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.

[00:00:01] Isaac Sacolick: Greetings everyone.

Welcome to this week’s coffee with digital trailblazers. This is episode 143 for us. We are going to take our usual slow ramp up to get everybody here on board and excited to talk about our topic today, the real cost of tribal knowledge. Why losing your people can bring OPS to a standstill.

This is a conversation that we’ve actually danced around a few times at different coffee hours talking about how important it is to work with our staff and capture knowledge, create processes, move away from gray work. And I’m really excited to have Bob Salai here with us today as our expert and we’re just going to give it a few more minutes to everybody joins. I actually clicked the Go live button a little bit earlier than usual just to see what would happen. It’s always fun to Mess around with LinkedIn and make sure everything’s working before you get started.

But really happy to see here folks. Say hello in the comments stream. Steve, thank you for getting that started and do want to hear your thoughts and your questions around knowledge management, around knowledge sharing and how we create processes. Hi Kristen, it’s good to see you again.

Hi David. Kristen and I saw each other just a couple weeks ago. Kristen, I’m going to be right near you Oddly I’m going to text you later on my drive back from Tucson, Arizona back to San Diego. So Valaba, good to see you and everybody just say hello in the comments stream.

This is an event I love to see people participating in especially this week. We’re talking about the real cost of tribal knowledge while losing your best people can bring OPS to a standstill and for everybody’s knowledge. Today’s episode is sponsored by Quickbase. Quickbase is an AI powered operations platform that is designed specifically for your unique processes and it’s not a one size fits all approach for those of you who are long term watchers and participants. You know we had a really interesting conversation, I think it was two or three months ago about the problems with Saspral and all these point solutions, the hundreds of different applications that exist in many enterprises and the impact of those and now today we’re almost talking about the opposite of this.

What happens when we don’t have processes, what happens when we don’t have documentation and over time it becomes a debt to the organization. We cannot easily onboard people, we can’t easily improve processes because you talk to one person and they know their little bit of what’s going on and so forth. So today we’re talking about the real cost of Tribal knowledge why losing your people can bring OPS to a standstill. I want to start with just an overall discussion around the knowledge management challenge and get some thoughts from everybody around what are some of the technical, operational and cultural barriers that prevent knowledge management from happening? I’m going to start with Bob. Bob is our expert from Quickbase. Bob, it’s good to have you here and just tell us a little bit about what you do at Quickbase and then share your thoughts around some of the challenges around knowledge management from a technical operation and cultural perspective. Welcome, Bob.

[00:03:45] Bob Salaj: Yeah, thank you, Isaac. And thank you to everyone here for joining us here today. It’s really exciting. Thank you for having me back. By the way. It’s always a pleasure here to join and to talk through this. And tribal knowledge, it’s always been really the hot topic, I think, of my career just to kind of go back, really. I’m a principal industry advisor here at Quickbase, primarily for the construction industry and really my career has spanned around lean continuous improvement. Starting off in manufacturing, going to Wesco distribution for electrical, distribute, distributing, but really for the last decade in construction, have been leading up innovation teams to kind of really look at their processes and how are they rolling out digital solutions for not just the office but also the field kind of going into this. So quite often focus a lot on lean continuous improvement, but use tools like Quickbase to help fill in those gaps and really kind of bring a lot of innovation to life in real time. And that’s really, I think I’ve seen a big change for it. So I see a bunch of hands being raised here. I want to give everyone a chance, but just to kind of like kick us off and talk through maybe one of the instances. Because culture, tech, operational, there’s a lot in each one of those and culture I think is the one that I really, really wanted to kind of just speak to first.

Really. I think before we even talk about like tribal knowledge and stuff like that. I’m kind of curious on the organization if they’re primed to receive feedback.

Basically, what is the culture at its heart and are they open to receiving feedback or are the employees feeling reluctant to kind of give their ideas for fear that either it might be misconstrued as complaining or maybe they’ve actually done it quite a lot of times and it just goes on deaf ears and no changes begin to occur.

So what happens a lot of times is that power is knowledge type mentality and they begin to hold on. So that’s something that I’ve seen across the board with a lot of organizations and curious from the rest of the group and panel other areas there, if they’re seeing that as well.

[00:05:52] Isaac Sacolick: That’s a really interesting point, Bob. I never thought of it in that order that you have people who are willing to volunteer information, who want to share feedback, want to improve things, and then over time, when they see management isn’t responding to that feedback, they just stop talking and they just do their own things. I never actually thought of it that way.

Let’s bring Martin in. Martin, have you seen it?

Have you seen that progression or where does it start from? What’s the underlying problem that prevents organizations from really creating a culture and processes and technical capabilities to share knowledge? Welcome back, Martin.

[00:06:33] Martin Davis: Hi, good to be here again.

I think there’s a couple of different things. You know, I agree culture is a big part of this.

You know, if I think back to some of my days in large organizations and part of my background is Ford Motor Company there for many years.

And there’s a couple of things I remember from some of those aspects of where people.

Some of it’s change fatigue as well because they see so much change, so they just kind of get fed up of it and don’t want to share stuff. So there’s too many initiatives kind of drives that. And it’s that cultural thing as well. But there’s also that fear thing. Yeah, the whole knowledge is power.

The person that’s been doing it for so many years has all of this knowledge about how it’s done and then what they’re scared about actually sharing some of that knowledge in case they’re no longer needed.

And I think that’s a mentality definitely for the. Maybe the older generations that are out there. Maybe not so much true with some of the later generations coming through. In my experience, others may disagree with that, but I think it’s kind of that whole thing around trying to help people to understand that for the good of the overall organization and it will actually help their job be easier as well. So kind of It’s Change Management 101 forms a big part of that whole knowledge sharing.

[00:08:02] Isaac Sacolick: You know, Martin, I think you bring up a good point about change fatigue and overload of information. I think that it’s almost like there’s too many voices in the room.

Right. And it creates this atmosphere of when do I get to speak up and when do I get to share?

But I think this, you know, I’m kind of interested, Derek, what you see here, you know, I told a story in Digital Trailblazer about walking into a broken ETL process. And when I went trying to discover what the root cause is, I found a stack of code that took an entire brick of paper to print out. And I did that to go back to my management team and said, look, it’s going to take us a long time to unravel years and years of, of engineering work that now only one person in the organization needs, understands how it works in this stream of undocumented code. We’ve all seen this before. So Derek, you know, what have you seen as you know, some of the technical and operation and cultural barriers that prevent knowledge sharing from happening?

[00:09:08] Derek Butts Yeah, you’re spot on with the, the code based stuff. I’ve actually seen it in industries where, you know, people are doing code, they’re developers on site and all of a sudden they leave and only to find out that they were knowledge hoarding. So they were holding the code, they did not document it properly, they didn’t document how different scripts were written and why they were written. So instead of the one person now having to go back and try to figure out what’s going on, it took several people to try to go back and figure out what’s going on, which again is going to delay progress in the company. It’s going to delay efficiency and Alfaro is going to, it’s going to extend the amount of money that company can generate revenue wise because now they have to go back and fix a product that they thought they had access to when they realized they don’t. I feel the lack of awareness with security associated with that is the biggest point because there weren’t enough security measures put in place to not allow that from a cultural barrier point of view. I look at other things when you talk about operational barriers, you know, these become single points of failure when you have a key person holding on to certain information that’s undocumented.

It affects different things down the road. Vendor relationships, it affects contract relationships. All these things are ripple effects across the organization which cripple the organization in that particular sector. Or maybe overall these are kind of things that we need to figure out. And the other thing I’ve seen, there’s not enough cross training.

