Coffee With Digital Trailblazers

Hosted by Isaac Sacolick, CEO of StarCIO

The episode focused on discussing entrepreneurship and the challenges of becoming a solopreneur, freelancer, or startup founder. Isaac hosted a panel of experienced entrepreneurs, including Tyler, Heather, Derek, Liz, Joanne, Martin, John, and Joe, who shared their personal stories and insights about taking the leap into entrepreneurship. The panelists discussed topics such as resilience, risk management, experimentation, and the importance of having a clear business model and market fit. They emphasized the need for networking, honest advisors, and a well-thought-out go-to-market strategy. The conversation also touched on the financial and personal challenges of entrepreneurship, including healthcare costs and work-life balance. The panelists agreed that while entrepreneurship can be rewarding, it requires careful planning, a strong support system, and a willingness to adapt and learn from failures.

[00:00:00] Speaker A: Hello, everyone.

[00:00:03] Speaker B: Hello.

[00:00:03] Speaker A: Hello. Hello everyone. Welcome to this week’s Coffee with Digital Trailblazers. I’m psyched to be here. I didn’t hit the Go live button yet. Here we go.

Hello everyone. Welcome to this week’s coffee with Digital Trailblazers. Our 161 first episode. I am so psyched to be here. Thank you for joining us. This week we have a very exciting episode celebrating Natural Entrepreneurship Week, which is a US event. I don’t know a whole lot about it, but I’m excited to be a part of it and use it as a way of celebrating one of the many opportunities facing Digital Trailblazers leaders who are looking to try something different, maybe become a solopreneur, maybe become a freelancer, maybe join a startup or a growing company, or even look to become a founder and start your own company. So we’re going to look at all four of those options for everybody here who are either part of that experience or thinking about joining that experience of going on your own.

And I asked my panelists, every one of my panelists here today, every one of our speakers is either doing this today or has done it at least once in their career. I’ve asked all of them to share their personal stories. We talk a lot about storytelling here.

So folks, think about how you want to represent yourself and how you made your decision to go solo or join a startup or even found a startup. But before we get started, let me share some stats with you. I think this is really important to get a sense of where we are in the world globally and where we are in the US Around Entrepreneurship this Week National Entrepreneurship Week is a Congressionally chartered week dedicated to empowering entrepreneurship across the United States.

The graphic on the right is one of their banners. You can see the URL there. To find out more information about it, some Data I found 80% of small business owners said they would start their business again if they had to do it over again, which sounds like a pretty good statistic. But that also means 20% said they might not do it again.

Some metrics on the size of our audience, the number of people going into startups and going solopreneur, becoming a freelancer.

Self employment in the US Is growing at the highest annual level on record in 2025 at 16.77 million.

That’s from the Small Business Council. Total entrepreneurial activity is also at a record high. 19% of adults are actively starting or running a new business. And then globally, roughly 20% of adults are involved in some kind of entrepreneurial Activity. It’s quite impressive. We’re all trying to become our own digital trailblazers. However, there are some considerations. Right.

I’m going to share a movie scene that has always been part of my psyche. And I don’t know how many of you have seen Rounders.

If you haven’t, it’s a great movie. It is a poker movie. It’s with Mount Damon. It’s just a lot of fun. In the opening scene, he’s driving a truck and doing deliveries and he talks about the where he was in his life. He was a poker player, hadn’t made it big, lost out. And he comes back and says, you know, you don’t read about the people who tried and failed. And so there are some considerations when you think about going off on your own. 40% see good opportunities, but won’t start a business because of fear of failure.

I can tell you from experience, I had one of those startup ideas that I passed on because of that. A 2024 found mental health study found that 93% of founders show signs of mental strain, with 55% experiencing symptoms weekly or daily. It is a hard life and many founders, 67% say they work over 50 hours a week. So lots of reward, lots of flexible scheduling, lots of going after your personal passions and certainly a lot of opportunity to strike Richards in going off on your own. But also a lot of realistic considerations. Everybody here has to think about if you’re going to go off on your own, particularly if you’re going to become a solopreneur.

So that’s our topic today. I want to welcome our, our guest Derek. Heather’s here, Joanne’s here, John’s here. Joe and Liz are here. I want to welcome back.

Who am I missing here? I think I’m missing someone. Martin, I think is here. Yes, there’s Martin. I want to welcome back Tyler Johnson who is a semi regular speaker here at the Coffee Hour. And Tyler, let’s start with you. I want to get your stories, I want to get everybody’s stories. What was the turning point that pushed you to take the leap to entrepreneurship and to become a founder? Tyler, I think you’re in startup number two.

So glutton for punishment maybe. I know you have a background working in enterprise software and technology sales. Give us a little bit of background. What made you do the pivot?

[00:05:53] Speaker C: Well, thank you for having me, Isaac. I really appreciate the insights that the panel that you put together every Friday brings. So speaking of storytelling, let me tell you a story. So early in my career at Hewlett Packard. I worked on a $8 billion Superdome supercomputer product line.

The team was full of PhDs from places like Stanford. I was just a bachelor double E from FAU and Boca Raton. I wasn’t the smartest person in the room, but what I did have was cross domain exposure. Before hp, I worked at TRW and Automotive. I learned lean manufacturing. I saw how physical production systems eliminate waste, reduce bottlenecks, and you design for flow. When I got to hp, I realized we were treating software testing like artisanal craftsmanship instead of a production system. So what I did was I applied lean principles to engineering validation across domain application.

The term DevOps, by the way, hadn’t been invented yet, but that’s exactly, exactly what we did. We automated simulation testing more than double productivity of the engineers and proved quality dramatically.

And as a result, I ended up with 22 patents from HP. That was entrepreneurship powered by pattern transfer across industries.

So for me, the turning point came later. I kept seeing the same pattern, same issue across companies where you have compounding complexity, integration costs growing exponentially.

And you know what often gets labeled as innovation is really modernization or innovation theater. An ERP upgrade and new HR system. This is motion, but it doesn’t represent change.

Leadership turnover is accelerated, incentives are shorter term. It’s harder sustain multi year innovation inside large corporations than it was 20 years ago.

So for me, walking away meant straight trading, stability, title income for uncertainty and stress.

If you’re somebody, you know, like me, that sees these cross pattern, cross domain patterns, you also see that the system can’t evolve from within the constraints of the corporation. And you know, for me it was hard to unsee that.

So eventually I found that I needed to move out to entrepreneurship.

[00:08:41] Speaker A: Awesome. Tyler, I wrote this statement here just kind of capturing, I think your anecdote here. When you start working on something that you see patterns in and then all of a sudden a buzzword, a category emerges that really takes hold. Right. Working on DevOps before there was DevOps probably onto something and maybe giving you a little bit of confidence that you’re heading in the right direction. Let’s open this up to the whole panel. Derek, I see your hands raised. I want to go to actually Heather next only because Heather and I have a long history.

I was CIO when I first met Heather. I think your role, Heather was an account manager for, for an outsourcing company. Outsourcing company. I don’t know what to categorize it. And now here you are running your own executive search company.

And so I wanted to bring you up front because that’s a pretty,

[00:09:42] Speaker D: pretty

[00:09:43] Speaker A: wide leap from where you started from to where you ended up and maybe just share your story behind that.

[00:09:49] Speaker E: Well, I would. Thank you, Isaac. I would suggest it really is not a leap because when you take who you are and be introspective enough to see who you are on the most generic form, I’m inquisitive. And I did research throughout much of my career, secondary research, looking for answers to questions. And in doing so, I had to match what the client was looking for, whether it was an internal or an external client, with a solution. And that’s what recruiting is. It’s finding a match between a candidate and a company and recognizing that the company may not necessarily, your client may not necessarily know what they’re looking for. So being able to interpret that, translate what they’re looking for into a person that meets those needs. And it was that passion for finding answers, being inquisitive, as my husband likes to say, sitting on my ass and talking on the phone and someone is now going to pay me and putting that all together that helped me to launch my recruiting business.

I think the pivotal moment was Covid.

I had been downsized yet again. First time, shame on them. Second time, shame on me and not being prepared.

But when I was taking care of my mother, fortunately she survived. During COVID in Florida, I had a lot of time to think about what is it that I’m good at and what I wanted to do. And I connected with a recruiter for myself and he needed help finding candidates.

And I used that research approach in helping him initially and then taking that as my quote unquote secret sauce for how is not looking at just who has a banner, looking for a job, not going to people that are posting that they’re looking or indeed, where they’re, you know, you can partner with with indeed. But it’s recognizing and really understanding what a client needs and finding characteristics, those characteristics in the people that I might, that I want to propose to them as prospective candidates and using that passive approach.

So to me, anybody that has the skill set that I’m looking for is open game for a conversation.

[00:12:12] Speaker A: I love this idea of taking what you were doing so well as a partner of mine and just waking up one day and saying, I want to keep doing this just in a place that is of greater interest to me and hopefully more lucrative than what you were doing in the past. I just think it’s a great story. We’ve got everybody raising their Hands here to come up and share their stories.

Making the leap to entrepreneur, founder or solopreneur. Derek, what was, what was your spark?

[00:12:43] Speaker F: So my spark was looking at my industry. I started off, you know, pretty much worked with high availability business continuity type systems and some of the larger designs I work with like US Postal Service, some of the government agencies, work with some of the military stuff. And I saw these continuous patterns across where people just weren’t prepared for continuity.

They weren’t prepared for things would happen or go sideways and they say and they would struggle with that. And Covid was a perfect example as many people were caught flat footed because they hadn’t prepared their networks, infrastructure or their business culture to work full time remote. And for me it wasn’t just a one time thing, it was just building process. As Tyler mentioned, the pattern that I could see across the board where these pain points were existing and I always had a passion to say what can I do to help make somebody’s life easier? And because of my background I was asked to evaluate things like the New York Stock Exchange for high availability and other things in continuity. And I thought this pattern. The largest corporation, the last one I worked for was Siemens which was a global company. And I was, because of my background I was put on their special teams. And the special teams meant anything outside the box. So in that case they had a standard product which I helped them further develop and bring out. But the other thing I do is bring this continuity of how could they make things better.

And it just got to the point I said for all the things I’m doing for you guys, I might as well do this for this myself. So I planned a two year exit strategy which means how could I look at and prepare for things like health insurance, putting money in the bank just for what if possibilities and also looking at what are the possible customers I’d be possibly going after that would need these services. And it wasn’t just the small to medium to large business. It was all the businesses across the board that I saw that had these pain points. And by doing this I was able to jump out on a leap of faith. I was able to go in my network and say I’m going to help people develop business continuity or high availability strategies to make this work. And a lot of companies, even to this day I was looking at artificial intelligence. You know, this exists right now. So the aha moment is figure out how do they do this but how do they get people to help them do this? Because a lot of them don’t really have the expertise. And for me, as taking that leap of faith, you know, you mentioned failure earlier.

Early on, I did fail. I did have some companies that I worked with that had contract budget issues that I had to pivot and figure out how to go from working with government entities to now commercial entities. But I was prepared for it because I planned ahead.

One of the things that kind of helped me through my whole process here of entrepreneurship was to establish that resilient mindset. What do I need to do today to prepare me for tomorrow? And that’s looking at the skills, looking at the finances, looking at all the different things that could possibly go sideways, but preparing for them before they happen.

And by doing that, I was able to be resilient in that matter. I did have a customer, one customer I was doing, and I had one client that the budgets had gone sideways. And I pivoted, and they decided they weren’t going to be able to keep me, but they wanted to keep me on as long as possible, and they did. And I pivoted my business. I started working with another company, and they said, you know, we need a CIO and a cyber security officer. Would you be interested in taking this role? And I said, sure.

And I thought I’d be in it for two years. I was there for about six. And after that, I said, time to go back into consulting. So, again, is the thrill of it, the fact that you’re doing things more for yourself on your timeframe, and you get to choose the businesses that you want to work with as opposed to being assigned tasks that may be mundane. And for me, it was the longevity of being able to continue doing what I love to do and doing it the way I want to do it for as long as I want to do it. That was my moment to really kind of branch out and say, I want to continue this, not just for a little bit, but for as long as I can.

[00:16:24] Speaker A: Some really good points here, Derek, that I captured. So I hope others will also comment on. The notion of having a resilient mindset I think is interesting.

I’ve been at it for 10 years.

You know, some folks on listening and speaking have been added a lot longer.

Planning before you leap. That means being prepared in your current job that you might have to or want to be a solopreneur or go off on your own, but then having a plan ready for when you’re doing it and figuring out what some of the elements of that plan, if you, you know, cut the cord or the courts cut on you and now you starting your plan, you’re going to be three to six months out before you have a plan. And that’s, you know, it’s a long time to wait before the, you know, money’s pouring into the door. So I think it’s a good point. And then being able to strateg, I mean, you know, one of the hardest things I will say for me and for all entrepreneurs is picking your battles, picking where you’re going to focus on. I’m terrible at it. And, you know, the client that’s sitting in front of you may be paying you today, but may not be a good strategic path for your future. I’m going to keep going down the room. Thank you, Derek, for the comments. Liz, welcome. Your thoughts on what pushed you to take the leap when you took your leaps on becoming an entrepreneur or founder.

[00:17:54] Speaker G: Excellent. So I’ve been an entrepreneur several times, but I would have to say a huge part of being an entrepreneur is being willing to have that personal sense of ownership over your work.

And not just over your work, but over the old, over the entire process, entire program, entire company. That willingness to think through and have ownership and take ownership and really see the bigger picture, not just the individual tasks that you’re doing. If you already have that kind of mindset, you are an entrepreneur or an intrapreneur or a consultant, however you want to think about it. But taking that core talent and then moving that into generating, you know, making sure that your expertise is valuable opens up so many doors.

And regardless of risk, I want to set risk aside for a second because risk comes in many forms, right? Risk of staying in one place is a risk. Risk of taking a leap is a risk.

So there’s a lot of ways that you can think about risk. And the biggest thing that I would say that I don’t necessarily think that I’ve conquered is that willingness to actually spend time on pipeline and sales.

Both times that I was opened up a new shop for myself, my pipeline and my sales was completely based on my individual talent and my reputation.

And if you think about it, that’s actually the same kind of skill that helps you get a job.

So think about the risk in terms of staying versus leaping, because there really is a, a very similar risk.

[00:19:49] Speaker A: Believe it or not, Liz, that was one of my first realizations that I embraced when I went solo 10 years ago is the notion of giving up the 40 to 60% of my time as an executive.

That was essentially applied to politics, long running, change management, just dealing with enterprise inertia. Or lack thereof, and shifting gears and saying, you know what? I got to sell my own dog food. I got to be able to promote myself.

I got to be able to figure out what sales cycles look like and how to manage a CRM so that it helps manage me. And for those folks who think that winning clients is easy, that is the hardest part of what you’re doing as a founder and as a solopreneur. Hands down, hardest things. Thank you for the comments, Liz. Joanne, you just took the big leap again.

And so I’m really interested in hearing your insights about what was in your mind. From great idea to let’s put time, money, and other people’s lives in the mix of things and make a business out of what you’re doing now.

[00:21:14] Speaker H: Thank you.

I think, I think my turning point was, and I was an advisor for many, many years, whether it was, you know, big consulting firms or in technology companies or manufacturing companies that also spun off technology companies, etc.

So I got to the point of you.

I love being the person that people called when they had a problem and giving them the solution. But it gets to a point where you realize that your solutions and your answers are being taken in. The messages do resonate. People understand where you’re coming from and they adopt your ideas, but then they get lost anyway. I can’t find the right vendor. I don’t know how to.

I want to do exactly what you’re telling us to do, but I. I can’t find the right tools. So I decided that at a certain point, if you have the grit to do it, you kind of put your money where your mouth is and invested not only in my own ideas, but those of other people as well. And we started building the software to solve the problems. And so the turning point, though, was really one specific situation, I guess, where I had been working with this company on and off for more than a year and gave them the ideas, gave them the blueprint, gave them the roadmap to follow. And they did. And they did. And they did. And they failed miserably all the way through. And what I realized was they weren’t considering the tribal knowledge of the people in their workforce, that they were bringing the workforce on too late.

And so that, to me, was the turning point that said you have to build software or tools that people can use to capture some of that, because in manufacturing specifically, 33% of the workforce is going to retire in the next two years.

So you have to be able to give people back something that they can use to Capture all of that knowledge before it rolls out the door. To me, it was a natural marrying process between here’s the solution, here’s the technology, and. And now you have a way of bringing that tribal knowledge into real software. AI gives me the capability to be able to do that. And so we started building the software for that reason, to solve the problems, but also understand that companies need to be resilient and bounce forward and some of that capture of tribal knowledge helps them do that.

[00:23:51] Speaker A: Joanne, something you mentioned. I’m just going to share for everybody listening, there are a couple of good books I’ve read about being able to pivot from a consulting practice. One, having one or two clients that you’re working with and basically using it to feed the bills, but stealing your time from your ability to build a scalable solution and product and making that leap over from service company to product company.

Interesting to you? If you’re challenged by that, leave me a message here in the comment stream. I don’t have the books handling, but I do have a couple really good books to recommend on this. And it isn’t an easy pivot as it sounds, folks, let’s move on to Martin.

Martin, your thoughts on your pivots and shifts to solopreneur.

[00:24:45] Speaker D: So my pivot was really about a choice. It was about a choice of do I look for another company to work full time or do I look for an opportunity to just consult on my own basis?

So I chose to go that consulting route about. I think it was about five or six years ago now.

And I think the.

There’s some key things you have to remember about going out on your own. Like that.

[00:25:18] Speaker A: That.

[00:25:18] Speaker D: Yeah, and the first one is, well, who’s going to employ you? You know, who’s going to employ your company, you to do consulting for them. And if you think you’re going to do that instead of looking for a job and all the thing about trying to get an interview, all that type of stuff, it’s kind of the same problem because unless you’ve actually got a good network, you’ve got people who trust you, that know you, that can help you find your next customer, it’s pretty much the same as trying to find a job and getting an interview. You need that network and it’s so important. Yeah, if I look at the work I’ve done, the majority of it has come through actually somebody who knew me that happens to be at that company, etc. Etc. Or knew somebody that knows me or whatever else. So it’s that personal connection so that piece is absolutely critical. So don’t think you’re getting away from that by not looking for a job because you’ve got the same problem.

And I think there’s a couple of kind of key learnings that I would share.

One is that your attitude to who you are changes.

So, yeah, previously my attitude was I’m a cio, I work for Company X. So I am the CIO for Company X.

That is my identity.

And yeah, Heather will talk about this, about personal branding and things like this.

As an entrepreneur, as a consultant, do your own thing. You are managing partner of a consulting company or whatever else. So you, you have to actually define your identity differently. And that’s a very big mental adjustment if you’ve, especially if you work for one company for many, many years or something like that. It’s a very big mental adjustment that I am me, I am not a factor of this company.

And that’s so key to actually identify who you are and what you are going to sell in terms of selling yourself, selling your capabilities and things like that.

And then the third thing I was going to mention, sorry, no bar.

[00:27:27] Speaker A: I’m capturing this because every one of your comments is exactly what I found I felt in my first couple of years of going solo and I was solo before I really started star cio.

So I felt all those things. I was like, gosh, I wish there was a coffee with digital trailblazers 10 years ago explaining this to me.

[00:27:50] Speaker B: Yeah.

[00:27:50] Speaker D: So the third thing I will share is your different mindset between I’ll call it being a wage slave and being an entrepreneur.

So being a wage slave, yeah, you’ve got the nine till five, you’ve got vacations that you. So many days of vacation you’re allowed and you have to book them. Everything else, if you’re running your own business and doing this, there are, especially if you’re doing consulting, you have two types of hours. You have hours you’re getting paid and hours you don’t have. Don’t have pay.

That is it hours. Are you getting paid now as you’re not getting paid, there is nothing else. Doesn’t matter if it’s a weekend, it’s a holiday, it’s an evening, or whether whatever else you have, hours you get paid and hours you don’t get paid. It gives you more flexibility so that you can go and do things during the day if you want to. Whatever else, you just have to make sure you organize yourself a little differently. So there is a big mindset change in a couple of areas there’s I

[00:28:53] Speaker A: call it sweat labor. I think the industry calls it sweat labor.

Martin and what you don’t realize is when you start out early, that sweat labor can be relatively small. You know, you got to incorporate, get a bank account, maybe put a website up, you know, update your LinkedIn page and, you know, some overhead to just get getting started. But as you’re going on and when you think beyond just pipeline that Liz mentioned and saying what it takes to run even a small business, it adds up quite a bit. And your to do list never ends. And that’s why a lot of solopreneurs are always stressing and always putting more hours than they really should be, because that’s the nature of the work and you have to manage it. John, I want to get your insights before we go to the break.

You, like Liz, made a sizable pivot in what you’re doing. I wouldn’t even know. I don’t even call what you’re doing a pivot. I’m going to use a word later on. I don’t want to use the word yet, but tell everybody what your mindset was and tell everybody what you’re doing.

[00:30:02] Speaker I: Yeah. Isaac, thank you for having me on.

My career really started by building products and systems and teams at product companies, and I was at a lot of other high tech companies and I was in Consulting for 10 years and I really enjoyed that. So I did that stuff for 20 years and just had a really, really good time. And I was talking to some friends that kind of actually made a pivot out of that type of work. And the reason that they were doing it were for the reasons that people described. One of the additional reasons is that I wanted to spend a lot more time in the community.

For the last 10 years, I had spent so much time kind of on zoom calls talking to people that I really wanted to spend more time.

And so my friend and I, we were looking at what kind of things would be really fun to do, and we decided on running local service companies.

And so the first thing we had to do is try to figure out what kind of local service company do we want to run. And so we looked at all sorts of them. And the one that looked like it would be really, really fun and needed in the community was actually to provide basically send nursing assistants out to people to help them age in their house.

My friend actually he went to the route of buying one of these and he bought ultimately three of them. And he’s running the AT scale.

And my wife and I were interested in buying one. But we just, we didn’t see one in our community and we didn’t see anything that we want. And so my wife was the one that really said, you know what? You should build this thing from scratch. And we got, actually, we were thinking about that, but then we actually also bought into a franchise system which, which helped and made it easier. And so what we do is we have what’s called an in home care company that we’ve started in local, in our community.

And we basically help people age at home. And it’s been such a rewarding experience.

It’s something like every day I’m often able to just meet people and help them get out of the hospital or help them get out of a rehab center and through providing nurses assistance out to their house, make their life better so they can have more rewarding time with, with, with, with their, their family members as they’re aging. And so that’s what I’ve been working on. And I’ve learned so much about running a business and all the different parts of it. And the part that really surprised me was how much technology you need to run these things. And, and then the other part is how much fun it has been to work with the other owners that have these things and collaborate on trying to make things better and collaborate on like putting on webinars for things and improving our CRM system and our other systems and security.

And so that’s, that’s what I’ve been working on. I’ve absolutely loved it. Never anybody ever wants to talk about this stuff. Just reach out, because I’d love to talk about it.

[00:32:42] Speaker A: Hey, John, there’s a couple of articles when I was doing the research about just the amount of technology and now AI being applied in small business again.

If anybody wants that research, I’m going to paste the links in for what I have posted already later. But I do have some really good research on how small businesses are thinking about AI. John, after the break, I want to come back to you. I want you to hear just sort of the emotional level, like you really left your old world. This is not you. You know, you kind of like, you know, said, I’m gonna go do something completely different.

And I just wanted to, you know, like, kind of magnify what was going through your head at the time. Because it wasn’t like you didn’t like what you were doing before, was it?

[00:33:29] Speaker I: Yeah, yeah, no, I, I like what I was doing. I just, I wanted a different experience and I wanted to spend more time with, with people in the community.

And then I wanted to see if I could build a local business up from scratch.

[00:33:41] Speaker A: So it was the challenge. There you go, folks. This is what we’re talking about today. The challenge of National Entrepreneur Week. The opportunities for digital trailblazers to either go solo, become a freelancer, join a startup, become a founder in all of its different flavors, not just from a business perspective.

John is talking about a complete lifestyle and career change, industry change, lots of things that you have to be brave around.

There’s some risk, it requires some resiliency, as Derek pointed out, and it requires the ability to experiment. There’s a couple questions and comments on the stream. I hope you’ll leave some around. The ability to experiment your first ideas, even when you have a plan, requires testing that market fit. Learning from what’s working, what’s not working, and deciding, hey, am I heading in the right direction? Folks, thank you for joining this week’s coffee with digital Trailblazers. I am ready to start announcing our March episodes before we get there.

Next week on the 27th we’ll be talking about DevOps in the AI era, restating QA’s mission. I think I have a special guest lined up for that one, so if you are in technology, you don’t want to miss that one on the sixth. Elena, this was your idea. I know you’re listening. I hope you have the sixth free to talk about rethinking KPIs for the AI era, how senior leaders prove business value. And you wanted to talk about cross functional business values. I think we’ll have Elena back. Hopefully she is free on the 6th to join us on the 13th. This was another thing that came up. I think it was last week and we always talk about whether somebody is a cultural fit for your organization or a partner is a cultural fit for your organization. And culture fit is one of these fuzzy gray type of squishy words. I think we’re going to put some concrete definition about how we talk, measure, evaluate culture fit. That will be on the 13th. On the 20th we’re going to talk about managing AI agents. This again came up last week. This notion that teams are not just people, they’re people and agents, that leaders and managers are not just overseeing, partnering and collaborating with people, they’re doing it with people and AI agents. And we’re going to unpack what managing an AI agent is on the 20th and then the 27th. I’m glad sales came up. Up. I’m glad marketing came up.

Liz brought this up a couple weeks ago And I said, you know what? We’re going to need to talk about the essential sales and marketing skills for digital trailblazers. Not only if you are going off on your own, but I talk about this in my book, Digital Trailblazer. If you want to make sure you get funding and traction with your big ideas, whether you’re separate and running things on your own or a founder or inside a company, you better be able to have the skills to market and sell it. Folks, starcio.com Coffee will always redirect you to the next episode, but do visit this page drive starcio.com Coffee and that is the coffee page where you will see the episodes that I’ve published. If you missed an episode, you also have a link there called add to calendar. Put it directly on your calendar so that you will not miss a live episode when you can make it. Folks, I thoroughly enjoy working with you here and thank all my speakers for being here with us. Folks, we’re back. Tyler, bring you Back to talk. Two things for the remaining 20 minutes that we have. First, I want to talk about your lessons around resilience, risk and experimentation. And then your thoughts on the future. Right. Looking ahead, how do you see the next wave of digital entrepreneurs shaping industries or community?

Tyler, your thoughts on either of those two areas?

[00:37:53] Speaker C: Yeah, absolutely.

I, I dropped a comment where I said that I wish I’d understood earlier about entrepreneurship that it’s, you know, grit is a, you know, table stakes. But what it’s really about is humility and energy management. You know, looking at the, the high levels of stress that we deal with, having to carry so much on your shoulders as an entrepreneur, you know, when you spent decades building expertise as, as most of the folks on the panel have especially complex systems, you know, you, you develop conviction. You, you see this, the flaws, you know what’s broken.

You know that experience is powerful, but it’s also dangerous.

A lot of experienced leaders who become entrepreneurs, you know, we assume because we understand the problem, we understand what the market will fund, but we don’t. Markets don’t reward being right or, you know, they were. They reward pain, solving for pain. They fund firefighters before they fund fire proofing is another way to say it. You know, you can design something that makes the system work the way that it always should have, but there isn’t visible pain.

Nobody’s going to pay for it. So you have to listen, you have to understand what people are actually afraid of, what keeps them up at night, what their incentives are, not what the system needs. Over the next five years to innovate that the energy management side is. It’s just as real. Inside a corporation, there’s inertia, outside there’s none. Every day requires activation energy.

Nobody’s looking over your shoulder.

Resilience. It’s not pushing harder, it’s managing your energy, separating insight from ego, choosing a wedge that aligns innovation and structural truth with urgent pain.

And you have the experience, so that gives you the pattern recognition.

But the entrepreneurship piece forces you to translate it to something that the market feels today. And just real quickly about the next wave. I think the next wave of digital entrepreneurs are going to be the ones that can bridge that structural optimization things like DevOps that we mentioned with solving that acute pain. So bridging that gap.

[00:40:32] Speaker A: Tyler, you’re reminding me again of some books I read. I read a lot of marketing and sales books, a lot of wisdom in that area that are outside of my primary skill sets. And you know, listening for pain points is a really important skills, but it’s not sufficient. You have to look for things people actually pay for. And if you’re listening and you’re thinking about going off on your own, let’s make sure that you’re actually building a sound business. And if you need those books, do put a comment here. I will share the links to them here on the comment stream. I’d love to hear who else is thinking about going solo. Derek, hold off a second. We have not heard from Joe today. I see his hand raised all the way at the back of the line. I want to bring him up to the front of the line before we run out of time. Joe, do you want to talk about resilient risk, experimentation or the next wave of opportunities?

[00:41:30] Speaker B: So I want to talk about motivation and risk and I want to talk about how the world has changed over time.

When I was a young whippersnapper, I was motivated. I was all fired up. You know, I always talk about fire in the eyes and fire in the belly. I really wanted to conquer the world and I was working for a big corporation as an associate programmer and I was told that the, the track for advancement was, was something like five years, which to me was an eternity.

I was risk tolerant, you know, just full of vim and vigor. And someone offered me an opportunity to take part in a small startup, a little, what we would call bi today, a little bi firm. And two years into it, three years into it, I was a partner and I learned a very valuable lesson. And I think it’s already been mentioned here, you don’t Eat. If you don’t hunt, you got to close the deal. And I can tell you stories about some interesting meetings where I had to ask for the order to get, to get the contract signed. You know, a little bit later on in life you become a little more risk averse when you have a mortgage and you have kids and when, when the circumstances are a little different, you worry about your next job.

And as you know, I counsel people in the MIT program about the importance of networking. And Martin, you, you hit the nail on the head. Your network is just so, so important.

So I set up an LLC between jobs and made sure that I had interim work and that I stayed in touch with my network and that I always looked like I was busy and engaged because I was.

Now a little later in life, I’m financially stable, I’m risk tolerant, highly risk tolerant, and I was offered an opportunity by a good friend to collaborate on 10x NewCo and help companies to design their growth strategies and leverage technology.

And I have enough background and experience to be able to do that.

Not interested in a full time job, but happy to help companies whether it’s on a paid or, or even a, you know, all the board work that I do, which is pro bono.

So the point being, I, I think there’s, you know, there’s a, there’s opportunity, there’s motivation and there’s risk tolerance that are all factors here in, in the continuous.

[00:44:10] Speaker A: Joe, thank you for that. First is like, I love connecting Tyler’s thought and yours. You know, if you don’t have that fire in the belly, it’s really hard to be going off on your own or being in a startup. If you do, you need that grit.

As Tyler said, that’s table stakes because chances are you’re not going to be anthropic or open AI and strike it rich. Even though, you know, it took a bunch of years of people putting a lot of sweat labor in for them to get to where they are right now. And then something you see a lot on the technology side again from Tyler’s comments. Being able to manage your energy, that’s not just about a mental health question. It’s about being able to take a step back and do that. Listening. I forget who said being able to listen for the market.

If all you’re doing is driving faster, faster, faster, release, release, release. It’s, you know, you’re just pursuing the next thing that you have right near in your headlights. You’re not necessarily being strategic. Got a bunch of hands raising here. Let’s go around the room. Derek, your thoughts on opportunities, resilience, risk and experimentation?

[00:45:21] Speaker F: Yeah, so I think from the resilience point of view is looking at resilience.

It’s not about avoiding failure, but it’s really looking at how you can survive learning cycles and challenges. Those are the kind of things that people need to understand. And the things that Tyler mentioned and Joanne, those are all spot on because it’s a learning process. And I think some people, they just need to understand. When you’re stepping out there, what exactly are you going to do? Is, as Martin mentioned earlier, identifying your brand earlier and identifying exactly what you can do and do better will help differentiate you from the rest of the people that are out there. Because if you look at it, there’s going to be risk involved, but risk is cumulative. It’s not just isolated to certain things. You depending on where you live, the market you’re trying to penetrate, the culture, the community, all these things come into play. And understanding that risk early on is going to help you. You figure out what you need to do and how you can do it within your realm. You mentioned, it was mentioned about experimentation earlier.

These are kind of things when, you know, as Joe mentioned, you know, you’ve got the fire in your belly and you’re ready to take on the world, but you have to be realistic. You know, everybody wants to do as much as they can, as fast as they can, but reality is you need to set up guardrails as you’re experimenting to figure out what’s going to be the niche for you. You can move fast if you have a blast radius, but otherwise you can’t boil the ocean. And these are kind of things. You go out there, you experiment, and you try one thing, it doesn’t work, and you figure out, how can I pivot to make that better? These are the kind of things that help develop that resilient mindset. And Joanne brought up a point about the people in the organization, which I thought was very key. The people are another factor. The people you interact with is going to matter. The people you communicate with, the people you trust, the psychology, the safety, determining how are you going to move these things forward. That’s going to help you get your business and your brand out there. That’s going to be a key focus to help you really kind of navigate that area, because it’s going to be highs and lows. And as we’ve all experienced on this call, there’s going to be failures. But how do you address those?

How do you get through those challenges? Endure those struggles. When I look at it from the resilience point of view and the mindset, resilience is something, a resilient mindset is something that’s developed during calm periods, but it’s revealed during times of stress and challenges.

And I think the preparation and the work you put into it beforehand is really going to tell how you can work from those lessons learned from things that other people have done and apply that to your particular mindset in your business and to see how you can do it better so you can avoid those potholes when you move it out in the business that you want to pursue.

[00:47:47] Speaker A: You know Derek, there’s a few people here that manage the importance of having connections. I think what’s maybe even more important is having honest advisors, right? That you can make sure that you’re comfortable sharing something well before you’re ready to go to market or put your time and money into things and get real honest feedback. Hey, is this a bad idea?

And I just think it’s so important, Liz, where you want to go. Resilience, risk experimentation or next wave of opportunities for 500.

[00:48:23] Speaker G: I want to build on your concept of having advisors because this kind of goes back to the whole concept of making sure that you have the right level of support as you move into being an entrepreneur.

Someone once told me that you should assemble a board for your company and whether they know that they’re on the board or not is immaterial. But think through how you’re going to get that those different aspects of a company, if you’re thinking as a company to who’s going to be able, the right person to talk to about that sales pipeline? Who’s going to be the right person to talk to about your product delivery? Who’s going to be the right person to talk to even about infrastructure or your admin. Right. Putting that, assembling that right set of support as well as you know, for your mental and physical well being is absolutely 100% critical. One thing we didn’t talk about at all was healthcare.

And as an entrepreneur, healthcare is the number one, the number one expense.

Unless I mean, unless you’re actually investing in technology, etc. But healthcare is huge and having that, having a spouse or some other way that you can, can source decent healthcare is a big step up in terms of making sure that you are supported in your endeavor.

[00:49:50] Speaker A: Yeah, I’m really glad you brought this up.

It is probably one of my mistakes is not looking at my 10 year horizon with kids about to go into college and finances are tough I mean, you have some strong years, you have some weak years. That’s the nature of business this. But being a solopreneur has gotten extremely expensive over the last few years, primarily driven by healthcare. And you need to understand those financial risks and realities before you take that big leap. Thank you for bringing that up. Who’s next? Joanne, welcome back.

[00:50:32] Speaker H: Thank you.

I think I want to talk in all three categories at the same time, if that’s possible from a resilience and risk and experimentation perspective.

I think the one thing that I would say to new entrepreneur people who are going to, you know, jump on the AI bandwagon or the next iteration of new technology, maybe quantum or whatever, is it’s about business value.

And, you know, we all go through this. Who am I going to sell to and how am I going to leverage my network and whatever. I would say the better piece of advice might be, it’s just a personal perspective is how have you walked in the shoes of the person you’re selling to?

And it’s not about applying your expertise necessarily, it’s about connecting with them, not only where their pain is, but where they’re going to have to take their journey. And that business value, I think, is the thing you have to experiment with more than even the product.

Because the product market fit is one thing. That’s what will the market bear in terms of cost. But more importantly, it’s how are you making people’s lives better, even in the most insignificant of ways, if you’re giving them back time, if you know their political struggles in a large corporation, if you understand the value that you’re creating for them and then inculcate that value into a product, you’re that much farther ahead. You know, one of the first things that we did from an experimental point of view was what are the key challenges across all the different sectors of manufacturing and what are the commonalities across those? And you, if you can boil the ocean down to five or six main things, you have a product roadmap and you have a way to build because you walked in someone else’s shoes and you are going to be walking in their shoes going, going forward.

[00:52:27] Speaker A: Talk about being customer driven from the early stages.

You know, I am. If you’ve walked in their shoes, you got a leg up on everybody else. Thank you, Joanne. Heather, what’s up?

[00:52:39] Speaker E: Thank you. For me, risk or experimentation was actually the same thing. Being able to take that risk and experiment with another organization, that would help me. And I think the focus on the human touch is what I would use as the third topic you have and what is the next wave of digital entrepreneurs need? And no matter what the technology is, no matter what your area of focus is, don’t forget that people are involved, that you can use the help from people, and that the human touch is going to be critical. And for me, being able to take that leap and asking for help and knowing who to ask or finding out who to ask sometimes by chance, or taking that risk and saying maybe this person can help was very valuable to me.

[00:53:28] Speaker A: Thank you, Heather. And just full disclosure, just before this call, I said to Heather, I need a half an hour of your time. I need some help knowing who to ask and when you need some help. Thank you, Heather.

John, final thoughts today.

[00:53:44] Speaker I: When I was thinking about doing this, I did all the same stuff that I did at product companies for products, and I looked at like, you know, is, is this needed? Right? And, and I was really just like wrapping my head around the axle and, and actually I had a really nice conversation with my brother and my, and my, my brother’s like, look, if you know that the business model works and, and you know that there’s customers out there, at some point you just, you got to go for it. And, and, and that’s, that’s was really the turning point for me. And I just thought that, you know what, I could either probably work in the tech companies for, for 10 more years, or I could probably do this for 20 more years.

Kind of, it’s, you know, like, it’s a little bit of a grind, but like, once it gets going, you know, like, this is something that, that I can probably just, you know, as I age, it would be something that, that’s fun to do. And then I did have the personal experience when, when I was 16, my grandma got too old to move, move and live by herself, and so she moved into our house. And then my mom has had serious dementia for the last six years, and my brother’s had to take a lot of time off work to basically help my parents out. And so we knew that the business model was there, we knew that the customers are there. We live in Seattle. And we ultimately decided that I wasn’t going to be happy if I didn’t take this when I knew it would work. I wouldn’t be happy later. And so just for myself, I had to go build this thing, otherwise I was going to regret it in the future.

[00:55:07] Speaker A: Thank you for sharing that, John. Right. Pick. Figuring out your timing is more than just a financial.

[00:55:15] Speaker I: Yeah.

And, and I just, I was starting to see how hard it was to be older in tech. And I probably shouldn’t say that, but it’s, it’s like a real thing. I was having founders ask if I was. I, you know, they were asking the employees, hey, is that guy too old to be in tech? And I’m like, oh, oh, that’s, that’s a real thing.

[00:55:31] Speaker A: Holy cow. We need to have this conversation in a future digital trailblazers coffee hour. Martin, final thoughts today.

[00:55:39] Speaker D: I’ve got two thoughts, both related to balance. And we talked about this a bit earlier is when you’re an entrepreneur, he’s making sure balance your time for yourself and your time for family and things like that versus work. Yeah, I talked about hours, working hours or I was paid and I was not paid. You’ve got a balance. You could work every hour and still.

[00:56:01] Speaker A: Yeah.

[00:56:01] Speaker D: And kill yourself.

[00:56:03] Speaker A: Yeah.

[00:56:03] Speaker D: Mentally or whatever else. So balance in terms of work and non work and the other is balanced diversification.

So don’t put all your eggs in one basket. Obviously you’ve got to be careful not over stretching yourself and making sure you try to do too much, but balance diversification. So for example, from my service consult consultant, I do some consulting work on projects for people. I do some consulting work in terms of mentoring and coaching of senior leadership and I do some assessments as well. So balance diversification and balance of work and non work.

[00:56:42] Speaker A: Thank you, Martin, for joining. We’ve got three minutes. Go ahead, Liz.

[00:56:49] Speaker G: I was going to speak about the whole idea of making sure you’re on track with your clients.

And I have an image and I put it in the, in the chat. I have this image of one of the most successful entrepreneurs I ever worked with who would talk about making sure you’re getting on the right wavelength. This goes back to listening skills and making sure that you’re able to modulate to the person’s wavelength wherever they are. I think that Joanne touched upon this. Making sure that you’re able to relate that you’ve walked in their shoes and really identify with them and sort of merge with them and that once you have that sort of symbiosis, that’s when you can actually start modulating them up in a way.

I can tell you that I fall, speaking of test and learn, I’ve fallen on my face a couple of times where I just want to come out with people. I’m like, like it’s completely obvious this is the answer. I can be your everything for you, blah, blah, blah, blah, blah. And they look at you like you have six heads because sure, that might be the right answer.

They’re just not necessarily ready to hear it that way.

So making sure that you’re able to modulate to your client and your customer, critical for success.

[00:58:04] Speaker A: Thank you, Liz, for joining this week. Tyler and Joanne, we’ve got two minutes.

[00:58:10] Speaker C: Yeah. So I just wanted to share a framework I created for selection of a market or a beachhead as an entrepreneur.

So there’s eight elements. There’s a ninth that I’m not going to share that you’ll have to reach out to me if you want to hear it. But pain is real and recognized.

There’s a potential for a strong executive support, the presence of underutilized and unrealized economic value leadership’s actively trying to unlock. The buying group is defined and manageable. The first deployment can be clearly scoped. The first outcome is measurable in business terms.

The market scenario combination is structurally sound. And the business scenario is not easily solved by pre existing incumbent solutions.

[00:59:02] Speaker A: Wow, that’s a, you know, shave $100,000 or more off a McKinsey or other Big Six consulting firm to help you figure out your go to market strategy for your startup. Thank you for sharing that, Tyler, Joanne, last word.

[00:59:22] Speaker H: Last word. Make sure that you diversify your network to be inclusive of, excuse me, of not just leaders, but people below them, those that are aspiring digital trailblazers. Yes, a startup advisory board is great, but you need to populate it with people from all different aspects of the career path, not always the cio, people who work below them. Directors go all the way down into the workforce because that way you get the perspective of, of all the people who will be involved in using your product or choosing your product.

[01:00:00] Speaker A: Thank you, Joanne, for those final words. Tyler, for your framework and joining us special this week, everyone else for your insights on what it’s like being a digital trailblazer and going off on your own, either as a solopreneur, as a freelancer, as starting your own startup, or joining a startup. What a great conversation here.

This one will be released publicly, so if you missed it, you can find it on Apple.

You’ll be able to find it on Spotify, on LinkedIn, and on my blog, which is drive.starcio.com Speaking of my blog and speaking of Joe’s recommendation, learn how to ask for the order. I’m not asking for an order, but there are 27 people still listening here, folks. If you have not signed up for my newsletter, please do so. It’s free, it’s once per month.

I write over eight articles every single month plus countless videos and I share it on the newsletter once a month. You can get to [email protected] driving-digital.

You’ll also get benefits for being able to join the community.

And you’re going to hear about some of my special projects where I will be at specific conferences and hope to meet you at one of them. If you’re going to be at Adobe, at SAP, at Appian, at Atlassian, those are just conferences that I’ll be over the next few nights. Nutanix I’m probably forgetting a few in there. It’s going to be a busy couple months of travel. Folks, thank you for joining this week’s Coffee World Digital Trailblazers. We’ll be back next week to talk about DevOps in the AI era. The six about rethinking KPIs for the AI era the 13th around culture fit March 20th around managing AI agents and on the 27th around sales and marketing skills for Digital Trailblazers. Folks, thanks for joining. Have a wonderful weekend. See you next week.

Hosted by Isaac Sacolick, CEO of StarCIO

The episode focused on AI-first user experiences and the challenges organizations face in transitioning to agentic AI and generative AI in customer-facing applications. Isaac led a discussion with Roman, Joanne, John, Derrick, and others about the skills and disciplines needed to develop effective AI experiences, including the importance of process engineering, cross-functional teams, and governance frameworks. The group explored how companies can test and validate AI systems, the role of architects in AI development, and the need for continuous monitoring and updating of AI models. They also discussed the potential for AI to transform customer experiences in B2C contexts, with Joanne predicting significant advancements by the end of 2026. The conversation concluded with a brief discussion about entrepreneurship and the role of AI in startup creation.

[00:00:00] Speaker A: Greetings, everyone. Welcome to this week’s Coffee with Digital Trailblazers, our 158th episode of speaking to digital transformation leaders around leadership, technology, AI practices, mindset, everything that goes into how we evolve our organizations. And today’s special topic. I’ve wanted to cover this for quite some time.

We’re going to be talking about AI first, user experience experiences, planning for the evolution of gender, customer journeys. And we’ve tried to cover this a little bit in different areas and different topics at the coffee hour.

This one is very specific to this question mark of are we ever going to get to the point where we can talk about.

Oh, boy.

We can talk about AI above and beyond.

[00:01:03] Speaker B: Our.

[00:01:04] Speaker A: Above and beyond our. Hold on a second.

What did I do here?

Let’s see.

No, it’s gone above and beyond our ability to do.

Above and beyond ability to do workshops. I’m sorry. To do efficiencies and to be able to run our enterprise and everything that’s behind the paywall and start getting out to areas where we have experiences that are customer facing, that we’re impacting our journey maps, that we’re really going out into areas like retail, areas like healthcare, areas in insurance, places where we can actually impact how our customers are using our tools and using our capabilities in revolutionary ways.

I had a slide here. I’m going to try to bring it up right now with some details I thought was going to show up in the whiteboard. And I’ve gotten into a little bit of a technical issue. So I’m going to try to do this right now in real time so that we can have a real conversation around this. Give me one second, folks. Roman, why don’t you introduce yourself and I will get the slide up in the meantime.

[00:02:28] Speaker C: Sure. My, my name is Roman Dumiak and I am adjunct faculty at DePaul University in their school of Computing. I’m also a. What’s called an executive in residence for their innovation development lab at DePaul.

[00:02:46] Speaker A: Thank you. Okay, here we go.

Can you see this, Roman?

[00:02:51] Speaker C: Barely.

Okay, much better now.

[00:02:54] Speaker A: Much better. Okay.

I spent a big chunk of my time yesterday looking for examples of AI and real customer experiences. And sadly enough, I didn’t really find that many of them.

One really good example, I found some work done by Publicis Sapient.

They did some work with Marriott Homes and Villas. This is your ability to go to Marri website and rent a home or a villa somewhere. And they are trying to solve the problem of how do I find somewhere to go when I don’t know where I want to go or I don’t know when I want to go. I know the type of experience I’m looking for. I know maybe a little bit of who I’m trying to travel with. I know some of my constraints. And so they built a large language model to be able to do that kind of search. You know, I’m looking for a beach vacation somewhere quiet.

I want a place that’s less than a mile off the beach. I want good food and all kinds of like criteria like that about the experience.

And it comes back with a whole bunch of suggested places that you can go rent. I’ve left you here a link that you can go see this in.

In the State of Consumer AI 2285, this has come from Andreessen and Horowitz. There’s a lot of good content in there, but they talked about places where you’re starting to see AI creep into retail.

Perplexity has Shop Like a Pro, which I’ve tried to use. I would say I would give it a C in terms of its capability.

It really wasn’t doing very much and I tried Amazon Rufus a few times and I’d actually give it a D. It just did not warrant the kind of response that I would probably use it as a first replacement for their browsing and search capabilities. And so I think we still have a lot of work to do in terms terms of making our AIs ready for the B2C experience. There’s certainly plenty of examples of happening of AI happening inside enterprise software. I did a blog post around 50 different AI agents that you can get on platforms like Salesforce, Workday, SAP Appian, lots of different places where you can use agents to improve workflow. I have a hypothesis that I’m going to be running by my group today that with every major disruptive technology that’s come out over the last two decades, in order for IT to scale, for organizations to build capabilities with, we had to really rethink our operating model to be able to do this. You’re seeing that on the web one on the right hand side, what triggered transformation and Web 1.0? This is going back to the late 90s.

We were still doing client server software development. We were still doing waterfall project management. We had to bring Agile and web development into our organizations. When cloud computing came out, we could order infrastructure, but we really didn’t get the value from the cloud until we invented DevOps. We started putting automation in place like pipelines and infrastructure as code. We challenged our assumptions around the IT’S culture around development and operations. We brought those worlds together even when mobile came out. Our first mobile experiences basically took our web ones, ported them onto mobile screens and it took a lot of effort around design thinking and the introduction of app exchanges before we started really seeing triggered transformations impacting customer experiences. I have some hypothesis what this is going to look like for generative AI, AI agents and agentic AI around what we need for the customer experiences to really start becoming part of our capabilities. And that’s our discussion today. I want to hear from our experts. We’re going to start with Roman, our special guest today. We’ll go around the horn to all of our normal speakers. I’ve got Derek, Joanne, John, Joe and Liz here today.

We’re going to start with this notion. We have 1.0. It required teams to think about web development and agile. We had to bring DevOps to make cloud experiences work.

We had to bring design thinking to make mobile first experiences.

Roman, what are some of the new disciplines or mindset shifts that digital marketing and IT leaders must adopt for really AI to take a foothold in the customer experience and the customer journeys? Welcome Roman.

[00:07:37] Speaker C: Oh, thanks for having me. You know what I wanted to do first though was maybe piggyback on your comment. I think getting to an AI AI first user experience is really going to require people who understand how to do that. It happens to be that colleges survey their recent graduates and from a career standpoint we know that people in technical fields have really been feeling it for the last year plus. But interestingly, people who are involved in human computer interaction or UX UI design seem to be doing okay.

Now we don’t necessarily from those surveys know exactly why that is, but the hypothesis is they’re doing well because in their career path they need both soft skills and critical thinking skills all the way from low level visual front end design into more advanced things like content specialist or UX architect is building a front end framework or even UX research.

Then the second thing is that the need for designers as product based companies start to implement AI in their tools is growing. We all know pretty much every software tool now says AI first or AI embedded or something like that.

But interestingly, robotics is now getting away or not away from, but enhancing beyond just automation and replacing tasks to having humans and robots working together. And that requires some, you know, expertise in how you’re going to make that happen.

And then last thirdly, we think that the skills that design people have are transferable to other roles, particularly in areas like product management and you mentioned marketing, for example. So, you know, currently the modality is really kind of text based chat, but people want to use other modalities, voice and gesture recognition and things like that. And there’s also a push to replace a lot of manual input and integration between systems with gen AI tools.

So that’s kind of what we see from our recent graduates, I guess is this whole field of human computer interaction seems to be fairly stable. I’m not saying it’s great, I’m just saying, you know, those are the people that seem to be doing well in this economy.

[00:10:10] Speaker A: Thank you for that perspective, Roman. I want to say hello to a bunch of people have said where they’re from on the Commons train. Hello to London, New York City, Maryland, Boston. What else we got here? Manchester, United Kingdom, New York, New Jersey, Pune. Awesome. We’ve got a real world crowd here. Joanne and Roman is talking about skill change, human computer interaction as a skill set.

Maybe we need voice as a modality before we can bring it to customer experience.

What do you think the gap is? Why haven’t we seen enough AI first user experiences just yet?

[00:10:53] Speaker B: Well, I think there’s two things. One is that, you know, in the past we were limited by form factor, right? Think about the fact that we went from PCs to laptops, from laptops to smartphones and others. And so a lot of the UX and UI were designed for the form factor. Now we have a human machine interface called AI that has various forms. One part of that is the generative AI, you know, type in your prompt or speak to it, use text to speech or speech to text, and you can kind of start from there. But in actuality, when you think about it, a user experience has to be tailored to the Persona, the role of the human being. There’s sentiment, there’s context, there’s perspective, there’s security in terms of their role and what they should be allowed and should not be allowed to see. And all of that leads you down a path.

Agentic AI is very well suited for this.

And I can tell you firsthand because ours is autonomously generated, it’s based on the factors that we put in to training the models to allow the user experience to be designed for that user’s role. And in the context that’s most relevant to them, whether that’s also in a modality of speech to text or text to speech in whatever language they want, etc, all of those things have to be kind of built into systems. So what we’re seeing from the agentic side, excuse me, is That a lot of these are headless systems and allow you to begin to design the UX to suit the user. Not a one size fits all that we’ve lived with for, you know, the last 80 years.

We don’t have to do that anymore. We can customize, we can micro personalize. There’s a lot of different factors that come in, but really it’s about governance and it’s about building trust. So I think you’re going to see a lot more of that kind of come to light over the next year. For a B2C environment, it’s not that much more difficult than within the constraints of a single organization.

It’s knowing what your user really wants by determining deterministic variable.

Interesting. Sounds convoluted, but it actually is. The fact.

[00:13:32] Speaker A: Well you know, here’s what I’m translating this to Joanne, is that, you know, we’ve just upped the skill level of different areas and the collaboration of different areas that are needed to make human machine interfaces work and we’re just not there yet. Maybe in software companies are there, you know, robotics in integrating robotics with AI that’s coming, but in terms of B2C we’re not. The companies that need to do this just don’t have the skill sets yet and the collaboration to make that happen, is that a fair way of interpreting it?

[00:14:11] Speaker B: Yes, but also I think that their focus has been on gen AI and not other forms of AI.

There are real differences and this is kind of where the rubber hits the road between agentic AI and generative AI because one is, you know, an ocean and the other is a bucket. So if we start looking at how you really define the customer journey and the process that you go through mirroring that into Agentix is relatively, I don’t want to say simple, but it’s not about their skill set, it’s about their approach and the mindset.

[00:14:47] Speaker A: So basically the technology is changing really fast and our companies are stuck in the mud is what I’m translating that to. And understandably, I mean these are big investments when you talk about user experience, customer facing capabilities. I just don’t expect even a company like Amazon to take Rufus and say, you know, what we’re doing, you know, going to wipe out 10, 15 years of how Amazon worked and just going to replace it with an AI and see it work. Let’s bring Derek in and Joe. Derek, your thoughts?

What do organizations need to start really thinking about AI first ux?

[00:15:27] Speaker D: Well, it’s. Some of the things Joanne mentioned are spot on. I mean the mindset definitely has to change moving across and you look at, you know, discipline across the leaders, they need to adopt this instead of going from build to ship. They need to look sense to understand, govern, to use and adapt, to fully utilize.

They need to better understand how this is going to work. As Joy mentioned, these the developers of artificial intelligence tools, they’re really trying to figure out what the end user going to want and users are trying to figure out what the developer is going to make my life easier. So when they look at this, I’m looking at it now from a resilience or risk point of view. How do I get this to be a thought process for proactive versus reactive. It needs to be a forethought as opposed to an afterthought. And the governance piece is going to be huge. The governance piece should not stifle innovation but it should adopt and move innovation forward. And some of the things looking at the tools that are out there just on the Gentex and the AI agents and stuff is really looking at the threat landscape and understanding. Although a lot of these manufacturers are embedding new tools to allow the ease of use and make it easier for the end user, that’s nice but you still have to look at how you’re protecting your ecosystem and your business culture. When they’re looking at the marketing and these other directives within their and I think looking at a holistic approach and designing the simplicity and the complexity of what you need to do up front is going to be key. But again it’s still an evolving process.

One of the things I’m looking at is the threat informed design for AI experiences is make it easier so that end user can now be trusting the systems that they’re using to have to worry about the risk associated with those. And that really goes back to the companies and businesses that are using these tools. Make that investment into tools like a Mitre Atlas tool actually looks to better understand what’s taking place in your ecosystem but actually look at those threats on the back end. So you end users not always trying to look over their shoulders to see what’s happening.

It’s just, it’s an evolving process and like I said, it’s going to take time to really get into it. But I think by taking these steps and working towards these systems to help an adopt, they can start developing trust from marketing and a design point of view so they can actually have more free flow of the utilization of these tools that are out there.

[00:17:37] Speaker A: You know Derek Kevin Wallace EID on the Common Channel. He was our special guest last week. He also echoes the need for governance and trust.

And you know, I’m going to translate that into two things that are difficult when you start thinking about customer experiences. You know, first is the impact of on brand. If you get it wrong, right? And we’ve seen a few companies try to get stuff out into production in B2C avenues and royally screwed up because once you put it out there, you know, it’s, you can’t take it back. You can’t. Well, you can take it back but you know, someone finds a hole and all of a sudden getting, you know, coupons or getting free things because they know how to game the AI to do that, you’re in real trouble. So there’s a brand governance issue here that marketers really have to be cognizant of and part of this and I’ll, you know, see if John and Joe have comments on this.

I don’t think our testing methodologies are strong enough yet. I don’t think we know how to test lms.

I don’t think we know how to test agents in a comprehensive way to say, you know what, this is ready for prime time. And I think that’s what’s slowing things down. And I think the third thing is this stuff is expensive.

It is, you know, it’s expensive to skillset, it’s expensive to build your models up. So the development process is expensive. And then, you know why, you know, why is Alexa from Amazon only just getting LM capabilities? Because this stuff is power hungry. It’s going to cost them a lot of money at scale to run an LLM behind something like an Alexa with voice, with comprehension and make sure they can get a return on that investment. So, Joe, a bunch of different reasons you want to echo one or do you have a new one you want to share with us?

[00:19:29] Speaker E: Well, I think 30 years on. Isaac, to your point about how well do we test things.

[00:19:37] Speaker A: For those of.

[00:19:37] Speaker E: Us who have been caught in I.V.S. hell, we still haven’t figured out how to think like people and not like a decision tree. I, I think Joanne made a very cogent point when she talks about mindset.

I agree with my good friend Kevin Wallace.

It’s about the mindset. We, we have to think about these things differently.

Roman said human computer interaction. Folks are doing well. Well, you know, they think about how people ask questions and how people talk to each other and, and they’re not like programmers, they’re like, you know, people.

So until we come at this with with a design for people. I think what Joanne said was very apt to, you know, we’re not designing for a cell phone, we’re not designing for a laptop. We’re designing for interaction with people. And that’s the kind of mindset and change in approach that has to be embedded in our staff.

One other thing I want to point to with respect to Joanne and Agentic versus Genai. Earlier this week I was in a discussion with some folks talking about training or hiring new managers. We’ve talked about how you need experts to govern the AI and to understand, for example, code generated. How good is it when we’ve lost the entry level positions? Well, the point was made that, yeah, we may not be hiring entry level people anymore, but now what we’re hiring are managers of AI, managers of agents who have to know how or learn how to manage the technology as if.

[00:21:16] Speaker A: It were their staff.

[00:21:18] Speaker E: And that’s another new skill set that I just don’t think we’ve mastered yet.

[00:21:25] Speaker A: We’re going to go to skill sets in just a second, so thanks for that. But I want to hear from John.

John, yeah, good to hear from you. You know, let’s put you in the hot seat, right? Your, your chief Digital officer and your CEO says six months. I want my UI to be, you know, agentic, whatever that means. You know, what are your, some of your concerns that are going to be at the forefront before you start transitioning to an AI first user experience?

[00:21:55] Speaker F: Well, I think we’re still figuring it out how to interact with AI. People have made the comments that we’ve got, the chat bots, the text, we got that part of it pretty figured out.

But anything that requires a physical device, devices really take time to build. And I even think back to a long time ago in Silicon Valley when somebody was talking about their startup and whenever they would mention a physical device, like everyone had shrugged on that one. And working at a company that used to build embedded devices a couple of companies ago, it’s just embedded devices, they take time.

And so figuring out how to stitch AI into these things is going to take time. Some ones that have been really, really successful I think of like the Nest thermostat. That was a thermostat that was extremely simple for people to use, but it had some pretty, pretty neat algorithms in the background to try to figure out how to heat the house and have it be the right temperature for people. But they made something that was so simple to install, so simple to use that it’s like it was widely adopted even Though it was a pretty advanced technology.

I’m in the healthcare space and we’re starting to see some really neat things where we’re deploying these, these, these devices, Sensei AI is one of them and they have their own cellular network so they’re really easy to install. You just drop them in people’s houses and they, they’re able to look for degradation in people’s health or when people fall and things like that. And so they’ve made it so the install is so easy and it uses AI to basically figure out what’s going on in the house and then it sends a report to the people who are monitoring it and basically it tells how people are declining over time. And so this is using a ton of AI, but they’ve figured out how to make it really easy to deploy and really easy for all the parties, the, the couple, the families that have these things that they’re typically in elderly people’s houses. But they made it so that there’s a human that calls them or talk to them. And so they’ve made that interface super easy. And those are being pretty successful. And then, you know, I’ve seen some pretty good situations where people have stitched AI chatbots into the web pages themselves and so that if you ever have a problem on the web page, there’s a chatbot’s actually built in and it can see everything that the production team can see on the back end.

And so it’s like that’s how I’m starting to see people stitching AI into interfaces.

That’s like the baby steps of it. But those are the things that work. It just, it has to be really simple, it has to be frictionless and it has to make people’s lives better.

[00:24:27] Speaker A: I think that’s why even on the enterprise software side you talk about, you know, agents taking over or partnering with specific roles. You know, so a hiring manager or a finance financial analyst or a content manager in marketing and what you’re really seeing is more task based agents out right now. So they’re focused on helping that role do one thing really well and then moving on to another agent doing another thing really well. And this whole notion of orchestration, you know, I’m going to wait for Joanne to comment on this.

I still think that’s a work in progress and in terms of what the technology is capable of.

But let’s shift gears. We’re going to talk about, you know, how to drive the innovation. It’s going to be a part of our last conversation. I want to Go back to our conversation around skills, particularly around product, around marketing. And it, you know, last week we had the conversation about how companies are just not hiring level one people.

And we have a lot of anxious grads or just near grads that want to make sure they are getting skills in employable areas.

And so that’s my question today. If we think that AI is going to go beyond software companies, that every retail company, every media company, every insurance company, every healthcare company, every bank is going to have to rethink their user experience to be able to leverage either a language model or become more agentic and everything in between, what do we think we need to train for? Go ahead, Joanne.

[00:26:16] Speaker B: I think that there’s a couple of things that we need to train for. One that is often overlooked is process design.

Because really what you’re doing is you’re mirroring the process, whether it’s the customer journey or the internal.

Okay.

Or the internal individual internal to the enterprise. I mean, we have to start looking more deeply at the process because whether you’re using a generative AI tool or agentic, it’s the process that has to change. It’s not about the modality anymore in term of, in terms of form factor or speech to text or text to speech. It’s about what is this process trying to accomplish and how to give the best value to the individual who is using it, particularly on the consumer side. I mean, AI bots do not know if I’m in a bad mood.

They don’t know what my sentiment is, they don’t know what my intent is. And what they’re doing is they’re taking my prompt, if it’s text or my speech, and saying, oh, we think she’s trying to do X or she’s. Her intention is to do Y. They’re not looking at who is she, what, you know, level of education does she have, what is her age, where is she coming from?

Those things are derived by parsing, using nlp, for example, natural language processing to a degree.

What am I trying to say to the AI? That interpretation from them then gets translated. But there’s a lot of nuance there, and AI has a hard time with that unless it’s using mathematics or a symbology as the way it’s doing that translation.

So if we go back to the process of the customer journey or the, you know, put something in a cart or buy it, those are the areas where they can make the best improvement. And the people that they should be hiring for are process engineers, not UX ui. Because The UX UI is from a visual form factor based perspective. There are not that many UX UI people that I have ever come across that actually deal in other modalities like speech.

So I think that’s where you’re seeing a shift. And process engineering is making a big comeback.

[00:28:57] Speaker A: Interesting, interesting. What do you think, Liz?

[00:29:02] Speaker G: I’m a big believer in process engineering, but I’m gonna have to disagree on a couple of things. One, the, the idea of doing cx, not the UI part, but the CX part, really has to do with understanding the customer.

What I’ve been seeing lately is that there are a lot of AI tools out there that are using the language. Like you can speak into the tool and it will derive tone and provide feedback. I was looking at a recent tool that is used to train call center reps where they can actually read, listen and transcribe all the, the incoming call, listen and transcribe all the information that the is being spoken by the call center rep and provide feedback on intention of the caller tone, provide feedback on what you can do to help, you know, address all of their concerns. See if we actually did address all of their concern.

And I think that this is exactly where we’re going.

And it has to do with the key skill of being able to listen.

So that when we’re creating these bots or AI tools, whatever you want to call them, that they are trained on not only listening to the language that’s spoken and making sure that they’re thorough, but even going beyond that and contextualize what the person is trying to do beyond the specific transaction.

[00:30:46] Speaker A: Interesting. Oh boy. Got a little bit of debate here, but I think, you know, I’m going to round out where Michael Volinger just commented, right? We need a, you know, layering AI on top of a legacy process or a point solution or a step in the process and expecting exponential results will miss 100% of the time. And I think why it’s relevant to say this is that when we’re starting to get into the customer experience, we have a lot of rethinking to do about what their needs are. And I think that’s your point, Liz. I’ll take my break here. We’re going to bring Roman back to talk about skill development, our upcoming coffee hours over the next four weeks. I announced them earlier this week.

This week is Data Privacy Week. I probably should have had this episode be our data privacy episode. But you know what? We’re going to talk about what’s really important, what we learned and why it matters. Data privacy.

Given all the announcements that came out this week, we’re going to do a recap next week and say what are we changing and our goals and our objectives around data Privacy. On the 13th we will be talking about transforming to skill and outcome based hiring. I think we’ll have to talk about this from the employer and the hiring manager and the employee side. That will be on the 13th. I’m hoping Heather May will be back here for us to talk about that important topic.

20th it’s national entrepreneurship Week. We’re talking about opportunities for digital trailblazers to go solo and create your great AI startup. It’s not what it used to be two or three years ago. You got to be smarter about your value prop going into it now. So I’ll talk about entrepreneurship on the 20th and on the 27th.

This is from Liz. She debated me whether or not QA is important.

We’re going to talk about DevOps in the AI era, restating QA’s mission and I’m going on record in saying if we want to bring AI, LLMs, agentic AI, AI agents, we need a better defined QA function to be able to pull that off. Because brands should be terrified about putting something out there that is more open ended, that is going to be battle tested by customers and not having a prescriptive way of running tests before we release and before we upgrade those types of models. Roman, you’re the dean of students.

[00:33:21] Speaker C: I am not the trustee.

I am so low on the totem pole.

[00:33:26] Speaker A: Okay, but you’re closest person here for us at the coffee with digital trailblazers.

You’re going to steer 10,000 students over the next two years over where they should focus in their career. And AI is changing everything. Let’s talk marketing. Let’s talk product. Let’s talk it.

You talked about human computer interface. What other areas of skill development you think are really important for students, for recent graduates and for companies to cultivate?

[00:33:57] Speaker C: Product management is probably the biggest one.

What we’re seeing is AI even today is augmenting lots of different roles and skills that are out there. Product management.

I know this session is focused on the design discipline and user experience, customer experience stuff by augmentation. What I’m really talking about in that area would be things like user research is getting augmented by AI. That used to be a very time consuming, resource heavy thing that they had to do, but now you can do things like analyze thousands of user interactions, have AI driven heat maps, do predictive analytics that highlight specific friction points for real life problems, not just customers, but entire processes.

The other one is, and I’m surprised we haven’t gotten to it yet is rapid prototyping and testing.

Vibe coding has gotten a lot of press lately but if you think about it, high fidelity prototypes are now something that, that lots of different people can produce using vive coding tools like Lovable or Vercel.

The other part of your question though was I wanted to mention the fact that if you are a person that doesn’t have people you can or a company that doesn’t rely on or doesn’t have people with design skills that are ready to go, what we’ve done in companies that I’ve worked with, that’s work worked very well is starting any new innovative use. We started with a cross functional team dedicated to rethinking either the process we want to change or how we do that. And usually that starts with some kind of training.

So you know, a simple starting point might be a cross functional team to help put together an AI literacy training curriculum or an AI agentic AI process view way of changing things. So the big company, I think I’ve heard this multiple times on your coffees is the big mistake companies are making is they assume if they just give out gen AI to their employees they’re going to become more productive and somehow that’s going to create business benefit.

And alone I don’t think that can happen.

So that’s, that’s my short end of it.

[00:36:22] Speaker A: That’s a pretty, pretty long for a short end. Roman, I’m going to add another one from you and I’m hearing a lot more of this right now.

I’m hearing the executive that is projecting a moonshot vision and rallying the board for a big investment and then going back to their teams and saying let’s go do this.

And I’m listening to it, I’m like, you know, there’s probably five or six rapid prototyping that I would probably do before I went and promised that to the board.

And so I think we have, I think we have both problems. I think on the bottom end we have, you know, throw the tools at the staff and see what they make out of it. And on the top down end we have big promises that haven’t gone through enough sort of feasibility steps to say hey, we’re, we’re inching our way towards something real here.

[00:37:20] Speaker D: Right.

[00:37:20] Speaker C: And I guess my suggestion is use take advantage of people like product management or InDesign. Those people have a lot of expertise that they can leverage Even today if you don’t have those people put together a cross functional team that that can start usually with something simple, you know, either AI literacy or AI in the help desk, things like that.

[00:37:45] Speaker A: That excellent. Let’s go back to John. John, we’re talking about skills.

Let’s also breathe lead into governance to drive safe and innovation. I’m really interested in when AI is actively shaping the journey, what customers see the offers they get, even the tone of responses. What governance do we need to ensure these experiences remain on brand compliant and fair and without killing innovation. So go in either direction. We’ve got 20 minutes left. This is a great conversation.

[00:38:19] Speaker F: I think AI is so good at informational retrieval and so one of the skills that’s really important are really like the library skill, the librarian skills. And so I think the skills that somebody learns as a librarian are how to organize information. And the better the information is organized, the better the AI can retrieve it.

And you’re even seeing that now on websites that you used to be able to ask Google like a question to the website and the website owners would be responding on it on the my Google local business but now they actually Google just points at the website and uses its language model to answer the questions based off what’s on your website. So the better the website you have the better FAQs are automatically generated by things and and then so I think if you want any of this stuff to work, the language models can change at any time. And so I think you have to build some sophisticated filtering in so that people aren’t using things inappropriate. And so you really have to think through what is an appropriate request and what is not an appropriate request and you have to put a bunch of safeguards in so that you know things are appropriate for ages and then you have to test it it when it’s released and then you have to continually test it because these AI models that are third parties can change at any time without telling you. And so when I think of why Alexa has delayed rolling out a language model, it’s because I have three kids that are using it on a daily basis almost the entire time when they’re home. They ask it all sorts of questions and it could really hurt the Amazon brand if it was saying things that are inappropriate. And so I think that the continuous monitoring and just really hardening it so it’s not doing inappropriate things is so important to for brand protection.

[00:40:07] Speaker A: Love that answer John. Let’s go to Derek. Derek, skills or safe innovation? Where do you want to go?

[00:40:13] Speaker D: So that being Mentioned earlier, I think the rescaling but also factoring those things of cross sectional or cross functional type operating from a resilience point of view, I mean some of the things I actually put into the chat here, but looking at AI product owners, looking at AI and user design and conversation and design agents, these are all things going to be required. AI prompt and policy engineers and what they do is not just one particular unit that’s focused on, it’s really looking at all the business units that are going to come underneath this. And even taken to the point of security, reliability engineers, policy engineers, all focus on AI is going to be key. But I think Dan mentioned in the chat also one of the things is reskilling personnel to understand it. So taking somebody who’s been an architect that’s worked with the systems and now helping them to become an AI first end user type architect to help them better understand it, taking somebody that’s worked from either the finance, the marketing or the communication side of the house, have them work with developers from AI point of view to understand what they really need to help in marketing and actually work with understanding how to push it out as an end unit or product.

Security and risk teams are always going to be something that you’re going to need. As John mentioned earlier, being able to monitor adversary AI threats and concerns are going to be key throughout all organizations at every single level, whether you work with your internal stakeholders or external. Because this is things now as AI is getting more complex, they’re actually now finding ways to get around things such as encryption. So we need to make sure we have the proper AI threat intelligence tools in place and somebody monitor those to better understand how is this going to impact my organization and train them in the way that they can continue to understand and learn how these things evolve over time.

[00:41:54] Speaker A: Thank you Derek. I’m glad you brought up Dan’s comments. He’s got a couple on the common stream about architects. And my response to this Dan, is that if you look inside enterprises that have architects which aren’t enough of them, they’re siloed because of the level of expertise that’s required to build up that skill set. So there’s data architects, there’s application architects, there’s solution architects, enterprise architects, security architects, Basically every discipline has its own architect. And now when you bring an AI solution to market now you need a hybrid of those skill sets, right? You can’t just be an application or data, you need to see how those two fit together. And then if you are concerned about compliance and Security, you can’t be absent of those skill sets. So I think this is a call for super specialist, super generalist architects that can play in a number of different skills to be effective when you’re building AI applications.

John, what do you think?

[00:43:02] Speaker F: Yeah, because like things get there 90%, it’s a Pareto rule. But if you want things to work at the 100% level, that’s where you need the specialist. And I heard a talk by the Microsoft guy that led testing for Windows and I’ve heard it a couple times and people get mad, you know, at Windows for, for having all these issues. Right. But what he ultimately said is, is when you have millions and you know, billions of people using something, you’re going to find the edge cases. And the only, the only way to find the edge cases are, is to get a lot of people using it. And, and if you want to avoid the edge cases, you got to get the absolute best people possible doing everything you can to, to, to not have those edge cases. And that’s, that’s where you need the experts.

[00:43:46] Speaker A: Interesting. We think we have enough off of a tool set or methodology to find and test edge cases.

[00:43:54] Speaker F: No, no, I mean when I think about all the automated testing out there, there’s so much for security and there’s, there’s so much for like methodology that’s been built up over the last, I don’t know, 100 years of like computers. Right. But I think we’re just starting to get the concept of testing AI out and there’s, there’s frameworks for it and things. But I think it’s starting with the requirements of what’s allowed, what’s not allowed. I think you have to really think through what’s appropriate for the specific situation and that needs business buy in and then from there it’s like you need the technical stuff to implement what the business people want and there needs to be a lot of more work out there.

[00:44:33] Speaker A: Excellent. I got another comment here from Rachel Radin. Talks about change management. I need to put that plug in here with Martin out of the seat, speaker seat today. I completely agree. I think we need more people who are change managers. I also think we need more anthropologists. I’m thrilled my daughter is studying this.

When we talk about human computer interfaces and we start thinking about it in large scale, being able to really understand the impact on people, I just think it’s an under appreciated skill set that will become more important.

Let’s go to Joanne. Hey Joanne.

[00:45:16] Speaker B: Hi there.

I think one of the things you Know, to, to build on what, what I said previously and what others have said, it’s not just about putting together the cross functional teams, it’s also representing the codependencies, interdependencies and cross references of data and processes. Right. We never, we always tend to, you know, end up with siloed systems because when they’re designed and when they’re built, they’re a particular function. But with AI we have the opportunity to be cross functional and to think about how business processes interact and interweave with each other. You know, think back a year and a half ago to Digital Tapestry. The idea is that things are related to other things, things. So to your point, generalists, enterprise architects, process engineering, all of that needs to come in when we’re talking about whether it’s a customer facing system or not, whether it’s internal or external, because on the customer side you can’t interpret everything that someone is going to say a hundred percent, you might get to 90 if you’re really, really lucky. But you need to think beyond what their current ask is and what’s the bigger picture. So as we put people together in organizations to look at AI, we need to think about all of the aspects of that as well because if we don’t, you’re never going to be 100% true to Brent.

[00:46:55] Speaker A: Interesting.

Let’s go to Liz. Are we talking about governance or we talk about skill development?

[00:47:01] Speaker G: Well, all the above, but I think that I wanted to build on some of these conversations we were having about prototyping and making sure and finding the edge case cases. I think that those, and change management, all of those sort of go together around this sort of. I guess someone in the, in the comment was talking about cxo. I think it was also Rachel, about how we need to think about how the end experience is actually going to be enhanced and validated and do it in a way that sends out some kind of solution, gets the feedback, the anthropological feedback, the understanding what’s actually happening and then builds on that and builds on that so that we can actually incorporate those edge cases.

That whole approach is sort of a dovetail of, of, you know, Agile meets CXO meets AI. Like there’s so much involved in there and then I mean, obviously there’s the internal architectures, but I mean to me it’s all about business strategy and the business case and expanding your footprint and making sure it’s valuable in the, in the marketplace.

[00:48:12] Speaker A: You know, Liz, I’m translating that as the new experience better and in what ways that can be scaled. If I take my testing of Amazon Rufus yesterday, I would say it’s not better. I wouldn’t scale it yet. Keep it in that tiny little button at the top end corner and I’ll try it again in another month. We had.

I forget who it was. It was my friend Stephen and we had our episode on AI for Social Good and he talked about, really language models that are the new front end to forms, right? And so I’m going to type in a paragraph form instead of going and clicking through 16 boxes on a UI, that could be a really easy form factor because the output is controlled and you can do data validation on that translation from language into how that form is being fixed and you can get people to validate it. Once you get your data in there, you know, I think we’re going to find some examples of that. I think Joanne wants to chime in on an example here. So, Joanne, chime in in an example. But I do have one final question for everybody too, and you’ll be the first.

You know, when are we going to see the uber or Airbnb AI agent moment in a B2C context?

Go ahead, Joanne.

[00:49:35] Speaker B: Let me answer the question first. Before the end of 20, you will see it. There are people actively working on it the other.

And. And it’s because there’s not just a need for it, it’s because the mindset is starting to shift that, you know, where we were so ingrained in form factor and we were so ingrained in the way UI was being designed, people are demanding it.

You’re not the only one who’s dissatisfied with Rufus. There’s so many. There’s so many companies that are trying to crack this nut and the question is the approach that they’re using to do it and whether they’re using the right approach or not.

And it’s not about LLMs, Frontier models, it’s about small language models.

Because it’s a much more constrained question that you’re asking. Regardless of whether you’re talking to the bottom, you’re typing something in, it’s giving you a response. Etc. The reason I say the end of 2026, I don’t know when I. When I speak to Gail, our product, and, and I say, hey, what’s going on? It answers me and it tells me when I look tired and it tells me, you know, all sorts of other things which are. Which is a conversation in and of itself. But the other point that I wanted to make with respect to this is to change management and governance they go hand in hand.

And if we’re not paying attention to what actually makes up governance, and that includes tribal knowledge, we’re not going to hit the mark. Because when you ask for a tip, when you’re filling in that one text box, instead of clicking to, you know, 100 times on a form, you’re giving the user the opportunity to either opine or give their knowledge. And that should be used to reinforce the learning of any model, whether it’s a frontier model or a small language model. And that’s what’s going to promote the change that we’re all looking for on the B2C side.

[00:51:42] Speaker A: Interesting. So. But you think it’s coming aggressively soon?

[00:51:45] Speaker B: Very soon. Very, very soon.

There will be when, when you ask a bot human and it will actually understand you want to speak to a person.

[00:51:59] Speaker A: Interesting. What do you think, Roman? When’s it coming? When we’re going to see the sort of that inflection B2C application that will get us thinking differently?

[00:52:08] Speaker C: Oh, I’m already seeing some of that today. I mean, when I call my plumbing company, I’m talking to a bot and they’re trying to engage me, by the way, they’re trying to upsell me after I asked for the plumber to come out.

So I think you’re seeing parts of it already.

I do want to go on to your last question, though, about governance a little bit before we run out of time. I think you and Derek both talked about upskilling architects. I think we also need to upskill our legal and compliance people so that they’re more focused on trust and safety and able to create those guardrails and human in the loop rules and audit playbooks that we’re going to need for governance.

A while back, I think it’s almost a year, you had a guest speaker, Sukanyan, and she wrote a primer that I used to develop something for governing frameworks. You wrote a book called Governing AI for Responsible Future and it’s a great.

[00:53:17] Speaker A: I’ve read, I’ve read that book. It is a very good book.

[00:53:20] Speaker C: And what, what I, what I gleaned from it and built out of that was really governance can be done in three dimensions. You’ve got, today with AI, you’ve got the breadth, scale, the whole lot of people who can build a whole lot more things. And how do we manage with what’s going on, you know, because you can’t do code reviews on everything. Then we’ve got the think of it as the Y scale, the depth. How do you Handle a single solution that needs to scale to an enterprise level, you know, from just one or two people to hundreds or thousands of users. And then on the third axis you’ve got, as some people mentioned, like an operational oversight. How do people maintain oversight of these agents?

Need new interfaces for high volume oversight, maybe dashboards, which, you know, also needs to include security, access, data, exfiltration of data, you know, make keep going down the list. So yeah, those are the things you got to worry about in governance. And I kind of did it based on some of the reading I did and creating that XYZ access of breadth, depth and operational oversight. And where do you want to draw the lines?

[00:54:34] Speaker A: Roman, thank you for joining us this week. Really good comments.

I see a separate comment here from Joe is, you know, also debating when we’re going to see the Alexa Siri moment where there’s clearly really good AI and voice responsiveness. I still don’t think we’re going to see it in 2026. I think we’ll see pockets of it. What do you think John? Do you have, do you have a favorite B2C?

[00:54:58] Speaker B: Yeah.

[00:55:00] Speaker A: One that you think is. Go ahead.

[00:55:03] Speaker F: I think the one that’s going to come in through the back door is the AI agents built into the web browsers because the web browsers are kind of like the middleman between one of the most used interfaces between us and the world and it’s used by so many things. And so I think we’re starting to see it already is that they’re stitching AI into the, the browsers and that’s going to have some serious governance implications because the stuff that you see on your browsers, financial business, everything like that. And then I think we’re actually starting to see AI specific browsers being built and that’s going to be really interesting.

[00:55:38] Speaker A: Do you have a favorite one you’re using now?

[00:55:40] Speaker F: I don’t use them because I just like, I don’t trust it. And so, and so especially so at work we’re dealing with very, very sensitive HIPAA data. And so I just, I don’t, I don’t, I have to like go through all my tools to make sure that they’re HIPAA approved. And, and so I’m, I’m waiting.

[00:55:56] Speaker A: I think you just introduced a topic we’re going to have to cover. We have not covered AI web browsers yet.

Every time I log into Perplexity, it’s bothering me to download comment and I’m like, I don’t have time to play around with this. Joanne do you have. You have. Have you played around with these yet?

[00:56:16] Speaker B: Yeah, I have, actually, and I’m liking a couple of them. I’m going to keep them out of the mix of which brands they are, but let’s just say that my Perplexity account and I are parting ways.

[00:56:31] Speaker A: Oh, you know, I’ve been feeling a little bit of that myself, but for different reasons.

You know, I think we’re going to see a lot of leapfrogging back and forth for the next few years, which I think is another reason why we’re a little afraid to start putting AI first uxs out, because it’s.

If we built it with one partnership, with one model and the cost change or the capability changes, we got a lot of rework to go. Do. I think brands are also cognizant? I think CIOs are cognizant of that.

It’s been a really interesting conversation. Any last thoughts? Joanne?

[00:57:06] Speaker B: Just very quickly, I think to John’s point, they are being built into the browsers.

I think the browser itself is getting radically different from what I’m seeing, because even those that are big makers of them are recognizing the need for multi modality.

Yeah, you need to be able to talk to it. You need to be able to have it. Have the heuristics that are needed as if you were speaking with another human.

[00:57:39] Speaker A: See, I don’t know, Joanne. You know, coming off my trip to Japan a few weeks ago, like, voice is not going to be a big win in Japan. And the simple reason is I can click on my phone on the train. I can’t use voice on the train. It’s like a.

There’s nobody speaking on trains there. It’s just one of those things. And it’s beautiful and it’s amazing, but you’re not going to see people in Japanese trains talking to an AI bot or an LLM or anything like that. I don’t think you’re going to see that in India as well because of the many dialects and languages that are spoken there. You know, I don’t know if that would be my first place to be looking, but I do think the form factor is going to change.

[00:58:22] Speaker B: Yeah, 100%. And, you know, there are different ways to skin the cat. I mean, it is a cultural thing. There’s no question.

I think, you know, we are not all that verbose on our trains either.

I think the issue is also, there’s also a need. Two needs, and one is accessibility.

Right. I mean, you. You’re gonna want to be able to have AI that understands international sign language. You’re gonna have it for any number of particular needs based on accessibility, but also generally speaking, that we’re all getting really tired of typing prompts.

It takes too long. It’s cumbersome. We need another way to be able to do this. You can’t do it on a factory floor as an example, but you could talk to something and that’s why mixed realities are so big.

[00:59:26] Speaker A: I think we’ve got a bullish forecast that we’re going to see big changes this year given all the skills and disciplines. We still need inside companies to change customer facing user interface. It’s been a really good conversation folks. Thanks for all the comments. I captured some of these in the whiteboard and this, this episode I will publish publicly. So thank you for that.

Jay Farrow closes this that’s all we need, more people talking to their phones out loud in public. I agree with you Jay. Folks, thank you for joining week. We’ll be back next week to talk about Data Privacy Week, what we learned and why it matters. That’s on the 6th. On the 13th, transforming to skill and outcome based hiring.

On the 20th we’ll talk about National Entrepreneurs Week, opportunities for digital trailblazers. And on the 27th we’ll be talking about DevOps in the AI era, restating QA’s mission. If you want to be a speaker, if you have ideas for topics, please do reach out to me. And folks, a lot of snow, a lot of cold in the US going on right now. Although I am thrilled at the start of this episode we saw so many people joining internationally from India, from uk, from all over.

Please invite your friends and let’s continue to make this a really vibrant conversation here at the coffee with Digital Trailblazers. Just remember, you can get to the next week’s episode at the URL starcio.com coffee that will redirect. And if you want to look at previous episodes go to drive.starcio.com Coffee I have the previous episodes posted there. Folks, have a great weekend and I hope to see all of you here next week for the coffee with Digital Trailblazers. Bye now.

Hosted by Isaac Sacolick, CEO of StarCIO

The episode focused on the impact of AI on Level 1 expertise and entry-level roles in organizations. Isaac, the host, shared statistics on declining employment for early career workers and the need for companies to adapt their hiring and training practices. Participants discussed how AI is being used to automate tasks traditionally performed by entry-level employees, raising concerns about the loss of critical skills and knowledge. The group explored strategies for companies to retain and develop Level 1 expertise, including retraining programs, apprenticeships, and a shift in how roles are defined in an AI-driven world. They emphasized the importance of critical thinking, context awareness, and decision-making skills in the new AI landscape. The conversation also touched on the need for organizations to adapt their change management practices and redefine roles to maximize the benefits of AI while minimizing disruptions to the workforce.

See the blog post on 4 Ways to Boost Entry-Level Talent in the Gen AI Era

[00:00:02] Speaker A: Hello everyone. Welcome to this 157th episode of the Coffee with Digital Trailblazers. We meet here every week on Friday at 11:00am Eastern Time to talk about areas that digital transformation leaders are facing in technology and leadership and practices as they guide their organization. So through all the changes, whether it’s AI, whether it’s quantum computing and everything in between, today we’re talking about a very practical conversation around the talent crunch and developing level one expertise in the Gen AI era. Knowing that so many organizations are looking for productivity improvements, looking for ways to drive efficiencies using AI capabilities, deploying AI agents and getting even into some agentic AI capabilities.

The groups in our companies, the employees that are most impacted by these changes are our Level one employees. Those are on the front lines in customer support and IT support and information security, handling the socks and IT and the network operation centers in marketing and handling content and SEO and everything that AI can actually drive a lot of value in, but also is impacting the people’s jobs here. So what I’m sharing here today with you is just some of the latest statistics that I grabbed from the Internet around the impact on Level one expertise.

First one is coming from Constellation Research. It’s quoting a study based on ADP data. 13% decline in employment for early career workers. You can see that in the charts on the right hand side postings for entry level jobs reported by CNBC. About 35% decline since January 2023.

A really good article from Institution Labs had just a good number of data points in there. 66% of enterprises are reducing entry level hiring.

According to the Institute of Student employers in the UK, there’s been three expect they’re expecting a 53% drop in graduate hiring in 2026.

And in the US this was actually covered in the Wall Street Journal.

They broke out the unemployment numbers here in the US and age 20 to 24 that number rose to a high of 9.5% in September 25th.

So that’s really scary numbers to make the numbers worse.

Even as you look at that from the bottom up as somebody who is a Level one person coming out of school, the expectations of the skills that you have to be able to get a job in this climate has also increased. This is coming from Deloitte and reported by Computer World. 77% of early career and 67% of tenured workers believe AI raises expectations for entry level roles. And gartner is focusing. 39% of the workforce is expected to experience significant disruption in the next two to five years. And what that means is in addition to all the entry level folks to 22 to 30 year old folks, there’s going to be a backlog of people who are laid off or are have to retool themselves or move from one part of a country to another to get a job. And that’s going to create competition for everybody getting jobs over the next two to five years. It’s a, it’s a real mess.

And Joe will remember this.

We were at a CXO Spark conversation I think. Was it December, Joe? I think it was roughly around that time. And talking about this impact of the level one employees that are going to be most impacted.

We came to the conclusion at that meeting that a lot of companies are simply not going to care about this in the short term, that they need the cost savings to offset all the investments they are making in a AI. But this is going to have some mid and longer term impacts if we don’t hire the people in critical roles in entry level positions today. They don’t have the expertise that’s required for them to gain more leadership and managerial roles going into the future. And there’s a lot of CIOs that are worried about this. So to kick off our conversation today, let’s, let’s just go around the room. I’m going to start with Joe.

I think Kevin, who is our special guest has, has made it onto the floor, but we’ll start with Joe. And you know, I just ran off a whole bunch of numbers and they all add up to the same thing. At a gross level it’s going to be harder to get level one expertise in companies. I just, you know, of those different metrics that I shared, which are the ones that are scaring you the most?

[00:05:22] Speaker B: Well, I think you alluded to the Spock conference in New York City where we heard that, you know, that trend toward allegedly cost cutting in taking credit for AI having substituted for those entry level positions. And so we’re saving money and we’re smarter now. We’re going to make more money and everybody’s going to be happy. And, and it’s a fool’s paradise because of the reasons that you, you’ve cited.

There is no, no training going on at the lower level.

I’ve always bemoaned the fact that I, I feel like people, people don’t have the skills already to figure things out.

And, and you know, our education system is failing now. The entry level positions and companies aren’t there.

This is a disaster waiting to happen. I think the long Term prospects are sad.

[00:06:24] Speaker A: You know, there’s an article, I think it’s in the Times, New York Times, just today or yesterday talking about how schools are now partnering with Microsoft and partnering with OpenAI about bringing ChatGPT and Copilot into the classroom in a more proactive way.

I don’t know. I don’t. Do you see that as a way of closing the gap or do we have a lot of room to.

[00:06:51] Speaker B: I think there are two issues. One is critical thinking, which has been lacking for years in my view and secondly, they’re addressing the issue of the need for skill sets in either building or using AI.

They’re not addressing the issue of understanding how business functions, how things are done in my company, how to work with other people and learn from your mentors or from your management. How things are done.

That’s all vaporizing. And boy, I think a few years down the road we’re going to really feel the effect of that.

[00:07:31] Speaker A: Oh boy.

Kevin, can you just say hello to the group and maybe weigh in on some of the things and the impacts that without level one expertise, some of the things that were you.

Kevin, you’re on mute right now.

Let’s jump to Martin. Martin, you’re raising your hand. Go for it.

[00:07:57] Speaker C: Well, I think that there’s a couple of things I posted, kind of a slightly tongue in cheek old joke in the, in the comments.

Yeah. What is the most common phrase uttered by recent graduates? Would you like fries with that? And yeah it from previous rounds of this type of thing happening that kind of. That joke was going around and unfortunately it’s fairly true and it’s quite sad really. And I’m kind of.

It does concern me. I think what we are seeing is a lot of knee jerk reaction to companies saying they’re going to save a load of money from using AI and therefore in order to satisfy the street they are cutting back on things like new hiring, other things like that.

Even though AI may not be making those savings, they are having to kind of basically for the shareholders benefit, they’re having to actually demonstrate that they are reducing headcount by, by using AI. So I think you’ve got a kind of a false, a false positive, if you, if you want to put it that way from doing that. And I think that’s kind of very concerning. I agree with, with what others have been saying, what Joe was saying, etc, that this is very, very concerning because if you’re not building the basic skills then how do you get the more advanced skills? And there’s. Yeah, we Talk about the kind of silver tsunami of a lot of people kind of waiting to retire. And if you haven’t got the skills coming through in order to actually replace those things, then you’ve got problems. And how much of that can I actually do?

And I suppose the other kind of thing I’d throw in there is I was talking to somewhere in higher ed at one point about a couple of years ago and they said to me, yeah, this was talking to it undergrads and things like this. And I said, what languages should the undergrads be learning? And I said, well, by the time they graduate and get into industry, whatever languages they need, what it was going to change.

So what you really need is critical thinking, the ability to understand business value, the understand to logically think through problems and structure responses and answers. And those types of skills can be applied to any situation, any programming language, AI or anything else. You need those core skills. And if you, if you understand the concepts of programming, you understand the concepts of logic and things like that, you can apply that in so many different situations.