One of the things I focus on when I go into organization is say I need to make sure the top level person knows what the bottom level person and the bottom level knows what the person below them. So if one person should be out for any reason, the show doesn’t stop, the show continues to go on. And this is important for the continuity aspect. To make sure there are no barriers to entry would need to do things like that. But the other thing I look at too is just the, you know, poorly integrated security tools are not actually addressing these things across the board.

If they’re doing it properly, they should be catching these things and labeling documentation, see how it. And you know those people that are looking at sending information to their personal devices as opposed to using business devices, that was not a big thing I saw in a lot of different areas. And it was okay because they didn’t have the proper systems in place to track it. And when you look at that across the board it just creates multiple barriers for management to kind of keep up because they don’t know one, it exists and two, they don’t know what they need to do to fix it immediately.

[00:11:23] Isaac Sacolick: Spot on, Derek. There’s also a comment here from Vape Hub in the comment stream. He says I think it needs to be top down, top down driven.

Liz, why are we so bad about this governance? I mean whether it’s security or operations or code, an end to end process. I mean why aren’t leaders demanding that we have good documentation before we deploy?

[00:11:50] Liz Martinez: Well, I mean why aren’t leaders demanding more documentation? I mean, I’m going to say something really simple and that’s because they’re cheap. They want, they want it, they want it now, they want it yesterday, they want it fast, they don’t want to spend any more money. And like documentation is seen as some, you know, basically a waste of money. It’s, you know, it’s a barrier to, you know, revenue in some ways overhead.

What they don’t see is that it’s actually if they don’t do it, what it’s going to cost them in terms of risk and maintenance long term.

I mean I’m going to talk about something that’s really basic which is, you know, an ounce of prevention is organizing, organizing your documentation. A big people have spoken multiple times on this call already about knowledge hoarders and you know, people in fear of losing their jobs and you know, all kinds of things like that. But really just having basically organizational of your document, of your documentation so that people know where to find things. I love what, I love what Derek said about cross training and making sure that you have redundancy or at least you know, some kind of succession planning so people know who’s second in command if somebody is God forbid, hit by a bus or out sick or whatever. But knowing where the documentation is is huge. Just knowing where it is and nowadays we should be thinking about implementing small language models so that we could drill into our documentation very easily. There’s no reason why we shouldn’t be able to find things super quickly if somebody is out sick.

So I’m a, I’m a big believer in making sure that everything is not only well documented, but really easy to find.

[00:13:45] Isaac Sacolick: Liz, I’m going to disagree with you in one aspect. All right.

[00:13:49] Liz Martinez: Okay. All right.

[00:13:50] Isaac Sacolick: Yeah. And then we’re going to let Joanne mediate this. I don’t think leaders see it as a waste of money. I think what happens with leaders is, you know, documentation and process creation is, is essentially an investment for the future. Right. And a lot of them are forced to think short term benefit, which you brought up, and are forced to think through what all the stakeholders are asking them to do and trying to get as much in there and so they don’t make that investment in the future. I think part of the issue, and I’m kind of interested in Joanne’s perspective on this part of the issue is we ask for too much complexity in our implementations.

Right. We need. And we have too much over engineering of our requirements. We have too much over engineering of our applications. I think as we start heading into agents and small language models, I think there’s a risk of that happening.

Another round of this where we wanted to do 100 things instead of doing 10 things well, and that drives either a complexity of process data or implementation and sort of looking for, let me just say lower Cody, ways of doing things where the natural expression of what you’re building is self documenting. Joanne, I’m kind of interested in your opinion. This, you know, it’s not worth it. Or are we just doing too many things wrong up front that make and create this operational and technical and cultural barrier around knowledge management?

[00:15:27] Joanne Friedman: I think we’re doing it wrong.

[00:15:30] Isaac Sacolick: There’s that mic drop I told everyone.

[00:15:35] Joanne Friedman: I mean, I wrote a very, call it a book several years ago now on knowledge management for the Conference Board of Canada. And one of the things that I sort of put out there was we have to look at knowledge transfer and knowledge management as in a different way than we currently do. Which, you know, from the IT perspective it’s about documentation, but it’s really not. It’s about community.

It’s about getting people over the notion of if you don’t keep your knowledge to yourself, you’re going to lose your job. And rather make it about the idea that knowledge sharing is the same as learning. And if we’re continually learning, we’re continually growing and that that brings benefit to everyone. So if you look at it from the what’s in it for me kind of thing and change the culture and incentivize people to share their knowledge, you’re going to have a different mindset that approaches it from the get go. So that could be leadership saying, hey, you know, we’re aware that like in manufacturing now, 33% of the workforce is going to retire in the next two years.

Oh, gee, we have to pay attention to this. How do we make sure that the tribal knowledge, or rather more appropriately termed institutional knowledge, is valued as an asset and does not walk out the door with the people who are retiring? Because then we’re really going to be SOL when we need a fix from somebody. Think about a maintenance engineer or someone in a factory who can walk down a line and knows just by the sound of the machine that something is wrong with it because they’ve been doing it for 25 or 30 or 40 years, that knowledge needs to be transferred to someone else. And the way to do that really depends on how you incentivize the individuals, how your culture allows for that incentivization, whether it’s, you know, a gamification or a reward system or a, hey, you get a gift card from Amazon, whatever it is, how that starts to flow should be much more conversational. And to your point about agents and agentic AI, absolutely. You can look at the prompts that people use. You can store the data that’s in the prompt and the data that is returned by the system as part of that knowledge management platform and then use it to inform learning systems, inform training systems, and also take it even one step farther and inform leadership about.

Did you know that these people are really, really subject matter experts in a way that you don’t expect them to be? In other words, let’s get away from the code and away from the paper and the documentation and have a system that allows people to contribute with an incentive without fear.

Agents can do that.

[00:18:48] Liz Martinez: I would have. Since you’re going contrary to my conversation about being cheap, I would have to.

I’m going to come back and say, first of all, Isaac, I think that what you said about more complexity versus simple is actually exactly the same point as I made, that they would rather invest in complexity than in documentation. So it’s actually the same comment as I made. But when it comes to what Joanne said, which is amazing, I would love to have that major culture change, it’s almost impossible to pull off. So in a situation where you get steering that Titanic away from the iceberg, what can you do?

And I would say organizing your documentation and getting it to the point where you can actually find things and is at least, at least something that’s tactical that you can do in a short term.

[00:19:43] Isaac Sacolick: So I want to bring this conversation into two industries that are really important around us. Right. Because we’re talking, when I think about the industrial applications in construction and manufacturing, technically lagging industries for the most part we’re not talking about digital experiences and workflows, we’re talking about hands on equipment.

There are safety issues with that.

These for the most part are slower changing industries in terms of best practices. There’s a lot of built up institutional knowledge around how to hang drywall, how to pour cement and things like that.

So I want to bring this into this fold of real world problems. It’s not just a knowledge sharing problem. This is how do we continue to build our expertise and how do we build processes and use tools to do more of the work that is in the physical world. Bob, I want to bring you back and then go to Joe, John and Martin after that. Bob, talk to me about the construction manufacturing fields. What are we most concerned about when we think about losing people and the knowledge that we’re trying to retain with them?

[00:20:59] Bob Salaj: Yeah, absolutely. And Joanne, I think you hit on a story that is all too really familiar with the construction industry. Right. But there’s a point here that I just want to kind of take one step back on and it’s just been out there because when we think about the term tribal knowledge, the very first thing that I’ve been asked to in a multitude of organizations is labor shortage. Let’s prepare for retirement. Let’s go out and talk to the foreman, let’s go talk to the office personnel and let’s start documenting processes.