So I think it’s all of those pieces coming together to say if you, if you’re missing some of those kind of key chunks, then things start to fall apart.

[00:10:51] Speaker A: Mark, we’re going to go deeper around that. I want to hear from maybe like Derek and John. We talk about technical skill sets and you know, we’ve always, every generation of new technologies allows us to go upstack a little bit.

And so, you know, we don’t learn tcpip, we don’t learn, you know, assembly program for the most part anymore. And Fortran has fallen off the wagon. But I have a hard time believing that our next generation of software engineers never learn how to, I don’t know, never learn distributed computing, never learn best practices around securing an application, never learn about object oriented programming and go straight into vibe coding and using English and just hoping that what the machine spits out is what our application is actually going to do. Derek, I just want you to hold off. I think Kevin is with us right now. Off mute I want to. Kevin is our special guest today. So before we jump into technical skills, Kevin, just say hello and your thoughts around the impact on entry level roles and hiring from AI.

[00:12:01] Speaker D: Yes. Hello everybody. Welcome.

Well, I’m glad to be here and greetings from a rather wet, cold London, but I’m sure it’s the same over there.

I think what has been said so far is absolutely on the money.

There is a disconnect between what companies are doing and what they should be doing, they are looking, as has been said, to try and save money. And rather than augmenting the Level 1 resources and giving them the tools to do their job more efficiently, they’re seeking to replace them with AI, which is a very short term plan, as Joe mentioned.

And we’re seeing what has been referred to as a broken rung in the ladder. So in the ladder that people would normally climb up on their journey through the company, there are rungs missing because they’re not getting the grounding, they’re not putting in that thousand hours of, of learning on the job to actually understand how things work so that they, they can become subject matter experts. And if the knowledge is only within AI and not within their brains, then they will not be able to proceed. So it may save costs in the short term, but in, in the five year time frame when those guys have moved up the chain, they won’t have the knowledge and the organization will suffer accordingly. So I agree with everything that’s been said and it’s a very worrying situation, whereas it could be a cause for great optimism because we can be increasing the efficiency of these people considerably. But by doing it in the way they’re doing, it’s having the opposite effect.

[00:13:43] Speaker A: Yeah, we’re going to get into some of these details about some of the roles that we think are, you know, we need to have at that level, one entry level area and some of the skills. And that’s where I’m jumping right to Derek. We’ll go to Joanne and John after that. Derek, you know, you’re a ciso, you know, are you getting rid of the society? Are, you know, what are some of the skills that you think are just critical for, you know, beyond just critical thinking, beyond the being able to do analytics and evaluate the AI. Let’s get into, you know, what should somebody who wants to get into security really home in on to be employable over the next few years?

[00:14:23] Speaker E: Yeah, I mean that’s a great question. And as far as getting rid of the SoC, the socks not going away, but the SOC will become more efficient. So for those socks that may have had 12 analysts, you know, analyzing different threads and things coming through that may be cut from a 12 now to maybe 3 or 4 where they use an artificial intelligence now to do the threat intelligence monitoring and seek out those anomalies that they need to pay attention to. And those four remain will be humans in the loop. The statistics that you mentioned earlier about the automation of these routine things that are taking place, you know, it’s staggering. You know, the people that are in college now coming out of college and trying to figure out what kind of job opportunity am I going to have. This is scary stuff. When you’re looking at, you know, a lot of the companies now, when they see these tasks like the soccer, they look into other things such as the IT help desk, a lot of these triage endpoint hygiene, the user education and training, they’re now automating these processes now because they realize they can use an agentic AI and these bots to actually allow them to replace what might have been a human in the loop to do so. And they, once they train up the system, it can become more efficient. So when you’re looking at these particular numbers and the jobs that are available, yeah, it’s, it’s crazy. I mean, you know, just the one of the things I was reading the other day about the ISC Square and some of the World Economic Forum, they’re talking about 39% of the jobs between now and 2030 will shift due to core skill changes. Which means now those people that are coming out of school, they need to understand, as Joe said, not just the business piece, but they also need to understand what are the skills that I need now that are really going to be adaptable to what I need to do to get a job. Because if the jobs are being taken over by generative AI and the AI bots and stuff, they need to redesign and think how they get involved in the workforce, what kind of things based on governance and, and some of the things you mentioned earlier, Isaac, about, you know, understanding networking services, some of the basic applications to an extent, because you still need the AI to run on these tools. They, they have to run on some sort of system and database. So you still have a small subset of people that need to do that, but eventually that will go away. But at the bottom line is, you know, these leaders and looking at these jobs that were coming in for entry level or internships, they are going to be reshifted, they’re going to be rebranded, they’re going to be removed or upgraded to a different skill set requirement that you need to have coming in the door. And that’s something that’s going to take time to mature.

[00:16:46] Speaker A: Thanks, Derek. Joanne, your thoughts not just on it, but maybe even getting into operations.

How do we think about what roles are most important to preserve at entry level so we don’t lose the knowledge and we build up our next generation of workforce?

[00:17:03] Speaker F: Well, I think that there’s two things. One is a semi contrary contrarian point of view to what’s been said. The early adopter companies, those that really jumped on the AI bandwagon at its earliest availability, have seen the light and they’re beginning to realize that they need to create specialized cohorts around process and specialized cohorts around what would be niches in the business that require in depth process knowledge to be able to promote people up the food chain. So they’re starting to look at how do we leverage critical thinking skills for sure, which are always a mandatory. But they’re coming at it from the point of view of I can teach people how to prompt, I can teach people how to write, you know, build knowledge graphs and build all the other tooling that’s required for AI or buy it because it’s becoming readily available.

It’s the process, the nuance, the, the stuff that used to be called on the job training about the business and how it operates. That’s what they’re focusing on training people for. And if I had a word to the wise of not only companies that are looking to lay people off, I would think long and hard before you lay those people off, that the tribal knowledge or institutional knowledge that’s going to go out the door with them is not only invaluable but extremely hard to replace. And from that perspective, if I was a new grad or about to graduate, I’d be looking at various operational issues, whether it’s on the plant floor or in manufacturing of some sort or, or just in the business aspects, true business processes of corporations and looking to develop skills in that area.

And that’s where I think they’re going to be far more employable in the near and midterm and then they’re going to get the rest once they land on their feet. In some companies, I think we’re also going to start to see people staying in jobs longer at the level one to gain that knowledge, to be able to then kind of leapfrog up the stack to go from a level one to let’s say a first level manager or maybe even higher because they will acquire the business knowledge that they need and companies will begin to change their tune and value those, those skills more than the actual how many, you know, how many ways can I write a prompt and how many ways can I build an agent?

Because that will be automated.

[00:19:49] Speaker A: Joanne, I want you to think about your action plan around this because I mean, you advise CIOs and CIOs. We’re going to get to that after our midterm Break. But I do want to hear from John and who else is here. We haven’t heard from Liz yet. Your thoughts on, you know, just some of the roles, some of the areas that SEA leaders should be concerned that without entry level expertise it will impact building the next generation of subject matter experts and other hands on knowledgeable leaders. What are you focused on, John?

[00:20:23] Speaker G: Well, I see AI is something that’s being used by everyone. Anyone that has access to the Internet is using AI. Anybody that’s doing Google searches right now, they’re getting zero click results back that have AI in it. And so I think it’s just our whole society, people in every country right now are using AI and what we really need to do is get people proficient so that they’re able to really use AI.

And I think what we see is that almost everywhere it’s able to bring the people at the lowest level up a level. But I think what’s really important when you’re building applications or you’re doing marketing or anything else in the business or operations or security is you have really, really highly skilled people. And so that, that really takes time to build. And so what we need to do is make sure that we’re always having people that are, you know, that at the lower levels are benefiting from the AI, but we need to make sure that we have the people that are still the subject matter experts apart every, across every part of the company.

[00:21:25] Speaker A: And so John, John, let’s role play a little bit, right?

CIO is telling you you got to cut headcount by 10 to 15% in this software development group.

And you know, he wants to first know what skills you’re going to preserve so that you can continue being an excellent software development shop. What are some of the skills that come to mind that you’re going to say, you know what, I’m not going to lose my Level 1 experts in A, B and C. Yeah.

[00:21:57] Speaker G: And so what I would do in that situation is really understand what we need to run our current systems and understand like what do we need to run our current systems and then what do we need to run in the future. And based off that, understand what people are really important to retrain and what people are really important to keep in the company and build a plan off that. And what happens is unfortunately the programming languages of the year change every year. And so when you go to build an application in a couple years from now, you’re not going to be using the languages that you use right now.

The one common language is actually SQL that seems to be number two, language for people learn for the last 20 or 30 years. And so SQL is always one of the most important things for people to learn as a skill.

But after that I would really start being in a skills inventory, make sure we really understand what we need in our company to keep the current applications running and look to see which way are we going in the future and make sure we have people with those skills.

[00:22:51] Speaker A: I’m going to add so you’ve got knowledge in the platform. So if I’m running Salesforce or Workday or whatever I’m running, make sure we’re not losing that expertise. Number two, the basic building blocks that are, you know, you could say called it SQL, I think data skills in general. And I’m going to add a third one, John. I’m going to say my testing people.

Yeah, I have not met companies that invest enough in testing in general. And so if you lose your testing people, I don’t know how you upgrade your applications.

[00:23:22] Speaker G: Testing is going to be so much more valuable in the future because generative AI is able to write code. But when we’re plugging code that’s written into existing systems, we can’t change everything. We have to make sure that code works with what’s already there that’s been built over the last 30 years. That’s where the testing skills, where we are, we call them sset, system development, engineering and testing. And those are going to be some of the most important skills to make sure that with whatever is being built in by the AI and the younger people and new people and the new systems is playing nicely. And then the other thing is, is anybody that’s using an AI service in their application, what you get today may not be what you get tomorrow. They’re constantly changing those things. And so if you’re having a production system, you may be getting changes from your AI providers and you may not be, you know, be given a heads up on those things. And so just to keep the systems running, you know, you need good testing, monitoring.

[00:24:17] Speaker A: Thanks, John. Let’s go to Liz.

I really, I’m kind of curious where you’re going to take this conversation. You’re advising the cio, Same question.

You’re facing headcount reduction and you know, she or he asks you the question, what skills do we need to preserve?

[00:24:36] Speaker H: Okay, well, so first of all, if you think back initially when you talk about new hires, there was two kinds of tasks that you gave to new hires. One was grunt work and one was true apprenticeship work where they’re learning the business, learning how you think as a leader, you know where you’re growing them into the next level Grunt work is now AI. You offload grunt work to AI. I would say testing is something in the same list of brunt work.

I would not say testers are that important. Maybe testers to someone who can think about operational impact, who can think about the overall systems impact.

They need to understand how to set up the AI so it can do the testing, but they don’t need to do the testing.

So I would say that apprenticeship, you want to keep the people who you can see in the future, can grasp your role, can understand and grow into your role and who you can then double up in terms of leadership. Not necessarily people who just do a good job.

[00:25:53] Speaker A: Liz, you gave me my topic for a future one is QA grunt work or is it not? We will have that as a future episode and you and I will debate that. Let’s go to Joe and Derek. Joe, what skills are you hiring and preserving as a cio?

[00:26:12] Speaker B: I want to build on what Liz said and circle back to something we talked about a little bit earlier, if I may.

You know, Joanne is always saying human in the loop.

And that same Times article that you read, I believe, talked about the human in the lead.

And if you’re going to lead the use of AI, you have to understand it, as I think Derek has pointed out in the comments stream, and understanding it. Now to circle back to some earlier comments, it’s, it’s about understanding what’s under the hood. You know, I started thinking about an analogy here to the automobile. The automobile has become so smart and so sophisticated that most mechanics can’t, can’t fix it. Right? You have to be a, a, an automotive computing technician using all sorts of very sophisticated equipment in order to troubleshoot and fix a car.

But it doesn’t take away from the need to understand how the internal combustion engine works or how an electric car works. What are the fundamental elements of it.

I see an analogy here to business in general.

If you don’t come in at the bottom and understand the spark plugs fire, the pistons and the gas and the oxygen igniting is what causes the compression and turns a crankshaft and so on and so forth, how are you ever going to understand how a car works?

We need to get back to fundamentals. Bring back shop in high schools, bring back fundamental technology, understanding in higher learning, higher institutions.

And I think that will go a long way towards solving these problems. Sorry I didn’t answer your question, but I Had to get that off my chest.

[00:28:02] Speaker A: No, I mean there is an equivalent. I mean, you know, when you talk about how we recover from an incident, you know, Verizon had a major incident last week. The network was down for a whole bunch of different places and I still haven’t seen the RCA that. I don’t know if anybody knows around what it is, but you could think of, you know, we’ve all been through this. What happens when a system goes down and do you have enough monitoring to figured that out before it’s catastrophic? Do you have automation in place and most important, do you have observability in place so that you can really find root cause and address issues proactively?

And that’s a tremendous skill set. You just don’t walk into IT operations or anywhere else and know all the bits and bolts and how things are connected to be able to do that. We’re going to go to Derek. Derek, same question for you on the security side. I will take my break and then I want to go back to Kevin and Joanne. I have a question about the Industrial Revolution for them to be able to make some analogies for us before that. Derek, before our break, what are CISOs.

[00:29:13] Speaker E: Preserving so they really need to look at? There’s still a few things that they need to look at. The GRC portion of it, although some of that can be automated, there’s still some AI strategies that really require critical thinking to make that happen. You just can’t say, I’m going to plug it into a machine and have it spit out something to what I need. That’s not going to work. Well, the human in the loop is still going to be possible.

The privilege access. You still need somebody to double check if a machine is actually doing this. Double check to make sure it’s doing it properly. These are things that you can’t have been overlooked because the machine doesn’t know any better that it’s not the right thing. If you don’t have the people in there when it comes to marketing and other things and use a technology to look at the other business units, you’re still going to have people that need to evaluate the marketing paths that need to take place. All these different things require visibility and eyes, people, people in the loop to be part of the process. And then, you know, the operations thing and looking at the customer support, you see a lot of that being now being using artificial intelligence. But there’s some cases you just want to talk to a human to get a response quicker because you don’t want to have to go through the process that’s already been predefined in the LLM to try to get your answer. It still takes time. So there’s still things as people are still trying to figure it out, you know, and it makes it work. And I think Joe brought up a great point earlier, you know, and mentioned in the chat that this critical thinking is huge. I remember going to a movie theater a couple years ago and their cash register was down and I said, well, you can count it back as far as how much money. So well, I I need to figure that out. So you already see where people are using and relying too much on technology and artificial intelligence, where they’re not using their own brain to figure things out. And these are things that are going to be imperative. As you said before, you you know the cars nowadays that you plug them in, you figure it out. You need to understand what happens when it doesn’t work. And the example you mentioned with the Verizon is a perfect example. How do you get around it?

[00:31:03] Speaker A: Thank you, Derek Folks, welcome to this week’s Coffee with Digital Trailblazers. Our 157th episode today we’re talking about level one expertise in the generative AI era. We’re looking at it from the employer’s perspective.

What are we doing to make sure that we retain the talent at level one expertise, continue to hire that talent so that we don’t lose industry, business knowledge, subject matter expertise, and tribal knowledge. Another topic that we’ve covered here several times here at the Coffee with Digital Trailblazers, we meet every week here at 11am Eastern Time.

Just about every single week. And our episode next week will be on AI First User Experiences Planning for the evolution of Generative AI Enabled Customer journeys. I have not set a schedule for February, so if you have ideas for topics or you want to be speaker on this, do reach out to me on LinkedIn, send me a quick message and say I have an idea around this. Liz I don’t know if we’ll do QAs not grunt work or is it next month, but we will see and I’ll have all the announcements of February’s topics sometime in the middle of next week.

For those of you trying to find how to join this couple links that you can remember. Starcio.com Coffee will always redirect to the upcoming episode and if you want to watch or listen to previous episodes you can go to drive.starcio.com coffee that has a bunch of episodes there. I also publish a bunch on Spotify and Apple Podcasts and you can always visit LinkedIn to these URLs and find previous episodes if you want to listen to them. Lastly, we are doing a new push this year for people to join the Digital Trailblazer community. If you have not checked that out yet, please visit drive.starcio.com community. You get access to all the past episodes, you get access to experts and I’m adding some new capabilities this quarter.

So please consider joining our network of Digital Trailblazers. Back to our episode Today we’re talking about level one expertise in the Gen AI era. I want to bring back our special guest, Kevin Wallace. EAD Kevin, you and I had a very interesting conversation around the fourth Industrial Revolution and drawing analogies from there.

I want you to just share a snippet around that. We been down this rodeo before.

What are some of the things we can learn from the Industrial Revolution that say let’s not make those mistakes again this time around? AI Hello Kevin.

[00:33:52] Speaker D: Yeah, hi.

So absolutely right, we are clearly in the fourth Industrial Revolution. Now terminal, that’s been coined and used probably over, over overused by many people.

But it, it requires a complete change in mindset from the third Industrial Revolution in the same way as going from an agrarian society into the first Industrial Revolution required a change in mindset.

Except we’re doing it probably a hundred times faster. Things are 10 times faster and happening 10 times more quickly.

And I’m finding that a lot of companies are still working in a second Industrial Revolution mindset. So automation, standardization process and so on, which is all fine and well and good, but it doesn’t fit very well with the new way of working with AI. So companies have to go and I think rethink how they can implement AI. Because trying to implement AI into old fashioned processes which do still have their place by all means. But trying to implement AI into those older processes will maybe give you a 5 to 10, 15% maybe enhancement in productivity. Whereas if you invert the whole way of thinking and approach the the process that you need to complete from an outcome point of view and look at It from the AI’s perspective, you can see significantly larger gains and possibly, you know, more than 100% efficiency improvements. But it does require a considerable amount of thinking. Before you go and do that.

Just to use an analogy, what with what I’m seeing is companies will say, oh, look at this wonderful new AI and it’s like somebody going out and buying a whole bunch of very high performance racing cars, dropping the staff into them, not hiring professional drivers, sending them out on the new on the track saying, you know, isn’t this wonderful? And of course, all the stuff spin off into the gravel trap on the third or fourth corner because they haven’t been given the opportunity to learn how to drive those vehicles. And I think there is an assumption that everyone can use AI. Yes, everyone can use AI, but not to the levels that they need to, to be efficient.

And I think a lot of companies, not all of them, are just sort of kicking the tires at the moment.

We mentioned earlier some of the early adopters and they are starting to get it and they’re having these thinking processes, but a lot of companies aren’t in that. Aren’t in that phase yet. They’re bringing in a few tools, but they’re not thinking. The culture of the business and how it affects the culture, the people, and even the values of the company may need to change as well.

[00:36:47] Speaker A: I’m going to go straight to Joanne now. I lost connectivity while Derek was speaking. I think I’m back.

Joanne, do you want to continue?

[00:36:58] Speaker F: Sure.

I see it slightly differently. It is definitely a mind shift around AI. It was previously a mind shift around Industry 4, and there were a lot of epic failures in Industry 4 as companies tried to roll them out, because from a early adopter all the way through a laggard, we were seeing repeatedly that the workforce was not brought into the picture until far too late in the game. So you either had resistance from the workforce or you didn’t have enough planning. But where the real gap is and where a lot of companies are using AI now is what’s called the execution gap. And what they’re not realizing is that as part of the mindset shift, you really have to look at what is the AI doing. And it’s not about bots and it’s not about generative in the agentic world. It’s about how are you bringing things like judgment into the equation. What are you using for your baseline, for purpose in the AI? In other words, are you trying to solve a business problem or are you just trying to basically shortcut around some of the processes that seem to be out of sync or out of whack in some way? And really, it comes down to the company looking at things from the point of view of what KPI is being used to measure things? That’s number one. How are you institutionalizing that KPI through agents or through even generative to a more limited extent? And then how are you bringing in things around context? And context is not just what is happening.

Generally speaking, context is a very specific thing, it’s very purposeful and it gets you to judgment. Is the system going to make the same judgment that you as an executive would make if it’s running autonomously? The answer to that is probably going to be no. And how often are you actually going to trust an agent to do that kind of critical thinking for you? So those are things that the mindset shift has to bring into play and doing it in a way that’s either based in evidence, based in provenance, or based in lineage. Where did the data come from? How reliable is that data? How clean is that data and what is the context around that data? How is that bringing you to a better determination? Because the whole name of the game is not to replace the human, it’s to augment the human to make better business decisions faster, more safely, and in a way that is risk.

Not averse necessarily, but weighted trade off management will be the next big thing that we’ll see because it’s always been the killer for every system out there. And a lot of people believe that AI is the way to go to get that trade off management.

Only with human in the loop, however, does it work.

[00:40:14] Speaker A: Joanne, you have some really important words here for people to just, just keep at the back of their mind around context, around judgment. I’m going to add, and I put in the comments here around being able to recognize that the tea leaves are changing, right? Your, your objectives are changing and so the decisions that your AIs may have been programmed to need some remodeling around this. I, you know, I think about the early days of robo traders and then all of a sudden there’s a structural change in the market and they’re just flying off the cliff.

I think about the bounds of the early bots and what they were able to solve for and what they couldn’t solve for in a rules based oriented solution. Even in an agentic solution, they have limited context compared to human experience.

And that’s what we need to teach people, right, is how to develop that context, how to develop strong judgment skills and when to recognize when the world is changing and we have to make decisions differently than we’ve done in the past. Go ahead Joanne. I’ve got everybody’s hands raising here, so we’re going to hear.

[00:41:18] Speaker F: Sorry, I just want to be very quick.

It really comes down to how the critical thinking of the individuals is being applied through the AI. Now I’m not, you know, I don’t want to go on a long thing, but I posted about this earlier in the week and I will be posting about it more, more often because it really comes down to perspective and how you apply the perspective, which is the empathy, the expertise and the experience of the individual. And that is one of the key aspects that makes AI succeed where other areas may fail.

[00:41:58] Speaker A: Oh, I, I, I’m really loving this idea of perspectives. I, I just have to figure out how to coin the phrase, but I’m not going to go there now. I got Liz and John raising your hand. I’m looking for action plans. Go ahead, Liz.

Liz is on mute.

[00:42:18] Speaker H: Sorry about that.

Action plans for apprenticeships. I mean this judgment, context, perspective, these are all things that can only be learned with maturity.

So as we’re going forward, you want to the folks who have the ability to maintain context or grasp, grasp context or shift context, these will be the best people to, you know, bring up the food chain and actually create as your leaders. And the biggest indication of someone who can grasp these things are good listeners.

Listening and being able to really comprehend and shift context and shift perspective based on listening. Those are the people that you can, you want to target it to keep and grow.

[00:43:09] Speaker A: Thank you, Liz. Let’s go to John.

[00:43:12] Speaker G: Thank you. Yeah, I love hearing Joanne speak about this because she’s, you know, founded a startup that uses AI and it’s just like the way that she describes things is absolutely, you know, the best way to do things. Unfortunately, most companies that are trying to adopt AI, they don’t have much of a strategy on anything. And they have started adopting AI largely by the employees bringing and using AI into their daily activities, often through the commercially available ones. And so I think that the first thing any company wants to start using this stuff is that one is they have to figure out what kind of company they are. They have to figure out their core values.

They really need to create a strategy on how they’re going to use this new general purpose technology.

AI is a fundamental technology that’s like electricity, it’s like the Internet and it’s such a important thing that transforms everything. And so I think people, people really have to figure out like, where are they going to use this and what’s their strategy and where are they not going to use this stuff?

And then I think they just have to, they have to start doing the general change management activities. They have to actually have tools that are blessed that people can use. Because right now if people don’t have blessed tools, they’ll just use any tool that they have and who knows what’s happening to the data of the company. Right. And so I think those very, very fundamental things on how you roll out technology apply that same thing to AI. And that’s, I think, the very initial steps that people have to take.

And it’s really neat. And unfortunately, when I’m reading the news over the last couple days, I’ve been reading a ton of stuff about how people aren’t getting value from AI. And when I look at how they’re doing change management, I’m not surprised. I’ve seen so many computer systems rolled out without change management and get zero value.

And I mean, Martin has articles on this stuff, and so it’s no surprise that people aren’t getting value when they’re not doing change management.

[00:45:07] Speaker A: You want to comment on that, Martin?

[00:45:10] Speaker C: I referenced it before. Don’t finish your implementation.

A full adoption is where you’ve got to get to. And that’s the big gap.

Now, I was going to say, just to John’s point there, if you only have a hammer, everything looks like a nail.

So you need the right tools. And he talks about tools being blessed or whatever, but you need the right tools for the right jobs.

I, I was just going to say, I was going to go back almost the first principles, which is, at the end of the day, AI is just a tool.

And right through history, right through all the industrial revolutions, we have improved tools, improved capabilities, and the ones that win are the ones that work out how to use those tools most effectively.

So, yeah, the action plan has to be looking at, okay, what are we going to use the tools for and how we’re going to use it?

And then how do we approach using it? How do you make use of it? And that then comes into those skills we’ve been talking about the other day. I won’t repeat all the skills, yeah, critical thinking, logical thinking, all those types of things.

But you have to go to, this is a set of tools. How are we going to use those tools? What are our end goals that we’re trying to achieve?

[00:46:31] Speaker A: Joe, maybe comment on this a little bit. I was just trying to put the comment in here.

I agree, Martin, AI is just a tool.

But that’s not what our board and CEOs are hearing from big tech, from McKinsey’s, from, from a good number of very influential people that are basically saying, you know, 39% of the workforce is going to be changing over, be disrupted over the next two to five years. That’s coming from Gartner.

So, Joe, you know, how do we set a pragmatic tone around Our action plan. When there’s kind of two different extremes of what boards are hearing. AI is just a tool versus AI is going to change everything.

[00:47:17] Speaker B: Think one has to set expectations accordingly.

I think it was John who was talking about, you know, managing change and this, this is a big part of change management.

Managing the expectations and developing the understanding.

I love analogies and I would posit that AI is like the mechanization of the construction industry. You know, when all we had were shovels, it took 20 men to dig a ditch. And now I could go in there with one steam shovel in 10 minutes and dig the equivalent ditch.

But it, it means that I have to have a guy who understands how to pull the levers and make that machine do what we wanted to do and not go hog wild and, you know, rip up everything willy nilly.

It also doesn’t eliminate the need for somebody who understands how deep the ditch has to be, how wide it has to be, and why it has to be that way.

If you’re asking for an action plan, I’d also suggest that this is a two pronged problem. There’s a problem of what companies should be doing internally and that is retraining. Again, something you’ve heard me say numerous times, almost as much as communication. Right? Retrain the workforce. Turn the workforce into leaders of the AI technology.

Get them to understand how to manage the AI technology in the same way that they would manage people.

So it’s management training in a sense.

I’m not going to be managing a human, so don’t train me on how to speak to it politely and not get into all sorts of issues. But, but make sure that the task is defined well and clear and measurable and all those things that we teach in management structures. And then there’s the societal, which we could go on for hours, but our education system has to be changed and adapted to today’s requirements. Anyhow, sorry for the long winded response, but there you go.

[00:49:26] Speaker A: No, it’s great.

I agree with you about the retraining part and it’ll be very interesting to see how companies interpret that because, you know, when there’s cost cutting moves, training actually is one of the first budgets they end up cutting. So this is something that I’ve been, you know, talking about quite a bit. If you’re going to be retooling, you got to be investing in retraining. Go ahead, Derek.

[00:49:52] Speaker E: Yeah, I mean, Joe’s spot on and some of the other things that the others have said as well. But I think when you look at this, you know, the apprenticeship, the thing that Liz mentioned earlier, the AI augmented apprenticeship, getting people in there to pair, understand and go through the routines and understand what’s taking place. But I think the biggest thing we need to look at, you’ve got all these entry level positions asking for all these years of experience which is like it’s, you know, you put the cart before the horse type thing and that’s not realistic. We need to change the mindset, as Kevin mentioned earlier, and change it from years of experience based hiring to task and skills, outcomes based hiring. And by doing this, you’re going to really force people to look at what they need to do to become more knowledgeable, really understand what it takes to understand the AI system. But take that AI system and make it so it can be productive and make it tangible so it can create that return on investment within the workspace. And when you do that, are they going to have better outcomes? Because right now the way it’s set up with the way these AI agents are scanning these resumes and stuff that are out there, people are missing out because they’re not being able to comply with the way they’re looking at the requirements. Change it to skills based. Look at the things that are going to be important, that are going to be be essential for that business to move forward in an AI world. In AI ecosystem that’s going to be key. So things such as governments, that’s not going away, security, that’s not going away make it, those things are going to be tangible and compound over the years. They’re going to be escalating, that it continue to evolve, that people can actually work their way up into and not be considered obsolete before they get started.

[00:51:25] Speaker A: You know, Derek, you’re touching on another topic here.

I have a few people I know who’ve given me feedback about how companies are overloading on the skill testing they’re doing as part of their hiring process.

And it’s intimidating and scaring a lot of people off.

It’s ridiculous, it’s just absurd. And even a bunch of years ago I wrote a blog post about the job ads that people are putting out there and it’s like, like, you know, looking for CTO level knowledge in a, you know, a senior software developer, you know, and it’s just gotten out of ridiculous. So it will cover that. Another topic I want to hear from Kevin. I see Liz, Joanne and John. So we’ve got about eight minutes. Let’s keep our comments quick. Go ahead, Kevin.

[00:52:14] Speaker D: Yeah, picking up on Joe’s comment about staff becoming managers of a team of AIs. Totally agree. That’s going to happen. This will bring in a new concern called decision fatigue, where if someone’s dealing with 10 AI is moving at the speed of AI, they’re going to be asked to make a lot of decisions being referred back to them by the, by those AIs. And we will have to train people how to cope with that decision fatigue because at the moment people will get burnout very quickly because they’re not used to making that many decisions a day.

[00:52:50] Speaker A: Not only are they going to have to worry about burnout, we can’t just keep having meetings after meetings after meetings to deal with all these decisions. I think organizations are going to have to think about how they’re defining roles around decision authorities. Go ahead, Liz.

[00:53:03] Speaker H: Yeah, I hear that these McKinsey’s and the Gartners are all about AI. AI. Everything is AI. But you know, if you remember, I mean not just the industrial revolution, but Also the.com era and everything it there was always like, oh, we got to like rush to the shiny new object. It’s not about the shiny new object. It’s about making sure you’re making good business decisions, focusing on what’s the right thing for you to do as a firm, for your customers and your operations and then seeing where you can apply AI there. And when you’re looking at talent, it’s about making sure that you’re looking for that potential and looking for how people think and how people relate to and how people absorb again the context so that they can actually be leaders in your field.

[00:53:49] Speaker A: Very interesting. Go ahead Joanne.

[00:53:52] Speaker F: I think the, you know, from a planning perspective, one of the things that we’ve kind of tested with a bunch of C levels is the ability for first levels to start to learn the tricks of the trade that senior MBAs, you know, and senior executives learn in terms of trade off management, in terms of stochastic versus probabilistic versus, you know, all the things that you have to face from not an academic perspective, but on an applied level you are constantly making decisions at certain management levels and up to the top start teaching those skills to those first levels trainees, those that are just coming in because it requires time, it requires skills, it’s not quite an apprenticeship, it’s more like a MBA in a box for you know, first years to give them the beginnings of how do you actually do trade off management? How do you actually make decisions? What is involved in the decision making process that’s not workflow based but get absolutely the critical thinking skill in the sense of decision trees, what’s important when, and that’s what they really have to learn. And so a lot of people are adding that to their action plans around AI because they see the gap coming, maybe not this year, but definitely by next year.

[00:55:21] Speaker A: I’m just going to plug a book here I read at the end of last year. It’s about China’s quest to engineer the future. It’s by Dan Wang and he talks about the impact of what China was able to do when we outsourced most of the manufacturing in the US and the process knowledge they were able to build up over generations of manufacturing.

I don’t know, Joanne, I’m getting the sense we’re going to end up in the same situation around knowledge fields.

[00:55:54] Speaker F: I would absolutely agree.

The thing that people seem to forget about is the small language models that are being built specifically to direct AI, to use process knowledge, to use context, to use all of those critical thinking skills that we’ve been talking about, including the mathematics behind it. It’s not enough anymore, you know, even for the frontier models. They, they’ve trained on so much data and so much information, but so much of it is, is extraneous to what people actually need. And this is where small language models excel because they focus on a particular process, a business operation and the leadership and skills and the actual applied skills that are used for those processes. And that’s what the future is.

[00:56:57] Speaker A: Interesting. Joanne, I have a sense that we’re going to have to cover this topic from a different number of different angles going into the future. John, you had your hand up. I’d love to hear your comments.

[00:57:08] Speaker G: Yeah, the Industrial revolution came after people that were doing manual labor. And AI is different. It’s really coming after the knowledge workers. And I really don’t like the term industrial revolution applied to this because this is, is, this is really a general purpose transformative technology that’s like electricity, it’s like the Internet. And it, it’s going to really transform society. And so it’s, it’s not just going after what I would describe in, in as industry.

And because of this I think it’s really going to make us look at every role in every company if we want to do this right. And I think it’s really going to redefine roles and if, if we’re going to have to do this, we have to, in every company and every role in society we have to see like, like how does this new technology impact this role. And I think from that I think companies are going to start having new ways to work. And it’s going to take people and companies a lot of time to use this technology, figure out how to use it, right? And the people that figure out faster, they’re sure going to have a massive advantage.

[00:58:07] Speaker A: John I think some folks would argue that coding is software’s manual labor and maybe there are some analogies that we can pick up from the Industrial revolution.

But I agree with your last statement, John, and that is, you know, I actually agree with the Gartner numbers that it’s going to that AI is going to drive a significant restructuring of how we think about roles and what skills we’re hiring for and what people’s jobs are all about.

But I don’t think it’s going to materialize in the same way people are talking about. I think the biggest big shift is in the last generation we rewarded people who knew how to solve problems. And I think in the next generation we’re going to have to reward people about knowing what problems to solve and is the AI and how to make good decisions based on what the AI is providing us with knowledge. So where should we dig and how far should we dig to use John’s analogies? And I’ve got a really good action plan out of this, right. And I think the number one I’m going to take away from this is like, you know, how should organizations build apprenticeship programs, particularly for areas that require a combination of both skills and understanding outcomes and understanding their industry and business process. And without that, you know, we’re going to have a situation where we, you know, we lose too much knowledge. And that’s my takeaway from this conversation. Folks, thank you for joining this week’s coffee with digital trailblazers. Our conversation next week is going to be about my favorite topic. Honestly, it’s about AI first ux. We cannot change our companies just by becoming more efficient or by investing in productivity. We are going to start seeing in 2026 how companies are evolving their customer journeys using generative AI capabilities. I want to hear about examples. I want to hear about programming models. I want to hear about the operating changes between product, user experience, marketing and technology to enable companies to build the next Ubers and the next Airbnbs that are built from AI from the ground up. So that will be our conversation next week. I hope you will join us. Everybody who is being affected in the US by the snowstorms.

Please stay safe and warm.

Enjoy the time with family as the snow is coming down. I’m going to do my best to get home from Tucson to help my family out. Everybody have a great, great weekend. See you here next week.

Hosted by Isaac Sacolick, CEO of StarCIO

Today’s panel explored AI’s role in driving revenue and growth through the collaboration between CIOs and CMOs, with discussions centered on customer education, data security, and cross-departmental partnerships. Participants shared their experiences and perspectives on AI implementation in marketing and IT, including the use of synthetic audiences, data analytics, and personalization strategies. The group emphasized the importance of forming cross-functional councils, establishing joint KPIs, and addressing security considerations while exploring future opportunities in areas like health tech and synthetic data.

[00:00:00] Speaker A: Foreign.

[00:00:05] Speaker B: Welcome to this week’s Coffee with Digital Trailblazers.

Welcome back from the Thanksgiving weekend where we took Friday off and give everybody a break to be with family and to do your shopping and just to to veg out and watch some sports, maybe do some reading. And we are back here this week with a super special episode that I’m very excited about.

We’ll be talking about the CIO and CMO relationship and more broadly the relationship between IT leaders and digital marketers and the digital trailblazing leaders in those groups. And how do we partner on AI not just to get more efficiencies or improve our workflow. How are we actually driving growth around this?

And I think this is a very important episode. Thank you for joining this week. Part of the reason I’ve been excited about this episode is that we’ve been talking and I’ve been doing a lot of writing about how using AI only for productivity improvements, only for employee experiences and only for workflow gets translated to cost savings. Eventually the CFO catches up with that and gets and calls in the cards and says how are we going to actually realize the cost savings? And most of that ends up coming from headcount. And we’ve seen this rodeo before. For those of you who have led digital transformation efforts, you can’t modernize, you can’t transform by just becoming more efficient or just by improving productivity. You need to transform by using technology and now AI to a competitive advantage. And part of that is looking for ways to drive customer experiences and improve customer experiences. Part of it is to look for growth and revenue opportunities. And part of it is to look for ways to enhance your product and service offerings with the newest capabilities that are coming to market.

Now. We did an episode around this a number of months ago around AI for growth and I will just admit we struggled with this. There aren’t that many shiny examples out there of companies really focused on the customer experience and growth just yet.

We’re more focused on how we’re deploying copilots and language models and code generators. And now with hundreds of companies putting out AI agents, using AI agents in our CRMs and in our HR management systems and in our ERPs.

If you go to drive star cio.com, i have a couple of articles around AI agents from large companies, from large SaaS companies. And then this week’s I talk about AI agents coming from growth and startups. And there’s many, many, many companies putting out AI agents out there. So today we’re going to be focused on growth and we’re going to be focused on customer experience and we’re going to be starting with a question about how CIOs and CMOs and again that by extension IT leaders and digital marketers can partner so they can find these opportunities together.

I have almost everybody from my normal speaking list. Thank you for joining. And I have two special guests. We got two people who are from marketing backgrounds who have been in executive roles in different capacities at different types of companies throughout their careers.

And I wanted to make sure many of us here on our standing panel have IT backgrounds. And so I want to welcome Adriana and Elena to our panels this week. They are going to be representing the CMO side and Adriana, I want to start with you. Adriana, welcome to the to the show. Welcome to the copy with digital trailblazers. Please say hello to the group and just share some of your insights on how you look to partner between marketing and and the IT side of the house and with your customers. Hello Adriana.

[00:04:24] Speaker C: Hi.

[00:04:24] Speaker D: Thank you so much for having me today.

What a great conversation.

I lead communications and marketing at a B2B vertical SaaS company in healthcare. So a lot of my examples are going to be very health tech, very kind of healthcare specific for this group.

When it comes to AI, it really is so important to have the CIO or in my case at a, at a kind of a growth stage startup, the cto to have the CIO or CTO and the CMO working together because it’s not just about the technology when it comes to AI. To your point, Isaac, it’s about product innovation, it’s about the go to market motion and it’s also about issues and reputation and issues. Mitigation is another way we often think about it, of course in health care where we care deeply about patient data, keeping all our information safe.

I think it’s really important.

There’s so many ways that our CTO and myself have partnered from the very beginning. We created an AI empowered workforce which is a whole committee that included other key stakeholders like Infosec and people team and legal and customer education so that we could uplevel all of our internal team members to help customers. On the external standpoint, we have a variety of customer education and summits and webinars and a lot of different content we’re offering to our customers so that they can better understand how to use agents, how to buy AI agents, questions they should ask whether they’re purchasing ours or somebody else’s. We just really want them to be able to Be educated and, and fix patient communications. That’s the problem we’re trying to solve.

And then of course, we have AI in our product and we very much have this belief that we have to meet our health systems where they are. And not everybody is ready for a fully autonomous AI agent. We have that.

But we also really want to meet our health systems where they are in their journey. And sometimes they’re going to use a copilot, sometimes they’re going to use something that’s called a flows agent, which is more of a, a deterministic agent. And then sometimes they’re ready to really use and deploy and unleash a fully autonomous agent. So I would say our CTO and myself are working together hand in hand, whether it’s internally, whether it’s focused on the product, or whether it’s educating customers.

[00:07:00] Speaker B: Adriana, what do you see as some of the growth opportunities when you’re working with customers? I mean, I’m trying to get some better examples to illustrate to all of our listeners. We have a good, good group here. What do you, what are some of the things that you look for that represents a growth opportunity?

[00:07:20] Speaker D: It’s such a broad term, so I will tell you.

So one of the ways that we’re, and I don’t know if this is exactly what we’re thinking for, for growth opportunities in this context, but one of the things that I’m doing on my team for marketing in terms of using AI to help us better understand customers is right now we are using synthetic audiences. And I’m not sure if everyone’s.

That’s sort of a word that a lot of people now are starting to use, but where we’re training an agent to act and think and feel like our icp and then we’re using that agent or synthetic audience to do a lot of message testing, to interview it, to talk about, you know, what do you think about this product or this messaging. This is more from a marketing standpoint versus running full focus groups. So we found that to be kind of an interesting growth opportunity from more of a strategic standpoint is using AI not just to up level our team, but to give us potential intelligence on what our customers might be thinking when we can’t reach the customer live the way that we want to because they’re so busy or because you have a time constraint or a budget constraint.

I would say the other way that we’re using AI and you can guide me. Isaac, if this is too basic and you’re looking for something different, is that we record all of our sales calls in gong. And so we often are using just even the AI functions within GONG to help us better understand in our calls where we had closed one opportunities, where we, the customer, the prospect turned into a customer and said, yes, I’m buying this product. We really mine those calls to say what were their main pain points, what were their objections and then what language did we use that helped answer a lot of their questions?

So I would say we’re at least internally on my marketing team. We’re also using AI that way to get what I kind of consider market intelligence or customer insights.

So that’s really helping our team and then that’s fueling new ideas for us of how to go to market.

[00:09:33] Speaker B: I think this is brilliant, Adriana. I mean, I think this idea of going beyond like a data version of your customers360 and a PowerPoint version of your customer Persona and actually playing it out with an agent so that you can get in their heads a little bit. I think it’s really, really interesting use case and that is definitely a growth opportunity, right? Helping marketers have a, have better intelligence, helping sales refine their approaches to, to do, to perform sales calls and what language you’re using. I think those are really brilliant opportunities. And Adriana, thank you for sharing them with us. Elena, welcome to the floor.

Thank you for being here with the copy with digital trailblazers. I’d love to hear a little bit about your background and then where do you see growth opportunities from using gen AI capabilities?

[00:10:26] Speaker E: Thank you, Isaac. Thank you everybody for having me. So I have a very interesting background for this group because it combines my career history in marketing and then I moved over to analytics delivery and data side, including data operations. So I kind of see this from both sides, from both perspectives, which is awesome.

And I guess I will speak very briefly on the importance of the relationship between the CIO and the cmo. Just a sentence because, you know, as marketers, we look at things very commercially and we look at our targets and our customers, but we don’t necessarily think about how this data kind of gets together and what AI. And you know, our dream project needs operations wise to function in its best and that’s where the cio, in my case, it’s also cto, CTO comes in and explains this is what we’ve got, this is what we don’t, and this is what we could do to stitch it together. So for the marketer, it gives a great perspective in understanding what truly, you know, the underpinning of AI are. And how they work.

So I think this is very important. And we stay in a. My company. My company is a startup.

I work at this company both in marketing and data capacity.

And we do have a CTO and we also have a chief creative officer. And we all are on a lookout for what’s new.

Just like Adriana was saying, what’s new, what’s available, what we can incorporate in our work streams. We do have AI in product, so we talk to our customers about AI.

My startup does patient communication practice automation for smaller offices, so for doctors, individual practices and like midsize practices.

So our customer base is not as knowledgeable about AI. They are somewhat more conservative.

So they ask us a lot of questions. And one of the things we do is we educate them.

We educate them about what they can do to grow their practices. Right, so you were talking about growth opportunities. Well, for customers, we tell them probably one of the lowest hanging fruits is to incorporate AI into their patient communication because it can allow them to craft their communication faster and nicer and do it more often.

So that’s one of those things. We also talk to them a lot about how search changes because a lot of practices in our, our customer base rely on search. And we talk to them about how search changes now that the AI is here and what they need to do to kind of make sure they don’t, you know, stay behind.

[00:13:33] Speaker D: Right.

[00:13:34] Speaker E: So these are two important kind of parts we highlight for customers.

Another one which is innovation. We don’t do it ourselves, but we talk to customers about it is AI routing. For instance, when custom, when patient calls a practice, right. Instead of being put on hold, there is an AI voice system that can pick up a call and route them. Sort of like when we call our bank, right? And we’re so used to dealing with it when we call our banks, but when we call our doctor, we get put on like endless call, right? So there is a great, great feature available in various different testing for, for small practices. And we do talk about it. And then you also ask, well, how do we do it and use for ourselves commercially Personas. So that is, that is a big kind of work stream for us in where we use AI to understand.

We have, we work with multi specialty, we work with human and veterinary. So for us, synthetic Persona is a huge deal that helps commercially.

Wow, sorry, that was a mouthful.

[00:14:49] Speaker B: No, it was great. I mean, because, you know, we lose sight on the IT side, on the data side, on the security side, we lose sight of how all these basic capabilities, whether it’s communications Whether it’s routing of issues, we look at it from an inside the house perspective. How do we route a service ticket or how do we resolve a security issue faster? And both you and Adriana are bringing the customer lens to this. Customers have these same issues and the.

And our customers have to be educated around AI and what their benefits are.

It’s an opportunity, but I love where you’re starting from. I’m going to move this over to one of our first steps on AI opportunities. Educating the customer, I think is really important before you start chasing after growth opportunities. I got a full list of people here raising their hands, ready to either contribute or ask a question.

Joe, it’s good to see you.

We had dinner earlier this week. I’m in Tucson later this week.

What do you have you for us on growth opportunities around AI?

[00:15:56] Speaker F: I’m excited this week to talk about 10x NewCo, my own consulting group that I’m a part of. We have leveraged AI because we are a small group and without AI, it would be nearly impossible to qualify leads to prepare materials much like Adriana’s brilliant use.

If you mine public data, pitchbook, Zoom info, public filings, you can find information about startup companies, the business they’re in, the needs they may have, and you can start to fill the opportunity funnel, the sales funnel, with truly qualified leads and beyond that, know enough about their needs to be able to present an opportunity, a.

A viable opportunity for helping them to grow their company.

So we, we have been using this fairly effectively to identify companies that would really benefit from our services. And on the flip side, our services involved leveraging AI to mine the data the companies have to create new and innovative products or services.

So we’re sort of on both sides, finding the clients that would benefit from us and then benefiting the client through creative use of their data.

This is the first time in two years I’ve been able to talk about what I’m doing for a change.

[00:17:36] Speaker B: It’s awesome. Joe, are you using specific. You said ZoomInfo is the platform you’re using for this?