But I also don’t want to forget this is not an age conversation. This is not about just the retirement. This is about your tribal knowledge of really the people and bringing it. Isaac, to his point.

Construction is starting to move forward with a lot of innovation around prefabrication and VDC and a whole bunch of models, twin, digital twins, etc. If you lose those people who have tribal knowledge irregardless of age and you’re not documenting in SOPs or whatever else you might have with that, you’re going to be impacted by it. And so when I think about construction, obviously our minds go to the foreman who can walk around and through smell and touch know exactly the percent complete of that particular job and where it’s going to be. But let’s also not forget about the innovation that’s occurring extremely rapidly and that it’s all about this culture. No matter where you’re at in terms of your tenure at the company overall.

[00:22:31] Isaac Sacolick: Let’S bring in some more expertise around this. Joe. Manufacturing and construction, long time working in this space.

How do we change a culture? Process technology in a place where like, like Bob said, there’s a lot of innovation coming.

[00:22:50] Joe Puglisi: I think you have to address some, some myths. And there’s a certain cultural arrogance in employees who say, I just know how to do this, or it’s too hard to explain, it’s too complicated. You’ll never understand.

You know, the fact is, we had, in one instance, we had an ETL process that took more than 24 hours, and it had been built and managed by one individual over a number of years. And so it was sort of sacrosanct. This individual knew it, no one else knew it, and everyone was afraid to touch it because if you broke it, the company would grind to a halt.

If you bring in some young talent and you explain what this process is supposed to do and you reinvent it and it runs in near real time, you have solved a huge problem.

So no one has knowledge that is completely unassailable or irreplaceable. That’s lesson number one.

Lesson number two is there’s a cultural arrogance at the industry level.

I worked for a construction company and they lived on a report called Anticipated Cost Report.

I then went to the reinsurance industry and at a meeting, there was a conversation about ivnr.

And I had no idea what IVNR stood for and even less knowledge about how you would create this thing.

So I stopped and asked, what are you guys talking about? Explain this to me.

And fundamentally, it was the ACR report. It was exactly the same concept.

So, you know, two. Two notions. No one has knowledge that. That can’t be deciphered or replaced in some way, shape or form.

I’m sure somebody out there is going to invent an agentic system that sort of becomes your apprentice and learns and replicates your processes. Right, Joanne?

[00:24:48] Joanne Friedman: Yes, sir.

[00:24:50] Joe Puglisi: And as far as, you know, industry uniqueness, I see this particularly in the hiring process. You know, if you haven’t worked in pharmaceuticals, you can’t work in pharmaceuticals. I call BS on that.

They all face fundamentally the same issues.

On a technology front, yes, there are some nuances, I admit it, but there’s a lot of commonality.

[00:25:14] Isaac Sacolick: Thank you, Joe. Let’s bring John in and Martin, we’re talking today about losing people and working through tribal knowledge. And I love this cultural arrogance versus learning culture. I love this idea of industry jargon as a killer of culture.

Bring us down to earth, John. Why are we having such a hard time when we get into agile teams and development teams and we’re building applications out, we’re building agents out.

I had some developers who told me the code itself is documentation.

I’d love to hear your perspective on this.

[00:25:54] John Patrick Luethe: Yeah, and I, I agree with almost everything that’s been said today, but the, the place that I think really has a lot of the blame is the management. And I just, I haven’t heard enough of the criticism of the management, which is, first off, if, if you’re going to have people tell people do documentation, like unless if you’re not going to go out and audit it, you really don’t know if the, the documentation is good.

And so what I’ve seen in companies that are really healthy, My last company, we did light manufacturing, light assembly, we, we would keep track of what people had, what skills. We would test all the skills on a yearly basis. We would keep track of like what business processes people can do and we would have people do different, different business processes over time that would allow us to have people a lot more fungible and we would systematically keep track of like the, the skills that people had in, in a database. And we would have people rate themselves and we’d have the managers review it on a yearly basis and we, we would look to see like when somebody left, what kind of skills are walking out the door.

And so I, I just, I agree with everything that people said. But, but like if you want to have good knowledge management, like there’s a ton of responsibility that goes to the management team. They have to make sure that different people are doing things, that people are cross trained. They have to audit the documentation. They have to have people that are unfamiliar with something run through the documentation. And if you do those types of things, you’re going to be a lot better set up if bad things happen in your organization.

[00:27:25] Isaac Sacolick: Martin, you get the mic, but you’re going to have to answer my question. Everybody’s pointing the finger at management. And you sat in the seat of cio, you felt the pressure, you still sit in that seat, you feel the pressure to deliver and improve.

And how do you handle this? Knowledge transfer, culture, teaching, learning, documentations, process cleanup, all the things that make us more operational, resilient. And I’m throwing this out so that Derek can also comment on it all these things and we’re not doing it as leaders.

[00:28:02] Martin Davis: Well, I think there’s only two examples and then a kind of a CIO perspective as well.

So two examples, both come from the manufacturing world and one is kind of a contrary to Joanne’s point earlier about incentivizing people to share knowledge. So I was involved in a company and there was a team of four people, three senior analysts, one manager.

And they had been looking after this specific system, a very important system, for 15 plus years as a team.

And there was significant issues of groupthink as they thought and acted as one body. The four of them, they were all heading towards retirement age and they did things their way.

And everybody above more senior management thought everything was fine because the system ran every day, there was no issues, etc.

All four of them took an early retirement package and left on 31st of December, one year.

That system didn’t run properly again for three months because instead of actually fixing things, they spent all of their time daily tweaking the data. So it ran. So sorting out small issues with the data rather than the system actually dealing with anomalies rather than fixing root cause and things like this.

So they were not interested in sharing knowledge, however much anyone incentivizes that was their way of doing things. There was no interest how you can incentivize them as much as you like. It wasn’t going to change. It was. This was their world, this is how they did things. So that’s kind of one example of where things, you know, that cultural thing don’t always give you an answer.

[00:29:49] Isaac Sacolick: I love that story, Bart. I just do.

[00:29:53] Martin Davis: So after somebody else came in and they reworked some of the system, made changes, the system ran with half a head, looking after it forevermore with very few problems.

So just that kind of where they weren’t letting the knowledge out. So just a kind of a great.

[00:30:12] Isaac Sacolick: Example, Martin, is the lesson learned here, like worst case, you’re going to have three months of pain when the people leave, or is the lesson here like management has to get into the weeds a little bit and see how things are being fixed.

[00:30:31] Martin Davis: I’m not sure how much management could get into the weeds because they close the doors around them. Yeah. And keep in mind their manager was part of that group. It wasn’t just the analysts, it was the analysts and their manager. So the outward facing thing, everyone was rosy.

[00:30:48] Isaac Sacolick: I think that the words you used here, this notion of group think as a barrier, I mean that’s what I would be smelling for, is the Leader saying, have they, you know, has this group created its own, you know, island of knowledge, island of operation? You know, they know how things work. Nothing goes out of this wall. And if you start sensing that maybe you do have to get in the weeds and get involved with this. Heather, I’m gonna. And Derek, I’m gonna bring you back in in just a second. I just want to welcome everybody to our conversation this week around tribal knowledge and why losing your best people can bring OPS to a standstill. Fantastic conversation around manufacturing and construction, in particular about the culture, processes and technology. We’re going to talk a little bit more about people and technology next in our second half and just stay tuned to learn more from this great group of folks talking about where we need to to lead our teams differently around simplifying and creating knowledge for our subject matter experts. Today’s episode is brought to you by Quickbase. Quickbase makes it simple to bring together all your data and teams into one centralized place. With Quickbase, you can collect and connect critical data from various sources into one location, create automated workflows based on your existing business processes, and get real time visibility into what’s happening on the front line. Customers use Quickbase for a variety of use cases like asset management, quality and process improvement, and field service management. I want to thank Quickbase and Bob Salai for joining us today as our speaker from there, talking about knowledge management, process reengineering and our steps to get toward AI. Bob, I want to bring you back in and talk about this because you said, look, you know, the technology is changing rapidly in the industrial space. We’re moving from industry 4.0 to 5.0. We’re bringing AI in and our AI is only as good as our knowledge. So let’s talk about some of the opportunities to close the knowledge gaps with technology and to better support the AI initiatives that are going to bring that culture together instead of separating the culture out.

[00:33:16] Bob Salaj: Yeah, absolutely.

And we’re seeing really the technology kind of move us into another era of really, how do we use AI to look at our entire process, not just the data.

And so we talked a lot on this call, really from the very beginning about standard operating procedures. And I could almost spend an entire four hours just speaking to that because that’s really the foundational thing of auditing the process. And I forget exactly who brought that up. But quite frankly, just to kind of camp on that instance, for one part, everybody goes in and says, let’s go create a standard operating procedure. And it’s almost like checking a Box in the moment we create that document, it’s almost kind of a myth. It goes into the ether and no one actually ever pulls it out again because no one’s auditing it.

So I use Quickbase to actually create an application so that we can actually have the dates and lessons learned of what is actually occurring with that SOP on a regular basis in terms of one instance so that you are actually going out and you’re setting a timeframe of six months, nine months, whatever it is, to keep your operations up to date because management may not have the time to go into every single process. Right. But we need to start using this technology to final the data into a centralized location.

Using Quickbase, you think about that for your estimating department. You’re capturing all the standard operating procedures for that and you’re going through all this time and effort for it just to die on a vine. But all of this information has to come back into one instance to educate our language models, to educate how we actually use AI to audit the process and find the gaps that aren’t documented at the end of the day. So it’s going to help out with ramping up. But to me it’s all about really that auditing and keeping your. Your language models up to date.

[00:35:06] Isaac Sacolick: I love this comment here from Vaibhav on the comment stream. He asks it as a question, is it a good idea to identify all legacy applications and take modernization project for the same on top priority? I’m going to switch his words around. I’m going to say it is a good idea to identify yes, this is exactly what we want to be doing. And I love this idea of a lineage or an audit log of learning.

One of the things I’m always concerned about when we talk about AI is the data you’re feeding dated and does it know how to think about what’s new and important versus what’s aged and outdated and really important. When you think about operations, you think about field and you think about construction, where so much of the type of work that we’re doing is changing all the time. Heather, welcome to the floor.

You’re our people person.

Lots of things are changing around us and we’re trying to create a culture where people are simplifying and using technology and using AI to do that. I just want to. Where are your thoughts going on this today?

[00:36:16] Heather May: Well, a couple of things and one thing that I want, a lot that I agreed with, one thing I’m going to take issue with is that it’s not about an age Thing I do think it’s a generational thing that the way people that have been doing work for 25 years, it’s generational, they’ve been doing it that way for so long that they don’t want to share it. And I think that concept of being afraid is brilliant.

How do you counteract that is you celebrate them, you bring them together, make it comfortable, give them a psychological safe space where they can talk about those stories and you capture those stories. And from that you can take document things. And certainly the technology is there to do the documentation. The process is there for the documentation, but you still have to collect the information. And so much of it will happen in their war stories, in highlighting their successes and their failures and being able to quantify and identify where those different steps are. So I think that. And the newer, younger generation will be able to do that. The technology is very familiar to them. It’s not familiar to the older person. But they can talk and they can share those stories. And if they’re given that opportunity, it can be there. And having them realize that this is just the first step. Because so much of what knowledge My background has been in research and as soon as something’s written, it’s old. So that documenting the ongoing and yes, and auditing, all that’s important. But it has to be in real time. It has to be all the time. It has to be a dashboard that’s shoved in your face that says, today, this is what happened yesterday, this is what happened. What can we do to fix this?

So I think that has to be really emphasized. And it is a question of a cultural shift wherever it’s going to start.

And it can’t be just in one place.

It has to be a pervasive feeling that this company, whatever company it is and whatever industry it is that you’re being able to, to say to people, we are changing, you are part of this change. You’re not going to lose your job. Don’t be afraid.

Let’s move this forward together.

And once that happens, and then you can deal with those succession plans and you can deal with the real time support and you can deal with the compliance so things aren’t left to chance. I saw an article that just said you can’t leave it to betting on the way things are going to be. And you’re right, you can’t.

[00:38:52] Isaac Sacolick: Heather, I agree with you. I do like the word generational issue, not an age thing.

Because when you look at the younger generation, we tend to use a different word. We call it a Hero culture.

The person who has figured out, has developed something, has created something and left it in such a state that she or he is the only person who can come in and fix.

Goes back to Martin’s story.

Who’s being brought in and how are they actually fixing things? They’re not fixing things in a way that others can contribute. And I think that’s all about not necessarily documenting things the way we think of in the past. Right. I still have books in my attic of the documentation guide for software that I created. I use it as like a memoir. We don’t need 400 page documents. What we do need is people to log their decision for people to share an insight to teach others. Derek, you’re in the security space. I mean like knowing how to solve a particular problem when it comes up again is not about creating a massive folder of operating procedures and security.

[00:40:06] Derek Butts No, you’re right. And the part of the problem is understand that you have a problem. I think that’s the first thing to look at. But just to kind of go back and then looking at the challenge you mentioned before, you know, when you’re talking about the risk areas, the two day I see a third party risk and supply chain cybersecurity risk. These are some of the most prevalent ways people have been breached because of lack of consistency going across the board, just, just accessing who has access to the data. And when you look at this and bring it forward and you talk about addressing these gaps, you know, we go back even further, we talk about the documentation. Well, artificial intelligence and the innovation. Artificial intelligence is only going to take that data that’s been properly marked and accessible and make it audible. You can make it automated. And if you don’t have good data, you’re not going to have access to data. Because you know, with artificial intelligence you’re going to have to train these systems to work with these automated tools to pull that documentation. I also look at things working with, you know, just the, the automated tools you can need to have in place now for the tribal knowledge and how do you close the gap? Having these tools that manage the threat associated with data and the tribal knowledge is being lost or building these dispersed systems, how to pull them all together so they can be unified.

These are things that we need to be happening at an enterprise level. And those management, management has to identify I’ve got a problem, I need to fix this. How am I going to do that? What systems do I need to put in place? I’ll kind of look at these knowledge gaps and graphs to show who’s doing what, what are dependencies, how do I visualize this? As mentioned by Heather, with a dashboard of some sort to show these are the things I have, these are the risk associated with the things I don’t have. How do I bring it all together? So it really is going to be looking at how to pull it all together, how I’m going to manage it from a governance and a policy point of view. You know, all these things come to remind you. Now you mentioned about, you know, the documentation and the amount of documentation. What is considered too much and I agree with you, 400 page documents or anything is going to be too much. But if I can automate it and assign accountability to it and assign roles and access to it based on certain individuals, it’s going to make it a whole lot easier to manage across the ecosystem.

[00:42:12] Isaac Sacolick: Yeah, look, I think this is about giving people tools where they can share snippets. You know, what did you learn today? How did you solve that problem?

What went into deploying this feature?

What was the change that you did in the operation to make this work better?

I think about all these. I think of it as a small problem of sharing that knowledge. And I’m looking at AI to my rescue, of being able to look at all the contributions people are making into these knowledge databases and saying, you know, now help me discern this into a storyline, help me query it. This goes back to something Liz had said earlier.