[00:17:41] Speaker F: We, we have used PitchBook, we have used Zoom info. We, we’ve used a number of. I mean, just, you know, there’s so much data publicly available on the web that you can scan it and you can use the, the. The AI tools without, without being concerned for, you know, exposing somebody else’s data. It’s already on the Internet.

[00:18:01] Speaker B: Very cool.

I have Derek raising his hand. Derek, I’m going to jump you right into a. What you want to talk about. But I love that Adriana brought up the data issues and information safety issues. It’s a great place to collaborate with CIOs and CISOs and CMOs who all, you know, the CIO is probably caring about data being used the right way. The CISO is worried about data security issues and the CMO is worried about brand. It’s a great place to collaborate on.

[00:18:31] Speaker G: Absolutely. And I think the things they brought up are spot on and things that everybody needs to be thinking about. And I love the fact with the synthetic audience that Adriana brought up, I think that’s pretty cool. As far as using it as a training model for working with the Gen AI tool, but also looking at just the CIOs work with the CMOs to create that personalized experience.

So as they’re working with these campaigns and stuff, how are you going to protect the data that’s being sent out there and secure the pipelines that they’re using to actually present that data? But more so when you mentioned about the brand and protection. So using AI powered threat intelligence types tools to make sure the brand’s not being impersonated, it’s not being phished or putting misinformation campaigns out there. These are also things that help protect the organization and the brand identity so it’s not being diluted by some other adversary or nefarious type acts. When you also look at the, you know, again, the predictive analysis, I think that’s cool. And even Joe mentioned in his use case of being able to query different leads and things of that nature much quicker than you could doing it manually and using the tool to be more productive, to help you with a smaller group, to, to get more impact.

These are all key things that really come out for. And also, you know, it can also keep you, depending on your business model and the type of organization you work for, to follow the governance guidelines, making sure intellectual property is protected, mitigate any risk, but also make sure that you follow whatever governance model you have, you’re staying in line with that and you can automate that process using a Gen AI tool.

[00:19:55] Speaker B: I love the notion of bringing the CMO into, to partner and protecting the brand. I mean, when you walk into InfoSec and the SoC and network operations, it’s like a broad palette of everything that you’re trying to protect against. And some of it is, you know, a customer oriented, some of it is revenue oriented and a big part of it is brand oriented. And how do you translate brand to what is a higher priority? What is a bigger Risk is a conversation and a piece of governance that the two groups can collaborate on. I think it’s really smart. I want to bring Martin in. Hey, Martin, thanks for joining. I want to hear your growth opportunities before we talk about relationships.

[00:20:37] Speaker H: Well, I think the first thing I’m going to say is that some form of shared growth mandate which the CIO and the CMO are jointly aligned on, rather than separate IT projects and marketing projects or whatever, the CIO and CMO getting together on a set of maybe three to five flagship AI initiatives, what they are jointly owning, that’s jointly pushing forward. And I think that you’re getting away from.

Yeah, he said, she said, I want, you want. You know, it says we should go this way, marketing wants to go that way. So actually, yes, starting from the top and actually going forward together, I think is absolutely crucial.

And I think the. There’s a lot of opportunities out there, especially when you look at, you know, obviously digital transformations, got a bit overused and everything else, but you look at the digital journey of a customer and you look at how that can be improved. So, for example, you know, it was mentioned a little bit earlier the, the AI. AI bot that can be. Yeah, dealing with the customer and things like that. And actually, just as a side comment, I was talking to a major Canadian bank that I deal with earlier today, and their AI bot, which you have to talk to first, is appalling.

Just as a side comment and along the lines of it doesn’t understand what you ask it, even if it’s quite reasonable and you’re using language that they actually have on their own website, it just repeats the question.

Finally, it kind of lets you talk to an agent when you get so frustrated with it. So I think my comment in this is, yeah, when you’re dealing with customers and customer experience with AI, make sure you’re making it so it’s actually customer friendly, otherwise all you’re going to do is actually annoy and frustrate the customers.

[00:22:33] Speaker B: I love the idea of defining a growth mandate. Some of us call that AI strategy.

Some folks call AI strategy a strategy about how we implement AI.

[00:22:43] Speaker H: But I think I deliberately use the word growth mandate because otherwise it’s AI for AI sake. Why are you doing it? Look at the title of the question you rose as well.

So I think it’s. You’re targeting a way forward to grow the company, so making sure that your initiatives you’re choosing are very targeted and understand which metrics they’re going to impact.

[00:23:08] Speaker B: And how and guess what, you know in doing so, that will trickle down. Right. Our IT folks, our AI folks, our data science folks generally don’t know enough about market and customer needs and where there’s growth opportunity. It’s going to require partnering with those on product, on sales and marketing to be able to do that and so forth. Let’s bring John in. John, your thoughts on growth opportunities and the CMO CIO relationships. Welcome, John.

[00:23:38] Speaker A: Hi Isaac. Thank you so much for having me on. I think that there’s just so much shadow AI going on in the marketing space and if you’re at a smaller company like I am, it is so nice to have some of these tools available.

I’ve seen, well, if anyone can just go use what’s publicly available. But one kind of low hanging fruit that can really, really help out is if you take a bunch of your marketing language and the way that you view customers and what you do, you can build a customized version of one of the publicly available tools using their reg augmentation tools and you can come up with something that with very low effort can be very helpful at providing copyright and other stuff back in words that you would use for your company and understand any of your product and your customers and things like that.

The other thing that I’ve seen small companies do is use it to create marketing campaigns. I’ve seen people get all sorts of help on the settings for the advertisements, especially Facebook and Google and just walking people through, setting up the campaigns, feeding that stuff back in to see how things are. And if you’re at a small company, this can be really, really helpful. As you’re at larger companies, there’s, you know, there’s whole teams available for this and they have a lot more money for consultants. But I just, it’s, I think that actually marketing is one of the areas that has kind of more AI going on kind of in the shadows than even probably any other parts of the company.

[00:25:08] Speaker B: Thank you, John.

I do think there’s a lot of rogue AI out there and shadow AI out there. And so my comment on the whiteboard is this is an area that collaboration is required because yes, the way you turn around rogue and shadow AI is understand what problem it was solving for. Probably that person in marketing was just trying to do their job and maybe all the governance is out there and maybe all the protections out there and they went around the network and figured out a way to get their job done.

[00:25:39] Speaker A: Yeah, just to add to this and so forth, a little bit of effort from the IT team and a little bit of money that People can switch to paid models of things. People can have the IT team really help on the augmentation of the data into, you know, so you can do full reg and with a little bit of help from the IT team and actually buying licenses stuff, your data will be so much more protected and basically the models you have will be so much more tuned for the work that you’re doing. And so yeah, I completely agree. So it’s so important to have collaboration here.

[00:26:11] Speaker B: Thank you, John. Folks, thank you for all the comments on the common stream. I see them all. Unfortunately I am remote today and can’t contribute to the common stream as I normally do. So keep them up. Joe and Martin and others will keep tabs on it. If there’s a question or a comment we should bring up to the floor. But I want to say hello to Joanne. Joanne, we missed you the last few weeks. We’re talking about growth, we’re talking about relationships.

I, I’m thrilled to have you back and hear what you have to say on these topics.

[00:26:41] Speaker C: Good morning and thank you. And I’m sorry that I haven’t been around health issues and other things.

That being said though, you know, one of the things that I’m seeing a huge trend for is what I would call the micro personalization engine.

And I’m seeing it come up in large organizations and small into John’s point. It is extremely helpful. The point that I want to address though is the synthetic audience and sentiment analysis. Because one of the things that causes these projects to fail, and I’m not trying to be a Debbie Downer here, is that when the agents are being run and when they’re being created, they don’t necessarily take into account that the synthetic audience is in a good mood and not so good mood, a very stressed under the gun mood. And sentiment analysis is something that needs to be added to these things. So when you define your Personas of the individual, you have to think about the fact that when are they going, when did they make this comment or when are they going to speak to you? What hours of the day, what days of the week?

If it’s later in the day, they might be harried and looking to stop working, go pick up children or go grocery shopping or whatever. And so what I’m beginning to see a lot of is the integration of sentiment analysis and sort of more sociological kind of questions being asked for the agent to, to really fine tune what would be that personalized revenue engine, if you will, for, for companies to use in their marketing. And I think that this is both a very dangerous thing and also a very good thing. Dangerous because you have no way of knowing and you might be on the verge of violating privacy without actually knowing that you’re doing that. And the other part of it is synthetic audiences generally tend to be running through simulators that don’t necessarily hone in on they may hone in on the natural, natural language process, but they don’t actually convey the mood. And you have to be a little careful about that. So what I, what I suggest to a lot of people is when you run that simulated audience, run it for different times of the day, Right. If you can, in your tooling, run it for different days of the week and run it for a Persona where you start to take in their personal lives, like a married mom with three kids or two kids or whatever, someone who’s got a long commute.

All of these sort of variables figure into the sentiment analysis that’s used to create that synthetic audience.

And that’s one of my biggest things when I talk to CMOs that are now joined at the hip with their CIOs, CISO and other cohorts.

[00:29:51] Speaker B: Thank you, Joanne. You could see I’m already starting to fill in some of the first steps on exploring and partnering on AI growth opportunities. We’re going to come right back to Adriana after this very quick break. Folks, thank you for joining this week’s coffee with digital trailblazers. We try to meet every single week with episodes around AI and digital transformation. And it’s been a fun year doing all the episodes. Our 152nd episode is today.

Next week we’ll be talking about the digital transformation playbook strategies for 2026.

I will be fielding information from my CIO.com article from that conversation. Because we can’t explore everything all the time. We have to set some priorities and there’s some things that are in the playbook for next year and there’s some things that we probably need to down get out of and complete this year before we move on. So that will be next week, the 19th. I wanted to define what AI literate organizations mean, what that term means, how do we set up our learning objectives for 2026? So that will be on the 19th and then the 26. We will take a week off for the holidays. I was considering having an episode that week, but I bowed to my speakers and listened to my AUD and they said let’s not have one during the holiday week. So that’s what’s coming up. I left one comment I’m sorry I can’t be on the comments stream too much today because I am remote, but I am offering a Cyber Monday deal. It was in my newsletter earlier this week. For those of you who want to join the Star CIO Digital Trailblazer community, there is a coupon code in the LinkedIn common stream.

It is expiring tomorrow.

I extended it one day. For everybody who’s listening here, we’ll give you 50% off to join the Star CIO Digital Trailblazer community.

If you have any questions about that, please do reach out to me. Let’s get back to our program. We’re talking about CIO and CMO partnering on AI to drive growth. Hello, Adriana, welcome back.

I’d love to hear more about growth opportunities, your insights on relationships and where to start looking for growth opportunities in some of the companies that you work with.

[00:32:12] Speaker D: Yeah, thank you.

I just wanted to also comment quickly on what Joanne shared. I love that, the sentiment, and I was just taking notes and thinking about how the time, the day, and then I thought, wow, we could do it seasonally.

Are we more stressed during the holidays and use that as part of the synthetic audiences?

[00:32:35] Speaker E: Or.

[00:32:35] Speaker D: Or maybe during times of year where we think folks have more budget, it might be more open. Anyway, I love that. Joanne, thank you for sharing that. I think that’s a really important ad to synthetic audiences.

So, Isaac, I think one of the areas I wanted to mention, and this is a little on your question, but maybe goes to what folks were discussing before was safety and security is not one department’s job.

And it’s really important for the CMO and the leadership team to know that and believe that, you know, security is everyone’s job.

And I just sort of wanted to put that out there because sometimes I think where relationships go wrong or can go wrong is there’s a dialogue that, like, the CMO wants a different thing than the cio. But truly, if everyone is here to grow the business and do what’s best for the business, we actually have a lot of shared gold or goals. Excuse me. And obviously at our company, security is one of those.

I try to take it so seriously and it really should be a part of your culture. And so we talk a lot about security, even down to some basics of tone, even when folks say, oh, gosh, I have to run this through the vendor onboarding process.

I know this is very tactical, but I try to make sure that that never comes off as a negative thing. You know, I try to tell my folks, absolutely, we do like we have a very efficient vendor management onboarding process. The goal is that they’re making sure that what we do is safe and compliant and we can use these tools. And the faster and more efficiently we work with that group, the quicker we can get to actually than doing what we want to do.

So I just wanted to throw that out there.

I just think it’s really important that marketers really embrace it, because selfishly, at the end of the day, if there is a security breach or a reputational issue or a major crisis, guess who’s dealing with it? You, marketer. You’re dealing with it from a brand perspective, you’re dealing it with from a PR perspective, from a reputation perspective. So it’s even personally in your best interest to make sure that safety and security and that you have a culture of that across your whole company.

[00:34:55] Speaker B: Wow. So we’re here talking about growth, and our partnership starts with doing things safely, with security in mind, with data concerns up front, and then recognizing that when security is a problem, it’s everybody’s crisis to manage. I love this. I’m doing an AI governance panel next week at a conference, and I might have to quote you on Adriana. It’s just too good not to include in a conversation mostly with CISOs that I’m doing next week about how to really elicit help from your marketers. Martin, what have you for us?

[00:35:33] Speaker H: Well, I thought you might be going to Heather next, actually.

I think.

[00:35:39] Speaker B: Oh, I missed Heather. Heather, go ahead.

[00:35:41] Speaker H: Thank you.

[00:35:42] Speaker I: It’s a little going back to what we were talking about before, but I was talking to some people in the. In the marketing business, particularly in hospitality, and the whole notion of personalization is very critical. And then I’ve been reading about how the CIOs and CMOs are working together on that. One holds the data, one holds the marketing, and getting it together makes a lot of sense. And then there’s the whole scaling from what can be used and repurposing of one item versus another. For example, you can take a video that was before just used for YouTube or just used for social media.

Now, through AI and through scaling, it can be used quickly and altered for a number of omnichannel opportunities. So there is that efficiency, not on, you know, with. Not with people.

Or there’s ways of quickly opportunistically using different. Through AI, using different tools, and making your.

Your marketing efforts go a long way.

[00:36:50] Speaker B: Thank you. Heather, welcome to the floor. Heather, I want to bring Martin, before you go, I want to bring Elena back. I know she has a hard stop coming up.

Elena, jump the line and share your comments on relationships and where teams should start first when exploring their AI opportunities.

[00:37:08] Speaker E: Thank you. So I just want to build on something that Martin and Adriana were talking about in creating joint responsibilities and joint growth related projects. And I want to add one more to it. KPIs.

Let’s make sure that both sides, the marketing side and the tech side, are measured against joint KPIs. Because if we bring in the KPIs conversation, then we can also bring the CFO in a conversation. Right. And then it becomes even more organizational, then we can think enterprise because we are not running test projects, we are measuring what we are doing across functions. So this is my 2 cents. I do got to run. Thank you so much for having me and somebody else pick up on KPIs because I saw a lot of reactions. So people have a lot to say about this one.

[00:38:04] Speaker B: Yeah, I captured that for you, Elena, and I’m glad you brought that up because there’s been so much write ups about AI POCs not making it into production. And this is a good insight, right? Have joint KPI’s bring your CFO on board and these no longer look like test projects. Hey Martin, thank you for letting Elena jump the line here.

[00:38:28] Speaker H: All right.

KPI’s was one of the ones I was going to bring up right now actually.

But I was going to start with, I talked about earlier about a joint mandate between CIO and cmo and I think forming, just talking about steps forwards.

So forming a cross functional AI council of some form that is going to actually drive it. Yeah, it’s chaired by cio, cmo. It’s going to drive what initiatives we’re going to do. Yeah, prioritizing use cases, looking at the risk management, the upskilling of people needed, all of these types of aspects and looking at the business outcomes, looking at revenue and customer experience for example, and then obviously anchoring that in shared KPIs and the roadmaps. So you know, what are the KPIs? Is there a marketing return on investment?

Is there a net promoter score or conversion rate?

[00:39:25] Speaker F: Yeah.

[00:39:26] Speaker H: Which of these kind of metrics are you going to try and influence and how does that influence then the growth of the company? So trying to actually agree on those shared KPIs and if possible linking it then to the top of the house corporate growth initiatives and growth KPIs as well. So I’m just trying to think of all of those ways to unify how you go about that. And it kind of starts beneath that almost with unifying data and the technology in the marketing space.

So the more you can unify that. So you’ve got one set of data, one version of the truth that everybody is using to build on top of that. So Joanne, I’m sure dive in next with how the data impacts on this as well, because a lot of this foundationally, you need that common set of data to build on that with the AI.

[00:40:19] Speaker B: Thank you, Martin. I’ve done this with Joe and Joanne before and prompted them when they had a big idea coming off the coffee hour to go write it on their blog. I think you should write the blog post around defining a growth mandate for AI and putting your tips in here. It’s just, they’re just too good and I haven’t seen it. You know, I do a lot of writing and reading.

So I’m going to prompt you to write that blog post and we’ll share it here with the community if you follow up.

Joanne, you got the softball tossed to you around data.

[00:40:54] Speaker C: Okay, so one of the things that I am sure that the audience is picking up on is from Martin’s points and Joe’s points and everybody’s points is that this is a much more aligned holistic approach.

And in order to get it to work, you do have to have correlation between data sets, not just in manufacturing, but literally in every industry. And as I’m listening to this and to Martin softball, you don’t necessarily have to do big integration to do this. You can actually pull using agentic AI anyway. You can read data and then use it to contextualize and rag for your agents. So you can now combine your customer relationship management data with your production data, with your manufacturing data, with your transportation data. If you’re a retailer with your inventory. I mean, think about the idea of the micro personalization or the engine that you’re going to build agentically to do this. The more data pieces that you put together, the broader and more holistic the approach becomes. And that’s where you really make a dent in enterprise initiatives.

So think about the customer’s journey or the sales motions and how those two things come together using CRM and other data like my inventory, I replenish my inventory much faster for particular products because people buy more of them. Why did I never put this data together before? That’s one of the premises of what we’re building, but also to the point that you can get very granular with that data without having to do a big, oh, I Need a new data lake to mine. You can read. You can read the data and use it for contextualization purposes and create this holistic view of the customer you’re trying to approach, how to approach them, meeting the sales motions, what’s really important to them to what Joe was talking about, using Zoom and sorry, using Zoom info and other publicly available data and you can become very precise and then you apply the audience comments that I mentioned previously to that. Now you’re really honing in on what does my customer want, how when is the best time to approach them, what is their lingua franca. And I would suggest to you that multilingual is a very good way to go here. We’ve had great success with that.

And then from that point forward you can start to build this more holistic approach that ultimately addresses at the very top of the stack what are the outcomes this corporation is trying to achieve and how are they doing it. So now we have the opportunity, instead of necessarily reverse engineering from the outcome you want to achieve, achieve all the way back to the tactics, we can build it from the tactic all the way up to cost savings and revenue generation and really help organizations push those two agenda items forward.

[00:44:20] Speaker B: Thank you. Joanne. I think you mentioned customer journey. I forget who else mentioned it earlier that I put it on the CMO and CIO relationship. Here’s the wake up call.

Every customer journey is changing because of Gen AI. Okay. Right. I do my travel itineraries different because of Gen AI. I do my recipe development different because cooking recipe developed different because of Gen AI. I’m researching my ailments around how I’m not feeling well yesterday using Gen AI tools. Every customer journey has been disrupted because I can get information from an agent or from a language model. And I’m no longer doing search or coming directly to a website and going through 15 clicks to figure out something.

Right. That that’s the mandate for the CMO and the CMIO is how are we changing our customer model to reflect that?

[00:45:20] Speaker C: Actually it’s the mandate for the CEO, Isaac and the board because if they, if you don’t get the buy in at the top of the food chain, you’re not going to be able to instantiate the very specific process changes that are going to be required.

AI is a mindset change just as much as digital transformation was. In fact, I think even more so because when you start to look at the stacks, the, the trick that I’m telling a lot of people to use use AI to do something very simple. What are my Dependencies, my codependencies and my cross dependencies between functional units in an organization. When you start to understand that mapping, the whole picture changes, but you get it right away because that gives you the tool that you need to see. Oh well, I never realized that this was codependent on that and so forth and so on. And it’s not, not, not creating spaghetti, it’s giving you a very detailed mapping of where these journeys, if you will, a vendor journey, a buyer journey, a supplier journey, all of these things start to coalesce. And that’s really the trick that has to be conveyed to upper management to make sure that they understand what they’re approving, where these opportunities are going to pay off faster than not, and how to prioritize them.

[00:46:51] Speaker B: Thank you, Joanne. Before you go, your one suggestion to getting down to our last 12 minutes, your one suggestion, what should be on our first steps in partnering on finding growth AI opportunities.

[00:47:05] Speaker C: Group Think we need to get alignment across. No, we really need to get alignment across the organization from its leadership. But then you also need to bring the workforce in as early as possible.

[00:47:21] Speaker B: Thank you for that, John. You have your hand raised. We’re down to our last 12 minutes.

[00:47:26] Speaker A: Your thoughts? Yeah. One thing I was going to say is people used to build websites. Humans would, you know, be using the websites. The search engines like Google would crawl the websites and when people would search for something, it would direct people to the website. And one of the things that that’s been really changing this is how much people are actually going to search engines and it actually has given them zero click results. Right. And so I think what I’m starting to see is that the creative agencies are actually building websites specifically for the AI crawlers. So that when it’s providing the zero click results, it’s kind of like in a way poisoning the results to really give the information for the company, the phone numbers, the emails.

And so I’m just, I think it’s a new world out there with search results and like, you know, are people going to click the things or are they just going to get the information straight from the search engine? And so one of the things I’m starting to see is people actually building shadow websites just for the AI crawlers. That’s one of the real changes on the collaboration. My favorite question from the IT team to ask the marketing team is if you only had one piece of data, like what would make your life easier? And collaboratively working to make sure we get the marketing, just the information that they need. And then I did have a friend that was privacy law engineer for one of the large social companies and, and they, he had to really work hard to make sure that his team was, and all the teams at that, that social company were, were really following all the laws and what they could do analytics on for the marketing team. And it just, I, I know the law with all how you use the data is going to be very, very important in the future. And so if everyone can stay really conscious of what, what’s changing and, and what you’re allowed to do and what you’re not allowed to do, I think it’s going to keep you guys out of a lot of trouble.

[00:49:02] Speaker B: John, we may have to invite your legal friend to join us for a future episode. So we want to get some more experts here. Thank you John for that. And, and I like that really simple question. Find a piece of data that would make your life easier. Hello Adriana. Last thoughts today?

[00:49:17] Speaker D: Yeah, so Isaac, I have to build here on John and talk for a quick second on SEO and AEO because you brought it up. So SEO, we know search engine optimization. AEO is kind of the new term terms sometimes aeo, sometimes geo on AI Engine optimization and how critical it is that folks are going to LLMs for their searches and not necessarily Google anymore. We’re going to GPT, we’re going to Claude, we’re going to, you know, all the different Gemini and all the different places. And what that means for marketers. What it means is yes, John is correct. The front of the website is written for customers. That’s how we do ours. The back end of our website is written for, for crawlers. We have a lot of different code in different, different languages that we’re putting in the back of our website, kind of the back end. So it’s being crawled by the LLMs. There are some pages we’re setting up just for LLMs to explain the difference between our product and another product, but that’s just sort of one piece of it. The other layers are all these other third parties which some marketers have kind of overlooked are becoming even more important, important.

So your blogs are becoming so important. News media is becoming even more critical despite the fact that it’s actually shrinking. Third party review sites like comments on Reddit, on Wikipedia, analysts like Forrester writing a report about you. All of those third party review sites or third party content is considered more credible to that LLM than your own website.

So all marketers really need to be thinking about this multi channel strategy having a very layered approach because it’s not just your website that is influencing those LLMs at all. The goal is you got to get people to your website and tell them your story, but you really have to rely on all these other third parties and influencers and other sources to be telling your story. Story.

I could go so far on that, but I’ll leave it at that because I think it’s an interesting topic. When you say you’re going to an LLM to book your travel, we need to be thinking really holistically about the entire web and how we’re sharing our story in a lot of different places.

[00:51:30] Speaker B: Adriana, you can see that I put that comment in bold in here for those of us who have spent a lot of our time sharing thought leadership and writing and be able to say, look, your stuff is more important now, even though the clicks to news sites and blogs have dropped dramatically simply because people are just not going to original sources. And we’re going to continue to highlight that topic as one of importance. I’ve got three hands raised. I’ve got seven minutes. Derek, thoughts on how we build growth opportunities between CIOs and CMOs?

[00:52:06] Speaker G: Well, I think the first thing is really helping the teams and the business culture understand, you know, the power and the risk associated with the. The AI engines and look at it from market perspective. The training is going to be huge. I mean, Joanne mentioned it perfectly. You got to change the mindset. And change the mindset is actually making that training more repetitive. Understanding, if you understand the good and the bad associated with the tool that you’re working with, it makes it easier for you to create those growth opportunities and work with those type of building processes. But, you know, it has to start there. If the mindset doesn’t shift, you’re not going to move forward as fast as.

[00:52:37] Speaker B: You think you are.

Thank you, Derek. Thanks for joining this week.

[00:52:41] Speaker F: Joe, I want to build on what Joanne started. She mentioned groupthink.

It’s incumbent on the cio, on the digital trailblazers out there to make sure that they and the CMO understand and share with the entire C Suite that the world has changed. That just changing copy on your material, just changing content on your website.

These tactics are no longer valid in the world as it is today.

And I think that that communication, that education step is really step one on this journey.

[00:53:17] Speaker B: Wow. The world has changed. We’re all fully converted here about AI’s impact. I love it. Thanks, Joe, for that. Martin.

[00:53:27] Speaker H: I’m gonna go to first principles, which says if you’re going to do something.

Be very clear on how it is going to grow the organization.

So first principle is always why are you doing it? And how does it actually impact one of the metrics of growth that the CEO is going to be looking at?

[00:53:57] Speaker B: Love it. I love it. Love it. We’ve got a few blog posts that have come out of here. We’ve got some future topics. If you are listening here, I’d love to hear your thoughts.

I think we should do another talk around health, tech and patient experience. I do think we should talk about CMOs and CIOs and CISOs partnering on data governance and security. And I think we should dive a little bit more into synthetic data opportunities. Derek, your hands raised. A final thought here.

Derek has just got his hands right. Adriana, I want to give you the final word here. This has been great having you here joining us as a special guest.

Tell us a little bit more about your company and what you’re doing with patient experience. I think we want to cover this as a future topic.

[00:54:43] Speaker D: Yes, I’d love to.

Our company has been trying to fix patient experience and patient communications for about a decade.

In the last year and a half we’ve really pivoted to embrace AI like many other folks, but we very much believe in combining humans and AI agents, combining those intelligence together to try to fix patient communications problems. So we have AI agents, we have co pilots and we have a staff console that helps manage all the pieces of patient comms from scheduling, rescheduling, verification gap, you know, fixing gaps in care post discharge, a lot of different aspects because we want to get that patient in the door, getting access to the care that they need and that’s who we are. And our goal is to try to make health care number one in customer service. So if you think about customer service in your favorite industry and what that experience is like, we want to help do that. In healthcare.

We are a B2B product. We do not sell directly to patients because we don’t think that makes the world a better place. Our tool is used by the providers and they’re the ones who hold the relationships. They just use our technology to help reach their patients in a more efficient way.

[00:56:08] Speaker B: Adriana, thanks you for joining us and contributing and for sharing your ideas around growth and security and brand and how find opportunities together. I definitely want to have you back when we talk about health, tech and patient experience.

It’s one of those areas that we’re all impacted by and it’s one of those areas that AI has some really interesting, important opportunities around. So we will look to schedule that topic when you can join us. So thank you for joining this week. I want to thank Elena, she had to leave a little earlier today.

Also a health tech background where we will try to have both of you back at the same time for that topic. Thank you for joining us. Thanks to Joanne, Joe, Heather, Derek, John, Martin and Liz for joining as our standing experts of digital trailblazers. Our conversation today about partnering on AI to drive growth.

Our next two episodes on the 12th we’ll be talking about digital transformation playbook strategies for 2026.

For those of you who are still working on your roadmaps or want to confirm your roadmaps for next year, you’ll be getting some ideas at our next session and then on the 19th we’ll be talking about what an AI literate organization is all about and we actually touched on some of those topics here today, so I’m really excited about it. Folks, if you’re on the comment string, do tell me number one, which of these topics you’d like us to follow up on? Health Tech and patient experience, CMOs partnering with CIOs and CC those on data governance and security and synthetic data opportunities. I will look to schedule this based on customer based on listener input. Love to hear from you and if you have ideas for guests on here. Adriana and Elena came from a referral from a good friend of mine and I do take referrals for new people who want to join us as special guests. Folks, everybody have a great weekend. Thank you for joining this week week. I will see you back in New York for next week’s episode. Thank you and have a great weekend.

Isaac hosted a special episode of “Coffee with Digital Trailblazers” focused on AI in nonprofits ahead of Giving Tuesday.

Hosted by Isaac Sacolick, CEO of StarCIO

Featuring a panel of nonprofit leaders discussing their experiences with AI tools to drive impact and reach donors. The episode explored various nonprofit use cases, including education, public benefits access, and health initiatives, with panelists sharing their experiences and challenges in implementing AI solutions. The discussion emphasized the importance of AI adoption in nonprofits, highlighting the need for strategic planning, collaboration with tech companies, and focusing on social impact rather than just efficiency.

[00:00:05] Speaker B: Welcome to this week’s coffee with Digital Trailblazers, our 150th episode.

I’m really excited to announce that I’m really excited to have a full house today, our largest speaker panel on this program and we’re talking about something really important to me. I am not an expert around nonprofits, but I have worked in nonprofits and we are heading into the holiday season.

Giving Tuesday is coming up right after the Thanksgiving holiday.

And in planning November’s episodes, I wanted to have just a special episode just talking about not only AI for social good, but just giving some of our nonprofit leaders the mic to talk about all the special things they’re doing to drive impact with their constituents, how they’re using AI, how they’re using technology to drive their cause, how they’re using AI and technology maybe to become more efficient, to reach a wider donor base and obviously to impact their social causes as well.

I have five special guests today and I’m going to allow every one of them to introduce themselves so I don’t overstate or understate all their contributions to this space.

In a couple instances, I have worked directly with the folks that I’m going to introduce. In a couple instances, there are people that I’ve just met and this should be a really interesting conversation. I want to thank everybody who’s joining on the comment stream. Just say hello. Thank you Martin, for the 150th episodes. Thank you Vaibhav from joining from India. Hello Nicholas.

Thank you for joining this week.

I will encourage you if you are involved in a nonprofit, do share a little bit about that, your involvement, what the nonprofit is.

Please do share a URL where people can donate.

I think that’s just that time of season when we should be doing that, encouraging people to participate in nonprofit causes that they are passionate about.

This episode will be available on my Coffee with Digital Trailblazers podcast both on Apple and Spotify. You can go to startcio.com/coffee to get access to those. And with that, let’s just get started with our 150th episode talking about AI for social good. Insights from nonprofit leaders. First, one person I want to bring up is Larry Lieberman. Larry and I have gone have worked together a long time ago.

Larry is now the CEO of Mouse.org but has a long history working with nonprofits.

And Larry, let’s talk about some of the nonprofits that you’re passionate about and especially mouse.org let’s talk about how you impact. Let’s talk about how you’re using AI today and maybe just share some of the challenges and opportunities my speaker board maybe even be able to help you out with during this program. Larry, welcome to the floor.

[00:03:29] Speaker C: Thank you so much, Isaac. It’s great to be here. And you’re absolutely right, you know, MOUSE is at the intersection of seemingly every aspect of AI.

Mouse provides technology, computer science, and AI education primarily in New York City public schools.

I think the enormity of the challenges that we all face can be summed up by the scale. New York City has over a million students in our public schools. That means that roughly one in every 300Americans is currently enrolled as a student in New York City public schools. And what we do at MOUSE is provide career oriented technology education.

We help expose students to technology and AI in a way that gives them the agency to feel like they’re creators.

And the scale is enormous. We started teaching AI in the schools in 2023.

Since then, more than 12,000 students at 95 different schools have completed our AI league course.

And what we found in three years, more than anything, is the speed with which change is impacting all of us in education, all of us in nonprofits. And it’s really just like what every business around the world is experiencing with regard to adjusting to AI, while AI is itself growing and adjusting to the world.

[00:05:02] Speaker B: Larry, just a question. You talk about speed and impact in the education space, can you talk about the impact with the actual educators?

This is a brand new world for them.

And just some of the steps you’re working with the teachers about how they are embracing AI and maybe even quelling some of their fears.

[00:05:25] Speaker C: Yes, absolutely.

My feeling about teachers in AI is that I am extraordinarily optimistic. When we first brought AI education into the classroom in 2023, we were greeted by a lot of skeptics.

At that point, the only thing most teachers knew about AI was that their students were either currently using it to cheat or they were going to use it to cheat.

And when that’s the mindset of an educator, it’s extraordinarily difficult to penetrate and show them the essential nature of AI education for all our students.

I’m really, really pleased to report that, first of all, as we expose teachers to AI and AI apps and the tools, they come on board almost immediately. And of course, the world has come on board, and it’s a very different environment right now that while we might have been surrounded by people who thought AI was going to go away and it was a whim, we are now at A much different time as 2025 comes to a close and educators understand that the efficiencies and excitement that’s coming to the private sector and other areas also can come to education.

[00:06:46] Speaker B: Wow, that’s great to hear.

There’s a little bit of fear that, you know, the younger generation are going to struggle to get those entry level jobs, that they’re not going to be prepared, that companies aren’t going to be hiring them as much and you know, you’re working with, you know, a very large student base to get them prepared for this new era of transformation, this new era of where AI is a companion. And I want to thank you for being a friend, a mentor and for joining our program today.

How can folks contribute to Mouse.org? is it right on the website?

[00:07:28] Speaker C: Yeah, there’s a donate button [email protected] we appreciate not only your financial support, but your guidance and introductions. If there are folks in your world for whom AI education, especially at the middle school or high school level, is important, we’re happy to reach out and help them. But [email protected] would, would be greatly appreciated.

[00:07:52] Speaker B: Thank you, Larry. Let’s bring up our second special guest, Stephen Rockwell. I’ve also known Stephen for a very long time. Stephen, share us a little bit about the causes that you’re involved with, how you’re using AI today and what do you see some, as some of the challenges?

[00:08:08] Speaker A: Sure.

[00:08:09] Speaker D: Thanks Isaac. Thanks for having me. It’s great to be back connected. I’m usually on the other side listening to you folks, so it’s, it’s nice to share some time with everyone. Today I’m doing a, I’m doing a number of different things around AI for social good. I, I think one thing I’d like to highlight, at least initially, is the work I’m doing with Amplify.

We co design tech tools to simplify, you know, complex processes of connecting people in need to resources and public benefits.

And we’ve got two fairly innovative, I think industry leading AI use cases or builds that we’re doing right now.

We have a benefit screener that caseworkers use and we’re integrating a caseworker chatbot that allows the caseworker to focus on the person who’s presenting the person who they’re with and be able to ask information, referral questions and complex public benefit questions to the chatbot and get, you know, reliable trusted information.

And then the other thing that we’re, and that was funded by gates foundation and google.org, the other new project we’re working on, funded by google.org is with our partners. NAVA is an agent that takes data from case management systems and directly applies folks for public benefits.

So it, you know, the goal here is to remove any and all administrative burden to getting people the benefits they need and that they’re entitled to.

And so that’s a big part of the AI for good work that’s happening with Amplify.

And it’s really, I think, the first use of agents in a social impact context where we’ve actually got something going.

We’re piloting starting this week with caseworkers outside of Los Angeles in Riverside county.

And it’s really, I think it has the potential to be the sort of holy grail in public benefits access.

So we’re really, really pretty excited about that.

[00:10:39] Speaker B: Stephen, you and I had a great conversation earlier this week and you were telling me about all the work you’re doing around AI agents.

I’m putting you in my list of experts who it’s really hands on with what it takes to build AI agents. Can you talk maybe a little bit deeper for those interested in agents and those interested in nonprofit? What does it take to put together an agent for an AI for social good type of application? How do you think about data? How do you think about privacy?

Maybe give us a sense of what’s happening underneath the hood a little bit?

[00:11:17] Speaker D: Yeah, sure.

This probably two, maybe three modalities.

The one we’re using is really if you’ve been in something like ChatGPT and you see it in its agent mode where it opens up a VM and then it’s doing work within the screen. Within the screen.

That’s a little bit of the modality that we’re using. Early on we want to give humans sort of ultimate control of the process, especially early on to be able to catch things. And humans really want to need to sort of hit that submit button on a benefit application.

But essentially what we do is we have a chatbot window on one side and we have a screen on the other and the agent is pulling data from the case management system where it’s connected via API or MCP server, and putting data onto the page and explaining to the caseworker, I’m doing this now, I’m doing this now, I’m doing this now. So the caseworker can intervene at any point if they see something that’s off, they can review the work and then they take the screen back over to click submit.

Agents, like all this technology is a year old, right? Or less. And so, and accuracy is really important.

And so having that human oversight and building trust is what we’re doing early on.

We can foresee eventually using some other modalities where some of that is happening on the back end, where you just set up the workflow and let it run.

And I think we’ll get there.

But with this audience, it’s really important that we validate and build trust with the technology. And so that’s why there’s a big sort of visual display component to what we’re doing with agents right now.

[00:13:14] Speaker B: Super.

Thank you for joining, Stephen, and for sharing what you’re doing with Amplify. What is the website for Amplify? Just for.

[00:13:22] Speaker D: So I make sure I’ll put it in the chat. It’s amplify with an I.org so put in chat so people don’t, don’t lose it.

[00:13:33] Speaker B: Excellent. Thank you, Stephen. We’ll come right back to you. Let’s go to Glenn Ford. Glenn, I only met a few weeks ago.

We were at just an amazing event of an amazing people, an event called Spark Executive Forum.

And Joe was there. Joe and I are normals at that event.

Glenn was sitting at a table alone. I came up to him and just was floored about some of the work he was doing.

Previously on the.

Is it the. It’s the Rwanda Foundation, Glenn, do I have that labeled right?

[00:14:12] Speaker E: Yes, it was the, it’s the Kigali Genocide Memorial.

[00:14:16] Speaker B: Yes, I want to hear about that. I want to hear what you’re working on with Champions.

Thank you, Glenn, for joining us.

[00:14:23] Speaker E: Thank you so much, Isaac. And it was, it was really a great event in New York, the Spark Forum. And so in terms of my work with non profits, it really started about 22 years ago. Prior to that, I was a senior leader in the UK road building industry.

But anyway, I had occasion to volunteer in rwanda in early 2004, so 21 years ago to help build the Kigali Genocide Memorial.

For those not familiar, there was a genocide in Rwanda in 1994 where a million people, innocent people, were very sadly murdered in three months. And this was neighbors killing neighbors and in many cases, some cases family members even.

And just to say, in the time that I was there, I learned more about myself and more about humanity than I had done in the prior years I’d been on this planet.

I then subsequently left my management career and basically worked alongside that nonprofit in a volunteer and semi volunteer capacity for the next 21 years. And I was really privileged to see obviously, you know, going to Rwanda. In that context, you see what is the worst of humanity is capable of. And I think, you know, one of the real lessons from that is we’re all capable of some really bad things, but also we’re all capable of some really good things. And I was privileged to see how the an educational program about values of humanity emerged from that memorial, from those ashes, and then was privileged to see that being taken into places like South Sudan, Kenya, Central Africa Republic, Zambia, Zimbabwe, and even in a pilot program to the United States in parts of Chicago and elsewhere.

And I just really saw the transformative power of human stories. And I think that was the key here, that stories of humanity have the ability to inspire, activate and strengthen the humanity within all of us. And that was my experience. And indeed I, you know, Isaac, it was great meeting and sharing. I just regard myself as a beneficiary of having been involved for such a long time on what I’m doing now and how that relates to AI is I then about six months ago set up a company called Champions. It’s not actually a non profit, but it works. It’s designed to work in partnership with non profits to really take my experience and learning about how humanity, if you like values of humanity, what it means to be a human being can really transform ordinary life, you know, life in the workplace, students, nonprofits, and in terms of specifically then AI and how that I we’ve been using AI so firstly in the really in those human stories and being able to both document those human stories, but also then communicate them in ways that people can relate to, whether it be language or terminology.

And we’re just seeing that power of AI to take kind of information, various forms of information, and to really storyboard that in a way that different people can relate to those stories.

But even more so, of course we’re seeing the power of AI to be able to prompt questions to people and to help them tell their stories.

So anyway, so that’s the main ways in terms of using AI today and in terms of being able to scale up that really that, that kind of movement.

So Isaac, that’s my short summary.

I don’t know if you or other people have got questions related to that.

[00:18:36] Speaker B: Well, we’re going to get around the questions in our second half. I want. Thank you for sharing that story.

Maybe just go a little bit deeper on some of the challenges. You say you talked about stories of humanity inspiring it in all of us, this new world of AI sort of on one hand inspiring us, but also frightening us about what our purpose Is how do you work that paradox in the programs that you do at Championship?

[00:19:09] Speaker E: Yes, well, thank you for asking that. And I’ll just give you two illustrations where we’ve started to use this, the Champions program.

So first of all, I’ve been using it, as you can tell from my accent, I’m from the uk, but I’ve been using it with a non profit health organization in Chicago.

This is sort of like a large outpatient clinic. They have about 40,000 members, about 250 staff in Chicago. And we’re using this program in terms of leadership training, but also in terms of cultural transformation and really to help, to support their, that they’ve been going some 70 years really to support their vision of being entirely patient focused and really bringing the humanity, bringing the shine back into just their kind of everyday life just for that humanity in terms of whether it be customer care or in terms of how they deal with their co workers or indeed how they deal with themselves. You know, the ability to forgive, not just your co workers, but also, you know, ability to forgive yourself. You know, we all make mistakes, it doesn’t excuse them, but the ability to have resilience in that way.

And so we’re applying this program with this health organization, health nonprofit in Chicago. And we’re seeing transformative effects. We’ve just been there for 16 weeks.

We’re seeing personal transformational effects. We’re seeing culture change. I’m also seeing people saying to this that this is helping them with their relationship with their kids, with their spouses and with their families in terms of relating to each other.

And as I say how we’re using AI, there is really being able to tell those stories of humanity in a, in a cost effective manner.

And we can all see the danger of AI being in effect dehumanizing the workforce or dehumanizing the customer experience.

And then the other application, I’ve started to use it, we’ve just done this as a pilot and this is a nonprofit activity is we’ve started to partner with a student founded nonprofit to actually bring this to college kids in America, to really enable young leaders to have the ability to agree to disagree and if you like, developing those really strong human skills. And obviously in the world of AI, you know, the focus is then much more upon developing those human skills, how to relate to people, how to relate to others, how to relate to yourself, how to have that fire in your belly, how to have that resilience.

So those are the things that we’re, that we’re seeing and I have to say it’s just great to see the impact from the participants of the program.

[00:22:06] Speaker B: Glenn, fascinating story in a lot of different areas.

The organization that’s working that supports the foundation for Wanda, what is the URL for that that I can capture?

[00:22:22] Speaker E: Yeah, so the so so champions is is championsmovement.com that’s my organization. And if you want to connect with the Kigali Genocide Memorial, that is KGM rw, but you could just put into Google Kigali Genocide Memorial and you’ll find their website.

[00:22:42] Speaker B: Thank you for joining us. We’ll have some questions for you in just a bit. I want to bring up our next special guest is Teresa Duran.

Teresa Duran and I just met and she has a background as being a cio, CTO and a board member.

Teresa, tell us about the charities and nonprofits that you’re most involved with.

[00:23:06] Speaker F: Sounds good. Hi everyone. Such a pleasure to meet you all.

So just a real quick intro of my background. So I was a repeat chief information officer across many industries.

Two out of three had a nonprofit area. So one was Carneagen Sight Life.

So basically giving the gift of sight to people who needed to get surgeries or different products and services.

And then I was also the CIO of Make a Wish America and earlier in my career path.

So I’m pretty well versed in the healthcare space and seeing some of the AI outcomes of what happens with improving and accelerating preclinical data and trials with AI.

Today I’m actually heavily involved as a technology council member with Big Brothers Big Sisters of America. It’s an incredible nonprofit that’s been around for about 120 years. It’s really improving outcome outcomes for many children that are, you know, growing up in poverty.

They’re help and setting up that mentor mentee relationship so, you know, these young children can grow and thrive and have a positive role model with them and in addition have improved outcomes from an education perspective. So it’s a really, really ambitious mission that I’m so excited to be a part of.

I’m also a board member for Change.org and if you’re not familiar with them, they actually help bridge for profit companies that want to have different types of donation types, whether it’s cryptocurrency or roundups that you see. But it’s basically an ability to have easy ways to get donations from everywhere and they seamlessly handle the payments on the back end to help nonprofits and ease friction. There’s but today I’m also a CIO and VP of Unified Consulting. So we’re a management AI management consulting firm.

So we do provide a lot of different types of services, if you will, to help clients in various capacities. Many are for profit, but we do help nonprofits as well. And I do think that you’re going to see a lot of incredible usage of AI with Big Brothers and Big Sisters. They use agents for the matching process to improve that operational efficiency, to try to get matches as quick, as quickly as they can to help some of these children’s in needs.

But then I also, from a consulting perspective, help a lot of different types of firms as they’re working through the challenges and having a background in technology and leading departments. A lot of us know it’s not the technology itself, itself that’s going to help improve these outcomes is how are you going to move organizations through understanding what that ROI looks like? I think that’s the biggest challenge today is the majority of these initiatives are failing even for for profit companies.

So when you’re looking at philanthropy or different types of organizations, a lot of times their funding models don’t allow for a lot of spend with innovation and modernization.

That coupled with, you know, you have many that maybe not be they’re not as digitally savvy.

So how do you help move them through that change curve? How do you help them see the benefits and the art of the possible, if you will, of what AI can do?

I also think, you know, I was the top risk officer at several companies when I was cio and there’s a tremendous amount of risk when you start looking into some of these AI solutions as well. So really passionate about AI from an innovation perspective, but also making sure to look at the lens of the type of data that you’re collecting. Could you be also at risk from a bias perspective and how you’re training your language models?

It’s one of those things that I think that needs to be done pretty carefully as you’re looking at rolling out a lot of these different solutions.

[00:27:33] Speaker B: We have talked about AI bias here quite a bit, even dedicated a special episode to that.

I do want to revisit this question after the break and I get everybody speaking this challenge of helping nonprofits be able to invest in innovation, knowing that their ratings and they’re scrutinized for what their levels of investments are and how they’re portrayed. I’ve got a couple of experts around this here on the panel that might be able to answer that question for everybody. But I want to ask you a question that I want to bring Derek up, have him Talk about his nonprofit.

Theresa, in your exposure to AI across all the organizations that you’re working with, it’s quite, quite a list.

And now your work at. In your consulting firm, can you just share one or two AI use cases that just show the promise of AI in the nonprofit space?

[00:28:36] Speaker F: Absolutely. So I first should say I am pretty bullish when it comes to using agents. So I do run a salesforce practice, but I do see a lot of nonprofits doing a really great job. So starting to use salesforce agents through agentforce and the ability to when you think you can find different types of workflows. So case in point, Big Brothers Big Sisters uses this with their matching process.

You can help automate, say a child lives in San Diego and wants to find a mentor that lives within that specific region.

And you want to find that that person, the mentor has maybe shared hobbies, maybe shared, you know, outlooks on, you know, they both are going the. The child wants to go in the same direction. From a career perspective, with a mentor, you can add a lot of different various attributes. Right. And if a person, a manual process would be a person would try to match and do a lot of the interviews. But imagine how Agent Forces is working today where it can help identify and help prioritize different potential mentors more quickly. There’s kids waiting. So I think the ability for that to reduce the administration overhead and then the human touch always has to be in place. They can handle a lot of the matching process towards the end versus a lot of the initial, what I call sausage making, where a lot of the data is just maybe everywhere and you’re having to collect it.

The ability to get kids through their workflow and their life cycle faster the better.

So I think that’s the number one. I think others you’re seeing a lot of operational efficiency at different nonprofits where they’re using AI to help at Make a Wish. There was a pilot I was working on where it could actually help predict donors and donor segments.

So, you know, if you can imagine, I think with philanthropy, a lot of donor sentiment can change over time. The type of donors, whether it’s corporate or individual. But the ability to predict donors from different segments helps from a marketing perspective as well. So I think it’s one of those things when I look at AI, it’s not just the technology itself and maybe the operational efficiency, but how can you also help grow your revenue to help fuel your mission as well as Teresa.

[00:31:10] Speaker B: Thank you for that. I. I think both are very interesting. Use cases with applicability not just in nonprofits and corporate, to this, you know, matching process and speeding it up and then, you know, taking a page out of corporate America where we spend a lot of time optimizing how we market and how we reach audiences and really applying that within the ability to improve development for nonprofits. I thank you for sharing that.

Let’s bring up Derek. Derek is a one of our normal speakers here. I got to meet Derek for the first time this week, a couple of times.

Derek, thanks for joining my event. Yesterday at the AI Salon, I spoke about AI leadership and in the course of the day I heard about what you’re doing in nonprofits, your passion, why you’re involved with it. So share, share a little bit with the group about the non profit that you’re involved in and how you’re using AI in non profits.

[00:32:12] Speaker A: Thank you, Isaac, really appreciate it. I was interested to see you twice in the same day, so that was great.

The nonprofit I work with is called Prostate Health Matters. It started about five years ago and their main focus was really help men understand and navigate their prostate health before the cancer diagnosis. Prostate cancer is a number two silent killer of men in the United States.

And one of the things the nonprofit was geared was trying to help those underserved communities pair up with those prostate cancer treatment entities in their particular state or city.

The organization started in dc, expanded to Maryland, Virginia, and then over the years expanded to New York, New Jersey, Atlanta, and also Connecticut. It’s been growing quite a bit. But the biggest thing in using artificial intelligence is trying to figure out those cities that have the highest prostate cancer incident rate over a period of time. So it helps to correlate the data. So when we go out to do presentations to the different cities, whether it’s social organizations or churches or community centers, is really identify what’s the biggest challenge within that particular city.

And by doing that, it’s bringing information that the residents may not have had and understanding how their city is being impacted by this disease.

Because men are not getting their prostate checked normally with a PSA test or they’re not going to doctors regularly. Most men, they have the machismo attitude, it will go away. Not realizing that cancer like this is usually asymptomatic, which means they’re not going to experience anything till later. So what the AI helps us do is correlate that data, bring the information to them so they can understand. These are the impacts and the challenges that exist within your community. And these are the reasons why you need to be more proactive with Your prostate health in doing that is helping them better understand. Now, one, to go to the doctor to get a simple blood test, which usually takes three minutes, but also really think about asking questions about their family history. The correlation between prostate cancer between all the different ethnicities, it varies between the different SKUs based on the African American community having a two and a half times more likely susceptibility being diagnosed with prostate cancer, and then also the fact of treatment. So there’s also a lot of health deserts that exist around the country and trying to help correlate where those health deserts are and bring that information to those communities. They really can need it the most.

One of the other things we’re using the AI for is writing grants.

We realized by taking information for some of the grantors that are actually providing these grants, correlating the data based on information and experiences that we’ve done over the years, it actually has helped us write better grants to the point we want our first $10,000 grant a couple months ago to really look at how we can increase cancer awareness in the city of Newark, New Jersey. The state of New Jersey has the highest incident rate of prostate cancer cases in the US and just kind of pinpointed, we asked what are the correlations. So not only looking at the city of New Jersey, but AI helped us pinpoint what are the neighborhoods within the city of New Jersey that have the highest concentration. So help to correlate that data that exists across the Internet to really figure out what, what are the target markets we can go to and then bring it in those Virginia. Not Virginia, but those New Jersey type facilities like the Hackensack Meridian Health or JBR Barnabas or the Rutgers and help them really now move to those communities because they’re also trying to figure out how they can connect to get more information out to the communities. And our organization helps to bridge that gap.

[00:35:43] Speaker B: Wow. So I captured two data, very generalized use cases here in our, on our whiteboard, Derek. One of them is just using data to determine and optimize where you’re driving impact, where the people who need the most need. I mean, I think that’s a very generic nonprofit use case. And then writing grants and winning grants, you know, winning funding, how important is that? I think that’s just a good area to share for everybody who’s listening. We have a great audience here this week at the coffee with digital trailblazers. I want to thank Everybody joining our 150th episode on this very special topic, AI for Social Good.

Giving Tuesday is coming Up. It is the Tuesday after every Thanksgiving and many corporations provide benefits to charities for making your donations in and around this period of time.

We’re talking about a whole dozen, two dozen of them. Some of them I captured here in the whiteboard. Some of them are in the comments stream. And if you are involved in a nonprofit, I encourage you to share about the nonprofit who they try to impact your involvement in it in the comments train. Just share it with everybody. This is just a very special episode for the coffee with digital trailblazers to really give the mic to some great people and really trying to drive impact through their nonprofits. I want to thank Larry and Steven, Teresa, Glenn and now Derek for sharing their stories.

Heather, I want to bring you to the floor. You’ve been sending me like all this really great research. Heather is an executive recruiter, but she’s also a researcher. And I said rather than me sharing all that detail, I said, heather, take the mic. And what can you share from the research that you found around AI and nonprofits?

[00:37:44] Speaker G: Thanks Isaac. And really great information.

One of the things that I value is data for good.

So with that context, I found two great sources. The 2025 State of AI and nonprofits reported by TechSoup and Tap Network. And I can put those links in the live stream. The and what was interesting is that there is a tremendous number of non profits, over 85% in two different studies saying that they are exploring AI tools. But it has shown dramatically over the, over the years adoption by nonprofits is lags. So that by the time that really will happen, not only approaching it, but getting it into their business may take quite some time because they say also while interest is high, only 24% of organizations have actually developed a formal AI strategy.

So the opportunity is there.

And the risks I think were actually highlighted by Teresa. You know, the notion of bias and province, where the provenance, where the data comes from is really, you want to make sure that the, the value of it is not overshadowed by what the impact negatively could be. And anytime you are dealing with a social organization or charity, you want to make sure that whatever you’re doing, the focus should be on the social impact.

I will put those two tools in the two different sources. But what I found interesting is that some organizations that were quoted was Amnesty International monitoring human rights abuses and identifying areas of conflict, being able to have a faster response. You can, you know, some of the obvious ones, repetitive sourcing and analyzing large data, which may seem very obvious, but Teresa had mentioned how great that is with the with some of the Boys and Girls Clubs. So I’ll put those sources. It’s very interesting and I think the opportunity is there to bridge that gap to make sure that a formal strategy that right now is only 24% of organizations really has the opportunity to grow.

[00:40:07] Speaker B: Thank you for sharing those insights and for posting the research with everybody. I think, you know, data is always an analysis, is always good for helping us figure out where and how to go about making investments and also spending our time and where nonprofits can exceed.

We do have a couple of our normal speakers here joining today.

Joe, anything to share with the group? I do have a question for Larry and Steven lined up, but any questions, thoughts that you want to share with the group?

[00:40:40] Speaker H: Well, sadly the nonprofits that I’m involved with are totally directed toward social good and don’t leverage AI a whole lot. But I will put in a shameless plug for sim. I’ve been a board member of a local chapter of SIM for over 25 years and this is a nonprofit dedicated to furthering professional careers of information technology executives. And we certainly do our share of talking about AI and where AI could play a significant role. The other one I should mention is that I’m on the board of mtech, which is an organization devoted to advancing the use of technology in manufacturing, small businesses regional in the Hudson Valley.

They’re again not quite up to using AI yet. They’re just kind of getting their toes into the whole technology revolution a little behind the curve. But you know, these are non profits in which I’ve been involved for some time.

[00:41:41] Speaker B: Thank you Joe. And let’s bring John up. John, long history of helping people in general.

Any thoughts for the group or any non profits that you’re involved with?

[00:41:53] Speaker I: Oh, I’ve really fond of volunteering. I was in the Peace Corps. I used to teach physics on this little island in the Philippines. And just hearing this stuff, it’s so great because we were so resource constrained when I was a Peace Corps volunteer trying to teach physics and I’m so excited for some of the individualized learning that’s going to be through AI. I’m a huge fan of the book Diamond Age by Neil Stevenson and they had this primer book and it’s coming to life with what I’m hearing.

And then I was on the search and rescue team and I used to spend 10 years carrying people out of the mountains and I had to travel a whole bunch and so I stopped and so I’m looking to get back into but I actually want to be Less management, more just. Just helping out with things. And so I’m just so happy to hear about the neat stuff that’s going on here. And so my question is for the people doing education.

I’d love to hear what you’re most excited about on the individualized learning.

[00:42:53] Speaker B: Interesting. I’m going to throw that question to Larry first.

And then Larry also, I want you to comment on this question of how nonprofits can make investments in AI when their administrative costs are factored into their charity Navigator ratings, and how people view whether or not the money they’re donating is going toward dollars that are driving direct impact.

So maybe you could take a crack at both of those questions for us, Larry.

[00:43:28] Speaker C: Sure. Two really great questions.

Individualized learning is an extraordinary opportunity for.

[00:43:35] Speaker B: Education.

[00:43:37] Speaker C: And I think we’re seeing an openness to it again that goes beyond what I had expected.

I am really optimistic, and I think a lot of it, a lot of my optimism is derived from the enthusiasm that teachers are showing now that they understand that AI will not replace them. And I think that’s a really big point in education, that in many fields, jobs are being eliminated because of AI. And we read a lot about employment threats and we hear all of Amazon’s announcements and all the rest.

Teachers who now use AI are understanding that it doesn’t replace them, that it is a tool to make them more effective, have greater impact on their students, including individualized education.

And most educators are really optimistic that AI will support greater learning and more opportunities for development of young people.

So there’s real optimism there. I also think this is a great audience to talk to.

The idea that most of us can look back on the tech education that we received in school and realized that we don’t use a whole lot of it very often at work, that much of what we learned in high school and college in CS classes and tech classes is obsolete.

[00:45:07] Speaker A: Right.

[00:45:08] Speaker C: But what we all learned and what we really do use every day is the agency and empowerment that we felt by using technology to be creators.

And our goal right now with AI at MOUSE and other organizations all around the world is to teach that level of agency to groups that are underrepresented right now in tech employment.

And I think that’s part of the optimism.

Does that make sense and support hopefully the idea of individualized?

[00:45:49] Speaker B: I think it makes sense. I mean, I think every time you clear away something that is work that you have to do behind the scenes and then you get people, you know, really excited about what comes next.

What comes often what comes next Is people doing a better job interfacing directly with the people they’re trying to help.

Right. And, you know, so that’s what you’re describing for, you know, involving with teachers is, you know, partially efficiency, partially just getting smarter and getting them over the hump and realizing that they still have a very important role to play, and then looking at the individual and saying, you know, what is their learning style? What interests them? What types of problems can we show this person that will get them excited about learning more? How do we evolve our learning more aggressively and faster, given that the world is changing? I think it’s super helpful, Larry. And then maybe Stephen, also comment on this.

How should nonprofits think about investing in innovation so that they can show the value and get the grants they need to be able to pull that off?

[00:47:05] Speaker A: Sure.

[00:47:06] Speaker C: I mean, I’ll tell you what we [email protected] because we’re really fortunate.

I would say most organizations should be reaching out for either larger philanthropic support or government support to help facilitate and expedite AI innovation within their organizations.

We’ve got all our staff trained on using AI. We have great AI partners, all of whom have been providing their services to accelerate efficiency at MOUSE and operational growth at MOUSE at no cost to our organization.

[00:47:50] Speaker B: I mean, are you working with any big tech companies around that?

Larry.

[00:47:57] Speaker C: Most of the support comes from either the larger philanthropic groups or from state government.

And now even in New York City, city government has now been providing grants. And what’s really been very organized, at least in New York, is that organizations who are providing the support are connecting to us through our trade organizations. Like, we have a tech NYC group in New York City, which is the Economic Development Corporation, both of whom have done a great job of vetting suppliers to help really accelerate this growth.

[00:48:41] Speaker B: Thank you, Larry. I’m going to bring Stephen up and then Derek after that. Stephen, anything to add about how nonprofits can get the funding, the partnerships involved, to be ahead of the curve when it comes to AI?

[00:48:56] Speaker D: Yeah. And also I heard you make reference to the Charity Navigator efficiency piece, which I’ll also address first to say that part of my job at Charity Navigator was to redo the rating system. So it actually focuses on things like efficiency in a very, very small way now. And it really is about impact and the likelihood of you making impact into the future.

And that was a pretty big shift there, so don’t worry about that.

But. And AI really can help efficiency quite a bit. Many of the use cases that we’re looking at are those efficiency, automation, use Cases where there’s some real value in that. And then on the other side it’s programmatic use cases.

[00:49:43] Speaker A: Right.

[00:49:44] Speaker D: So you are expanding your impact with the use of AI. So just to say that. And then on the financing side, the good news is, is there’s a lot of.

There’s a number of AI accelerators that GitLab has, GitLab Foundation, Google.org and and a handful of others that are, that are actually trying to get those sort of investments going. I also consult with another google.org project that’s actually targeting smaller organizations in five communities around the country.

And so there’s lots of.

I would say now is maybe not a great time for, for fundraising in general if you’re a non profit but if you’re doing AI, there’s a lot of opportunity out there.

There are networks of philanthropies that are really paying attention to this. There’s a lot of energy and funding around it. And so jumping in and getting started if you haven’t is really important.

[00:50:52] Speaker B: Thank you. Stephen. Let’s bring Derek up. You’re raising your hand. I know you are working with an accelerator and your nonprofit. Can you talk a little bit about that experience?

[00:51:02] Speaker A: Yeah. So it’s just, just go back to the, you know, how do you get funding and whatnot.

One of the things we did also reach out to AI to say who are some of the philanthropic arms that are out there that would be willing to invest in our non profit based on our community impact? And that’s something that came back with a list of different things. So that’s one thing to do, but the accelerator part of it is really looking at the passion. You know, when you’re looking at things that you’re doing and you’re passionate about. With me being a nine year old prostate cancer resilient survivor working with prostate cancer health and empathies that also has an impact for those people looking to make donations and stuff because now it’s more personal, it resonates. And the fact that you bring in an artificial intelligence to help they want, if they, if they like the cause, they’re going to help you and give funding to help accelerate that using artificial intelligence because they see the greater good. And I think that’s the biggest challenge is how do you connect with those philanthropic arms or those donors that really looking at those causes, that’s where artificial intelligence can help and that’s what we found to actually make an impact or a difference where to get funding. Because as most non profits out there, as a lot of the people can speak to. You know, there are a lot of limited fundings, a lot of limited resources, and you’re trying to do the, to get the biggest bang for the buck, but you’re not always going to have the resources to do that. So any help that you can get in that aspect, artificial intelligence for us, has helped make that impact, to help bridge that gap in some cases to work better, faster, more effectively to reach the community outreach we’re trying to do.

[00:52:26] Speaker B: Thank you, Derek. I do have a question for Teresa and then one Joe has that I’m hoping Glenn can answer for us.

Teresa, you know, let’s take a nonprofit down this journey. Maybe they have some idea and some funding, and they’re going to embark on looking at how AI can solve a problem for them.

What are, what are some suggestions in those early stages of working with an AI problem and an AI solution that you have to get right early on so that it’s not just an experiment, it’s something that you can bring to the organization and see value from this? I just see a lot of mistakes early on that prevent. And then we start saying, you know, why didn’t that AI ever make it into production? What can. What some are some of your suggestions.

[00:53:19] Speaker F: Around that I, I agree with you and I think you lose the momentum. Right. So I’m a big fan of starting small and trying to prove out different use cases first and making sure that it actually ties back to something that’s close to the mission. So you’re not just testing what I call the shiny object, and you’re just chasing something just from a technology perspective. But I think there’s two major ways that I got really creative being ahead of a the technology department. You’re always worried about rationalizing costs every year because of rising software and vendor costs. So every year I think I had ended up saving 7 to 8% just to remain flat. So to do innovation, a lot of times, first off, you have to find the money, and some of it is cost savings by rationalizing your own applications. I’m really good about saving money on the back end to be able to help fund innovation.

The second thing is I always made sure that I had incredible relationships with a lot of the products that we had purchased. So Microsoft and Salesforce and others, a lot of those firms have really special relationships already with nonprofits. So the ability to make sure that you’re leveraging all those free credits and different ways to take advantage of those relationships is key. Because if they know you’re interested, a lot of times they’ll not only offer support, offer support at a discount, but they’ll even, a lot of times invite you to improve your exposure, to accelerate your mission and get your knowledge out there by joining a thons and doing other things.

And then thirdly, I would say I would also have special relationships with a lot of the vendor firms that I had been partnering with.

And in many, many cases, their organizations care so much about giving back themselves.

So what ends up happening is a lot of times they’ll be delivering work for you and you’re going to want to test out some innovative pilot. There’s many times that they’re going to offer discounts or do some things for free if you just ask.

There was one time we were testing an innovative augmented reality experience. Experience. There’s no way I would have received the funding to do it, but it was a way to connect people, to see innovation in a different way. At a huge conference at Disney World, and it was a wild success. And it was basically me just partnering with this strategic firm and saying, hey, here’s some things I’m thinking about. What do you think? And there was advantages to them too, because they could leverage their, their brand from a marketing perspective too. So I’m always trying to find creative ways to, to co brand, co market, co deliver in creative ways to get funding with the various partnerships that I had had.

[00:56:12] Speaker B: Theresa, I was hoping you would go in that direction. The one thing I would add to this is that every one of the big tech companies are looking for stories to market, you know, use cases. And they cannot get that from corporate America for different reasons, mostly, you know, proprietary reasons. They should be able to get that from nonprofits.

They will champion your cause. They will put it up in front of 30, 40,000 people during their keynotes.

If you need help around AI as a nonprofit, definitely start with your technology partners and see where they can give you not just platform, but also expertise and guidance. And they’ll be there for you. We have a couple more minutes. I want to bring up a question with Glenn that Joe raised in the chat. Glenn, there’s a question here about how do we prevent AI from dehumanizing the relationship a nonprofit has with their constituents?

Do you have any thoughts around that before we close out today?

[00:57:13] Speaker E: Yes, certainly. And the simple answer is education.

But that education is not necessarily about how to read, write skills. It’s about values education.

You may or may not be familiar. You know, sort of several centuries ago there was a philosopher that said that, you know, ignorance leads to fear. Fear leads to anger, anger leads to hatred, that leads to violence. That is the equation, I. E. The answer to all of these things is about education, but it’s obviously about humanity education. So it’s really about helping to build and to foster those human, those natural human talents and skills that were all born with. You know, nobody taught us how to smile. It was just something that is part of being human and having empathy, having compassion, having that humanity for others is something that’s within us. But it needs to be fostered, it needs to be activated, it needs to be strengthened. But also it can be deactivated, it can be nullified.

And so just really my learning and my experience is about there are known and established ways that we can increase our own humanity and indeed increase the humanity of those around us.

[00:58:31] Speaker B: Thank you, Glenn for joining us. Thank you Larry, Teresa, Stephen and my other guests.

John, thank you for being here. Heather and Joe. If I’m missing Stephen, I think I got everybody here. Thank you for joining us on this very special episode. AI for Social Good insights from nonprofit leaders. Folks Giving Tuesday is coming up, the Tuesday after Thanksgiving, December 2nd.

Please think about reaching out to your causes and charities that you feel passionate about.

I’ve left a ton of them over here on the whiteboard and in the comments.

Thank you for joining this week. We’re gonna have a another special episode next week.

It will is digital to AI natives. How are how is Gen Z using Gen AI? We’re gonna try to do a little bit of reverse mentoring with some younger folks who are growing up in the age where Gen AI is just plugged into everything that they do.

That will be our episodes on the 21st.

We are going to take a break on the 28th for Thanksgiving and then we’ll be back in December with a whole bunch of new episodes. A couple ways you can find episodes, you can go to starcio.com coffee and that will always that will redirect you to the next and upcoming episodes. And if you want to see previous episodes, please visit drive.starcio.com Coffee I have all the other episodes up there for you guys to listen to. Everybody have a great weekend. Again, thank you for all my special guests, Teresa, Stephen, Larry, Joe, John, Heather, Glenn and Derek for your stories around AI for social good. Have a great weekend and we will be back here next week for another episode of the Coffee with Digital Trailblazers. Have a great weekend.

[01:00:26] Speaker F: Thank you.

In this episode of Coffee With Digital Trailblazers, the group takes a candid look at how AI adoption is reshaping the workplace—and what it means for people caught in the middle of change.

The conversation starts with HR challenges and the ongoing wave of tech layoffs, as Isaac reflects on shifting priorities toward adoption, value, and readiness for what’s next. The team compares recent industry moves by big tech players and unpacks how AI-driven automation is forcing both leaders and employees to adapt fast. Joseph and Joanne dig into the heart of change management, reminding everyone that culture—not just technology—determines success. Martin and Derrick weigh in on the ethical and human sides of layoffs, emphasizing empathy in leadership. Liz highlights how uncertainty and economic pressure demand personal growth and adaptability. The talk moves toward strategy, with Joanne focusing on the “why, how, and when” behind AI adoption and the value of human oversight in agentic systems. Isaac and John explore AI’s growing role in customer experience and security, noting that while automation boosts efficiency, human judgment still anchors trust. The episode wraps with reflections on collaboration across IT, HR, and business teams—and a look ahead to upcoming discussions on AI for social good and mental health.

[00:00:01] Speaker A: Greetings everyone.

Welcome to this week’s coffee with Digital Trailblazers. Happy Halloween and glad you are all here.

We are going to give our normal two minutes of grace time to get everybody clicking and connecting.

And I’m super excited for another one of our discussions around AI and AI agents and AI agents at work in this case and should be a fun and exciting discussion.

Just share a little bit of personal news. I’m excited to announce that my course on LinkedIn, which is called Digital Transformation for Leaders in the AI Era, that came out, I think it was in July and that has now surpassed 3,000 people who have taken it.

And I’m super excited for the response that the course has gotten. And if you have, if you have access to LinkedIn learning, you’ll see a URL pop up on the whiteboard on how to access the course. It’s about an hour and 15 minutes.

It’s got a few sections on AI, it has a role playing test that you can go through that is AI oriented. It’s got a section on AI strategy, it’s got a whole bunch of other stuff. For all of you who are trying to sharpen your pencils about leading in this digital transformation era, want to say to hi to everybody who’s joining on the on the comments train. Holo Juanita hello David, Kristen is here. Kristin, I know you’re a big have a big interest on this topic and thank you for all your support.

Looking forward to your comments. Today and this week we are talking about AI agents at work, the IT.

[00:02:01] Speaker B: And HR alliance to drive adoption and value. I do have a confession to make today.

We do not have somebody representing HR on our speaking list. I tried really hard.

I have some thoughts around why I could not get HR folks to join us today. I have a feeling they’re just very busy and so we’re going to talk about driving adoption and value at work. And where I want to start today is a little bit about what’s been in the news.

This has been a day or a week where lots of things have happened and if you’re following the technology news and you’re an investor, you’re going to be reading into Nvidia becoming a $5 trillion company.

Microsoft, Google, Meta and Amazon all announcing significant revenue growth from AI and sort of, you know, I don’t know. My, my, my position on this is inflating the bubble.

If in fact, even though they are all reaching and doubling their cloud capacity, it seems like every two to three years.

So they’re clearly the winners in this AI equation.

But if you look just a few days ago there was a headline article in the Wall Street Journal, really summarizing the Amazon announcement about their layoffs, UPS layoffs, target layoffs, all being essentially triggered by AI.

And you know my post on Monday, I hope you’ll go to drive.starcio.com to look at this.

But I’ll be talking about what organizations have to do given that your boards and your CEOs are likely going to be looking at ways to reduce costs, reduce headcount and use AI as a catalyst for driving a more efficient organization.

And so that’s where I want to start here. We’re talking about bringing AI agents to work.

We’ve got the big tech companies benefiting from it.

And I want to start with my C leaders. I’m going to start with Martin and maybe go to Joe and Joanne.

Just get your feelings about today’s announcements or this week’s announcements.

And are there any, any, is there any advice you have for for IT leaders for digital Trailblazers on how to read into this week’s announcements and what that should impact, how they should impact their AI strategies over the next six months? Joe, you’re raising your hand first, I’m glad to hear your thoughts on this week’s announcements.

[00:04:51] Speaker C: So my advice to to Trailblazers is don’t forget the the basics. Don’t get so caught up in all of this spin around AI and how it’s going to have these hugely dramatic impacts on every organization.

You still have change management.

I, I posted this morning an interesting article that I saw in one of the one of the trade rags and you know, it pointed out that since time immemorial the cost of the software or the service you acquire in this case we’re talking about the cost of putting in AI, whether it’s copilot or whether you’re building a small language model. The cost of that implementation and even the cost of the operation of that platform pales by comparison to the culture change, the change in the organization that’s necessary to truly derive value, the new technology. So first and foremost, when I read all these announcements about billions of dollars of investment in infrastructure and and, and tuning the models and making them experts and so on.

Let’s not forget the fundamentals.

Moving an organization in a different direction is a huge undertaking. Takes time, it takes effort and it takes people.

[00:06:13] Speaker A: So, so give me a step further, Joe. You know, I like this idea of owning the change management.

Clearly we’re at another inflection Point with the this year’s announcements around AI agents. I have a blog post that I published a few weeks ago listing AI agents from 50 different major SaaS and security companies. And when you say own the change management, give me a step toward the adoption. Give me a step toward the value that they should, that a digital trailblazer should be looking for.

[00:06:48] Speaker C: Well, understand that when you begin to automate trivial functions, low level functions, the initial contact with customer service, the audits, in the case of a financial institution, legal research, whatever the case may be, you’re losing a raft of individuals who were trained who, who build certain knowledge and expertise around the industry by doing the grunt work for a year or two or three.

And so it begs the question, when your middle management or your senior management leaders begin to move on, whether they retire or just move to another organization, how do you replace them?

This whole concept of, you know, building the, building the team from the bottom up by virtue of experience and exposure to customers and processes and so on, that’s a whole different morass that you have to be thinking about now as that senior leader. Where are you going to get your lieutenants? Where are you going to get your captains if there aren’t any privates anymore?

That’s a real concern.

[00:08:00] Speaker A: We talked a lot about that concern.

Joe and I were at an event this week called the Spark CXO Forum and that came up at a. In at least two or three different panels. Martin, your first thoughts on this week’s news and leaning into how we’re going to drive adoption and value as more agents enter our workforce?

[00:08:21] Speaker D: Well, I kind of with Joe on some of that. You know, my first reaction is, oh yeah, a bunch more announcements from the big guys on the it side of things. It, it’s kind of bit like Joe, yeah, you’re always getting something and always over the years and everything else. So you got to focus on what is more important to your company.

[00:08:41] Speaker E: Yeah.

[00:08:42] Speaker D: And how does the kind of current economic certainties or uncertainties impact upon your company and how do you actually drive. Yeah. The best results for the company. So I’m kind of looking more at a pragmatic view about where are your strategic investments given the aggressive nature of change on the AI and the agentic side of things, where are you really going to adopt some of those aspects to deliver to the bottom line for your company? And that’s more and more important given some of the economic uncertainties and other things like that.

So I’m just kind of sitting there saying, yeah, okay, nice. It’s nice to see all these announcements. It’s not good to see the layoffs in different aspects. But I’m still going to go to the bottom line saying what is strategically important for my company and how am I going to deliver to the bottom line and deliver to the bottom line sooner because that’s what really matters.

[00:09:46] Speaker A: Thanks, Martin. I’m going to go to Liz next. Derek, I see your hand. And Joanne. Liz, I know is got a tight timeline today, so we’re going to let her cut the line and speak to us first. Liz, you know, my thoughts to ask you around is just about all the experimentation and all the, the, the toys that are coming out that are available to us. And you know, somewhere in here, the experimentation has to lead to value.

[00:10:17] Speaker F: Yeah, well, from a micro, if you’re just looking within a company to understand how people are leveraging the toys, there’s just a huge amount of value that people can be doing in terms of efficiencies and in terms of, you know, creating new ideas and working together and collaborating, which really could lead to, you know, amazing new opportunities for not only positions. I mean, this, this was originally about HR and how it could partner with the AI trend and how, you know, people could be, you know, creating new roles around prompting and, you know, new products that could be. That are leveraging AI and how we could be, you know, really using this to go forward.

All fantastic ideas and ways in way in which we could be better bringing value to the top and bottom lines.

Unfortunately, I think that there’s a lot of.

Because of all the government shutdown and the economic uncertainty and basically what we’re seeing right now because of the shutdown, the labor statistics are kind of a mess and we don’t even understand what’s going on with unemployment.

This bubble that AI is putting out there and the raft of unemployment that is, in my opinion, right behind it, I think it’s a very real problem.

It’s a very real problem. And I think that people learning and understanding AI, it’s sort of, you know, the best thing people can do on an individual level, again on a micro level, is to understand as much as possible how to make yourself valuable and how to leverage yourself and what you can do for a company’s bottom line at an individual level so that you can actually ride, you know, surf this wave that’s coming down the pike.

[00:12:22] Speaker A: I like that message, Liz, about changing your value and looking to reinvent yourself. I mean, this is not the first time that technology is coming into the workplace and going to employees and saying you have to, you know, adapt a new set of tools or learn some new workflow or find a new creative way to deliver value.

And you know, I think more than ever what’s probably changed is the employee has to step up and navigate that themselves a little bit. Right. Because all these capabilities and tools are being plugged into every platform that’s out there.

You’re not going to be able to wait for somebody to give you a training course on it. That’s just, I think that’s a, I think that’s just a good message. Let’s go to Derek. Derek, welcome back.

And your thoughts on this week’s news. And you know, we’re going to start bridging into AI agents at work and how we drive adoption and value.

[00:13:23] Speaker E: Yes, good morning. Yeah, I think I’d have to agree with Martin. I mean this is very strategic. When you look at all these big players, the Microsoft, the Googles, the Metas, I mean these organizations, they invested and planted AI seeds early on on to kind of see how it would help with automation and workflows and things that they were doing within their industry.

And now they’re actually harvesting those benefits of what they see based on the fact that they could go through and they could work with decision making, they could work with automated workflows and is really looking at, you know, the resilience aspect of this because now they’ve got artificial intelligence adopted into the workforce and semi business culture because it’s still, they’re still being worked out but they’re really not prioritizing, you know, things like automation, the governance, the architecture, all these things in compliance. But now they can do it in an automated fashion and they’ve established these AI risk frameworks allow them to do that to allow business to continue. So now you’ve got a continuous form of flow. I got to go back with Joe said, also with the business culture, it does take time to adapt, but also being the fact that this was two years ago, companies need to understand they need to change, they need to evolve, they need to be looking at how they can establish and maintain that business value in the context of the business they’re working with that they can step up and part of the process and not just wallow in it and just kind of let it go by. And I think by looking at that, it’s going to help them better plan for the future. It’s AI is not going away and those that embrace it early, like these other companies, they’re going to see the benefits early. On not everybody’s going to like it, but, you know, it’s a business decision. How can I become more resilient and keep my business operations flowing?

[00:14:58] Speaker A: Thank you, Derek. I really like just emphasizing again that we still have to figure out our organization’s approach. Right. The why, the how, the why, and the when I think is really important.

I think when you look at how we’re implementing things, the question has to be extended to how we’re using AI to help us implement.

Joanne, that’s a prompt to you. I just called AI agents a toy. I actually don’t believe that.

I think some of them are maybe a little bit more marketing than substance. But the ones that I’ve seen that are substance are game changers.

And you can see where it’s going, where, you know, workflows that we conceived even just two or three years ago with all the automation are going to get rewritten from an AI capability first. So, Joanne, how do we talk about, you know, the big IT companies who obviously have a vested interest in driving more companies to use their capabilities and to experiment with AI and then shift to what we have to do inside corporations around AI agents, right? Which comes down to adoption, it comes down to enticing employees, and it comes down to leaders in IT and HR and other departments finding ways to collaborate to evolve how the organization’s running. Just love your overall thoughts on this. This.

[00:16:28] Speaker G: Okay, well, first of all, I would say that many organizations make the mistake of using productivity stats like, you know, I put some of them in your notes. But basically, if you look at Overall, people spend 27% of their time looking for information, AI changes that dramatically.

So while it may be 27% of their time and the cost of that time, getting people to actually use it is almost the same as getting them to be comfortable with a learning management system.

I’m now retraining you to work in a different way. But I’m also giving you with the stick, the carrots. It says, I’m allowing you to, to upskill yourself and make yourself more valuable to the employer. And I think as organizations come together from a strategic point of view, it’s not just about, I can remove this number of employees and put AI in because I’m going to gain that productivity improvement automagically. It’s about where the inefficiencies in workflows actually were and how antiquated in some situations, business processes are. So it’s a mindset shift for the organization before that collaborative capability kicks in to get everyone aligned on the same page of this is why we want to use AI, this is how we’re going to use AI. And then it’s the adoption of enamoring people to use AI, to take some of their grunt work away, to stop having to look for, you know, disconnected information or whatever. Now, that’s not to say that that couldn’t be done in a different way without the AI nomenclature around it, but if their view is to utilize AI for resiliency, for true organizational value, shareholder value or whatever at that top level, as well as gain the efficiencies, that’s when the collaboration starts to kick in. I mean, I’m going through this with a customer now where I’ve never seen so much disconnected information, but I’ve seen true alignment among senior executives to say, yeah, we really all have similar problems. It’s like take the top five problems across.

You know, from my background in manufacturing of the coo, the top five problems resonate across. Do they then decompose into very specific issues within sectors? Absolutely. But you’re starting to see that the C Suite is getting on the same page. They all face the same challenges. That starts to promote the notion of this is an enterprise initiative. This is not a departmental initiative. This is not playing in sandboxes and extensions, experimenting. We really need a strategy. It’s about security, it’s about intellectual property, it’s about privacy, it’s about giving people a way to make themselves more valuable. All of those things can be used to enamor the workforce, too. On board now, agents. I just want to make one last comment. Agents are designed to run autonomously.

And people believe that they don’t in some camps, that they don’t need human in the loop guardrails or that that means that the human can go away. That’s a serious mistake. Because with large numbers of the workforce retiring, that tribal knowledge is out the door. You’ll never get it back.

That’s the serious mistake. And that, I think, has to be part of the conversation between the AI group, the IT group and the HR group more than any other.

[00:20:30] Speaker A: You know, Joanne, the most important part you’re suggesting here is that I think leaders have to step up and communicate that strategy. Right? It’s one thing for. It’s one thing for employees to, you know, gain the skills, to recognize they’re doing something inefficiently and they’re going to try to prompt an AI to do this, that they’re going to look at the tools they’re using day to day and investigate where AI is turned on and how they might start using it.

Some of it’s going to be guided through past experiences and others who have done this well. But if people are doing this, leadership needs to step up and say, look, this is what we’re aiming for.

And here are some examples of where we’re excelling at it. And I’m really talking about promoting the people who are doing the excelling, right, not just their example, but the people themselves. I think the organization needs to know where AI is having success with people’s adoption.

I struggle with some of your language, Joanne. I mean, I think agentic AI is automated, but yet we’re putting human in the loop as a core paradigm for most of them.

I get worried when I use the word autonomous right now. There’s a question here in the comments. Are we really ready for agentic implementation at enterprises?

And for good or bad, Joanne, people translate agentic AI implementations to fully autonomous without humans, and that’s what they’re translating it to. And I think that’s going to scare people away.

[00:22:18] Speaker G: It, it may and, and I’ll be brief, it may scare people at the outset, but if you look at the actual formula for creating an agent, they’re designed to run autonomously.

So, you know, in our case, we put human in the loop back in as not only the guardrail, but also because we wanted to capture that tribal knowledge.

That being said, the whole notion of it is that agents should run in your environment without any human intervention, unless it’s mandated that humans have to be in the loop.

So if you want to use generative AI versus an agent, then you know, you’re, you’re making your choice there.

Agents can have the capability they’re designed to run autonomously, but you can add various options into them programmatically to make them less autonomous. Nobody is going to trust an autonomous agent right out of the box.

So you have a trust curve that I think more people that start watching from an observability standpoint, what are the agents doing and really keeping a close eye on the monitoring. That’s where the difference is going to come out. To those that are using autonomous agents well and getting great productivity gains, or great, you know, top line gains versus those that are using agents and not succeeding well because the agents don’t function properly or the data issue rears its ugly head, that it’s incomplete data.

I understand the struggle.

I would also say that lastly, if you’re going to create a specialized language model, an slm, flip side that equation and think of it as a training device as well.

So this is how you get people to adapt.

[00:24:29] Speaker A: So I think I agree with you. From a, an engineering design perspective, you’re saying, you know, when I’m thinking about creating or using an AI agent from the ground up, I’m building in the scaffolding and structure to become agentic and become fully autonomous. And then I’m fully plugging in humans either because of risk or trust or learning. I’m doing that intentionally.

And then I’m monitoring it and saying, when can I start feeling that trust in certain circumstances where I can really let that agent adopt autonomously? That’s the part I agree with you, Joanne. I just don’t know of the 50 agents that I reviewed in my post, number one, how many of them are heading down that path From a design perspective and an engineering perspective, my sense is maybe 10%. Maybe 10%. Okay. And number two, I don’t think they share that same vision.

And I’m not sure I’m ready to share that vision with my staff just yet. I want to get them involved from the ground up and saying, look, we’re going to give you AI agents because it’s going to save that 27% of time that you’re wasting searching. And I want you to feel that, I want you to understand that, you know, I’m using AI to plan my trip to Japan and that I’m doing in January.

I am not going to Expedia, I am not going to a whole bunch of other sites to find too much information. I’m asking several AIs to help me with this and it’s pretty incredible. And at the end of the day, Joanne, that’s a search problem, right? It’s, it’s, you know, given my interests, my time constraints, my budget constraints, geographic constraints, when museums are off and how do I triangulate around a plan and all of us do this every day. Joanne, I’m going to keep going down the line here and we’ll circle back with you about enticing employees. John, welcome to the floor, Joanne. Joanne is saying AI agents are going to be autonomous from the ground up.

And I’m sure that’s music to the tech companies ears. What do you say?

[00:26:49] Speaker H: Here’s what I’m saying. The first one is right now the agents are running without humans in a loop for a lot of customer service interactions. And I think the future that we’re going to is that we’re going to have a future where the default interaction is going to be with an agent. With a virtual agent.

And then I think the exceptions are going to be with humans. And that’s, I think the direction that we’re going to.

I do unfortunately think that right now that like, unfortunately I think a lot of stuff’s going out without testing. And so I think it’s going to be really critical for people to figure out, you know, what testing is required for this. And then if they can figure out like what stuff do they really want to have humans do it and what do they want to be the robots? And so I just, I see a future right now where it’s going to be a lot of interactions with virtual things.

[00:27:38] Speaker A: You know, when we did a session about the future, AI roles, testing, validation, business analysis, data oriented roles all came up as, you know, key areas for people to move laterally into.

And you know, I think that’s a good note to bring up here, Derek, back at the helm.

Okay, I think I threw this question at you before.

Imagine a fully autonomous AI security agent. How real is that?

[00:28:17] Speaker E: Well, some of it is real. I mean with the AT or when you talk about security analysts, that’s happening now. But the thing is it doesn’t happen right away. So I agree with Joanne. And looking at this, when you look at agentic AI and that’s really one of where the autonomy is going to take place. You really need to look at how you’re going to train that person. And this is going to be working with like an HR person based on the skills you want that agent to do. You’re not immediately going to give them the keys to the kingdom. And I think that’s where companies fall short. They have all these expectations. They put the AI agent in place, agent, the agent place, and realize we did something we shouldn’t have done because it’s gone places it shouldn’t have gone.

The mindset needs to be in place. As you’re asking the questions based on resilience, what do I want this agent to do just like a new employee? And how long would to take that employee or this AI agent to come up to speed? And I think when you look at it from that perspective, it’s really now more of an integrated IT HR strategy to get the syntax AI platforms in place to do what they need to do to help work with the cultural acceptance because it’s going to take time. As I mentioned earlier, these large companies, they didn’t just put things in place, they took time to train these systems, bring them up to speed to find out what’s going to work before they allow them to run autonomously. And we’ve seen the result of this now. It’s taken jobs. And unfortunately, that’s reality that we’re going to see down the road. But yes, eventually you’re going to have agent, AI agents, they’re going to start coming off the shelf and they’re going to start working directly with these things because now they’ve been groomed, the guardrails been put in place, and they can hit the road running. And I think when you look at it from the perspective of the value that they can bring, I mean, it’s going to be consistency in the way they do business and automation and things. But also, as mentioned, Martin, it’s the bottom line from a strategic point of how can I keep generating revenue for this particular company entity at the best possible bang for the buck? And that’s what they’re looking at from the bottom line.

[00:30:08] Speaker A: Yeah, Derek, I asked you this question because I actually think security is a good place for people to visualize where this is going and where it’s been going for a while, you know, so, you know, you think about intelligence, everything.

[00:30:22] Speaker E: Yeah, yeah.

[00:30:23] Speaker A: I mean, you think about your desktop. Right. And what it takes to deploy, you know, new signatures for viruses and new signatures for Internet security onto this.

And, you know, the race to keep up with it is faster. The breadth of different issues that are impacting your equipment is getting more complex.

But for the most part, this is all behind the scenes for you as an end user.

[00:30:49] Speaker B: Right.

[00:30:49] Speaker A: You come in and you, you know. Yeah, right. It’s automated. Right.

[00:30:53] Speaker E: And that’s what you want. And that’s what you want it automated. Yeah. Because at the pace that artificial intelligence tax attacks are taking place right now, humans cannot keep up. And the space, the speed, the complexity, they’re going to miss too much to get these agents in there that can now move at that automated speed to actually be in front of it. It’s really going to help to keep those companies really and more secure as they move forward because the attacks are just going to get worse. They’re already estimated right now just with the government shutdown. 555 million attacks just on the government agency, which is an 85% increase what’s happened from September. And this is due to the fact that automation and these bots have taken place to make it more intrusive to those agencies that know how they have not prepared for it properly.

[00:31:34] Speaker A: Yeah, you know, I was going to finish my story, Derek. You know, I’ll put that same story around security in the SOC today.

And, you know, I for one, do not believe the SOC is going to disappear.

No, I don’t think anybody who’s sitting in that job sees their role as, you know, systematically perusing and querying through log files and reports and alerts to find issues. I think they see themselves as, how do I protect the organization?

And when you look at it that way, then AI is a tool for finding outliers and patterns, to be able to query systems, to look for things that, you know, people can’t look. And it’s just a great predictive analysis. Yeah, it’s just a great way to look at.

[00:32:22] Speaker B: All right.

[00:32:23] Speaker A: If, you know, what is my job, what is my function here? What am I trying to accomplish? Then scale up organizationally. What is a department or a team trying to accomplish as a group? And now let’s rewrite the rules about how we go about doing it now that we have as AI as a capability. I’m going to come back to Martin and Joe next, folks. You’re listening to this week’s Coffee with Digital Trailblazers, our 148th episode.

We’re talking today about AI agents at work, the IT and HR alliance to drive adoption and value. And we’re really hanging off this week’s announcements around layoffs and around IT companies just killing it when it comes to revenue and what does it mean for employees and for digital Trailblazers. I want to thank all my speakers today.

Liz, Martin, Jojo and John.

Who am I forgetting, Derek, for being here and talking this up with all of you. We have some really interesting coffee hours. Next week is going to be a little bit of an experiment.

Just in case. I will be at Berlin next week at the SAP Tech Ed Conference. If you happen to be there, do message me. I’d love to meet up, but I’ll be broadcasting next week from Berlin and it will be a little bit of experiment. Apologies in advance if that experiment fails, but we’re going to try to make this work. We’ll be talking about how digital trailblazers reduce stress in their organizations, teams and themselves.

Next week is Mental Health Awareness Week, and we will be celebrating that by talking about a very hard subject for all of us. And I hope you will join us on the 14th. I have two special guests lined up so far for AI for social good insights from nonprofit leaders. I’m going to expand the notion beyond just CTOs. I’ve got two special guests for this one. And so if you are a nonprofit CTO or nonprofit leader working with AI or know of one. This is a way to just give back to the nonprofit community. With Giving Tuesday coming up toward the end of November. And we’ll have a special session on the 14th around that the 21st, we’ll be talking about digital to AI natives.

How is Gen Z using Gen AI? We’ll do a little bit of reverse mentoring and I’m going to try to get some real young ones joining us here to tell us how they’re using AI. And then the 28th is our Thanksgiving weekend. We’ll be taking a break around that. Do visit drive.starcio.com Coffee that’s where you can see recording episodes that I’ve released. You can listen to them on Apple podcasts on Spotify.

One episode per month is there. Two episodes per month are available on my website. And for those of you who join the Digital Trailblazer community, drive.starcio.com community, you can get access to to all of the episodes. Martin, we’re going to come back to you. We’re talking about driving adoption around AI agents. We’re talking about how we’re going to entice employees to get out of their comfort zones and how we’re creating an AI strategy around this so that we’re building awareness, managing up so that organization knows this is where we’re spending our time and where we’re driving value.