Martin, where are we going with this?

I mean, the reason I asked this is because I just do feel there’s a moat there. You know, going back to what Bob said, that if we don’t find a way to have really strong, real time, up to date information about what we’re doing in our operations, it’s going to really be hard to put AI in place now that it’s becoming more accessible and at some point more affordable for industries like manufacturing construction to take advantage of it.

[00:43:30] Martin Davis: So I’m going to take you to the manufacturing floor and this is kind of building on an example I’ve used in the past.

So you’ve got a maintenance engineer in a manufacturing plant and they’re walking the line and they have a head up display, an augmented reality display that is showing them information about the equipment they’re looking at.

And that information is coming from lots of different sources. It’s coming from sop. So I’ll go to where Bob was going with sops, small language models of all the data we have, manufacturing information coming from sensors on the actual equipment. It’s coming from the actual equipment manufacturer themselves, providing maintenance routines and other data like that.

So person can be looking at a piece of equipment and seeing how it’s operating.

Let’s say they need to do some maintenance work on that piece of equipment they’re looking at and be able to bring up the previous maintenance history of that equipment. They’re able to bring up and show them on, on the head up display. They’re wearing the actual maintenance steps. So the sop, the steps to actually change, change a bearing or whatever it might be, or they’re actually able to then. So we’re taking it from the data that’s captured from various sources being brought together using the latest technology, using a small language model, using AI, using your sops, using the data to help the person actually conduct the task. So it’s funneling everything down to just what the person needs to do.

[00:45:11] Isaac Sacolick: Just kind of.

[00:45:12] Martin Davis: I sometimes think it’s very good to make some of these things real in terms of this is actually how it can work.

[00:45:19] Isaac Sacolick: I love that picture. I even love it in multiple use cases. Other than just maintenance. I’m thinking about putting that AR display on somebody who’s learning what equipment is on the shop floor and how it works and what the different functions it does. I think it’s a learning tool. I think it can be a management tool as well to see how things are performing and where things are having quality or performance issues. I think there’s a lot we can do as we’re using tools to collect data and then using AI and using display technologies to bring it right to the people who are working on the floors or in the field. Hey John.

[00:46:02] John Patrick Luethe: Yeah, Isaac. Yeah, I, I love what I’m hearing and I, I agree. If you want to have better documentation, you got to improve the tools to make it easier for people to submit information in blog tickets on the documentation. I was talking to a guy that he works for one of the large car companies and I saw his jacket and I asked him about it and he said he loves that company because management listens to people. And he said that every time he finds an issue with the documentation or he finds a better way to fix something, he writes it up and if it gets accepted, he gets 300 to $1,000. And so that’s like such an amazing thing for him that people actually listen to him and then he’s so happy to get the money.

The other thing that happens is that if you can put your steps, put your repair guides in electronic format, take a look at what people are looking, you can look to see if people have recently looked at that thing. We were at a company, we found that it’s really hard to diagnose things, but once you know you diagnose it, it’s easy to fix because the repair guides are real good. We were keeping track of which steps did people do and what was the success or failure of those. And we were able to look at and get the. Basically by doing machine learning on the repair guides and the steps that people were taking and the success or failure of those, we were able to really improve the repair guides and serve up kind of dynamically what people should be doing for diagnostic steps. And so if you invest in better tools, the documentation gets better. And if you incent people and treat people well and listen to them, the stuff gets better too. And that improves culture and, and then they actually, you know, if management listens to them, a lot of good things happen.

[00:47:36] Isaac Sacolick: I think you talk about better tools and incentives. I love this $300 to $1,000 bonus for recommending process changes.

Joanne, you’ve worked in this space, you have a startup in this space and it’s all about process and data and culture of sharing. Where do you want to leave us today, Joanne?

[00:47:57] Joanne Friedman: Where I want to leave you is with two points. If I take Martin’s example that he was just relating about the heads up display. The one thing that I did not hear from him was the inclusion of the individual’s knowledge as well, the tribal knowledge that was the piece that was missing. And you know, when I look at these things, one of the things that we have and are working on is agentic digital twins. In other words, taking a digital twin, which is basically a mirror of what a piece of equipment is on the factory floor or the entire process. And then adding to that the notion that you can now take and go from what if meeting simulation all the way to what’s next as you’ve put all of this data together, the inclusion of the tribal knowledge and the ability to inculcate the value that John was talking about as well, which is, as I said at the outset, incentivizing people, you giving them a different way to learn and to teach meaning, to impart their knowledge. To Heather’s point, this is all the capability that can be built into a gentic AI because the agents can be differentiated by the task. You know, take the big process, break it down into smaller chunks, etc. Etc. We’ve all heard about that, but it’s what you do with each of those chunks that makes the difference on how productive the agentic system may be. In the case of a digital twin, you can actually take a visual image to what Martin was speaking of, see exactly where the problem is, see what’s happening with it. I’m noticing the same thing is happening. You know, my son is a master electrician. He’s getting a lot of innovation in his trade where he can now use some of the more sophisticated tools on the market. Not only to help him get to root cause analysis faster, but he can add his own perspective. Like, listen, I’ve run through, you know, thousands of lines of code books, I’ve run through manufacturers specifications.

X does not work, Y is the fix because there’s been an error in the documentation, or there’s actually a better way to do it. Faster, cheaper, you know, more successful in a particular situation.

Those kinds of nuanced information are what is absolutely required going forward. How we, how we institutionalize new knowledge, not standard operating procedure, but the changes of the standard operation needs to change as well. And, and the tools that we give people, whether it’s, you know, I know one company that we’re working with where they’re using 30 second video clips on TikTok.

What, what did you fix today? What was the problem? How did you solve it? Impart your knowledge and they have a contest going on where that’s how they’re using new technology like, you know, video stories or storification to get the knowledge out of their workforce. And the younger people may be more inclined to do that. The older people may want to sort of have it as a conversation where they’re not being filmed, audio, video. We don’t need the physical piece of paper mentality or capability that we’ve had for too many generations because the original standard operating procedure is no longer the same. The documentation issue, whether you’re using an AI tool to do it or not, is too much.

And you don’t necessarily need to know everything that’s in that documentation to get to a root cause analysis and a fix. Time is what’s important here.

So the more we can impart that through, whether it’s agentic AI or another form of AI physics, AI, for example, or math, we’re ahead of the game. And those companies that start adopting that capability, they’re the ones who are going to lead because they’ve taken into account that tribal knowledge or institutional knowledge is actually one of the most valuable assets of a corporation. It’s time to treat that data as an asset.

[00:52:27] Isaac Sacolick: Well said, Joanne. Joanne, you and Bob both mentioned standard operating procedures and I think what we’re really talking about here is dynamic work management. It’s really absolutely.

Quickbase has a lot of writing on this on their website, I think about weather factors, supply chain factors, people who should be in the office or at the field today who aren’t there because of a personal issue. Right. We tend to think standardization means a nice straight line from point A to point Z and a finite number of exceptions that we can manage to. What I think about today’s world is lots of things can go wrong, lots of things can surprise us today.

And where our subject matter expertise comes in and the antithesis to this is tribal knowledge is what to do when there’s an ex, when there’s something you didn’t expect.

How do we.

[00:53:32] Joanne Friedman: Well, this is.

[00:53:33] Isaac Sacolick: Go ahead.

[00:53:34] Joanne Friedman: Sorry, sorry for interrupting you, Isaac. This is where, you know, newer tools like knowledge graphs start to paint a much better picture. And I would say, you know, as an example, something that we’re working with at the moment is a business process that is multivariant, has all of the kind of complexity that you’re discussing. Weather factors, you know, closed loop supply chains, open loop supply chains, remanufactured goods, greening, all of these capabilities coming together.

Knowledge graphs is a way to paint that picture for people in a visual representation where they understand the what we couldn’t understand from a standard operating procedure, which is not only the nuances but also the dependencies and codependencies.

And this is where I think all of you know, change that we’re about to see, not only driven by AI, but just generally speaking as a result of AI being part of our world now is going to come into play. We’re going to be much more aware of those cross dependencies, ways to reach, you know, through silos and, and, and have a bigger picture to deal with.

[00:54:55] Isaac Sacolick: Thank you, Joanne. Great, great comment stream happening here on LinkedIn Live. If you haven’t said hello on the common stream, do drop in and say hello to everybody. Want to thank Steve and David, a whole bunch of people who’ve been sharing their insights, some that I have put on the whiteboard that you see here.

Joe, love to hear your thoughts on how we close the gap with tech and AI and then we’ll bring Bob on for some final recommendations. Hello Joe.

[00:55:23] Joe Puglisi: I think Bob, Martin and Joanne have all said very eloquently what I would say is documentation is not static. It’s not a point in time, it’s not a task, it’s an Ongoing effort with a continuous feedback loop. Like many other systems in the construction industry, electricians use a lockout tagout mechanism. Right. And it’s, you know, pilots use takeoff checklists, but those, those things are static.

And when we can bring technology to bear on delivering that kind of knowledge and expertise about how to protect yourself and protect your customers, your passengers and whatever it may be, when we can bring that in with real time feedback, as Martin and Joanna both suggested, that’s when you start to really win.

The companies won’t win that have the best knowledge management systems.

They’ll be the ones that make tribal knowledge obsolete by making expertise ubiquitous.

[00:56:32] Isaac Sacolick: Making expertise ubiquitous. Wow.

What a, what a leading statement for the age of AI where we need to be able to ask the right questions, where we are going to use AR to bring more, more real time relevant information in the field where sharing is more important than doing at times.

Bob, how do we get there?

[00:57:00] Bob Salaj: Wow. I think we’re all going to have to come back and rewatch this entire episode because of how many good points are kind of made across the board. And I think the real cost of tribal knowledge, right, I think really what we’ve been talking about and there’s a lot of impacts it to me, I start thinking about the intersection of like workflow, person and data. We talked about the culture, we talked about the operations and the tech side where AI is going and really how do we kind of move past the static ness of really it’s taking too much time. And I’ve been watching the comments and tremendous comments there and I think really at Quickbase we can imagine a world of AI not only for building applications that really match your workflows exactly and mining the data, but it should also be documenting itself so that you don’t have to pull out a number two lead pencil and actually ask someone because of that change of culture of actually documenting is so tenuous and tedious to kind of move forward with the system and AI should all be doing that all together so that the impact isn’t taking 17 months to ramp somebody up or losing three months. I think in terms of the. If people leave then that’s going to be the impact to your process. So how do we move all that together into one Venn diagram so that we can help out the output. So I think it’s exciting times ahead.

[00:58:19] Isaac Sacolick: I thank you for that, Bob. I think most of the folks here know that I’m a big fan of Quickbase. I use it to run my own business.

Every one of our coffee hour episodes. Every one of the articles that I write ends up in a Quickbase database. It’s how I manage my business.

And you know, increasingly what I’ve been doing more with it is using it as a knowledge tool, using it with AI and to just be able to ask questions around simple things. What should I write about next? What should be our next coffee hours?

And you can’t do that unless you have centralized knowledge, unless you have things really typed with strong metadata and at times being able to permission access to the information so the right people have access to it. So a lot of really good knowledge here. You mentioned we should go back and relist listen to this episode. This episode will be on my blog drive.starcio.com Coffee.

It will also be up on Apple and Spotify podcasts. Be about a week before we get it up there. This dashboard will be cleaned up and you will see a blog post with me from me on Monday with some of the best practices here. So lots of really good knowledge here. Thank you everybody who’s been participating on the Common Stream. My speakers here. Bob, thank you for joining us from Quickbase.

Quickbase is what they say to me. Say goodbye to workarounds and hello to workflows that work with Quickbase. Learn [email protected] Solutions if you ever want to see a demo of Quickbase I have loads of them.

Happy to show anybody who is interested in what it looks like in my world. Obviously I don’t work in the manufacturing or construction industry. We but I’ve used those examples in some of my previous episodes. We talked about SAS sprawl before and lots of opportunities to use Quickbase to use to cement out field operations. Folks, thank you for joining this week. If you look in the top right hand corner I have the upcoming episodes for October up listening there in our whiteboard. You can also see them on Drive Star CI.

Thank you for participating this week everybody. Have a great weekend. And again thank you Quickbase for sponsoring this episode and thank you Bob for being our expert on this particular episode. Everybody have a great weekend.

[01:00:56] Bob Salaj: Thank you everybody.

This episode is brought to you by Quickbase.

The views and opinions expressed herein are those of the speakers and do not necessarily represent the views and opinions of Quickbase.

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.

Isaac Sacolick:

Greetings everyone. Welcome to this week’s Coffee with Digital Trailblazers. Great to have you here this week I’m going to give my usual two or three minutes for everybody to join. We’ve got quite a bit of signup for this one, so clearly people are interested in the future IT and what AI is or will not do to it. I’m not surprised that we will want to hear how leadership is going to change, how the different functions in IT is going to change how you’re going to change your career. So very excited to see this. Hello, Steve. Hi Chris. Chris is my supporter of the week. I want to just thank Chris for just being such an outstanding partner. I think I announced a few times I was scheduled to do a keynote out in Atlanta next week on AI governance and he is helping me reschedule that because of things that I’m going through personally.

So I just want to thank Chris for just being such a strong friend and supporter and go check out net woven and give a date here. Chris. I think we’re doing it September 10th as our rescheduled date for those of you in the Atlanta area. We’ll be doing an AI governance summit around then and I’m really excited for it. Of course, that means I’m going to have to update my deck again because by the time we September rolls around everything we’re talking about AI today, we’ll be completely obsolete or at least different and we’ll have some new things to talk about. We’ll see Martin and Joe, we’ll see if that continues on that way. Hello Jay, welcome for joining and our conversation this week. AI era transformation is the ai, the end of it as we know it. There are some who might suggest it is the end of it every four to six years there’s some maverick in Harvard Business Review who comes up with some kind of article suggesting that it has done and then of course we’re like, well, not so fast.

We still have some coba lying around. And by the way, yeah, we have some no code and low code and self-service it, but you know what? We still need governance, we still need process. We still need architects and experts and none of it actually goes away. A lot of the services that we do tends to get commoditized automated. We move up stack, I don’t know how many IT departments have storage engineers anymore. If you have a data center, you might have a storage engineer, but we have more cloud engineers probably than storage engineers and network engineers combined. Our software developers are doing increasingly more stuff with automation. They’ve accepted and embraced low-code technologies. Our testers are largely doing automated testing, continuous testing. So we’ve certainly gone quite a bit of distance in terms of what it is doing and how it’s functioning and how it’s organized particularly over the last five years is things like agile and DevOps.

And again, low code cloud has all given us basically capabilities and technologies to do more maybe with fewer people but certainly with more expertise. And so now along comes ai, and I’ve been writing about AI’s impact on it for quite a bit over the last six months. I’ve talked about AI’s impact on software, AI’s impact on requirements on testing, on data governance, on IT operations, just to name a few have a whole series of articles on InfoWorld that talk about what’s happening in these different disciplines and how AI is really changing the world in these different areas. And the one you all know about is around code, right? So latest research, 30% or so of code suggested by AI is being accepted by IT departments. And it’s not saying much necessarily about the quality or the steps it takes to get AI code into production, but clearly there’s some value there because we’re putting 30% of it into production.