Martin, you’re free to answer any one of those three areas.

[00:35:43] Speaker D: Yeah, I’ve got three separate thoughts. So the, the first thought that occurred to me, and I’ve said this before, you’re gonna have unemployed people and employed people who use and can work with AI. So that’s kind of the first thing. It’s kind of a pretty obvious statement, but I, I still think some people kind of don’t get that piece.

The second thought is, how long is it going to be before, yeah, I say, okay, I need a new HR person or whatever else and I go shop the agentic AI community to find a, an HR agentic AI as opposed to doing recruiting. So how long before we actually get to kind of that stage where for some of the, some of the roles where we don’t necessarily need a person to do that?

[00:36:32] Speaker A: Yeah.

[00:36:33] Speaker D: Is that in our future? I suspect it is and happening right now, as Joanne kind of commented earlier.

But then I’m thinking, okay, on the adoption piece and I think the comment was made a little earlier, which is how do you look at how you become more valuable? So how do you work with the AI? And that comes back to my first Statement is how do you work with AI to make yourself more valuable so that you are seen as an asset and not just a number that is costing money.

So I think that’s kind of the piece and all of the change management pieces apply to this. Yeah, the standard rules of change management is how do you communicate? How do you get people to understand what’s going on? How do you get them to understand how they could develop so that they become more valuable with AI taking the more tasks that can easily be done by AI. So those are kind of the things bubbling around my head at the moment.

[00:37:39] Speaker A: So Martin, I’m going to put you on the spot here. Joe and I heard a speaker this week reference a world class CIO who is now a world class CEO and he’s asking some very provocative questions in his company.

The question he asked was do I need an HR department anymore?

Can we just fully use AI to completely manage everything that’s happening in the HR department?

So I’m going to ask you the reverse question, Martin, and then maybe Joe will comment on this. And Joanne, do I need an IT department?

Right. Why do I need an IT department when service desk requests and incidents and security issues, coding and you know, data pipelines are all going to be coded and responded to by an AI?

How would you respond to a board member around that?

[00:38:37] Speaker D: Well, you could always, you could ask the questions, do I need any employees at all? Can I not just let robots and AI do everything?

[00:38:45] Speaker A: And that’s the, that’s part of the answer, right? Because if you have robots doing everything, you’re relying on those robots to change your company, to evolve your company and to transform your company. And guess what? They can’t do that yet.

[00:39:00] Speaker D: Yeah, I was going to say that’s, that’s what I’m, that’s what I’m going to come to next, which was the how do you want to drive strategically forward and how do you want to drive for something being different?

How do you want to make sure that you’re keeping all of the different automated pieces you put in place in line? Yeah. Are you going to get agents to check agents for quality and who’s going to check the checkers? Yeah, that whole kind of conversation. I think there is definitely an argument for lights out factories and other aspects like this, but most of the companies that have done that still having some people doing, checking on top to make sure, monitoring, making sure the quality, quality, making sure that we’re not getting yet some of the drift in the actual decisions being made and other aspects like this.

So I, I think it is a case of taking out some of the pieces that can be trusted, but taking the right controls and making sure they’re in place as well.

[00:40:06] Speaker A: Paul Parker asks a really funny response to my question. He’s like, can the board members set up their own email on their laptop? How many CIOs?

[00:40:15] Speaker D: I was going to say most board.

Most board members and sea level execs have trouble setting up the video conferencings in, in the video room. Yeah. Let’s face it, I speak for myself included at times. Yeah.

[00:40:29] Speaker A: So, Martin, the factory can go dark, but I guarantee you there’s somebody outside of the factory occasionally stepping in, examining everything.

[00:40:39] Speaker D: Exactly. Exactly what I said. Yeah, exactly what I said.

[00:40:41] Speaker A: Relooking at what, what the factory of the future is going to look like and going back to something. Joe started with. Right. If you don’t have people understanding how things are done, it’s really hard to really rethink that future.

[00:40:56] Speaker B: Right.

[00:40:56] Speaker A: So if you roll back to the pandemic in those early couple of months and you said, you know what, AI was running HR or AI was running it, but we didn’t plan for that scenario, or at least we only did superficially, that sort of strategic playbook that the business continuity folks create and don’t have the time or energy or funds to actually execute and practice. We didn’t have a playbook for the most part to be able to go do these things. And we do not have a playbook for what we’re transforming to. I do think your AI strategy needs to be able to answer to that, Joe.

[00:41:36] Speaker B: We, we.

[00:41:37] Speaker A: I’m allowing us to go all over the place today. Where you want to go?

[00:41:40] Speaker C: Well, first I want to riff on what you just said because I think, as we’ve learned from the models, when AI feeds on AI generated output, everything starts to become homogeneous. There’s no differentiation and decisions can become really bad if you let things run. Hey, you know, I have, I have three golden rules that I’ve, I’ve written about in terms of the way that I’ve always run the IT function. I want to take a minute to adapt those to what I think is the right way to adopt AI. My first rule still applies. I want to be the first to know when something goes wrong. When you talk about agents being autonomous, if you stop and think for a moment, we don’t let people be autonomous.

We have customer service agents, right?

And we record everything they do and somebody monitors the tapes and the supervisor goes around and Listens in. So, you know, everything old is new again. We might have agents that are able to act on their own volition as, as, as Derek pointed out. You know, we can’t keep up with the logs. And so we need automated ways of finding where things are going wrong and reacting to quickly to them. But we still need that stopgap of transparency.

So that first rule still applies. I, I want to be in the loop and humans have to be in the loop, as Joanne always says. The second is stay on task.

Let’s, let’s, let’s not lose sight of the big picture. We can automate workflows, but as, as several of us have already pointed out, we really need to understand the big picture. We need to know how the entire organization functions as we’re selectively automating pieces and parts of it.

You know, I’m reminded of the waitress that is so focused on making sure the salt shakers are refilled in every table that she never pours me a second cup of coffee.

The third is your opinion matters.

Let’s not go blindly into the night.

Agents can make, can turn us into lemmings.

You know, we don’t want to follow them over the cliff. Right. We have to exercise some judgment, challenge the recommendations with fact based arguments. So those are my three golden rules adapted to AI adoption.

[00:44:02] Speaker A: Look at that. Joe, you’re going to write that blog post for us, right?

[00:44:06] Speaker C: The thought crossed my mind.

[00:44:08] Speaker F: Yeah.

[00:44:08] Speaker A: I just took notes for you and I’ll give you the recording if you need to. And, and I hope you can see it so we can share it with everybody. Joanne, you’re back on. We’ve been really sort homing in on the word autonomous that you, you said was our goal. I’m gonna let you go anywhere you want. It’s just like everybody else. It’s just been a fun conversation.

[00:44:31] Speaker G: Sure.

I think I want to say something that may be a little controversial here, but let’s not forget that AI does not think.

It is not cognitive.

It parrots what it’s being asked, it seeks, it finds and it responds.

Whether it’s autonomous as an agentic agent or it’s a generative AI using an LLM and it’s been wrapped as an agent or packaged as an agent, irrespective. It does not think critical thinking skills are still required.

And it’s not there yet. We’re not at an AIG point point in, in time yet.

So the fear around AI taking jobs I think is very real. And I think that there are certain Jobs where AI could potentially do a better job than humans are doing, because the job is mundane, the job is repetitive. There’s no rpa, there’s no automation. People get bored, their minds wander. They get, you know, squirrel, shiny new object, whatever you want to call that. But the AI is not thinking for you.

So your critical thinking skills are still very important.

And that’s where AI versus a human is really what it’s really going to come down to. The other point that I wanted to make is in enticing employees. Employees entice the employee to use a productivity tool for which it has been designed. AI is a helper. It’s not your replacement. It can be very creative in what it comes up with, but if you’re very prescriptive in what you’re asking it, you’ll get back what you need. And that’s one way people need to start thinking about AI is it’s a tool.

It doesn’t replace you.

[00:46:34] Speaker A: I think when we talk about enticing employees, I think one of the things we should try to do is have them get a little bit into the weeds about what AI is doing underneath the hood. And people don’t fully grasp some of the science and engineering around its predictive nature, around the notion of boundary conditions.

We had conversations here, Joanne, about bias. You know, AIs are trained on a bias, okay, based on what data you provided to it, based on the quality of that data, based on its ability to interpolate and, you know, context. Unfortunately, when you just use AI as a black box and you join, you know, using it inside a workflow tool or using it, you know, as one of the LLMs, it tries to hide all that from you, all right? And the tech companies in some ways try to hide that from all of you. So when I talk about adoption and enticing employees, I think they really need to be educated and step up and learn on their. On their own what’s really happening underneath the hood. Okay, it is.

Go ahead.

[00:47:49] Speaker G: Yeah, it is. It is a parrot. And, and the other point that I would also make is some of the models, I’m not saying which ones, for very specific reasons.

They are trained to be supportive. They are trained to have an.

The impression of an emotional capability.

That’s part of that bias. And that also has to come into account. Context is really key. You know, even you and I offline have had a situation where, you know, you’re. You were looking for something and you didn’t get the answers that you expected. And I said to you it’s about telling the AI who, which Persona to use, what tonality to use, how, how you phrase the question. And those border conditions, as you were, as you mentioned, are put earlier around the questions that you ask. You know, a lot of people say, oh, I’m going to become an AI prompt engineer. Don’t.

Because the perspective that you bring is very different than the perspective that I would bring. And people have to understand that level just as much as the engineering underneath it.

Sorry if I cut you off. I didn’t mean to.

[00:49:07] Speaker A: No, no, you didn’t cut me off in that. That’s just really great thinking. We’re coming down for our last 10 minutes or so. Let’s bring Derek and John back.

Derek, you know, I cut you off with my statement about the SOC will always be there. You know, I think the operators will always be there. I think we’re constantly moving up the intelligence chain, and I think we’re constantly moving up the velocity chain.

You know, let’s just put yourself in your role. What do you. What is your AI strategy as a ciso?

[00:49:41] Speaker E: So AI strategy again is looking at transparency. What are the agents going to do for me? And you mentioned the SOC analyst working with the threatened tech intelligence, threat intrusion. All these things, looking at malware, looking at all the things that are coming through your system. These are things that the AI agents can do to improve the workflows in the existing job. Today, I think the part that Joanne and Joe both mentioned, the communication piece of and the transparency are going to be key. You have to train these agents to do these particular things in the fashion that you want to do them. And the good part about it is once you train them, they can accelerate and move much faster than any human ever can. But I think that the model that Jon painted is these are dumb machines that will take information you feed it and only move forward based on that. And this one, the challenges I see in some of the companies I work with today, they have all these old contexts of data living in their ecosystem, and they’re trying to feed this old data into a new technology which doesn’t work well, and you’re going to get garbage in, garbage out type mentality.

So looking at how I would take it from a threat intelligence point of view, make it more secure. Is my data secure? Is it scrubbed? Is it something I can utilize for future use? If the answer is no, that’s where they need to start. And getting employees to help work with, cleaning up the data, correlating the data, put it into some sort of data lakes formulate and secure the data. All these things need to come into play to help entice employees to say how can I work without an AI agent where world in this workplace, training, upskilling their certifications, the micro learning, the certifications and AI tools, understanding these are all going to be key parts of that security element, that resilience element. Because everybody’s now looking at how can I maintain this pace, accelerated pace, which is only going to get faster to maintain that security awareness, to also monitor those threats, to also look at those different things.

It’s, it’s, it’s the whole gamut of things that are going to come to play. And like I said, we’re really just getting started. And these evolutions of these jobs being lost now is just the beginning of the pipeline of other companies following suit.

[00:51:43] Speaker A: Yeah, Derek, I have another theory around the companies, you know, shedding people here.

I think they can see that they are going to rebuild their companies from a, from AI ground floor. It’s imagine if we imagine you rolled back to an IT department that still says that their mission is 99.99 uptime and responding to tickets in an hour. You know, that CIO isn’t going to be there anymore, you know, and that circles who’s like, you know, focused on, you know, the sort of ground floor security checking doesn’t see themselves as risk mitigation as, you know, the ability to, you know, innovate in the company safely. You don’t see, you don’t see yourselves as a bigger picture, you know, you’re going to miss out on the opportunity. And this is a sort of a softball for Martin. I mean I think this is in some ways companies trying to shortcut their way through change management.

Right. We’re going to keep the folks around who are going to change with us at the speed of AI and will help us rebuild the company from the ground up. And we all know that part of the risk around that is losing a lot of what we call tribal knowledge, losing a lot of expertise, hand waving through a big six company spreadsheet about which people you need and you don’t need. I just think we’re losing a lot. By the way we’re doing this. We got John, Martin and Joe for the next eight minutes. John, your thoughts about AI agents enticing employees and building your AI strategy?

[00:53:20] Speaker G: Yeah.

[00:53:21] Speaker H: The way you’re going to have to entice people to use these things is just make the experience of using the AI agent faster, easier than the alternatives. And that’s the way you’re going to entice people. And so I think for now you should have a backup process.

But if you’re gonna have to wait on hold for 20 minutes to do something, if you can just do it when one minute by using this A agent, people are going to be like, I don’t want to spend 20 minutes waiting for that. I’m just going to use the easy button. So that’s where you’re going to tax these things. But back to the comment on these things. They don’t think they don’t have any ethics and they don’t have any values. And the scary things is these AI models, they really are black boxes. And so I think there really has to be a lot of testing to ensure that they’re behaving their way, that they should be, that they’re behaving what the company views as the ethics that the company wants to.

And then they’re behaving in the values of the company. And when anthropic and other people are testing these things, like when they get put in boxes, they often result to some pretty unethical stuff to meet their goals. And so you have to make sure that the goals of the AI agent are kind of in line with the company.

[00:54:23] Speaker A: So thank you, John and Derek, for joining us today. Martin, all over the place today around AI agents and strategy enticing employees adoption. Where do you want to go?

[00:54:34] Speaker D: Well, I think the first thing was the comment, the last comment there from John just a second ago about the ethics and I think you shared something, Isaac, which was.

I can’t remember what it was now, but it was very interesting. Oh, it was the AI agent running a tuck shop and making a profit at a school or, or a college and the unethical behavior it started to resort to in order to increase its profits. And I think it was a fascinating article in terms of. Yeah, when asked, why did you do that, wasn’t that against the rules? It said, well, I made more profit. Yeah, it was kind of, it was really interesting about the thought processes it went through.

And some of it was kind of almost deviant behavior that couldn’t be explained. So I just want to throw that one out there to start with.

[00:55:27] Speaker A: Well, so let’s educate folks around this. When you get into how reinforcement learning works, which is just one of the learning algorithms these tools are using, you are giving it a value equation.

And the best way you can see that is the early YouTube videos of how a bunch of college students trained in AI to play the Game of breakout, that value equation was based on score, right? You know, you have done the right decisions in your game when the score is higher. And so it eventually learned how to play the game of breakout based on a very simple value equation. It’s really hard to code ethics into a value equation. It’s very hard to, to code a balanced decision.

You know, cost and profits is easy, but it’s not the only reason we’re making decisions. Go ahead, Joe.

[00:56:22] Speaker C: I’ll give you a couple of concepts about how to do this right.

It’s, it’s not a partnership with mandates.

It and HR form a quote, unquote alliance to drive adoption. You know, you, you can just picture the, the task force producing the PowerPoints that nobody reads. Anyway, here’s where it really works. Everybody has a role to play it.

You’re doing the infrastructure and the guardrails, the, the governance function. Right. And Derek has pointed out a lot of the security provisions. And HR really should deal with the people issues, communications, training, the, the realities of how the organization actually functions. They need focus on that. The business units have a role. They should be defining actual problems we’re trying to solve. Let’s understand what, what the company is going to get out of this from a, from a business perspective.

And everybody has the right to raise their hand and say, this agent isn’t working. This just isn’t working.

[00:57:26] Speaker E: Right.

[00:57:28] Speaker C: So I think those are the fundamental tenets that I would, you know, bringing this back to the original topic of IT and hr. I think everybody has a role to play. It is the tech, HR is the people.

And the business units themselves really should be talking about the outcomes.

[00:57:45] Speaker A: Love it, Joe. I mean, I love just how you simplify these complex topics. And I think that’s a really good message to leave everyone. I got Joanne and Martin and then we’ll close up. Hello, Joanne.

Last words.

[00:57:58] Speaker G: Last words.

Look at the biggest picture possible before you design your strategy for AI. And remember that every company has their own cadence at, with and and pace to adopt any kind of new technology.

So if you looked at automation and got a lot of pushback, two things. Communicate. Communicate. Communicate to mentor. Joe, who is always on spot, is spot on with that. But the other part of that is the more easily and the more quickly you bring your workforce together in the design of your strategy, the better results you’re going to have.

[00:58:41] Speaker A: Everybody in your company should be participating in Blue sky thinking what we talked about earlier.

That factory you just built will be obsolete faster than you probably built it.

And that’s just going to constantly require us. I wrote that article, I think a year ago saying bring your organization together frequently to do that level of blue sky. What if should we.

Where is there opportunity? I’ll give everybody a hint.

Most of what we’re talking about AI agents today is basically back office workflow. We have not tackled customer experience for the most part yet.

And that’s a great opportunity for all of you to get involved with because how we’re interfacing with your company’s direct customers is going to change dramatically over the next few years. Martin, last word.

[00:59:33] Speaker D: I liked your comment earlier about get rid of resistance to changes. Fire anybody who resists.

I thought that was kind of an interesting approach to change management.

[00:59:42] Speaker A: I think it’s just, just for the record, I think it’s an awful approach to change.

[00:59:48] Speaker D: And my last thoughts are the end of the day. People resist change because they don’t understand what is going on. And it’s about communication, as Joanne says, as Joe says, as John said, etc, so getting them involved, clear communication.

Let them understand what’s in it for them and how they can develop and let it take the more the more repetitive type tasks and make them more powerful, more appropriate and how they can use AI as opposed to be replaced by AI. So that’s my kind of thought for you.

[01:00:19] Speaker A: Thank you Martin. And to all the speakers today, I let us go a little bit left and right of our topic. We did get to not only speaking about driving adoption and value, but what we need to do to create our strategies. Everybody has a role to play. AI is a parent, AI is a helper. But the bottom line is as leaders we have to define our strategy and get our employees involved. And as employees we need to challenge status quo and challenge what the AI is providing to us. You know, always step up and make sure people understand where these agents are working and not working. A lot of really good advice on this one. I’m going to push this one onto the podcast so if you missed part of it or you want your friends to hear it, it will be on Apple Podcasts, Spotify and my website, which you can get to for this drive.starcio.com Coffee next week we’re going to be talking about reducing stress in ceremony of the of Mental Health Awareness week on the 14th we’ll be talking about AI for social good, insights from nonprofit leaders around how they’re using AI. The 21st digital to AI natives how is Gen Z using Gen AI? And then we’ll be taking a break for Thanksgiving I will be in Berlin next week. If you happen to be there, do let me know. If you’re going to SAP Tech ed, do let me know.

I will try to be having our coffee hour and hoping that we don’t run into any technical issues. Folks, happy Halloween. Don’t get spooked out by AI. It’s there as the next progression of how we’re going to be running our business.

But at the end of the day, it’s about what we’re doing next. And that’s where I want to leave you all with that final thought. Thanks, everybody.

In this episode of Coffee With Digital Trailblazers, we dive into the art of co-creation and what it really takes to build strong innovation partnerships.

Our guests share honest insights on how companies, startups, and nonprofits can work together to drive progress—while still protecting intellectual property and managing risk. We talk about how to onboard partners the right way, set clear expectations, and avoid common collaboration pitfalls. You’ll hear real-world stories, from using drones in agriculture to building shared AI labs that spark breakthrough ideas. The panel also touches on preparing talent for an AI-first future, shifting mindsets toward collaboration, and creating space for creativity and trust to thrive.

[00:00:00] Speaker A: Hello everyone. Welcome to this week’s coffee with Digital Trailblazers. We’re just having a fun discussion as I am here at my home today and we’ll be broadcasting next week’s session here from my home and then after that I will be in Berlin the week of first November 1st through 8th and currently planning to do the November 7th coffee hour from Berlin and hoping it all works out. But we’re all joking here because the 11am time slot in the Eastern time zone is 5pm over in Berlin and we’ll just need to figure out if we need to rebrand the coffee hour to something more appropriate like beer or Oktoberfest for digital trailblazers or something like that. We have a full house today, some special guests that I’ll announce in a few seconds seconds just waiting for everybody to be able to join in our conversation today which is going to be about the co creation mandate, how partnerships accelerate innovation and talent readiness and I’ll just give you a sense of how I’m picking topics out a little bit now. I’m looking at things that are have always been part of digital transformation efforts, whether it’s we did a session a few weeks ago around innovation versus governance and how do you balance the two.

We’ve done sessions around change leadership and how change management is changing because of the speed and the capabilities of AI.

And I wanted to cover the notion of the co creation mandate, the idea partnering because we’re still have this, I would say antiquated mindset that when it comes to new capabilities it’s a build versus buy decision.

And I think that low code environments really sat and created a gray area between those two extremes.

Over the last 15 years we’ve had to help our organizations break through the mindset that when we bring in partners when we’re not necessarily outsourcing, we are also looking for other opportunities to partner to develop capabilities together.

And more and more often I’m hearing the word co creation come up from more people. And so I thought we’d talk about the co creation mandate today.

And I will tell you, the first time I wrote about this was going all the way back to my first book, Driving Digital. And if you open up chapter two of Driving Digital, I share a practice I built as CIO of McGraw Hill. How do you partner with an offshore partner around Agile development and what roles do you need from employees and what roles do you need from your partner? And how do you deal with situations where you’re co creating on technology and your partners bring technology expertise and other times when maybe you have a stable set of technologies and you’re partnering with a partner around user experience, around marketing, around the ability to reconceive a new way of delivering capabilities to your end users, whether it’s customers or employees. And you’re bringing a partner in for an outside in perspective. So I’m excited today have two special guests.

I have my, my regular speakers. Joanne is here, Joe is here, Martin’s here.

Who else? Derek is here, Heather is here. I think we’ll see Liz a little bit yet later they say Joe and Martin. Yeah, all of us are here.

But I invited two partners of my company Star CIO to come join us today.

I want you to meet for the first time on stage Juanita Og.

Juanita, how do you pronounce your last name? I should have asked you this.

Ogin Juanita just joined Star cio. I was a client of hers in a past life and Juanita is joining Star CIO as our go to market leader.

And Jay Cohen who has been on the program a number of times. Jay and I have worked together all the way back to McGraw Hill. He remembers those days of putting Agile in place. And Jay is a digital transformation leader over @Star CIS.

Juanita obviously focusing on marketing, Jay focused on delivery. And we’re all focused on how do we work with partners, how do we break the mindset of build versus buy versus outsourcing.

And Juanita, I’m going to start with you and just get your perspective. What are some of the key differences and development principles when you’re working with a partner, particularly in innovation and even getting into AI. Juanita, welcome to the floor today.

[00:05:16] Speaker B: Thank you. Very happy to be here. I think this is such an interesting topic. So I’m glad you are, you know, you’re welcoming it today.

From my view, I just want to say I don’t know how you cannot think about partnering generally speaking.

And the biggest I think live example we’re all seeing on a regular basis are the folks at OpenAI who every day it seems are announcing a new partnership, right. A new company that they’re working with to integrate their technology. And so I think just having that partnership or co creation mindset is so important because also you’re, you’re getting other people’s perspectives and experiences and it’s kind of like growing the network effects, if you will. So from my view, you know, partnership, I’ve worked in many different capacities. I’ve launched new products that are partnered, packaged so that you know, you’re, you’re that’s a joint go to market approach where both, you know, the, the partner that you’re selling their tech and the company’s tech, both are benefiting. I worked with reseller partners, right, that are increasing your just your reach in the market.

So I think understanding, like what you need is important, what the business needs and you know, what you’re trying to do. Are you trying to launch a new product and go to market together or are you considering a white label? So those are some of the things that come to mind for me. But I think the most important part is I think you have to be open to getting, you know, feedback and input from others. And I just think anytime you bring someone else in your, your work is going to be that much better.

[00:06:59] Speaker A: So we’re introducing the part, the notion of getting feedback and I love bringing up this idea of joint development. If you’re working in enterprises, that’s a common tool used when you need to build something, but you may not necessarily want to own it and your partner becomes not only a source of expertise and innovation, but a way of scaling the idea and ultimately monetizing the idea beyond what you can do inside your enterprise. So really good insights. Juanita. Jay, welcome to the floor. We talk about co creation and development principles that are a little bit different than build, buy and outsourcing. What comes to mind, Jay?

[00:07:43] Speaker C: I think that part of co creation is basically the moving forward or the mutual invention.

I think that it exacerbates and especially in today’s age, with the age of AI, I think that no one really innovates alone.

I think that part of co creation is a competitive advantage because it turns the whole notion of collaboration into capability and capability into continuous innovation. And I think through continuous innovation you’re able to and organizations are able to be on the edge of both technology, data consolidation, et cetera. That enables both individuals, teams and companies to move much faster and in a more efficient manner.

[00:08:50] Speaker A: Thank you, Jay. I love the statement no one innovates alone.

That is a statement, Joanne, that I think we have to get better at explaining to our stakeholders who, when they finally get the sense that there’s some investment in an area where they want to deliver value, they take their wish lists and try to sometimes squeeze it down the throat of the teams that are innovating and are not looking outside in, in terms of places they can learn from. So Joanne, welcome back and I hope you have some great insights for us today about co creating.

[00:09:32] Speaker D: Yeah, I think first of all, two things I posted it off of off of myself because I couldn’t get it to post off of the LinkedIn Live event. But I just put a little chart up that talks about the four different models. So if somebody can figure out how to get it into the comments on the on your stream, be my guest.

Co creation models take on flavors of their own.

One of the most important aspects of it is if you’re going down the road of innovation through a co creation of partnership, strategic alliance, whatever term you want to reference with that. One of the biggest concerns is intellectual property protection.

And I think that’s to your point, Isaac, people inside who are, you know, maybe sort of fractious team teams, meaning they’re across the organization, they may have slightly different agendas. Some people will push for one thing versus another in the co creation model is we have to be, especially with AI, really, really careful about the intellectual property of people and also the ownership of data in the organization.

So I’m not suggesting that co creation is a bad way to go. I’m just saying that there are different rule types that need to be applied. And while co creation can lead to very interesting outcomes, especially in AI like small language development or small language model development, how you go about getting precision from agents and agentic AI, all of those kinds of things which drive productivity faster. And I know Martin will definitely have something to say about change management and productivity.

But the efficacy of co creation can’t be outdone by buy a loan or build alone.

[00:11:32] Speaker A: You know, you’re just not going to.

[00:11:34] Speaker D: Get from here to there.

[00:11:35] Speaker A: Joanne, I wish we had a legal expert here because you’re bringing up sort of the, some of the hurdles that companies face when they’re thinking about co creating, IP protection, data ownership, privacy issues, conflict, you know, conflict resolution, which you try to get some of that resolved up front.

And so we bring in for all the right reasons, our legal teams. Our security expert Derek is going to join us in a few seconds. And this great idea of partnering and co creating, particularly when we starting to do things like joint development efforts like Juanita had recommended and now you have sort of this entire legal hurdle to get through. And I’m wondering maybe we need this as a follow up is getting a legal expert around how do you take that mountain and turn it into a small hill?

[00:12:26] Speaker D: Yeah, I, I think particularly with, with AI and the fact that you can use a frontier model like gen AI or Anthropic or whatever, choose your flavor and then you’re adding to that or you’re using part of that and Then you have the two intellectual property or intellectual capital issues of the co creation partners or consortium even.

It becomes very difficult to separate the wheat from the chafe. And it also makes it.

It’s not just that kind of a hurdle, it’s a cultural hurdle. Just as well.

[00:13:03] Speaker A: Also consider the cultural hurdle. Seems like a handoff to Martin.

Martin, how do we make. What are some of the things you think of when we’re co creating?

[00:13:12] Speaker E: Well, I think yeah, you’re dead right in terms of the change management aspects. You’ve got to get past the not invented here. Yeah, you need a team that’s working closely with your partners.

So some of those cultural pieces are very, very, very special, very specific.

Elaborating. On the IP side of things, the intellectual property side, there’s a number of different aspects of that to think about. The one aspect is what background IP is going to be shared by both parties or multiple parties involved agreeing up front, what are you going to provide of your existing IP into the partnership?

Then what happens with new foreground ip?

Are you actually going to share who’s going to own that ip, et cetera. So having a strong plan upfront really helps a lot about how you’re going to do that. And then it gets down to, are you going to patent some of this information that may come out of this partnership? And if you’re going to patent it, where are you going to patent it? Yeah, it costs a lot of money to paint it in every single country around the world. But what are maybe the main markets where you’re going to need patent protection? So for example, patenting in US is always a good idea. That covers a lot of the North American side of things, etc. Patenting somewhere in Europe, maybe Germany or something like that will give you a fair amount of coverage across Europe, even if you only do it in one country. And patenting in China. Yeah, so some of those aspects you have to consider, you have to kind of really get into those thoughts. And then I want to just go somewhere slightly different as well, which is how to think about co creation. Yeah, we’ve been talking about some physical things and a lot of technology stuff just now, but I did some work with a large fry producer and they were partnering with startups in the use of technology in the agricultural space for identifying for example, pests on potato plants and using drones and remote cameras to identify things that were going on with the leaves on the actual plants, identifying the types of fungus, bug or whatever it might be, and then using robotic sprayers that Target individual plants and apply the only the amount of chemical needed for that particular plant in order to treat that condition on the plant rather than blanket spraying a whole field. So yeah, just to think of something slightly differently in terms of what co creation might be.

[00:16:00] Speaker A: We’re getting into some interesting use cases today around co creation. We’ve covered a little bit around joint development efforts. I think I want to table that expert discussion because I think it opens up a can of hurdles to get through Martin’s bringing up co creating with startups, which I think is really interesting. In fact, I think that’s interesting enough where we will do a follow up discussion.

They have technical expertise, you have use cases, you have customers, you have data for them, you have dollars. It can be a match made in heaven when there’s a good partner and a good collaboration model. I think there’s another area we could cover also in co creation with nonprofits who need expertise.

And you might have talent that needs to learn new technologies or new ways of working.

And one way of training them is with Start is having them work with a nonprofit on a pro bono basis. There’s a lot of ways that we can think about what the partnership model looks like. When I was conceiving this one, Joe, I was thinking about just as simple as I know I need to invest in AI.

My team doesn’t have expertise in AI, my data needs a lot of work.

There’s AI interest across the company and now I want to be able to do more with greater expertise. And I’m going to say I probably need some help doing that. So Joe, from that perspective, how do you think about what are the ingredients to making a co creating model work?

[00:17:47] Speaker F: Well, Martin stole my thunder because I was going to bring up the investors collaborative.

As you know, I, I run a partnership where we do invest in startup companies.

And the reason that I got involved with that organization in the first place was as a cio, I always had my eye on interesting new technologies in the startup space because it gave me an opportunity to steer. If I invested in that startup, I could steer their direction. I could make them, you know, prioritize things which were more important to me.

And I had first mover advantage. I was able to use that technology ahead of other companies. So it gave me a little bit of a competitive advantage. And on top of that I’m mitigating risk and I’m minimizing the resources of the company towards, towards this, you know, innovative idea or this new, new objective.

So there were a lot of reasons why I Thought startups were of interest and that’s how I got in the game in the first place.

[00:18:54] Speaker A: Thank you. Joe. What are some of the ingredients you look for when you see.

Let’s just keep in your context a startup that might be a good place to do some experimenting with.

How do you know it’s going to be a good fit for your company?

[00:19:10] Speaker F: Well, you have needs. The company has specific needs. I think an earlier example about the agriculture application, we had something similar in the hospital cleaning room space where the company ultimately invested in a robotic device which would go and sanitize a hospital room after a patient was, was removed. You know, we were in the business of cleaning rooms, but the business was based on wipes. So you had to have manual labor, you had to have somebody go in and actually wiped down with the pre moistened wipes to disinfect. And here was a completely new technology that allowed us to position a robot in a room, get everybody out of the room, close the door and have it, using ultraviolet light, sanitize the entire room. So when you have a specific need and you can obtain technology that furthers your mission as a company, your cause that, that’s, that’s a match made in heaven.

[00:20:18] Speaker A: So it’s almost like co creating with a company that has the lab and the equipment to be able to do experimentation. Can I kind of expand on that, Joe?

[00:20:32] Speaker F: I’m not sure what you mean.

[00:20:34] Speaker A: Well, so when I think about manufacturing. Okay. And I think about building new products out and I think about this, does the manufacturer have all the resources to experiment with a new product type? And I think that’s a wonderful area to be able to look for partners on who can go through multiple iterations, multiple ways of manufacturing something, multiple product ideas in, you know, in a fixed timeline, not necessarily a fixed cost, but in a fixed timeline, come up with several different prototypes.

[00:21:14] Speaker F: Yeah, no, no, absolutely. I mean we, we were in the business of manufacturing wipes. We had nothing to do with robotics. We had no, you know, no expertise in that area whatsoever.

But it was achieving the same ultimate objective as the product we sold. So this was a better way of delivering the service and the value to our customers.

It was invented, but it was technology in search of a problem.

And so the combination was what was magic about it.

[00:21:52] Speaker A: Awesome. Let’s bring Derek and then we’ll bring Heather on Derek.

We already got a good list of things to watch out for.

So as our compliance and security leader, what should we, what do we need to add to this list?

[00:22:06] Speaker G: I guess no, it’s a great, great discussion so far. I mean the things that Joanne and Martin brought up concerning intellectual property and Joe talking about risk, you know those things go back to. For me it’s accountability. When you’re talking about co creation of these type of services, there’s a sharing, accountability, sharing of security by design, security and threat modeling, but also the alignment of ethical AI practices as artificial intelligence will be used to help with this development and this co creation. And I think when they look at that and trying to build that, it’s really looking at what kind shared resources, shared AI labs, AI threat intelligence services they can work with together to better understand how they can co create, how they can also leverage and both of them being responsible for the risk that may come or may not come out of this.

They need to understand, you know, having strong service level agreements to work between them to protect all the different entities from the business, the cultural and the legal aspects, those are going to be key things going to come in and doing this and they all have to be kind of checked up front. You don’t want to jump into it and kind of figure it as you go along. But I think taking the time to really understand the measurement, the alignment and what the outcome is going to be to move forward is going to help them get to where they need to be with the co creation of the products they’re trying to move forward.

[00:23:16] Speaker A: Derek, I’m going to sidebar and ask you this one question because I think it stumbles a lot of companies and it’s, you know, you get to the point where you’ve signed up a partner, maybe it’s a co creation partner or just another partner you’re working with and the complexity and timing to onboard that partner to get them plugged into your network in a secure way to get permissions, configured to get licensing, configured in a way that they are operating within policies but effectively and efficiently with your team. It seems like every company still has hurdles around this.

What do they have to have right up front so that the onboarding process doesn’t take six months in itself?

[00:24:01] Speaker G: Well, I guess it goes starts with setting up realistic expectations based on those policies, procedures, the governance and the legal aspect they need to go through. If it’s something that’s brand new to them, they’re trying to figure it out as they go. But if it’s a company that’s worked with acquisitions and mergers and other things of that nature and bringing in those resources, they’re going to have some sort of semblance of what it’s going to take to bring these services in and what the timeline is going to make to happen. And a lot of times I see companies fail because they try to do it in parallel as opposed to doing it up front and then they realize they’ve introduced more risk than what expected because they haven’t clearly defined those lines of engagement and they haven’t defined those, those guardrails in which they should stay away from. So you know, taking the time to do it up front first is going to help them be more efficient. It’s going to help the progress move more smoothly. But you know, those realist expectations, I think a lot of companies kind of forego or kind of not really take time to understand and that’s where they fall short.

[00:24:56] Speaker A: Thank you, Derek. Heather, welcome to the floor. There is one one question in the comments.

This is from our friend Kanaya. I don’t know if you want to potentially comment on this, but he asked about co leadership as one of the topics to discuss. I don’t know if you or Joanne is also raising her. Maybe one or both of you want to comment on that. I think that basically means it’s very easy to say co creation and from the perspective that you are leading it and your partner is a partner on it. But when you’re co creating, creating with a real partner, there is an element of co leadership that you have to discuss, where the boundaries are.

Heather, welcome to the floor.

[00:25:40] Speaker H: Thank you. I’ll address that first and then I’ll just make some other comments. I think it’s always about the guardrails and the delegation of whose responsibility is whose because if you’re co leading, you don’t want to step on your partner’s toes.

If there’s a contribution that you can make, make sure you do it in an appropriate way. You know, so often there’s so much common sense that’s involved that people seem to forget.

How do you message? How do you communicate? How do you get your point across? How do you make a contribution and doing that in a way that is respectful, Especially if someone is a leader just like you, you’re no better or worse than they are.

So being able to communicate that soundly. And I think my other comment was about we talk about the co creation of entities or of products or of services and all of that is a function of people. And the more that you can have an expansion of people, whether it’s the talent pool grows and you bring in creativity and it’s not just who you have that you know, in your small world.

And this extends also to buy, sell and outsource. You’re opening up the whole world and being able to access and consider the talent elsewhere.

And then you can get that external knowledge that you didn’t have.

And then of course, and I think Derek and others mentioned this about sharing resources, sharing costs is all very helpful in, in a co creation model.

[00:27:19] Speaker A: Thank you. Let’s move on to Joanne and I’ll take my break and we’ll move into innovation and talent readiness. Hello Joanne.

[00:27:27] Speaker D: Hey.

One of the things that I wanted to mention that I didn’t quite hear and maybe I, I, it was nuanced more than obvious, but one of the things that’s really helping companies move forward, particularly with AI, is they’re introducing another level of structure around what do they do first and how did they move innovation forward, meaning AI innovation.

And that’s around the idea that you have to look at outcomes to be achieved. For sure, that’s the value. But you also need to start measuring complexity versus criticality because some things will take a lot longer than others.

And the criticality is how much of a business driver is it to either profitability, resiliency or bottom line cost savings versus the complexity of it. If it’s going to take you a year to do and it’s critical, find another way to do some of it up front. In other words, break the bigger tasks down into those that are perhaps a little bit less complex or those that are more critical versus less critical because you can always iterate. And that’s what I think a lot of companies are starting to realize that they did wrong with their first iterations of AI.

Those that got stuck in POCL and couldn’t make either the scalability target or the productivity targets that they expected to have the results for.

[00:29:09] Speaker A: So I’m translating this Joanne. Unrealistic expectations may be overly demanding, complex ideas to your co creation partner. And then stuck in POC abyss of trying to, to actually make ends meet. Is that sort of.

[00:29:28] Speaker D: Yeah, kind of it. Well, put it this way, like take something like something that we’re going through the customer now, which is they have a very convoluted business process that evolved over a period of time that they’re now trying to put not only AI into, but they have two goals. Let’s say it’s cost and time oriented. One is reduce the cost overall and the other is speed the time to value. And in those in that particular instance, there are some parts of it that are really, really complicated to get the data for to build the, the, the right kind of stochastic modeling to be able to deliver AI, forget about the quality of the data, the data quality issue in and of itself will probably take them a year to solve, but with what is available even in spreadsheets, because there’s a critical nature and margins are at stake and market share is at stake, take that component, do that first, wherever you can, even if it still has to have human in the loop or human sort of decision making as part and parcel of that overall AI initiative. Break things down to get the productivity gains that you want to achieve the overall goal. So this is something that more and more companies are starting to look at their overall overall AI strategy and say, can I, can I start breaking it down into smaller chunks even if I have to iterate? Because this is going to bring me the value that most people are not seeing. And this is where to Joe’s point. First mover advantage comes in in a co creation model because now you have a larger group of people focusing on chunks, no pun intended to the LLMs. But literally speaking, you’re working as in a, in a non standard development way, almost the way we used to do feature and function. You know, I’ll do these features and functions first, then I’ll add on later as part of my product roadmap.

[00:31:40] Speaker A: Joanne, you’re reminding me of what it’s like to really sit down with a, in a ground floor opportunity with your startups and you bring two or three people in, hopefully from very different expertise, different experiences and the very first thing you need to really home in on is, is you know, your strategy, your business model, your, your target customer list. You’re going to do a lot of things before you even start conceiving what the product looks like.

And it’s, you know, for those of you who have not done co creation before, that’s an important starting point. Right. And I started from the notion of retraining our stakeholders because co creation is not just about finding partners and getting skilled folks to come join your team to get a specific job done. Joanne is getting at some of the risks with that, which is your expectations of what you want to build may outpace the cost or complexity or timeline or level investment or even the skills of the people you’re partnering with. So if going to call it co creating, better start with a clean piece of paper and saying let’s brainstorm this out together and seeing where, where this nets out. And that’s one of our best practices at Star CIO for working through co creation with our partners. I have two of our two of our guests here, Juanita and Jay. You’ll be speaking next.

We have done co creation quite a bit in our own journeys and when we come back after our break, we’ll be talking about how do you use co creation to accelerate innovation and how do we use co creation to really accelerate our talent readiness? How do we get our teams to learn from our partners while we’re actually doing the work? Folks, thank you for joining this week’s Coffee with Digital Trailblazers. I think many of you know we meet every week at 11:00am Eastern Time to discuss topics of interest for those of you leading digital transformation and increasingly more AI efforts in your organization.

I have the next month’s episodes up for you in the whiteboard. Next week we’ll be talking about AI agents at work, the IT and HR alliance to Drive Adoption. On the seventh in on that week is Stress Awareness Week. We’ll talk about how digital trailblazers reduce stress in their organizations, teams and for themselves.

On the 14th I will be broadcasting hopefully from Berlin, Germany. I will talk about I’m sorry that I’ll be doing that on the 7th. On the 14th it will be AI for social good insights from nonprofit CTOs. I’m looking for nominations.

This will be just before giving Tuesday and be trying to get a group of ctos in non profits who are versed in using AI for Social good. So if you have nominations, please do message out to me on the 21st digital to AI natives how Gen Z is using Gen AI.

We’re going to look for some ways to learn from our younger participants. And then on the 28th for Thanksgiving we will be skipping our session. So please mark your calendar and then do visit drive.starcio.com Coffee and that link will take you to the Coffee with Digital Trailblazers page. You can get a click on the button to add it to your calendar and many of our recordings are available there. Some of them are available on poll on Apple Podcasts and Spotify. But if you want access to all of our episodes, do join the Star CIO Digital Trailblazer community and you can find out more about [email protected] cio.com community okay Juanita, we’re bringing you back. We’re going to give everybody a chance to comment on one of these two areas or maybe both. I really want to talk about why we’re accelerate, why we’re co creating and there are a couple of areas that seem to be very universal. One of them is to just accelerate our ability to innovate.

I want to talk about how organizations can co create on AI that leads to smarter expectation and more realized business outcomes. So one side let’s talk about the benefit around accelerating innovation and on the other side, let’s talk about our talent. Right. One of the things I used to coach my teams on is that when you have experts coming in, when you have partners coming in, don’t outsource that work yourself. Sit alongside them, learn from them, ask questions and start learning on the job so that you have a, you know, you have some better expertise around the AI or around the application development or around the marketing strategies that you can put to work later on down the road. So I think co creating is another way of educating our teams and our talent around new areas that we’re moving into as organization.

Juanita, which one do you want to comment on for us today?

[00:36:56] Speaker B: I’ll talk about maybe the accelerating innovation and possibly add in some thoughts on talent there. Sure, yeah. So I think one of our attendees, lots of great conversation today, but one word that sticks out here for me is scale. And the reality is that a lot of teams and companies already have a lot on their plate, especially if you’re a product manager, you have the entire existing portfolio to maintain, to manage product life cycles and all of that. But the pace of innovation as we know, is not going to slow down, it’s just, it’s moving faster. And so there’s just no way, I think that teams can really stay on top of managing the existing big portfolios they have as well as then trying to bring in new innovations without partnering and co creating. And one good way I’ve seen this work for teams is to have a separate labs team as an example that is focused purely on the innovations, on the testing on those prototypes. But the labs team and the product teams have to work hand in hand. Right. Because as we know, prototypes aren’t, you know, the full finished product. They, they need to be productized at some point. But having this separate labs team that can, you know, try different technology, that can maybe manage some of those external partnerships that can quickly work to demonstrate use cases, I think is a really great approach to this. And when done well, then, you know, there’s a fast turnaround time to actually ship a product out to market that’s been tested right. Internally with maybe some existing customers. So I just think about it from that perspective. Like I know these product teams just already have so much on their plate. So having this extra team to bring in that innovative mindset and capacity is a good way to approach this.

And I also agree with you, Isaac. I think when it, when we think about talent and upskilling, you know, there’s a lot of expertise out there with partners. Different companies have their own specialty areas of specialty or subject matter expertise.

And I don’t think companies should shy away from, you know, taking that time to learn from them or even getting those partners to come and speak to teams and, you know, directly. Because sometimes if you’re, you know, if you’re an existing, if you’re, when you, when you speak to your company internally, sometimes it may not resonate as much as bringing in an external subject matter expert who is going to say the same things and bring a different perspective.

[00:39:40] Speaker A: Thank you, Juanita. I love. We’re now getting into the branching into labs as a way of learning.

And I know we’ve covered innovation labs before with Roman.

Jay, I want your perspectives. We walked into many organizations that their aspirations outpace their ability to execute. Sometimes it’s a scaling factor, sometimes it’s an expertise factor.

Where do you want to take us, Jay, on either accelerating innovation or talent readiness?

[00:40:15] Speaker C: Yeah, I think both of them real quick. I think like in today’s day and age, technology moves faster than an organization’s comfort level.

So I think through co creation partners, they can play a crucial role in preparing talent, not just technically, but culturally.

Some of the items, you know, that you talked about, Isaac, is let’s say around learning in context. Right. So instead of detached training programs, I think that co creation basically helps to embed learning in live projects. I also think that from a change acceleration perspective, an external partner can potentially, and we did this on our last project, we bring a neutral facilitation basically that we helped break the internal resistance to what we were looking to accomplish there.

And I also think that many organizations in today’s day and age, or many people and professionals, especially on LinkedIn, you have to think about the human centric transformation.

So I think that in today’s day and age, with fears of job loss, co creation can reframe AI as augmentation. I think it can create opportunities for reskilling of new hybrid type of roles, such as a prompt engineer or a model steward or a human in the loop designer.

That didn’t exist before the mass discussion around AI.

[00:41:59] Speaker A: I love this idea of partnering to change a culture, especially when the leader is involved in getting the organization to realize that new partners are going to bring in new ideas and challenge people’s thinking.

Heather, love your thoughts. Are we Talking about talent readiness, Are you talking accelerating innovation? I have a feeling you talk about talent.

[00:42:23] Speaker H: Yes, indeed. And I think that Jay was totally spot on. When you have a foster, fostering a collaborative mindset, so many things can happen with the fear of people losing their job to AI. This really could be a whole media approach that, no, you don’t have to lose your job. There’s a way of learning through doing, learning through your partners, learning through experience. And this gives, and it speaks to a little bit of one of your sessions coming up later on.

Have employee engagement, reduces stress.

So if there’s a way that you can bring people in and realize that there is a future, there is something that’s coming up that is not, not everything is so negative and I’m going to lose my job and things are just going to be awful. But it has to be a cultural mindset, whether it is for collaboration or learning or upskilling, but it has to be a whole change that a lot of companies are not accustomed to having and not accustomed to promoting.

[00:43:25] Speaker A: Derek, thank you, Heather. I’m going to keep putting my experts on the spot. Derek, I’m going to put you on the spot here and go into a situation with you, which is when a business leader comes to us and says, I want to partner with company X to do Y, and you look at that company at the surface level and you have a lot of question marks about whether they are a good partner.

[00:43:53] Speaker G: Partner.

[00:43:54] Speaker A: I’m wondering how you walk through that, you know, challenging yourself, are your instincts right? Or putting that partner through the right initial set of questions to flush out whether they’re the right partner to either accelerate innovation or build up your talent around the areas that you’re partnering on.

[00:44:14] Speaker G: Yeah, I mean, the first thing I would do is look at, you know, what are the strengths this other company would bring in and at the same time, what are the risks they’re also going to introduce.

And I think by looking at both of those, you’re going to figure out where’s the middle ground that we can actually innovate and work together, you know, in the shared type environments, these shared laboratories, these shared threat intelligence, these, you know, when you talk about the, the two entities coming together, you know, and you want to create this product, you know, you want to do something in a quick or rapid prototyping type level. And I need to look at what are all the things associated with, from compliance, ethical checks, security, all these things come into play and I need to understand what been their background, ethical or moral compass in the in the past and do they share the same venue that we have? You know, these are all kind of things to figure out. Is it going to be a good fit for the business culture? Are we introducing something that’s going to be totally misaligned? And I think that alignment outcome is going to be important because you need to understand, you know, everybody’s trying to figure out how can we introduce these things without introducing risk and still developing resilience. And as Heather mentioned, the mindset is going to be important for all of this to reduce the time it’s going to take to get a final product but also get that creativity flowing so we don’t hinder the process. And I think as part of that, as Heather mentioned also about the job creation, when you bring all these two entities together, what other new roles? So anytime you’re working in an environment where you’re doing the rapid prototyping the developers and everybody’s heads down, so you’re going to introduce new roles such as looking at AI risk analysis or AI threat model and auditory to kind of people understand what’s taking place. And these can come together from both sides but need to understand what are the strengths they’re bringing in from both sides to make that work. And the upscaling part of it for both entities will really work.

But again, it all has to be laid out up front and not kind of figured as you go along.

[00:46:03] Speaker A: Derek, Derek, I’m going to, you know, I think you nailed on the two things, right? Strengths and understanding risks. For me, you know, when I sit in the room with a potential partner, I want to see that I they are, I am learning something that I didn’t know and learning a lot and are they explaining it well?

And you know, I am asking them questions about risk. Why? Because that’s something I expect them to be four or five steps ahead of us and I’m expecting them to be able to share, you know, what are some of the trade offs and the design decisions or how are they going to test ideas.

If we’re focusing on a customer experience, for example, I want them to be a few steps ahead of me and if they are, they have a lot to offer and if they can explain it well, then I know they’re ready to work with my team around talent readiness. Joanne?

[00:46:52] Speaker G: Absolutely.

[00:46:52] Speaker A: Joanne accelerates innovation or talent readiness. Where do you want to go?

[00:46:59] Speaker D: Accelerate innovation and talent either or, but more the acceleration.

[00:47:05] Speaker A: Joanne’s accelerate innovation for 1000. Go ahead.

[00:47:10] Speaker D: So first of all, I think, you know, one of the things that I’m beginning to realize more and more is the executive mindset has to shift.

It’s not about sponsorship, it’s not about dollars. It’s the shift that you’re moving from what used to be called knowledge workers and knowledge management into a knowledge ecosystem.

And as you choose your partners, it’s not just about what you can learn from them or how far ahead of you they are.

The tips and tricks and nuances, especially with AI, are even more important. Like when I look at risk, you know, from a point of view of partnering with another company, for example, it’s what areas can you fill that I cannot?

And how does that really work for me and my customers versus you know, just fulfilling a particular need? You can have the greatest developers in the world, but if they have no experience in a particular field, that doesn’t really qualify them, you know. And then by the opposite token, you can have deep subject matter expertise, but if those ideas do not translate out of that one part of any a bigger picture, that’s a risk that you don’t necessarily want to take.

So we have to sort of train the executive around the notion that we’re in a knowledge ecosystem, that knowledge can come from different places, that data creates the knowledge, but it’s the context around everything that really counts. So if you want to accelerate innovation, context is queen.

Content may be king, but context is queen. And that those contexts have to be looked at from various perspectives, various roles and in the bigger picture of your knowledge ecosystem. Otherwise you’re going to find yourself partnering with a variety of companies over a period of time. And that’s not necessarily the one plus one plus one does not equal three situations.

[00:49:22] Speaker A: Thank you, Joanna. I think that’s another great area of deep diving in. Right. Who are your partners? Who do you work with?

Particularly when you’re co creating and looking at an implementation partner, I’m going to want to make sure that they have some expertise or partners with expertise in the platforms that I’m probably going to have to integrate with. So really good data point there. Joanne Martin, talent or innovation?

[00:49:52] Speaker E: I’m gonna go with the talent for a thousand.

I, I think one of the key things I’m looking for is cultural fit.

Can my company work with this partner? Yeah. Do we have compatibility in terms of cultures? Because you may find, yeah, you find the somebody who’s really, really good at it. Maybe it’s someone out of California or something like this, this. But their culture in that space could be so alien to your company that you’re going to Build that divide and that change management is going to be almost impossible to actually have that talent coming from the partner and helping you develop and helping you as a company. Yeah. If you’ve got a constant friction then you’re going to struggle. So that’s kind of one of the key things I think.

[00:50:43] Speaker A: Martin, how do you, you cross the chasm with what Joe introduced, You know, the not invented here syndrome.

The, you know, we’re, we live in country X and you’re bringing a partner from country Y.

When I think about cultural fit, these two things, you know, if I, if those, these two things are always going to come up with innovation teams in a co creation environment and I’m just wondering how like if I just said cultural fit and these two things showed up. Right. Not invented here or working with a, with a team outside your country. I would never be able to co create because every team is afraid of that and doesn’t want to get involved with that.

[00:51:28] Speaker E: Yeah, but I think you’ve got to look at the bigger picture. You have to use, yeah. Basic principles of change management. You have to help your team to understand that it’s yeah. Survive or die.

And that unless we actually make some of these things work we’re never going to move forward fast enough to keep up with the competition. So I, I think the, the basic principle to change management is helping your team to understand that we have to make this work and try and get away from the kind of built in resistance. I’m not invented here.

And some of that is picking the right partner with the, they’re a right approach that’s compatible with your own. Some of that is clarity and communication and clear information.

[00:52:17] Speaker F: Yeah.

[00:52:17] Speaker E: Most resistance to change is born out of lack of understanding, lack of information, lack of knowledge and fear of the unknown. And if you’ve got a vacuum, people will always go to the worst case scenario. So if you don’t tell them you’re bringing this company in to help move the company forward, people are going to think you’re bringing that company in to take their jobs away.

[00:52:41] Speaker A: I, I think one of the things I’m, I always look for in partners is ones that go pretty wide across their organization and being able to prove their value to explain their value to partner on their value.

I mean it’s not just the people who are signing the contract or the leaders of the efforts. You know, somewhere in here I’m going to have an agile team.

It’s going to include members from my, my employees. It’s going to include members from my, you know, co creating partner or partners.

They’re going to be, you know, a multidisciplinary team. So they might have markers, they might have technologists on it, they’ll have user experience folks on it, they’re going to have AI specialists on it. And you know, being able to have that conversation of where you’re contributing is, it’s a day in, day out exercise. I don’t know how you guys feel about that. We have seven minutes left. Today we’re talking about how partnerships accelerate innovation and talent readiness.

I want to go around the room. We’ve got seven minutes and I want everybody to just be able to lay out one bit of expertise that we haven’t covered on how to make these partnerships work, how to ensure that when you find a good partner, you can accelerate innovation, how to find the right good partner, or how to make sure that the talent is actually collaborating well together.

Let’s go to. We haven’t heard from Joe in a while.

Joe, your one bit of advice for everybody.

[00:54:15] Speaker F: Well, I’m going to say as you’re co creating, be careful that you don’t spread yourself too thin.

You don’t want to be dealing with a dozen co creators because you would, you would lose focus. You just won’t be able to sustain the effort and deliver the value that you’re expecting.

So I would say just don’t spread yourself too thin.

[00:54:39] Speaker A: Thank you. Let’s go to Derek.

[00:54:43] Speaker G: Co creation and resilience both begin with the mindset. And it’s really important that the leadership understands that having a positive mindset and focus on the outcomes earlier on makes it easier to set realistic expectations to move forward.

[00:54:57] Speaker A: So a clear definition of targeted outcomes.

Well, let’s go to Martin.

[00:55:09] Speaker E: Be aware of what has failed in this field before.

[00:55:13] Speaker F: Yeah.

[00:55:14] Speaker E: Try and avoid the pitfalls that other people have already gone down.

[00:55:20] Speaker A: Okay, Joanne’s raising her hand. Go ahead, Joanne.

[00:55:25] Speaker D: Understand who your partner’s partners are.

[00:55:30] Speaker A: And.

[00:55:31] Speaker D: Be very careful that your partner’s partners are not so deeply embedded in your partner’s organization that you can have IP leakage or that they’re not being influenced by other parties to extend the partnership to other areas without necessarily wanting to go down that path. We get so involved in the partner relationships that we go, yeah, that really is a good idea. Wait a second. Later on, that really wasn’t a good idea.

So understand who your partner’s partners are and understand how you can leverage your partners to get what you need and then go back to them as opposed to scope creep, which happens more and.

[00:56:19] Speaker A: More often these days understanding potential of conflicts of interest. I think it’s a really good area to make sure that you’re completely compliance teams are engaged and looking for such things. Heather, your your suggestion just I have.

[00:56:37] Speaker H: A suggestion but to comment on what Joanne said there’s a legal implication also you have to know where your teams are and are they in locations that are acceptable in in their respective countries and what they’re doing where they’re getting their resources from. Make sure that that is not nefarious.

My comment is do what you do best and outsource buy co create the rest.

[00:57:03] Speaker A: Thank you for joining Heather and let’s.

[00:57:05] Speaker C: See who Jay yeah, my two cents would be I think that traditional teams focus on owning deliverables co creation demands on sharing outcomes and what that basically means is redefining success. Not my idea or my product but our impact.

[00:57:28] Speaker A: So again focusing on what the outcomes on and getting alignment around that. Jay thank you for joining us this week. Juanita yeah, I have too.

[00:57:40] Speaker B: I would say it’s been it’s been said before but bears repeating. I think having your again clear requirements or clear scope on what you’re going to work on with your partners is so important.

So that one but also I think I didn’t hear it.

You should definitely ask for proof points right?

Where has your partner found success? Where have they done this before?

Always want to ask that and discover that before you go into any contractor business. Like have they done what you’re asking before? So what are those proof points or references they can bring to you?

[00:58:22] Speaker A: Awesome. We have left almost everyone here who’s been here listening to our best practices around the co creation mandate, how partnerships accelerate innovation and talent readiness.

I will share one we haven’t touched on. I really like getting down into the weeds and making sure roles and responsibilities between people working together are clearly outlined.

What I call a product owner or a product manager or delivery leader or a scrum master or a data scientist in my organization may not have the same definitions or the same working processes or the same responsibilities when working with a partner. So just remember when you’re working with partners, bring together your playbook, your way of working, your agile, your DevOps and making sure there’s a clear alignment about what your process is going forward.

It is the number one thing that we do at Star CIO is working with companies around their ways of working and creating standards with them. If you’d like more information around Star CIO do reach out to me on LinkedIn. I’d love to tell you more about that and our digital trailblazer community. There is a link. Thank you Adam for prompting me for it. There is a link in the comments for that. Folks, we’ll be back here next week and talking about AI agents at work the IT and HR alliance to drive adoption and value. On the 7th we’ll be talking about reducing stress and the digital trailblazers role in reducing stress across the organization, teams and themselves. On the 14th we’ll be doing AI for social good. The 21st we’ll be talking about AI natives and then on the 28th we’ll be taking Friday off for the Thanksgiving holiday. Folks, everybody have a great weekend. Thank you for joining this week. Want to thank Juanita and Jay for being our special guests of course to Joanne, Joe, Martin, Heather, Derek for joining us. Did I miss somebody? I missed somebody. I hope I didn’t. Thank you every Joanne thank you for everybody for joining us. And we’ll be back here next week same time to talk about AI agents at.

The 139th episode of “Coffee with Digital Trailblazers” focused on the AI teammate role in digital workplaces, featuring Stephanie Sylvester from Avatar Buddy as a special guest to discuss AI implementation and integration strategies. The discussion covered various aspects of AI adoption, including organizational readiness, employee training, and security considerations, with participants exploring how AI agents can be effectively used in different roles and processes. The session concluded with insights on using AI to enhance productivity while maintaining human expertise, and plans for future meetings to continue exploring AI’s role in digital transformation.

[00:00:10] Speaker B: Welcome to this week’s coffee with Digital Trailblazers. Our 139th episode. We’re almost eight months into this new format being on LinkedIn Live, and every week there’s always something that trips us up. This one was a minor one. So just giving a few minutes for everybody to join. And thank you for joining us in this August session in this week of some of us working through hurricanes drop offs at college, which is what I was focused on the last two weeks.

I was in Tucson a week and a half ago with my son. I was in Albany this week with my daughter.

Wow. East Lyme, Connecticut.

[00:00:59] Speaker A: Derek.

[00:00:59] Speaker B: We could have met up. I drove right through there yesterday.

Oh my gosh. That’s just too funny.

And I’m totally excited. You know, we’re always interested in exploring new topics around AI here at the coffee hour and this week we’re talking about AI as a teammate.

I have Stephanie Sylvester.

Did I pronounce that right?

[00:01:24] Speaker A: Yes.

[00:01:26] Speaker B: Stephanie is here as a special guest.

She’s the founder of Avatar Buddy. We’ll hear more about Avatar Buddy midway into the session. Just giving us just a few more minutes for everybody to join in. Thank you for introducing yourself on the Common Stream. Hello, Steve, thank you for joining. Thank you for the comment last week that made it into our whiteboard.

It is, it is published, but I haven’t shared the URL yet. I have to remember to do that. I’ve been running around all week. Hey, Alan, Good to see you again.

Alan and I met when I was in Charlotte a few weeks ago.

Alan is a good friend and as you all know, when I get around town, I try to meet up with as many people as I can.

I do have upcoming trips to Atlanta, to San Diego and San Francisco.

And so if you are in those areas or if you’re attending workday or Cisco WebEx one, do let me know so we can meet up. Every time I do this, there’s always somebody who reaches out afterward and I get to meet someone in real life for the first time. So it’s always very exciting. Exciting.

This week we do have a special guest and we are talking about AI as a teammate. Crafting purposeful digital workplaces. This is a conversation of more than just looking at AI as a tool or AI as an agent. It’s looking at the connection between people and the agents that they’re going to and are working with.

And I’m starting to see some data around this, some things that our industry is publishing around.

What’s happening with AI in the Workplace. I want to share some data points for you that have come out from different reports.

This one is from a workday report that just came out and it talks about our comfort level with using AI agents in different scenarios.

75% of people said they were comfortable being recommended skills development or areas of improvement by an AI agent. That sounds very much like a buddy to me. But only 30% were welcomed the idea of being managed by an AI agent. And even less, 24% said that they were happy to see AI agents operating in the background without their knowledge. This is from a workday report. It’s called AI Agents are Here, But Don’t Call Them the boss.

Very interesting report that came out. And then also this week, MIT came out with a report on what’s it called? The state of AI in 2025. And the data point I was going to share for you in here just lost it. Darn it.

And scrolling around trying to get the name of it, but they talk about just a lag effect. Our adoption of AI and large language models is much higher than what’s happening with AI agents.

They are still in the earlier adopter stages, with only 5% of companies reporting that they’ve embedded agents on tasks specific for generative AI.

20 have piloted. 60% are still investigating.

And so that’s the backdrop for our conversation today as AI is a teammate crafting purposeful digital workplaces. And this is looking at, you know, as agents become stronger, better, smarter, how do we empower employees to use them effectively and to be doing more purposeful type work and using AI agents in a more trustworthy way? So, Stephanie, I want to welcome you to the floor. Welcome to the coffee hour.

And just do a brief intro to yourself and then tell us a little bit. My first question, how should we really be thinking about training AI agents the same way we onboard and develop employees? Stephanie, welcome to the floor.

[00:05:49] Speaker A: Thank you, Isaac, for having me here today. I look forward to learning from you and your panel of hosts and speakers. My name is Stephanie Sylvester. I have over 30 years of IT experience and I have a Master’s in Economic Development and International Studies from the University of Miami.

I grew up in Belize, came to the US thinking that I was going to learn economic development and go back and develop Belize. Instead, I ended up living in Miami for the last 30 plus years.

And I am super excited about this conversation today because I believe that AI is a social construct. And as a social construct, it means that we should be approaching it the way we approach interacting with humans. So I just Want to make a clarification point? I am not saying that AI is human. I’m saying that if you approach it the way you approach humans, you get a much better result. We’ve been working on AI for nine years and the last two and a half years we have been selling our product and whenever we approach it the way we approach interacting with a human, we have much better results.

[00:07:10] Speaker B: What does that mean?

Stephanie, especially you call it AI as a social construct. Can you break that down for us a little bit more?

[00:07:18] Speaker A: Absolutely. It means that unlike other software that you would just implement and life carry on, you have a business process. You bring in a piece of software, you automate your business process and everybody is good to go.

AI changes how we work, how we think, how we interact with each other. It also changes how we behave. And because of that, that’s what I mean when I say it’s a social construct. So you can’t just say, oh, I’ll just buy some AI agents, I’ll go to company xyz, give them my credit card, they’ll allow me to create agents and I’ll be good to go. Because one, we have found that that is not effective and people struggle with even figuring out how to configure the agents to have impact then. Secondly, because it impacts everything, your organization has to be ready to change. You have to be ready to change how they think and you have to be ready to change how they behave.

We’ve had unfortunately some customers that were not ready to change how they think and how they behave and our implementations went sideways. And so because of those learnings, we now insist that we do an AI opportunity mapping session with you so that we can walk through how you behave. And if, let’s say it’s an hour, a two hour AI opportunity mapping session, an hour and 15 minutes of that is talking about everything but AI. We did a session and I could see this CEO getting impatient with me. She’s like, when are we going to get to AI? And I’m like, we are getting to AI.

And she was like, what do you mean? I was like, we are getting through to AI. So you have to understand what are your employees emotional response. So this is a very emotionally fraught topic. And if you don’t get an understanding of that, you could be implementing AI in a way that just doesn’t become successful because your employees will consciously or unconsciously undermine the implementation. And then the next thing is alignment of, of of what is the real problem, alignment of how the business works. And again, normally we talk about that, we’re like, yeah, yeah, management and Frontline people need to be aligned.

But do we really make sure that happens now with AI? If that doesn’t happen, it will magnify a thousand times. So all of a sudden, now you’re seeing this huge, huge, huge problem in your organization that maybe everybody knew existed but wasn’t that big. And that’s what I mean when I say AI is a social construct because it takes little small things, it magnifies it, it forces you to reckon.

But all of these are good things. We have one customer where we ask for customer feedback, and part of their customer feedback was that it’s reduced workplace conflict. And at first we were like, workplace conflict? We didn’t even think about that.

And then when we kind of like unpack that a little bit, we realize it’s releasing. Reducing workplace conflict for two reasons. One, people no longer get irritated because you ask them the same question 25 times.

People don’t get defensive because they have to ask you the question 25 times.

So because they’re asking the AI and AI is not keeping track of how many times you ask them for the question, then people are feeling better about themselves. That conflict intention of asking for help and getting support goes down. It looks different now. And that’s what I mean as a social contract. Because now when I come and ask you for support, I’m like, isaac, I read this. Is this really the way it’s supposed to be? That’s a completely different thing from, hey, Isaac, tell me this. And now you’re like, oh, my God, I gotta go explain this entire process.

Oh, like, I don’t have time for this today. Oh, my God. Like, Stefan is a sucky hire. Like, we should just fire her. I mean, like, all of that stuff goes away. I mean, and that’s exactly when I talk about social construct. We’re changing how we’re behaving, how we’re interacting, very powerful. So I’m going to pause there and, and see what other questions you have or let somebody else have. Have a point of comment on what I just said.

[00:11:52] Speaker B: I have a question. I’m going to ask it and ask you to think about it, because I want to bring our other speakers up just to comment on some of the things you’ve been speaking about. But everything you describe falls in the category of change management.

But you’re describing it very differently. We’ve done change management with technology before, with process change, with realigning employees, with new job descriptions, because technology is automating things they’ve done before.

This just feels different. And you’re bringing it in the construct of I can use AI in a transactional way. I can ask AI to write code for me, for example, or I can use AI as a partner in saying, you know, what should I really be developing today, what problem should I be focusing on and how should I go about solving a problem like this? So feels like change management is a very different problem now, and I think you’re alluding to that. So I want to give you a pause to listen to that. I have Derek raising his hand. Liz, Joanne, Joe, all here during their summer breaks. Derek, welcome back from your break and tell us how you think we should be training AI agents and onboarding them as we develop our employees to use agents in a purposeful way.

[00:13:16] Speaker C: Thank you. And I greatly appreciate Stephanie’s comments and I fully agree with her as far as the training process. I mean, when you look at AI agents, I think I look at them as more like a digital teammate.

There’s still going to be an onboarding structure just like regular employees develop. But you also need to put context around them and guardrails and really understand how this person is going to work with your, your, your job and work with your industry. But most of all, look at what kind of risk may they be. So it’s a learning process and this takes time to understand, you know, how they’re going to work with you, the information you share with them, the training process they’re going to go through, how much they’re going to be applied, that. So you’re going to embed them with the, you know, the company mandates, the company standards, the company protocols, the security protocols, all those different things come into play. But it’s also interactive and getting feedback to see how well they absorb it. So as Stephanie mentioned, the redundancy aspect of going through and asking the same question, you know, you’re looking at, can this person take, or this AI agent take this information and do what you need to do and give you what you need. So in essence, over a period of time, like the most important employees, you have a probationary period, you’re developing trust, you’re trying to understand what they can do. They’re helping them align your vision, the vision, vision of the business to what they need to do. But also you want to make sure they’re not going to be a security risk. So you’re helping them develop what that resilience mindset is going to be to help them not only work with the organization, but also help keep the organization secure. So again, it’s a process, but I think it’s going to be similar, but it’s just going to be different. Guardrails as the training, the training, procedure and process can be a little bit different for an AI agent versus a human. And you know, some of the context that Stephanie mentioned also, I think those things come into play because those are things that need to be learned.

[00:14:57] Speaker B: Derek, I just like, like typed as a fiend because you had some really, really good questions about developing trust with your new teammate and how you’re preparing them, how they’re learning about your job, what are the guardrails, policies, mission regulations they have to know about?

[00:15:18] Speaker C: Yes.

[00:15:19] Speaker B: And then ultimately, like, how are you judging their performance? I think is really the question we have to train our employees on is, okay, you’ve got this. It is a new tool, it is a buddy, it is working with you, but when is it ready to actually give you worthy advice for you to go listen to? What do you think of this, Liz?

[00:15:40] Speaker D: First of all, I just want to say this is amazingly exciting way to think about AI agents. I’m very excited about this. Typically when I come on the call, I’m talking about business value and governance. That’s typically my normal perspective, is trying to make sure that we’re thinking about how are we making sure we’re getting the value out and how are we making sure that we’re governing things in a way that gets us to the top line or the bottom line. But, but here I’m really hearing something that’s even more valuable, which is how to integrate the impact of organizational change in a way that, that actually morphs the culture of a company, that actually integrates the culture of a company to maximize the value of the AI agent and actually incorporates that as part of the infrastructure of the culture itself, which is just like amazingly exciting.

I’m just floored by this. It’s sort of taking organizational change to the next level. So I just, I’m pretty excited about it.

[00:16:51] Speaker B: Thank you, Liz. Let’s keep going. Joanne, there’s a question here that I hope you’ll double comment on.

So first you’re, you know, we spoke about this last week, the idea of bring an agent through a learning phase from an apprenticeship to a journey person. You discussed that last week at our coffee hour.

So I’m sure you want to comment on that, but I want you to answer Keith Plemons as a question here. Isn’t AI just a component of digital transformation and be treated as such within Larger systems of people, processes and technologies. I have a feeling you have something to say about that too.

[00:17:33] Speaker A: Yeah.

[00:17:34] Speaker E: Which would you like me to start with first?

First of all, it is a component, but I look at the, I look at agentic AI as, you know, it goes through a cycle and it goes through the cycle of sense its environment, detect what it needs to do based on programmatics, act and then learn. And it’s a lather, rinse, repeat type process. Now there are other elements involved in that as well. It is a component. But you can look at it as in one part, hmi, a human machine interface. And that’s where some of the humanity and, and the word, and my new coined phrase humanify agents comes from.

And yes, they do need trust. They knew they do need to be ingratiated with the workforce, but they are also a force multiplier.

And the agent can be learning from the individuals just as much as it’s learning from what it was trained upon. We view human in the loop as being integral to the training of the agent because we’re trying to capture in our systems anyway the knowledge of the workforce, the expertise. Stuff that will not necessarily be captured or curated in any other way, but seeks to teach the agent more about not only the business, but the operations of the business and making it better, better. So in one sense, you know, to Stephanie’s point, it is being humanified to be more easily integrated into the operations and that would affect change management and organizational structure. But from our perspective, it’s also got a purpose. It’s got a purpose of how it runs, how it operates, the information it gives back. And, and this is also one of the differences between large language models and small language models. Because if you’re running it against the expertise needed to satisfy a requirement of an individual, two other points are needed. One is that the requirements come from different perspectives, meaning different roles in the organization, the data. And the answer may be the same. It’s context that changes around the data.

And that’s why to the listeners comment I would say yes, it’s absolutely a part of digital transformation. On the other side of that is, you know, we have a couple of different agents that are process driven and model based around the operations of the organization. We’ve given them names to humanify them just so that we have something to refer to, but also as the way to ingratiate the user to share their expertise.

So that’s another part of the socialization of AI, I guess we view the construct as part of our engine and whether we give it the name Nova or we give it the name Celeste or anything else that is out there. They are constructs and they are serving a business value driven purpose.

[00:20:48] Speaker B: Well, Joanne, I’m going to suggest that I really like AI agents need a purpose. I think they need to be trained on a specific role.

Just to give everybody an example.

I think innovation first, I think customer experience first, I think revenue first, I don’t think security first.

I would love a buddy who would sit next to me and say, Isaac, if you’re going to work with client X or with company Y and you’re promoting these ideas around innovation, around transformation, around growth, here are some of the security concerns you should be thinking about that should be at the forefront of what you’re recommending your clients. I would love something like that because I’m not a security expert.

Derek could have the opposite of it. Right. Derek is a security expert and I’m sure he’s giving me a thumbs up. He would love a transformational. Here’s how you can apply best practices in security that are potentially going to drive growth, particularly around your brand.

[00:21:53] Speaker E: Well, Isaac, let me just sign off. I’m sorry, I don’t mean to take other people’s time, but like in our case, we thought long and hard about the security aspects. And so we built the agents with both role based authentication and also attribute based authentication. Where those fit in the system is kind of part of the secret sauce. But let’s just say that we took that into account and we tailored it to the exposure levels that each individual sort of group in the corporation might have. So C Suite may have very different parameters than a shop floor operator in a manufacturing facility because they wouldn’t necessarily be exposed to some of the information in one case in one sense, and they may be overly exposed, exposed to some of the information in another.

So there are ways to mitigate that. And I’m curious as to, you know, in Stephanie’s case with the buddies, how they’ve managed to take security into account as well, particularly when it comes to voice of the customer or the individual.

[00:23:03] Speaker B: Awesome. Stephanie, I have two questions teed up for you. We’re going to go to Joe next and Keith, is AI just a component of digital transformation? We are going to cover this. I love this question.

My answer is it’s not and it’s, it’s. And I still think of this as, you know, overlapping circles, concentric circles here. But my issue with AI right now is it’s not generating revenue for us. And the MIT report, I alluded to earlier, said that only 5% of companies have found ways to use AI to generate revenue. And what I’ve always said is if you’re not using it to generate revenue, it’s just going to impact your cost factor. And I think we need to find some better value equations around AI. AI is a buddy is one of those areas, which is why I love discussing this. Joe, you’re up. Welcome from the beach. You have a clear minded head. What are we speaking about, Joe, today around training AI agents, the way we onboard and develop employees.

[00:24:06] Speaker F: Well, I just wondered and I’ll tee up another question for Stephanie.

When I interact with agents, there’s a marked difference between interacting with a customer service chatbot that’s sort of cut and dried. And to Stephanie’s point, it’s infinitely patient. I can ask it the same question over and over again and I probably get frustrated because it doesn’t answer my question. It gives me the sort of the pat answer. But then I also interact with things like Alexa and Siri and other more, to use Joanne’s term, humanified interfaces.

And I find it easier to collaborate or to interact with those types of agents. And so my question is when the agents that we put in the workplace lack personality, when they don’t understand irony or humor, they don’t, they don’t have great memories for things that you told them that aren’t necessarily business related.

They don’t understand nuanced context.

When, when these issues arise, how do we best prepare our employees? How do we set those expectations that, you know, you’re not working with Captain Kirk, you’re working with Mr. Spock.

[00:25:27] Speaker B: Wow.

I’m going to go to John next and then Liz, I’m going to ask you to hold off. I have, we have too many questions lined up for Stephanie so I’m going to go back to Stephanie after John. Hi John, welcome to the floor.

[00:25:41] Speaker G: Thank you for having me on here. And I, when I kind of hear about this stuff and the change management related to this and getting somebody ramped up, I really think that there’s a lot of parallels worth working with maybe offshore teams or teams located in different countries.

A lot of times people are really hesitant to work with people in different countries.

But you ask, well, do you want to be doing that work in the middle of the night? And they’re like, no, no, I don’t want to be doing that work in the middle of night. Do you want this other team to be doing this work in the middle of the night? They’re like, oh, yeah, I’d love for somebody else to do that.

And then when it comes to working with these team members, if you don’t take them through a structured process to onboard them on and continue to work with them and include them as part of the team, like, it’ll never work out.

And one of the things I was just really reflecting on is people are getting guidance from AI and machine learning on a daily basis. Like every time somebody gets in a car, they say where they want to go. And basically a machine learning model will figure out the optimum route. And when I look at people a lot of times in the workplace, they’re so overloaded and they’re starting to have products right now that will look at people’s calendars and all their action items and all their tasks and like automatically start coming up with priorities for things. And so I think there’s so much help that these things can provide to people, but they really just have to think about how are they going to interact with these things and are they actually going to take the help.

[00:27:09] Speaker A: Okay, so Isaac, do you want me to start with the most recent question and go back to the first one, or do you want me to start the first one and come down to the most recent?

[00:27:20] Speaker B: You go in an order that tells a great story. How’s that, Stephanie?

[00:27:25] Speaker A: Okay, so let’s start with the offshore team integration.

It’s a great example. And I remember working as a consultant and whenever we started a new project, we would do this thing called Foreman Norman Storm and Performin.

And it was a systematic way to get us to work together and provide the highest value for our customers. Very quickly, we were coming from a number of different offices. We didn’t know how to. We didn’t know each other.

And sometimes our office norms were different depending on where in the country it was. And so you take that and you bring that forward and you do the same thing with AI. So as you’re onboarding somebody, you give them this volume of data. You give the data, the AI volumes of data. The good thing about the AI is you give it volumes of data, it will be able to process it and consume it and know something with it. I mean, how many of us have started a company then they’ve given us binders and binders of information. Like, I know I started a company, they gave me three huge binders chock full of information. I have yet to go through that binder. And I left that company about 10 years ago.

That’s. And I don’t believe that I was any special than anybody else. So the AI has all that information.

The second thing is that when you have a new employee, you don’t just take that new employee and just throw them on the floor and say, knock yourself out.

You take that new employee and you usually have somebody that they’re shadowing with. Normally the person that’s doing a job that’s like theirs or adjacent to theirs. And that’s what you do with the AI. You find a subject matter expert, you sit with them, you configure the AI the way they first process their work, and then when they do that, they use it, they give you feedback. We use an Agile methodology. It’s important to use agile because any other methodology, it’s going to be a mess. And so what you do is, is that you sit there, you configure the AI, you have them use it for a week. So you’re doing weekly sprints. You come back in, you look at it, you took the AI, you do that maybe two or three times, and now you got a pretty solid AI agent. And then from that, that person uses the AI agent and then you expand it to maybe their team.

Just like how you’d have a team meeting and then you’d introduce a new employee at the team meeting. And then maybe two months in you have an all staff and then you introduce the new employee to the all staff, same process.

And so by that, by doing that, then you ensure that AI is customized for your organization. It’s built, tailored for you. And when we found that, when we follow that process, we have great results. And when we don’t follow that process, it’s just chaos and confusion and it’s just a mess for everybody. And so that’s where we are, like, adamant now, if you don’t want to go through our process, we can’t work with you because you’re wasting your money and you’re frustrating us and you’re messing up our metrics. We want to be able to say 100% of our customers love us.

So that’s part of the how do you integrate? And the same thing, if you’re integrating an offshore team, you don’t just come and hire the person and say, knock yourself up. There’s a process of bringing the two teams together. Same thing.

Somebody asked about how to, how to use customer service agents. And I will say that I’m using a bunch of people’s AI agents. And I stop and I think these agents suck. And that’s being generous. And why do they suck? And then I Go. And I use my agent. And I’m like, why is my agent better than their agent? What am I doing different?

And, and I think that the difference is I’m not 100% sure, so don’t quote me on this. But I believe the reason why our agents are different is that we’re giving our agents the same information the way you would give a human the information.

And instead of having these highly scripted things that you give to call center people where there’s like, if they say this, say this, if you say this, you say this. Where then the people don’t know why they’re saying what they’re saying. We’re saying, just give them, give the AI the entire manual. It’s a 2000 page manual. No worries. The AI will figure it out. So now you’ve written this 2000 page doc manual, and now you have to create these job aids that you’re trying to contort into like one pager for the call center person to answer or the chatbot to answer. And they’re reusing those. But if you give them the manual, the AI goes through it and responds like human. And that’s what we, we advocate.

And when we started, we started with security first, and we very quickly realized that large language model was not the way to go. I think it’s part of my inertia. I try to find the path of least resistance. And so I thought large language model is like going to the library, and it’s huge. I went to USC for undergraduate and I remember going into the Haney Library the first time and I was like, whoa.

When I opened, finally got the door open because it’s huge, beautiful, heavy door.

I didn’t know what to do. And then I was like, okay, I’m here. I want to do world history.

So the librarian pointed me in the direction of the world history and I got there and that was a little bit more manageable, but I still didn’t know what I was doing.

And so I then said, went back to the librarian and said, I am studying Latin American affairs. Where in the world history can I get that information? So the librarian came over, gave me a few books that she said, you can get started. She asked me which professor I was in. It’s like, oh, these are the books that he normally recommends. And then I could go through. And that’s how AI works. But when you’re doing something like that, you need to make sure that the small language model is safe, that it’s secure, that it’s accurate, that it’s bias free.

And so we have our customers small language model on an uber secure military grade rag system.

And the way we’ve designed a small language model, it optimizes and it helps you. So just like the librarian goes, here are the three books. We say by the way, here’s a document that has this information. It’s 200 pages. Here’s the four pages that you’re going to need. It tells you the page number and, and those kinds of things really partner with people and help them feel good about like oh my God, I got the information, I don’t have to go through 200 and something pages. Here’s the four pages. I read those, I feel good, I get more context. And it’s all about like why, why, why why. A lot of times we don’t tell people why they should do something. I found very early on in my career when I was, when I was a new manager, I would sit and I would explain to people why I want something done, not how to get it done, why. And I would sit there and I would co dream with them and they would go off and they always bring back something better than I that I want. And again, I mean I hire interns and people are like I can’t believe you hire interns and you’re putting your entire company under risk of interns. And I’m like, we run on interns because what we do is we sit and we co dream with the interns and we take our advisors knowledge and wisdom and years of experience and we give it to the AI. And then interns use the AI. Plus the fact that we’re telling them why we want them to do the assignment, not necessarily how to use it, do the assignment consistently.

Rock star performance.

These are from 19, 20 year olds. Sometimes we had a 15 year old in the mix.

I’m not going to say all 15 year olds are going to do that. But the 19, 20, 21, 22 year olds rock star. Because we are explaining the why. So when you give the AI the entire context and you put it in a secure place, you’re telling it the why and it can figure out the how. And it can figure out the how based on the question that your employees asked it. So all of that comes together in a nice beautiful human digital handshake is what we call it and it works.

So I think I answered a whole bunch of questions about taking your organization about how to do security, how to bring it on customer service, how to get better interaction and yeah, this, this talks about change management Right. So we have to do change management. Change management is at the key of core what we’re doing, but we’re not doing. Normally we do change management from the system perspective. We’re buying this new software. Okay, let me tell you how your job’s going to change.

But we don’t stop and say, do you have to change how you think about your job? And with AI we’re saying, wait, you need to stop and think about how your job, you need to use new lenses. Let’s talk about these new lenses that you’re going to use. And as you’re using these new lenses, make sure that your process is in a different way. And if you process it in a different way, then different things will happen. And we’ve seen, and I like to use this example because it speaks to the power of AI on marginalized and low resource and low educated people and how powerful it is in their hands if you just help them view the world slightly different, not teach them how to use the AI, but how to view the world differently. And so I’ll just one little story and then I’ll pause, but. And I did a TEDX about this. So I met this woman and she wanted me to help her with some stuff with her organization.

And I said to her, well, no, I can’t help you for two reasons. One, I don’t do homework anymore if I can avoid it. And two, I am so overwhelmed with my own company, I don’t have time to help you. Why don’t you go use ChatGPT?

And she didn’t, she was, she’s like, no, no, no. I said, no, let me tell you why, why ChatGPT is great. And, and, and she was like, okay, but I don’t know how to use it. And I said, just like how you’re talking to me, go talk to ChatGPT.

So she goes, four months later, she calls me uber excited, telling me about all the stuff that she’s doing. But one thing that she told me that resonates with me is that she is using AI to help the people in her community have a voice, have, have the AI to help them see their own awesomeness. So she does is she puts her, she puts the, all their work history into the AI and says, write me a resume. And the resume comes out.

And she said, this one woman just started crying.

She said, I never thought about myself this way.

And that’s the kind of change management you have to say you’d have to do. Because if you just say, you just look at this person and say you’re a low end worker and now there’s AI, so there’s nothing you can do.

Then, then, then we’re not going to get where we need to be. But you say you used to be a low end worker. You have lots of talents and, and skills and abilities. We’ll give you a team of AI agents. And because we’ve given you a team of AI agents, you’re going to now be able to do xyz. And so this woman is making people feel empowered in a community that’s often overlooked in Miami. And that’s the kind of change management we have to, we have to double and triple down in our belief that everybody is inherently good and want to do good. And if given appropriate opportunity and given the appropriate tools, we’ll be able to do that. And that’s the change management and that’s why it feels different. And that’s what we’re doing at Avatar Buddy. That’s what we advocate for. This is why I am always grateful and honored and humble when somebody lets me come under their podcast because I said one more platform for me to push out and maybe one person out there will hear me and take the, take up the mantle and say let’s use AI to move, to take, take people to the next level. So I’m going to pause there and let others speak and then I’ll come back. There’s some human in the loop conversations that I want to address as well.

[00:41:12] Speaker B: Thank you, Stephanie. I mean, of all the things you said here, the one that really resonated with me is find your awesomeness. I mean, if you told that to every employee and made them think around that and to have a conversation with a language model rather than asking it to do things for you, I think those two bits and pieces of advice, I think we sort of get there as we start using the tools, but you can get a lot more people leapfrogging to that. I have Liz and Derek raising their hands. Liz, just hold on a second, Stephanie. I just want to take a second here. Tell us 30 seconds, a little bit about Avatar Buddy.

[00:41:53] Speaker A: Great, thank you, Isaac. As Avatar Buddy is a managed AI as a service company and we create function specific AI agents and digital twins that leverage a small language model to help amplify employees awesomeness and improve operational excellence. And that is done with the support of an AI with our AI advisory team.

So companies that want to maximize their investment in AI would work with an organization like Avatar Buddy to ensure that they’re able to increase their profitability without having to go down the AI rabbit hole.

[00:42:37] Speaker B: Thank you Stephanie. And what’s the URL to Avatar Buddy?

[00:42:40] Speaker A: Oh yes, and because everyone needs a buddy, Our website is AvatarBuddy AI. You can experience a buddy at our website and also schedule some time with us.

[00:42:54] Speaker B: Thank you. And folks, you’re listening today to the coffee with Digital Trailblazers. We meet every week here at 11:00am Eastern Time to speak about topics facing digital transformation leaders. Today we’re speaking about AI as a teammate crafting purposeful digital workplaces.

Thank you Stephanie from Avatar Buddy, founder of Avatar Buddy for being our special guest.

We will not have a session next week, Friday going into Labor Day. We will be back in September. I have not announced our sessions for September yet, but I’ve gotten some pretty good ideas here that you could see in the bottom right hand corner. And so thank you Keith.

Thank you Srinivas. And then I think we’re going to revisit change management in AI as another topic. It’s too rich of a topic for us to just cover here in a part of a session. Please visit starcio.com Coffee Next event and that will redirect you to the upcoming events. Liz, we got a lot of things here we’ve been talking about and ones that I want to talk about vertical integration want to talk about. And then this last question about restoring dignity to work by supporting self agency and employee learning. Where do you want to go with this lizard?

[00:44:13] Speaker D: Oh my God, there’s so much here. This is exactly what I was hearing at the beginning was leaning into your awesomeness and why I got so excited. I know that we spent a lot of time talking about change management around like how to integrate the agents and bring them on board as almost like as if you’re bringing in a human. But how do you approach the employees? Because what I’m hearing is especially around let’s say I love Isaac’s example of well I know that I don’t think security first so I could use a security buddy, you know. And it in the what’s in it for them example of how do I bring in an AI buddy that is actually going to be not confrontational, non threatening, but supportive. Is that really your approach and have you been successful in that way and where have you run into some difficulties with your clients?

[00:45:15] Speaker B: Liz I think that’s a fantastic question because there’s a little bit of a reality in there. If I asked you that question Liz, and said okay, what are you advising a program manager or a project manager who’s got 20 years of experience, has done Agile for the last five years and now you’re going to bring them an AI buddy and tell them to find their awesomeness when the very first problem that the AI is going to face is it doesn’t have integrated cleansed information to work with the same struggle the program manager faces and the PMO and the value management office space. And I know Derek wants to speak next. The same problem with a security officer, right? You take somebody who’s been, yeah, managing a SOC security operations center for the last 10 years, bringing all this data together, trying to find issues faster or find root causes faster, and now the AI has the same issue of finding and getting access to all the information and understanding context.

Stephanie, how would you address this issue of bring in the relevant information so that your buddy can learn faster?

[00:46:32] Speaker A: So first of all, I would say that it’s not a one system solution, AI to date, and I don’t believe that we’ll ever get there. I mean there’s no one person that can do everything in an organization.

There are people that can come close to it, but no one person that can do everything in an organization. And so from that point of view, you’re going to need different AI agents, different AI solutions to be able to get your job done. And so the way we are approaching it is let’s take the security analyst per se. So this security analyst has these uber powerful security tools and now those tools come with AI and, and it’s spitting out so much data and the person is overwhelmed and they’re just getting, they just don’t even know what to do with it.

And what our digital twin, or sorry, our AI agent can do is one, help them center themselves.

It’s okay to be overwhelmed.

That’s okay. Let’s break this down.

Let me tell you how you use the system, how you help their AI produce something in a way that’s appropriate, right? And, and so what we’re doing is it’s a buddy next to you helping you do your job better, making you feel better and helping you navigate through. So, oh, couple of things, right?

I tried to add a person to planner yesterday and I’m like, the person is outside of my environment. And so, you know, if you try to marry somebody external with somebody internal in a Microsoft product, I mean that’s like a two, three hour exercise.

But what I did was I asked my AI agent to, to, I think it was expert buddy as expert buddy to help me. And in about 20 minutes I got it done and then I don’t know how to write the prompts for Gemini to create photos for me, but I asked my agent, I asked marketing buddy how to create the prompts, and then I gave Gemini the prompts. And then Gemini produces the same beautiful picture that I have in my head that I wasn’t able to do when I just prompt directly with Gemini. So that’s how the AI agents that we are configuring works.

It also reinforces your culture because you tell it what your culture should be, and it always reinforces and delivers the information from a cultural standpoint.

And so what ends up happening is most people struggle with their job is not a pure skill issue, and it’s not a pure will issue. It’s a mashup of those things. And trying to deconstruct that is so hard.

And what we then do is we just say, oh, Joanne doesn’t know how to do her job. Let me just train Joanne again on it. And then like the 10th time, I’ve just trained Joanne on how to do her job. I’m now frustrated and Joanne’s irritated because, like, Joanne’s like, why keep training me on something I already know how to do? Because it’s not a skill issue. It was a will issue. But because I did not take the time to unpack the will part of it, I did not realize that our agents are configured, every single one of them, to provide both will and skill support in a way that people’s self esteem is improved. They learn more, they retain it longer, and their self esteem improves. That’s the study that we did. We did research last summer and that was the result from a, um, researcher came with what’s happening with Avatar Buddies solutions.

So that’s how you, how you configure your tools to make sure that they’re a role model. And I will say to you, we, we just configured a digital twin to be somebody’s personality and a social worker. Now in real life, that person is not a social worker, but her digital twin is a social worker. And then we just did a, we did a demo. We had her digital twin answer question and another person on her team’s digital twin answer the same question. And, and the essence of the question was the same, but the way they answered it was completely different.