We don’t know how robust it is and we don’t know if it’s introducing tech debt yet, but we are hearing our leaders, our AI leaders who get to see everything I’m talking about the Google leaders and the open AI leaders and the anthropic leaders coming out and saying there will not be coding or software development somewhere in the next three to five years. And CIO magazine online has an article, I think it was out yesterday that boards are now putting pressure on CIOs to reduce staff in the belief or the reality that we need fewer people in it because of ai. And so that’s what’s happening right now, AI era transformation. Is AI the end of it as we know it? Yes. I’m playing off the song by REM. It’s the end of the world as we know it. Don’t know if that will really be the case, but I’m going to give the floor over to Martin. First. He suggested this topic and I’ve broken this down, Martin into three categories. Let’s stick on the delivery development agile low-code side and just talk about maybe look at this on a one to three year horizon. Let’s not try to go too far out. How is AI changing these disciplines and is it the end in your opinion or is it just changing? Although Mark

Martin Davis:

The world of IT is always said, who needs it? If you’re a CEO and you’re seeing all this AI and you’re seeing AI generating code, you’ve got to be thinking to yourself, oh, I’m a CEO. Why do I need a CIO anymore? Why do I need an IT group? But there again, we’ve had that in the past and we’ve had it with outsourcing and things like that. I don’t need an IT department. I’ll hire a company X, Y or Z to do it for me. And we’ve seen through all of those cycles, things start to become clearer and we find out that we do need it, but the role of it and what it does does change and evolve same as it does right across. I saw a great quote recently that said, you will have two types of people in the future. You will have unemployed people who don’t use AI and employed people that do use ai, and I just thought that was fairly true and it’s becoming truer and truer regarding your question in terms of development and DevOps and SDLC and Agile.

I think that’s going to continue to evolve. AI’s fundamentally reshaping the IT landscape, so repetitive tasks, tasks that can easily be done will actually continue to evolve and how we’re doing them, how we’re using AI to generate a lot of these things, it’s going to change the whole delivery aspect, but you still need IT involved because it’s role, if you think about it, it’s not just to generate code, it’s more and more strategic. It’s more and more it does it work with the business to identify how the business can be improved, what could change, and a lot of those higher level functions, the lower level functions. Yeah, as you said, you don’t have a storage engines anymore and at some point you’re not going to have coders either yet, but the ability to actually understand and work with the business to define how things could be improved and then let AI generate the solution, I think is where we’re going to end up.

Isaac Sacolick:

I think that’s a good vantage point and I think that’s a good tee off to say. Look, the reality is every time we’ve built a layer of technical capability, it means the entire skillset moves up stack in some way. We’re doing things more robustly, we’re doing things more efficiently, we’re building better integrated connected applications, more modular ones. Certainly there’s going to be an opportunity for folks working in the software delivery angle to move on to the business side. So asking questions, what should we focus on? We can’t do everything. What’s going to deliver business value? That’s certainly going to be an increased opportunity, but let’s go back down in the middle. Maybe John, maybe you’ll jump in on here and think about this. If I’m a software developer today, and I don’t want to jump onto the business side, I love development. I know two of my daughter’s friends are going to university starting next year to study computer science and engineering, and I’m like, gee, what’s the world going to look like for them when they’re out in four years? You got a perspective on that, John?

John Patrick Luethe:

Yeah, I think if you’re in that role, I think a lot of what you can do has just got accelerated. And so I think it’s still going to be really important to have the really strong developers because I think the developers are going to be people that glue the different components and Lego blocks together and AI could be used to help write a lot of the code, but if you wanted to work right, that’s where you need the people with a strong development skills. If you need to troubleshoot something, that’s where you need people with the strong development skills. And so I think the people in those roles, they’re going to be able to do their jobs a lot faster, make a lot larger impact, and it’s still absolutely critical. The other area that I see AI really increasing is the amount of testing that goes on because a lot of times people, you’re using technology that’s developed outside the organization and it’s often not entirely controlled by the organization and can change at any time. And so having the stuff behaved the way you want is you’re going to need those technical people just making sure the system responds the way you want when you launch and as you go on, making sure the system responds the way you want.

Isaac Sacolick:

I’m glad you brought up testing because in those situations, John, most companies dramatically have underinvested in testing over the years, even though the tools have gotten fairly robust in terms of automation handling, test data sets, synthetic data, automated performance testing, scaling up and down infrastructure to do the automated performance testing, taking live data, feeding it into your performance test to make sure you do some realistic testing. There’s all kinds of capability there and one of the things that’s been a big gap is just enough people in the IT organization to be able to do this either with the scale or just the amount of money the CIO has to spend on testing. And so if you’re involved, I think testing is a great area to think about going into. Joanne, I’m sure you have an opinion on this one.

Joanne Friedman:

Yes, I do. I look at it from a couple of different perspectives. To me, AI in the agenda form of AI will have an impact and generative AI as well, but a slightly different one. But overall, I see this as a catalyst to the IT organization to become what is needed for a long time, a true impetus and catalyst to become a business unit. To me, this is a way that it becomes not only has a seat at the table, but becomes a strategic organization to the business. And this is also what will take the CIO to A CEO role because you’re going to have new roles being defined within IT that are somewhere around things like data translator. How should this data be interpreted? You’re going to have a lot more people doing things like metadata introspection, so the roles will change the actual developers and testers and the environment around them.

I think that’s going to become more around things like testing for the ethics of ai, is this the right thing to do? This is a really good opportunity for people to upskill themselves in ways that they never imagined would be an IT role because the maintenance of storage and keeping the lights on and all of the things that are cost center driven in it will be handled by agents as soon as they are trusted. So to the point about testing, yes, test more because that’s the only way you’re going to develop that trust is constant reuse and constant refinement, but that’s the level of testing that I think will happen. I think a lot of folks that I speak to in it, particularly within the organization who used to be called programmers and developers and operators, are now taking on significantly more valuable responsibility in determining what’s what and letting the AI do the sort of grunt work, if you will.

Isaac Sacolick:

I love this idea of it becoming a business unit. Joanne, I think we’re going to have to have another topic on that, which is why I wrote this down and the reason I think it’s interesting in this scope is that when you look at where agents are being built by the vendors and in their platforms, they’re largely workflow, departmental oriented problems either in marketing or finance, human resources and things like that. When you start getting into industry workflows, I would put those as emerging but likely to come. And when you start putting them into customer facing and not just end user employee, but customer facing capabilities, moving from apps to agents and where you’re going to deliver real business value to your customers, I still think that’s an emerging area. I don’t think companies have figured out their agents in that space. I mean, if they did, we probably would’ve seen Amazon with a buying agent out there. It’s not even there yet, so there’s still a lot of runway there when we start talking customer facing. Do you have an opinion on that before we go to Joe?

Joanne Friedman:

Yeah. Oh, sorry. Yeah, just quickly, there’s a bunch of startups that are doing exactly what you’re discussing, but I just want to take a step back for one reason and there’s a lot of confusion about what an agent is and what an AI agent is, and I think we have to be very careful in our definitions because agents that are designed ultimately to run autonomously are not the same as AI agents, meaning you’re using AI and identifying a task. These are agent agents, literally sense, detect, learn, and act autonomously. They’re designed for that capability that is still emerging. I a hundred percent agree. I think what a lot of companies are doing now in trying to figure out is what part of the, I hate to use the word maturity curve, but what part of the maturity curve they want to aim for as opposed to where they are.