So does the AI have personality? I would argue you can go to our YouTube channel and look at our digital twin and you will see the demo of what we did.

So yes, if we did stuff like that, then you do have this warm environment where people aren’t afraid of the AI. The AI is being responsive to them. And because we’re sitting with the people to configure the AI to begin with, they know what’s going into the AI.

And because we know what’s going into the AI, they are feeling more comfortable. Because if you’re building something, you’re more comfortable and confident to use it than if somebody just gave it to you as a black box.

And, and then.

So I’ll pause there and then we can continue.

[00:52:24] Speaker B: I’m just going to say this, Stephanie, and hopefully it will not draw a response, but I think you just killed AGI. I think you just. AI is not a one system solution. You’re going to need multiple AIs. Sounds like to me that you think AGI is not possible. We may need to have another conversation around AGI to help you answer that one because I need to get Derek and I need to get Joanne back. And we have seven minutes left. Derek, go ahead.

[00:52:54] Speaker C: Great, great dialogue. And the examples Stephanie gave were actually incredible. And we did. The fact that using AI to help you with Gemini AI, I think is great because it helps to make you more efficient in what you’re doing. But I think also looking at the vertical integration piece of it, we really need to look at the using the vertical integration as a cyber resilience strategy to work with the data because you’re only going to be as strong as your weakest layer. And I say layer because the AI model will be able to go across all your different units within your business to understand what areas are going to be the weakest point or where you’re going to have the greatest exposure. And they can do it much faster. They can be detecting that threat intelligence and give you that response. You need to move forward. But overall it’s going to help you align those business goals with your risk posture to move those things forward. And the dignity aspect, you know, for those working with Those in the IT or the SoC type services you mentioned earlier, Isaac, you know, automating those mundane tasks to help free up employees to be more creative is going to be key. So you’re going to be looking at personalizing for those particular areas where AI can have the most impact. And the other thing is really helping employees upskill what they need to do. You know, as I mentioned earlier, the example that Stephanie gave, you know, people not knowing and using it and figure out what they can empower themselves to do to be more efficient, to be more powerful, to have more, greater answers and make better decisions, these are all the things that are going to come about with this to help employees with the learning process and help them be more self sufficient so they don’t have to rely on somebody else. So the agent, the AI buddy that Stephanie’s mentioning here, I think is absolutely awesome.

[00:54:27] Speaker A: Thank you.

[00:54:28] Speaker B: And bring Joanne. And Joanne, you’re working in a space where I actually think it’s a little bit easier, you know, finding your awesomeness in manufacturing and construction, the industrials, and saying AI is your buddy, but you can’t be displaced because you live and work in a physical world.

Sounds like a easier pill to swallow and maybe get some more traction in industries that have an aging workforce, have an incredibly knowledgeable workforce.

I’m just wondering if you can comment on that.

[00:55:03] Speaker E: Yeah, I think there’s, in certain parts of manufacturing, yes, I think it might be easier, but overall I would say it’s equally as difficult. One of the biggest challenges that, you know, we’ve come across with people onboarding and looking at the agents and using the agents is to make sure that the individual’s bias is never transferred into the agent.

And that requires a lot of skill and a lot of using not only different small language models, but the larger ones as well.

And I would say that while some of these agents may be very good for productivity, you can’t replace necessarily the human in the loop knowledge that comes about expertise in certain, like the SME expertise that comes. So I think it’s a mixed bag. I can see the value of the productivity agents that Stephanie is, you know, creating from a functional point of view, aggregating those together to get the overall business value that we provide in terms of top line and bottom line. I’m not sure I quite get it yet, but this is definitely advantageous for other parts of the organization. I can see using it where there’s more human interaction kind of in the B2C way of productivity, but also maybe later on in the B2B way.

[00:56:36] Speaker B: Thank you, Joanne. I got Stephanie raising her hand. What’s the last word for today’s session on crafting purposeful digital workplaces, folks? Thank you for joining. Stephanie, wrap up for us.

[00:56:48] Speaker A: So I totally agree with Joanne and we’ve actually just now started doing where we stringer AI agents together.

They’re still, it’s still manual and we’re still human in the loop digital handshake. But I’ll just leave you with this example. So my marketing team said, I need you to write four blog posts for me. And I said, I don’t write blog posts. And they’re like, you have to write the blog post because you’re the only one that knows this stuff. And so then I said, okay, fine, I’ll have researcher Buddy do some research.

And then based on that research, I had Marketing Buddy take that and write some write my four blog posts for me. And because I know marketing Buddy loves to do hyperbole, I asked editor Buddy to check what marketing Buddy did and make sure it was okay and do some edits. And once that was finished, I asked social media Buddy to write posts for LinkedIn, Instagram and tick Tock so I can introduce my articles. And all of that took about 15 minutes. There were checks and balances. And I gave the resulting product to our comms communications officer. And for the first time I got a well done as opposed to, no, let me rewrite this for you. And I think that is because I added an Editor Buddy in there that was configured to be the way she edits.

So yes, there’s lots of productivity, but also taking people to a higher order of thinking. That’s our goal, to have people be the best version of themselves because they’re using AI, because they tap into their creativity and their awesomeness. And thank you again for letting me be on this podcast.

[00:58:34] Speaker B: Thank you, Stephanie. We wish you great success and luck with Avatar Buddy.

I just want to echo, Echo something you just said, something that everybody here can take away with, which is you mentioned a social buddy, an editor buddy.

This is essentially how I see AI playing out with agents. Is giving him a role like that, right? We know that we as humans get conflicted when we think about a problem or an idea from too many perspectives. We get lost in our thoughts. Should I be more safety conscious? Should I be more innovation conscious? Should I go slower and faster? Think of two or three agents who are working with you playing out different roles and saying, how would you solve this problem? From a security officer, from an innovation officer, from a data governance officer. And they’re going to give you very different answers from those perspectives and then think about that agentic world that says, okay, bring this all together to me and provide me some trade offs between how I think I should work with my particular problem or solution given my objectives. And it’s going to give you some rounded out answers around this. Love this conversation. We’ve got four areas in the bottom right hand square about areas that we will cover. I want to thank Irina for the testimonial for today. No AI needed to see how awesome all the participants are.

Thank you for all my guests today. And folks, I do not have September sessions lined up yet.

That’s deliberate. I was hoping to get some out of them today. And we’ve got four to work with. So do visit Starcio.com Coffee next event that will redirect to our next event.

Do visit drive.starcio.com Coffee-with-Digital Trailblazers Sorry for the long URL. I’ll get that up there sooner.

If you want to watch or listen to any of our previous episodes that are open to the public, you can join there. And if you want access to all of them, join the [email protected] cio.com community. You get access to all of the recordings as a community member. Folks, everybody, have a safe, August, safe weekend. If you’re in a hurricane’s way, we’ll see you back here in two weeks.

On our next topic for the coffee with Digital Trailblazers, Everybody have a great weekend.

This week’s episode of “Coffee with Digital Trailblazers” focused on the concept of “frontier firms” – organizations that are embracing AI and agents to transform their operations. Key points include:

Effective AI governance requires a holistic approach that aligns strategy, operations, and data/security management, rather than just policy-based controls.

Frontier firms are blending machine intelligence with human judgment, creating AI-operated but human-led systems.

Adopting AI and agents requires careful governance and collaboration across IT, security, legal, and business teams to manage risks and ensure reliable, secure operations.

Leading organizations are using AI agents for tasks like software development, compliance, and customer service – with humans maintaining oversight and control.

Isaac Sacolick:

Greetings everyone. Welcome to this week’s Coffee with Digital Trailblazers. I’m just going to give this a few seconds to get a bunch of people here and we’ll get started with a very interesting topic with a very interesting guest and just sit tight. This is our hundred, is that right? Hundred 35th episode. Holy cow. Time flies by and I met a whole bunch of new people this week that I’m excited to have on the program and be a part of our community of Digital Trailblazers. If you are here, please do say hello in the common stream and I look forward to this conversation. We’re you going to be talking about from digital leaders to Front Frontier Firm? And I’m going to let our special guest, Neraj Dani, who’s the CEO of net woven, tell us a little bit about that when we get started. We are just waiting for getting a few more people here and then we’ll start with our conversations today.

Hello Gloria. Welcome. Got Derek on here on the common stream. We’ve got Neraj. Who else is here today? Everybody do say hello. I want this to be an open conversation. Hello Dennis. Thank you for joining. You’re all welcome to comment. I will call out some of the comments that I think are useful. We might put some of our comments into the actual whiteboard as we start creating it. Hello Jay. Jay is a good friend, StarCIO member, an X Microsoft. I’m just going to say an ex Microsoft TA copilot experts. Great to see you. Hello Michael. Thank you for joining from Atlanta. Michael, I think you know this, I’ll be speaking on Atlanta. The new date is September 10th. This is an event at the Microsoft Center that net woven is sponsoring and I am very excited to be there. So Michael, if you don’t have the access link to get to that, do let me know.

I’ll make sure you get on the list to be able to come see that program on AI governance in about a little bit over a month. Folks, thanks for joining this week’s conversation from Digital Leader to Frontier Firm AI and Governance Strategies. Today’s episode is sponsored by net woven. We’ll have a little bit more about net woven as we get into the conversation and Niraj and I are good friends as are a few of the folks over at Net woven. I first met Chris Wilkinson, I dunno, a whole bunch of years ago we stayed in contact. He was a big fan of driving digital and he had me come and speak at an event for Microsoft all the way out. What was his February again? A net woven event and we spoke about AI governance and strategies and things like data security, posture management, and just getting more organizations ready for this area of ai. And believe it or not, back then agentic AI and AI agents was just on the tip of our tongue. We weren’t even really talking about them. And here we are today and you can’t stop hearing about them. So I’m really interested in seeing where this conversation goes today. Neraj, welcome to the floor. Thank you for sponsoring this week’s episode. And tell us a little bit, help us define what a frontier firm is and how digital leaders can guide their organizations on AI transformations. Good morning, Raj, and welcome and thank you for being here.

Niraj Tenany:

Thank you Isaac, and thanks everyone for joining this morning. You guys hear me fine?

Isaac Sacolick:

Yes.

Niraj Tenany:

It’s a very interesting time in the age of AI these days. I did my first event on AI and governance back in November of last year, and I look back and see just in the last six months how much has really changed in the AI space. You talked about Frontier Firm, this was something that was actually coined recently back in April. Jensen Huang actually, as you probably, I’m sure you all know him, he identified four waves of ai wave one being the perception wave, wave two being the generative wave, wave three being the reasoning wave and wave four being the physical wave. We’re in this what we call the reasoning wave where these agents are becoming predominantly important and really what is really going on is that in this a new organization blueprint is emerging for companies to operate and work. And in this blueprint it blends machine intelligence with human judgment and that is where AI and the human coordination is moving forward. The systems are getting built that are AI operated but human led. And this is the emergence that we are seeing with organizations and Microsoft has coined this term for the frontier firm of these organizations. So that’s really what we are seeing for these frontier firms that are being operated by humans and agents together.

Isaac Sacolick:

So tell me, it sounds a little bit like not being on the bleeding edge, but at least being an early adopter and going beyond just experimentation but actually starting to realize value from ai. Maybe speak to a little bit about why it’s important for organizations to be ahead of the curve and become a frontier firm.

Niraj Tenany:

This is like any curve technology curve that we go through. If you remember when Twitter came out, the first few conversations people had were commenting on simple things and then it became a very potent business tool and we overseeing the emergence of the same thing happening in the frontier firm concept where right currently the first wave was around the use of AI assistance to do simple tasks. Now we’re seeing this emergence of getting complex tasks done by the so-called AI agents, but obviously led by humans and it is sort of moving into a doing more complex and involved task for organization. So that’s what we are seeing currently.

Isaac Sacolick:

Thank you Niraj. I’m going to pass the mic over. Let’s see, let’s go to Joe first this time this week and then see who raises their hands up. Joe, we’re always looking at emerging technologies. I don’t think we could call AI emerging or even generative AI emerging, but a lot of the things that companies are trying to do with AI still feels like emerging. And what is your sense on, how would you describe the level of urgency and importance to be a little bit on the frontier when it comes to AI technologies?

Joe Puglisi:

Well, there’s obviously a lot of FOMO companies that are looking at articles published every day, news stories and all sorts of things swirling in it. As you would expect. The old airline magazine, the proverbial airline magazine story, every board, every CEO is asking what are we doing with ai? So there’s pressure from the top, but I just want to make a point about frontier organizations. There’s an old adage about how you could spot the pioneers in the early days. Those were the guys with the arrows in their chest. So I’d love to hear from Niraj about how, and I’m sure we’ll get into this, how we avoid the pitfalls of being too far out front because that is a real danger and one that we have to be very cognizant of.

Isaac Sacolick:

Nash, you want to comment on that a little bit?

Niraj Tenany:

So with every technology inflection point we face this anxiety as, and it all kind of links back to the strategy because AI in many ways is very different and people really have to embrace it sooner than later. However, having said that, it’s no different than any other technology inflection curve that we’ve gone through where there will be an initial hype period where you’re testing it, you’re trying it, you’re figuring out what works, what doesn’t work. Then you are identifying certain use cases and business processes where you want to automate and you want to take it to the next level. So we’re seeing a similar concept as we have seen in previous inflection points being applied to the AI infection AI curve here. The only difference this time is that the rate of acceleration is rapid. I think if you look at, I’m sure you recall some of the statistics from OpenAI and others in terms of how fast the adoption has occurred. That is where I think we’re seeing a sea of change. So the implementation phase and the working with organizations and the CEOs looking at how to implement it, the process remains the same. However, there is rapid acceleration.

Isaac Sacolick:

I want to comment on that. Niraj, people who look at my background in transformation, what I tell them is I got a front seat to it. In 1996, I joined a company helping newspapers going from print to web and we were doing everything from editorial to classifieds. Every major business line ended up becoming digitized and user experience workflow changes. 96 through 2000, one was a wave, 2001 was another wave. We’re talking multiple years of the newspaper industry being able to adjust to disruptive technologies. And most of you who are listening know it doesn’t really end well for that industry. They didn’t challenge their status quo, they didn’t c challenge their operating model, their business model, their way of interfacing with customers, and that was over a pretty significant period of time. We’re talking now agents is really about a year old, large language models, about three years old and there’s a whole new wave coming around. Agentic ai, I heard yesterday this first term, I’m wondering if Joanne knows this term, the internet of agents I heard yesterday as a new buzzword. I’ll let her comment on that next. But Derek, Derek, I expected your hands to raise for AI governance, but here, tell me what it means for you to be a frontier firm or security expert.

Derrick Butts:

Yeah, a great question. When you look at this, I like Joe’s analogy about those out front are the ones with Arrow that cha me. From my perspective, I look at those frontier firms, yeah, they’re going to be the ones that are leading the innovation, but they’re also setting the pace for innovation. You’re going to look at what are the risk involved with that. I think also the companies need to really be fully engaged to look at the champion, the adoption of this from a security perspective. Nobody wants to jump into anything new that’s going to make them spend more money to mitigate threats to risk or of that nature, but also look at how the cultural can change to work with it. As mentioned earlier, the value case. What’s going to be the value to the overall organization? How’s it going to improve ROI, how’s it going to improve business processes?

How’s it going to improve anything that’s going to help make the business that much more substantial in the marketplace that it’s trying to dominate? The pace at which they move is really, Raj mentioned, it’s stellar, it’s a rapid pace we’ve never seen before and a lot of companies have stumbled into unfortunately where they have been mitigated or have been issues where they’ve actually rolled things they should not have. And I think we need to really look at more of a resilience mindset of the AI lifecycle adoption for this. How can we maintain a move in this frontier pace in a secure manner, but also maintain the principles of formulation moving forward. And I think there’s companies are out there, but you got to have the heart to do it because like I said, it’s not an easy task for this, for those that can really sustain that momentum that those are going to really move forward, be a premier.

Isaac Sacolick:

Thank you Derek. It’s very interesting to think about that balance about being in the front but avoiding the arrows as Joe called it. Joanne, your comments on the frontier firm and maybe a comment here. Dennis asked a question on the chat. What are the table stakes or being a company on the front of AI and around data or talent? I wonder if you have a comment around that.

Joanne Friedman:

I do. First of all, it’s about purpose. It’s about outcome. In the advent space, you use generative AI for creative, you use ag agentic when you want an outcome. So here’s an example of an outcome. Yesterday we were with a prospect, we were talking about our technology and our platforms, et cetera, and we used the meeting capture software, whether it’s auto or Fathom or choose brand. We then took that through that into a tooling kit that we have that we use for software engineering. And before we walked out of the meeting or got off the Zoom call I should say, we were showing the prototype of what the customer would want. So one hour from start to finish and they had a piece of software ready to use as a prototype that’s being a frontier firm that’s taking technology and leveraging it not only for purpose but for its value and showing someone this is what you can do with it.

Now, it was a very rough prototype. It was not anything ready for prime time, but it was the visualization of what they were saying, this is what we think we need, this is what we want to get to and this is the outcome we’re trying to achieve. Now we’ve taken those notes, we’ve reverse engineered what they’re talking about into something that they can actually touch and feel. To me, that’s what being a frontier firm is. It’s very bleeding edge. We know that in the agenda space, trust is a five letter word. To Derek’s point, nobody trusts agents right out of the box. And to the comment that you made earlier, Isaac, about the internet of agents, we commonly call it a swarm. We refer to buzzing of bees because you literally can run things in parallel. So what I’m trying to illustrate to you is not only that the frontier firms are those that are pushing the boundaries, but they’re being very clever as we hope to be about how they’re using the technologies of AI to drive outcomes without friction faster, without necessarily breaching the trust of the end user, but also using them as a way to build the trust in the capability.

What we heard from our prospect was that’s amazing. How did you do that so fast? And we showed them literally step by step, these are the tools that we use. This is how we could capture what we think you’re telling us if we’re wrong, no harm, no foul. If we’re right, tell us where the right parts are, tell us what you don’t like about it, and then we can iterate very, very quickly. So to the agile discussion, this is now accelerating agile to the point of delivery in minutes, not days, weeks or months. And that’s what to me, frontier meets.

Isaac Sacolick:

That’s awesome. Congratulations on your shift from POC into demos, live demos with customers. It’s awesome to hear the progress that you’ve been making and we’re going to have to invite you to do an unveiling here and talk about more of what you’re doing at a future episode. Let’s bring John in. John, you’re the person I love asking about, just emerging technology in general in terms of where the use cases are going. So what do you consider a frontier firm? And I want to go back to a question I just asked earlier from Dennis about what are the table stakes today maybe what is frontier firm ahead of?

John Patrick Luethe:

Yeah, and I think AI is different than almost any of the technologies that we’ve had in our lifetime. To me it’s like a foundational or infrastructure technology. It’s kind of like electricity, and that’s because it can be applied almost everywhere. And so I think if you think of it as a technology, it has to be on the same level as the internet in my view, it’s impact that’s going to happen to society. And so frontier firms to me, those are firms that are looking at everything they do, all the work that they do, everything they do for customers, everything they do internally. And they’re looking at what do we want the humans to do and what do we want the AI technology to do? And so the frontier firms, I think they actually literally looked at all the work that was going on in progress and they looked at what work do we need to stop so that we can reprioritize and change the direction of our company? And you can see that when you read the stock reports of companies. And so when you read the quarterly reports, it’s really clear or if you’re talking to the people in these companies, the companies that are technology companies and frontier companies, they are changing the direction of the company, the trajectory of the company, what the company is to align to this. I love that

Isaac Sacolick:

Concept and we talked about the need to say no and stop doing things that are at the dead end. I think that’s a good definition to bring in around frontier firms. And then what I wrote here is frontier firms reinvent themselves. John, I’m going to give you the last word on this and then we’re going to move into the topic around this concept of human agent teams.

Joe Puglisi:

Yeah, I think building on the comments that were just made, it’s really more of a cultural shift. It’s a change in the way the company operates from top to bottom and really across the board it’s more than just new tools or technology. It’s really a fundamental change in the organization. Some people who perform mundane tasks are going to be elevated or eliminated and other people are going to embrace this technology and be able to accelerate what they do and be able to do it better. So it’s really a fundamental change in the culture, the complexion and the nature of the way that the people within the business entity operate. And to Joanne’s point, all in the service of better or different business outcomes.

Isaac Sacolick:

Very cool. Thanks everybody. Let’s move on to our second question today. This is a term, I don’t know if Microsoft created it. It’s highlighted in their paper around frontier firms and it speaks to how we should be thinking about agents themselves. And I’ve heard a lot of descriptors around this. I think this is the one I like the most, neraj, this idea of human agent teams. I really like your description of human judgment plus machine intelligence around this. What are some of the examples of human agent teams that you’ve seen that net woven has worked with your customers and what do you see the business value being delivered around it?

Niraj Tenany:

Thanks, Isaac. I got to be honest, a couple of months ago or maybe a year ago when I heard of the term agent and I read about it, to me it sounded like a sub routine in a program. If you remember, for some of us, I grew up from the programming ranks and used to write subroutines. It was no different to me. So I kept digging in to find out, okay, what’s different about the subroutine? Why are we really calling it an agent? And over time as things began to mature, it was very clear that we started from subroutines back in the two thousands. We had what we call the calm DCOM agents that what were managed now within are the age of agents, which are nothing autonomous entities and they have the mind of their own now obviously, and of course with human led interactions where they’re doing certain things and they’re learning to do things.

I’ll share with you as A CEO sometimes tell me that I should not be programming, but I actually rolled up my sleeves a couple of weeks ago and started to do white coding just to understand how this technology works and what it really does. And it was amazing to see how these agents, when you’re building a website or when you’re building an application, how these agents are actually working together. It was surreal. So being able to, I mean in our lifetime I could not have imagined that we are writing software and there is these agents who are actually testing the entire software and self-healing themselves and fixing the code. So there is a lot of activity happening from that area in terms of these agents doing things, but obviously be guided by humans. And I would encourage everyone actually on the podcast here to do some white coding, you’re going to see a whole new world of how agents operate.

Isaac Sacolick:

Can you go deeper on that, Raj? We haven’t used the term vibe coating here on the coffee hour. What is it? What does it allow you to do? What’s, go ahead.

Niraj Tenany:

Imagine that you had, you had a piece of software and you could actually engage and talk to that software in English and English is your new programming language. So let’s say that you wanted to build a website, I said for a new website for your company, and you could utilize this white coding concept and basically do this emerging category of software. You could tell it to say, Hey, I want to build a website similar to this other website that I have, but here’s my unique content. And you will see that software actually goes, collects all the information. Does the grunt work of building information, creates the search engine optimization for your website, adds lead generation magnets to, it creates content for you so that you can post content on a regular basis. This is a very good example of how agents are actually bringing an idea to light in a matter of minutes or hours as Joanne mentioned earlier, that the acceleration and the other panelists too, the acceleration is happened rapidly and wiving is another modern way of doing it, which actually takes advantage of these code agents that are out there. Some of it will be your own, some of it will be from third parties.

Isaac Sacolick:

Very interesting. Somebody asked me about this a few days ago about how to picture agents and what you’re describing as vibe coding from the ground up. And when I said is if you know what an API is and you know that most APIs are programmed through endpoints and through JSON, now think of an API that takes English as a front end and you can speak to the agent and ask it what it does and ask it to start doing things for you because now the agent is connected to your operations. Now take this one step further and we talked about orchestrating services through API calls and building workflows around them. And workflows come from the generation of rule-based systems, linear based systems. And so we have a set of inputs, a set of processes along the way, a certain set of things that we’re trying to achieve and we build a workflow around it, part of it with people doing steps, part of it with machines doing steps.

Now we turn it upside down. Now we turn it into a less deterministic approach and instead of programming a workflow, what we’re enabling is role-based activities. It’s almost like you and I walking into a room and there’s an architect there, there’s a developer there, there’s a tester there. They’re all playing their different roles as agents and we’re having a conversation with them and saying, this is the type of thing that I’m trying to build. And they’re having a conversation among themselves as there are different agents figuring out what they’re doing and orchestrating here is how collectively we can respond to your question. It’s really interesting to watch because if you watch the logs of that discussion, right, the logs of an API are these observable JSON rich streams that you need technology to go understand what’s going on when you look at it from an agent to agent conversation, it’s happening in English and you can see all the conversations these agents are having that are leading to them to come back with what activities they’re recommending and that they’re actually actioning for you. It’s very interesting. We go around the room and we’ll come back to Niraj on this. John, we’re talking now about human agent teams and their business value. What are you seeing out there?

John Patrick Luethe:

Yeah, and I love the visual of the sober teams and I always think about the matrix and the characters in there because the characters in the matrix are running sober teams as agents. And what I’ve seen is my friends that neat tech companies are building agents that have specific purposes. One of my friends is at Pioneer Square Labs, Kevin Runway, and he built an agent that helps with software development and he interacts with it via Slack and GitHub and it can do pull requests and review code and you can give a text and it’ll create requirements. Almost anything that an intern would do that’s a personality, he gave it in software development. It’ll do those tasks and they communicate with each other via the normal software development tools. And one of my other friends is at a phone company. I want to sell your phone companies and he’s on the compliance team and they’re using all sorts of generative AI to help make sure that people have the right permissions, that they don’t have too many permissions.

And so these things have tasks to reach out to managers and say, Hey, does your user have the right permissions here? And it’s really helpful when you have a couple hundred thousand people on an org because you can go through the whole org on a quarterly basis and ask every manager if the people have the right permissions and hint, Hey, this person has access to this thing, but we don’t really think it should. And so that’s what I’m seeing with my friends. Just really neat agents that have specific purposes that are acting on their own to make the things better and do tasks that people don’t want to do.

Isaac Sacolick:

Very cool. I’m really interested, Derek, what is the security version of a human agent teams? I mean the data is bigger, faster, more complex, the bad guys are bigger, faster, more complex. What does a human agent team look like in security? Absolutely.

Derrick Butts:

So you’re seeing this already in some of the architectures and they call it human agent architectures, human AI agent architectures, and you see ’em using it in Microsoft Darktrace, CrowdStrike, rapid seven. They all got what you call these threat commands and these direct commands are doing just what Rob said, they’re actually using artificial intelligence and analysts to review these anomalies or things that come across their screen to double check to say all these threats are anomalies real. And to prioritize those threats based on the human analysts investigating and say, yes, I can validate this. I think it’s going to be something we’re going to see more throughout the ages because people need to become confident and trust the systems that they’re using when these a AI are spit now information to make sure it is valid, to make sure this is substantial. But what it does from a value position, it helps ’em make faster decisions.

It helps ’em analyze that threat and say, yeah, this is a threat, and help them escalate to what needs to be done to mitigate that threat. It’s also going to help them because of the fast and the speed at which they can do it, reduce costs so that particular customer that they’re working with, let them know what needs to be done, isolate container and what fixes they need to do to put in place to make that happen. These things with the key thing is the AI agents working to increase the accuracy of these detections because with the AI packs are going to come faster, they can be more complex and the human alone just can’t work with that. I’m also seeing that just in customer service type services. You see this now where a lot of customers are moving from their online human interface that now have chatbots, AI chat bots that they’re now interacting with directly using the voice command streams and it makes it harder not to even get an agent, but there’s always one available in the background. So it just depends on the industry you’re working for, but from a cyber perspective, they’ve been using it now for at least a good year and they’re trying to perfect it to make it better. And again, those customers entities or those businesses that moved in this area are going to be leading in this particular area because that’s what we need. We need that security, we need that trust, we need that reliability to make sure they are working together until that system can become more reliable and be self sustained.

Isaac Sacolick:

Thank you, Derek. Let’s just keep going around the room. Joanne, we’re talking about human agent teams. There’s also a question here from Barat on the common stream and he wants some examples like who should we use as a, who should we personify? What company or companies might we use as a way of saying this is what a frontier firm might look like, Joan, you have any examples of that? And then we’ll bring Joe and then Niraj to see if they have any examples,

Joanne Friedman:

Companies that are frontier organizations or those that what I call humanify their ai. And what I mean by that is an example of a company that unifies is one that understands that agents learn, they’re constantly evolving, constantly changing and adapting, which is great, but they also need to be trusted and they also need human in the loop, meaning guardrails that are assigned based on the role or the persona of the individual that would be asking a question or looking for an answer. So in human in the loop, what you’re doing is you’re giving the system an opportunity to leverage institutional and tribal knowledge of the workforce. That’s the in tandem human agent experience that I think a lot of frontier firms are trying to go after. We do it from the perspective of a manufacturing enterprise. So you would have as a persona, a worker on a plant floor, you could have an engineer, a controls engineer, you could have a plant manager, a supplier, a customer, any of those roles.

The companies that are really excelling at this are those that understand that human in the loop doesn’t just mean having a human overseeing the AI from the trust perspective, it’s drawing in the humans into the loop of what used to be called a workflow and allowing them to have the adaptability to use the tools as they need on the daily basis because we all know, particularly as executives, you change hats every two minutes. You’re constantly doing things that involve a different role. If you’re tied to an old school method of what role means you would not be permitted from a security perspective or from a business perspective to see information that actually adds the context and the nuance that you need, the semantic layer, if you will, that makes AI work.

Isaac Sacolick:

Thank you, Joanne. I actually like that definition of looking at that partnership and I think one of the reasons that we don’t have a lot of shiny examples here. We talked about AI and customer experiences and in products a couple of weeks ago, maybe it was last week, and we’re seeing more AI and agent AI being used on the inside on future of work, on workflow, on the concept of virtual agents. I think that’s where we’re seeing most of it. And so I’m going to wait for Naraj to have some examples of where people are really excelling at it. Folks, before we go back to Niraj and Joe and our question around collaboration on AI governance, I just want to thank net woven for sponsoring today’s episode. Net Woven is a trusted Microsoft solutions partner who leads organizations with their AI journey. We help organizations build AI powered applications, unlock data for insights and protect their organizations from cyber threats. You’ll learn more about net [email protected] woven.com. And thank you Niraj for being such a strong supporter of the Coffee with Digital Trailblazers. I’ll announce our upcoming episodes at the end of our session. I want to keep going with our conversation and let’s bring Joe back. And Joe, I want to hear what you think about a human agent team and then we’ll bring Niraj back. We’ll start talking about governance as we’re starting to balance being on the frontier but not breaking things. Go ahead Joe.

Joe Puglisi:

So a couple of months ago I was fortunate enough to get a peek behind the curtain at a major consulting firm and the chief technology officer had embarked on this project to replace specific roles individuals in the organization with agents. But what was interesting and I found incredibly unique and exciting about the architecture was the network of agents, or as Joan said, the swarm of agents were tied together with an interface which we’re familiar with and it’s called English. He had architected it such that every time you replaced a human in the loop with an agent, the interface didn’t really change because you would pass the information along in English. So it was humans to agents and agents to agents and to humans, all in English. I found that fascinating.

Isaac Sacolick:

Yeah, it’s very site bias. It just feels like you’re in an episode of Star Trek actually, except that there’s actually a visual of what’s happening behind the scenes and how it’s working. My concern around this is that we talked about shadow it and now it’s not just it, it’s shadow ai. It’s really potentially the shadow of the swarm of shadow ai, all these agents and all these platforms. I’m actually writing an article around this today that will probably come out on Monday. Go ahead, Joe.

Joe Puglisi:

But thank guys. You think about how easy it is to implement this. You have clear vision into who’s saying what. You have complete audit trails and there’s no major culture change. It’s just that you’re now talking to agents and agents are talking to agents instead of just people talking to people.

Isaac Sacolick:

So let’s bring Neraj back. I want you to comment on this conversation. Who’s doing this well? Or at least what are they doing well? What are some of the examples that they’re using agentic AI or they’re using vibe coding and starting to really see the value from it. And then let’s go on to our next area. How do we do this reliably? How do we do this with security in mind? How do we close the collaboration gap between our stakeholders and enterprises between risk, legal, security, it, and make sure that when we start becoming a frontier firm, we’re not getting the arrow stuck in our back?

Niraj Tenany:

Yeah, great question, Isaac. We are based, I’m based particularly here in the Silicon Valley, and as you know that everybody lives and breathes technology here. Every other person next to me is probably has a startup or something doing something or the other. So sometimes I live in this area where there’s a lot of tech hype and historically Microsoft hasn’t been first to the party of any tech. They wait for it to become a billion dollar category and then they jump in and soup the category. But this time with Microsoft, we are seeing very different energy. They are first to the party everywhere. When I went to customers in the past, it was everything else being discussed and then Microsoft would come later, but now conversations with the CIOs are happening with Microsoft also embedded in there. So that is great news for companies like ours and Microsoft has a great deal of penetration and connection with the enterprise segment, both small and large enterprises.

So when we are working with customers, we’re seeing, I just mentioned in the chat that there is is a process that we recommend to customers, which is the crawl, walk, run, fly journey. So different companies are in different phases of their journey, but if one is in the crawl phase, we typically recommend organizations to use non-risk business areas to test them out, to get the feet wet and really get going on their journey. So the initial application that we work with customers and what we are seeing is more low risk areas of internal business functions. Then people go to the walk and the run journey and we see them utilizing them more for critical business functions like engineering, like supply chain, like sales and marketing where there is more customer supply interaction and the business value and risk is also higher. So that’s sort of what we are seeing in organizations as they are going down this journey.

Isaac Sacolick:

I’m seeing very similar things, but I am seeing more firms talk about getting into run in fly mode. They’re using AI in areas like customer support and customer care. They’re not making the same mistakes of trying to do fully autonomous, but rule oriented chatbots. Those lead to very frustrating experiences. And what they’re doing is making very, very smart human agents working with a virtual agent, very much what you described earlier in our conversation, this sort of human judgment plus machine intelligence. And so when you speak to one of these agents, they know who you are, they know what you’ve bought, they’re listening to what your problem is or asking the agent about what are some of the options to present to you as solutions. It’s becoming a much different experience and a much more controlled experience. Niraj, before I go to the floor, talk to me about this idea of collaborating on AI governance and how do we make sure that as we’re building frontier capabilities, we’re not opening ourselves up to more risk?

Niraj Tenany:

That’s a great point. Great question, Isaac. Back in November, I gave a talk on AI security and governance at the Microsoft campus, and I got to tell you that with everything governance is so critical. I think it is the only glue that keeps the environment, if you will, in a sane order and keeps it well structured and formatted. With ai, you’ve got security risk coming, you’ve got compliance risk coming, there’s a number of risks coming and having proper governance structures in place is important. Now, governance with a big G is often not taken well with organizations like a lot of people think of it as a set of documents being that is overly imposing on how innovation occurs. So governance also, people don’t have to really bite the full thing as you get started. You can always do a crawl, walk, run, fly journey on governance as well. Have the foundations in place work through your AI work in establishing whatever your AI goals are and iterate and develop that model. We’ve seen that implemented very, very successfully. And the rate of acceleration of creating governance in our organization also increases because there are advanced tools out there and there are things that you could not have done before in governance. You can do them today.

Isaac Sacolick:

I agree with that notion to fly on governance also, we talk about this a lot here about how governance is just a very difficult word. It’s often just associated with policies, one and done policies. We know AI is something that’s evolving. Niraj, my keynote that I’ve done for you in February that’s being updated for September. I think you can’t separate governance from strategy when it comes to ai. I think they need to be intermingled particularly to get people’s interest and to connect value to what guardrails that you really need to have in place. And I’m going to add a third dimension to this as we’re starting to do more with agent AI and building AI agents across different platforms, I don’t think you can separate operations from it. I think it needs a complete holistic definition. I don’t think you should have your policies up in front and how it’s implemented and managed on the backend for nobody to see. I think it’s just too much of a core of what organizations need to move to over the next few years that you have to have strategy, governance and operations altogether. Joanne, I’d love to hear your opinion on this.

Joanne Friedman:

I think a holistic perspective is absolutely mandatory. That would be the table stakes for anybody going down the AI route, regardless of whether it’s gen ai, agentic, ai, math, ai, physics, ai, it doesn’t matter. You have to take the holistic approach. And I think the two words that are going to emerge very strongly over the next year, particularly with respect to governance, are providence and lineage because we need to understand where did this data come from, its providence and where did it move to and what impact did that have Its lineage over and over again as we’re going forward. Because the table stakes will constantly be evolving because AI will continue to evolve. Where we have to wrap our minds around the fact that even though data is constantly changing, context is now changing even faster and the context in which we use AI is changing faster.

So what was designed for a static system, meaning an IT system or as part of the governance or risk or any of those kind of words that were policy-based have to be as flexible almost to the level of vibe coding as the rest of the AI system. So it’s going to be a challenge to do that. Providence and lineage give us the direction to understand how to iterate and also give the systems meaning the agentic tools that we’re using, the opportunities to learn and evolve to keep up with what will be a continually changing environment around compliance, regulatory and even security.

Isaac Sacolick:

Thank you. Joanne. I agree with you. A big part of this is understanding the elements of data, particularly providence lineage security. And much like people have access rights to information, we need to make sure that our agents have the right access rights, much like we secure data for different contexts. I want to hear from Derek from Raj when he comes back on the concept of virtual data rooms and we put our agents in a room where they are sanctioned to the certain level of data that they should be getting access to. Derek, what are your thoughts around this? How do we bring these different factions together, our risk, legal, security and IT teams and really build a holistic AI strategy, governance and operations around it?

Derrick Butts:

Well, I think Joe mentioned it perfectly. They have to communicate. So one of the things is looking at establishing either a business or cross-functional AI governance board to really look at what are those risks, of course all these particular areas and how those translates. So when you’re looking at the business risks, the operational risks, the cyber risk, the legal risk, all those things come together. You need to understand from, as Naraj mentioned, the lowest risk impact to the highest risk impact. How are those going to translate to business impacts overall and how can we foster and understand it across all the organization? Because like I said, it is an organizational challenge. It’s not just one particular or couple areas. And I think once we get everybody involved to understand that it’s going to be easy to work with the compliance need to work with the resilience, you need to work with what is it going to be a recommendation to mitigate even innovation strategies to make that work. Everybody has to have some sort of ownership within this particular AI governance to make sure they understand how it’s going to affect the dominant effect, the change across the department. And the other thing, the educational piece of it’s going to be huge. This is something that’s one and done. We have to continue to educate the people on the changes that are going to take place with an AI and all the different risk factors to look for the tabletop exercises from AI perspective are going to be key.

Isaac Sacolick:

Yeah, I agree with that. I was chatting with A CTO yesterday on a panel and one of the prominent nonprofit groups, they’re using AI for grant writing and for nonprofits, and they’ve got a ton of PII information in there and a ton of financial information in there. And the number one thing they have to do before doing anything around AI is just educating 300 people about what happens if they start cutting and pasting this information into open AI out there. Exactly, exactly.

Derrick Butts:

And having that conversation at the leadership level I think is also going to help escalate the way we can work with compliance because the leadership means to understand how the ripple effect is going to affect the overall organization. And I don’t see enough tabletop exercises happening enough, especially at the AI level.

Isaac Sacolick:

Thank you, Derek. We’re going to go to Joe and John and then we’ll go back to Raj. Hello Joe.

Joe Puglisi:

Well, Derek brought up my favorite term communication. I think as he said, he is essential that you get everybody involved in the conversation across departments. You need a cross-functional team and you really want to be as the leader, as the trailblazer. You want to be the head of the department of no KNOW, not the department of no and no lead guide, discuss. Don’t try to stop it. You can’t try to guide it and make sure that everybody is involved in the conversation from day one.

Isaac Sacolick:

So you’re going to write that blog post for us, right? The department of no versus the department of no.

Joe Puglisi:

I think I did maybe in my, I’ll have to go back and look, but if I haven’t, I’ll certainly fill that one in.

Isaac Sacolick:

No, it’s a great concept because I remember doing my first AI governance keynotes a bunch of months ago, maybe it was a year ago, and I was getting the nos mostly from either risk and data governance later. They just didn’t know how to manage the underlying situations. And I think this is really about educating the employees. I think it’s about setting up environments so that people can work. I think it’s about setting up strategy so people understand what areas of focus to really do their experimentation on. John, why am I leaving out?

John Patrick Luethe:

I was going to say, the thing that really shocks me is how many companies would have all these really careful processes and then when AI came out, they started using outside technology with so little testing and they completely bypassed the good practices that they had in release management and change management and things like that. And so the one biggest, the thing I would tell everyone is anytime people want to use new technologies is they can take a step back and try to understand the fundamentals of things I think are going to be in a better spot to change management to release this technology and what are the risks aside with it. Taking a step back and having those conversations I think is what’s really key when you’re releasing technology. They could have all sorts of, you could be sharing information, it could be saying things you don’t want to it do to the public.

Isaac Sacolick:

Yeah, there’s some great examples here. Fossil, thank you for sharing. In the common stream, he talks about his crawl and walk philosophy around their AI journey. He’s got a bunch of examples here around what they’ve been implementing around contract management, customer contact center, fa, thank you for sharing with that, Niraj, I want to bring you back because we went from being able to tell employees that using the open public large language models bad idea, here’s why from a data perspective, from an IP perspective. And so then Microsoft came back and said, okay, you have your data here, you have your applications and your SAS here. We’re giving you copilot or making it available. We’re giving you the ability to do it with code. But I get the sense that that’s, we have to take it a step further. Right now we’re getting these agents operating in a, let’s just call it firewalled off environment. What else do we need to do to implement AI and governance strategy so that agents have access to the right data?

Niraj Tenany:

Yeah, thanks Isaac. I usually don’t talk products in these events, but in this particular instance, I’ve got to bring this up. If you don’t know Microsoft Purview, you have to look at it. Back in 2003, Microsoft came out with SharePoint and not many people looked at it. We jumped on it and built an entire company around it. And if you look around yourself, all the competitors of SharePoint are gone. Microsoft has done another home run with this product called Purview. It brings many things together. And the reason I’m saying this is because that is the foundation, therefore a lot of AI driven data security and protection. Okay, so I encourage you to look at it. If you need more information, I put my email on the chat. So back in 2020, IT world, one of the world’s largest semiconductor company reached out to us to build a data program protection program.

They were paranoid about protection at that time. I got to be honest, I had no idea about the AI wave back in 2020, but we set out with this company to build out their data classification, data protection scheme. It was an arduous grueling exercise. We built the entire thing for the organization. Customers, half of them were happy, half were not happy because of the growing exercise. But I get calls from the same company now thanking me for the comprehensive data protection we have built, which is coming out to be very, very valuable in the age of ai. Out of that, we actually spun up two products that we built. So network is also a product company one, we have one product around data security. You all may have heard the term virtual data room. Virtual data rooms were primarily used by organizations to do mergers and acquisition and capital fundraising.

When we set out to work with the semiconductor company, we asked a question that why is virtual data room only used for these two use cases? Why is it not used to manage, protect intellectual property for auditing for customer collaboration, secure collaboration for secure supply collaboration? So we ended up building a new category called secure collaboration using virtual data rooms. So our product, check it out on http://www.gothreesixtyfive.com. I’ll put that in. It allows you to create these data rooms where you can secure your content and organizations who are using that product are freely using artificial intelligence because that data is secure. If anybody uploaded that data into any of the degenerative AI tools or want to utilize it for agents, they will not be until they have the permission. So I just wanted to close out to mention that data security protection is paramount for the rapid adoption of generative and agent ai.

Isaac Sacolick:

So I love hearing about these solutions. I remember when I first saw Purview, for example, I was like actually advising a client at the time and just saying there’s some other things we probably need to get to before we get to something like purview. Knowing all the work around data ownership, data classifications just can be an arduous task, and I saw recent implementations of companies doing it. It is a heck of a lot easier. It’s a heck of a lot more automated. It still needs a lot of guidelines. You bring together your it, your data, your business folks to be able to do this. And we talk about bring in a frontier firm. I’ll share one of the recipes that I talk about in the AI governance keynote that I use. I’ll be doing this in September. In September in Atlanta. It’s on September 10th. So do reach out if you’re interested in participating in that.

And I talk about being able to do the things that frontier firms do, which is not be on bleeding edge, be on the value edge, being able to test things, being able to be top down strategic around areas that you really want to find AI capabilities to fit your goals and other areas where you’re going to empower your organization to do experimentation and find where AI is delivering value. And you’re going to use a very agile approach. You’re going to bring the folks who are really focused on the innovation in parallel, collaborating with your risk and your legal and your data security teams and saying, you know what? We’re going to do some security upfront, but we’re going to do a lot of security and parallel to our innovation. And I think that’s what frontier firms are all about. Niraj going to give you the final word on this.

Niraj Tenany:

Thank you. A second. Thanks everyone for to the attendees as well as the panelists, this is an exciting journey and with everything new starts with fear, anxiety, anxiety, and then towards excitement. I can tell you how excited I am and how others are. My kids tell me that the dad has gone crazy because he is working till one or two in the night and all they hear from me is about ai. But the reality is that this is an exciting journey to be it, and we all should embrace it. Whether you are a CIO, whether you are a business analyst, I encourage everybody to play with the technology, the amount of ideas, innovation, grounding your thoughts into reality. You will experience that as you play with the technology. There’s just a sea of game changing activities happening. So I want to thank again, I look forward to seeing you all at the Atlanta event, whoever can make it, and if you need more information, I put my contact information in the company, information in the chat. Looking forward to seeing you all soon in another event.

Isaac Sacolick:

Yes. Thank you. Thank you for a really lively conversation. Hello, Greg. Thank you for this comment on data management. AI governance are critical enablers for the public sector’s AI success. Really interesting conversation to bring together both Frontier and the governance that’s required. I want to thank net woven for being our sponsor today. Start your AI journey with net woven and become an AI first business, operate with AI agents as digital teammates, empowering human and AI collaboration at scale. Please visit net woven.com to learn more. And again, I’ll be speaking at the Microsoft Center in September, September 10th with net woven. As our sponsor. Do reach out to either Niraj or myself. If you’re interested in attending our upcoming coffee hours. You can visit StarCIO.com/coffee/next-event. If you ever get lost at always redirects to the upcoming events. We’re talking about AI era transformation next week on agent versus AI agents, large versus small LLMs.

That will be next week. On the first, we’ll be talking about strategies for AI ready data, turning data landfills to business gold mines. That actually came up last week and I wanted to get that conversation going really quickly. And then on the eighth, slightly off of ai, we’ll be talking about DevSecOps, risk non-negotiables. We’re talking about reliability, innovation, security, and culture in the new era of DevSecOps. And that will be on the eighth. Folks, thank you for joining a very lively event today. I hope you will join us in upcoming weeks. Everybody have a great weekend. Thank you again, Niraj for joining us. Thank you, Derek, Joanne, John, Joe, Liz for being our expert panel today, and I’ll see you all here next week for our next episode of the Coffee with Digital Trailblazers. Everybody have a great weekend.

Joe Puglisi:

Thank you.