And that’s one of the issues around AI in particular. But yeah, the customer focus one, a little shout out to May for be heard. They’re in the process of doing customer facing stuff and it’s very interesting in the way that they’re using agent ai but also in the way their customers are reacting to it because they’re driving true business value right at the get go. I think a lot of companies could take a lesson in what are you really trying to accomplish with these agents, AI agents or agentic ai? Are you looking for efficiency? Are you looking for a better customer experience and how are you really going to measure that? There’s a lot that’s still influx. One could call that nascent or one could call that emerging.

Isaac Sacolick:

Interesting. I’m really interested to hear Joe’s opinion on this. Before there, I want to give a shout out in the comments to Sandy McCarran, good friend, colleague, partner of StarCIO and who has been on here before. She says her organization safety partner is developing a workshop, a three day AI design sprint with a 24 hour MVP. Very interesting. And she also asked this question, I think it’s relevant to this, Joe, what does human in the loop look like? Does it increase when we talk about the end-to-end delivery life cycle and then what happens when your competitor hires that person? So Joe, welcome to the floor talking about ai. Is it the end of it as we know it? What are your thoughts around this today?

Joe Puglisi:

So I’m going to answer your question in a short and concise yes, and I want to introduce a new term into the conversation evolution. We are moving forward as the IT specialist from the days, I think back to the days when you wrote an assembly and it took a lot of time. Once we introduced compilers, we were able to have higher level languages. Now we have interpreters that generate code. Now we have AI that can write code, but all along the way, this was a natural progression of capabilities. It was not the end of programming. We’re always going to have to build applications, build workflows. The caveat here, when it comes back to your comment about human in the loop, the caveat is that when we turn these engines loose on fixing up the company, making it run faster, better, smarter, more efficiently, it may suboptimize, and by that I mean leading it to an individual to use tools to automate their process might be in the old adage, paving the cow path.

Where is the vision that is a broader perspective across the organization that typically the CIO has today to understand how the different functions across the organization interrelate and interplay and how do we streamline that? And I think Joanne’s point was very cogent and you’ve heard me say more than once that in my role as CIO, I often said we were BT business technology because the B comes before the T. I think it’s more true today than ever with ai. We have to focus on how does it accelerate the business, how does it deliver more value to our customers, to our clients, how does it make us run smarter and faster? That’s really the future role and the future role of the CIO.

Isaac Sacolick:

Thank you Joe. I put this comment sort of bridging off your thought, how computing languages have evolved and their sophistication and simplicity of vernacular available of libraries, APIs of services. We’ve gone from assembly code up through Java low-code and now the new language might just be prompts and is the ada, you get what you pay for. Maybe the adage here you get what you ask for

And if I asked you to write some code to do something and I’m not specific about my requirements, my acceptance criteria, my non-functional requirements, my security considerations, who the end user is, what their real objective is in trying to accomplish this, that same concept probably has dozens if not hundreds of prompts and probably only a handful of them of the right ones to be asking for what you’re really looking for. So maybe we won’t be coding, but maybe English and other languages is the new prompt. But I tried an experiment this week, Joe and I gave Chachi PT a pretty high level request. I essentially told it I’m trying to build this app, write out a CSV for me with all the features and user stories, grouped them into an MVP release and a release 1.1 and develop a dashboard that illustrates the features and when they’re going to deploy.

And I kid you not, it gave me six very broad boring features and I didn’t give it enough detail to really fill this out and give me a full end-to-end application. So if I really want something that’s potentially a prototype or a pilot, I’m going to have to spend a lot of time going and investing in my prompts and I probably could get it there or I might have to get it there in bits and pieces and then assemble it myself, but I still building an app, just the approach and the tools I’m using are different.

Joe Puglisi:

That’s exactly right. And also you were very focused on a specific app. Remember that the function of the technology leadership is to help take the entire company forward. And so my fear is as we abdicate responsibility for building solutions to individuals throughout the organization, you lose that perspective of the overall functioning of the organization and the service to its customers.

Isaac Sacolick:

Sweet. So we’ll go Joanne, and then we’ll switch gears to our IT operations functions. Go ahead Joanne.

Joanne Friedman:

Sure. I just want to comment on your experience. This is where the rubber hits the road and the difference between the tools that you use, the AI tools that you use to develop and also the difference between generative AI and agen ai. If you want an outcome, use agentic. If you want discovery like you were getting the broad feature function, generative will do the job, but there are very specific classes of tools that are emerging and I think this is germane to the notion of IT and IT organization overall. There are going to be new roles emerging in the ITO that will look at the various tasks, subtasks, subtask of subtasks and start opining about which tools should be used for which purposes. For example, knowledge reps, which are now very awa, everybody is looking at them, they’re a very different mindset, but it’s a mindset shift that is going to hit it faster than anything else.

I’m not used to using these kinds of tools, which is the best one for me to use. Is it cursor, is it this or is it that? It’s not about the name brand of the tool, it’s the function of the tool. We now have so many more choices and so I would say to you if you wanted to run the same experiment and philanthropic, you’d get a different set of results. If you chose to take that same prompt without tweaking it and run it in a different tool set like client for example, you’d get a completely different set of results. So it’s not about the prompting so much as the asking the question of the ai, which is the best tool set for me to use to ask the prompt and what is the best structure of that prompt that I should use to start paring it down? Because although a GI, the basic general information, general intelligence is still often the future. The roots of it come from the large language models and that’s part of where people get very frustrated and where the hiccups start to happen where you get large management consulting firms reporting, 85% of the projects fail. This is the root cause of it. So that’s I think one of the areas where it is going to expand its purview in a much more user-friendly way.

Isaac Sacolick:

We shall see. Joanne, I don’t know about the user-friendly part. I mean we’ve had to put in design thinking and figure out where that belongs into our process. How do we, you discussed last week getting technologists to go out to the factory floor and see how things are actually done. We don’t have a good history of doing that effectively, which goes back to where Martin was move up stack a little bit, really understand end users, really understand workflow, really understand good design principles. I mean all those are just great ideas for people to go to. And I want to give you the first crack at this, Joan. We’re going to talk now about ITSM cloud computing end user compute. Before I go there, I’ll just say that I did leave a link in the chat a little bit. OBAs had asked, what are the new roles that AI is going to enable?

We brainstorm a list of them. 25 of them I think came out of this group at a previous coffee hour and I did share them in a blog post and I left the link in your comments. Sandy is saying creativity is the hardest thing to replace. Maybe I should have put that on there. Maybe we’ll have another topic just on that because what is it Google announced the ability to make motion picture quality AI video now through AI and trying to attack the entire workflow and cost structure of Hollywood. So we got to be careful. AI is going into everything and everything. It doesn’t mean it’s going to completely eradicate people getting involved with things. It just means our roles are going to be very different. Joan, lemme just do my quick break here and we’ll jump into this folks. We are in our hundred 30th episode of the Coffee with Digital Trailblazers.

If you are here for the first time, we meet every week on Friday at 11:00 AM eastern time to talk everything from AI to Zed, everything that digital transformation leaders are facing in their jobs, in their technologies, in process and governances and leadership and sometimes in life. Our next two episodes on the 13th, we did an excellent episode last year around celebrating moms. We’re going to do this year’s episode on celebrating dads in tech and the focus this time will be around improving work life for everyone. Just thought that’d be good timing to introduce that concept here. And then on the 20th, dovetailing off of my daughter graduating and her friends graduating, preparing grads for AI errors, opportunities and disruptions, and I think we’re going to talk about design thinking here, Joanne. I think we’re going to talk about making it into a business unit. I think we’ve got two great topics to call off of for my July upcoming ones, but let’s just jump into some nuts and bolts, right?

So it used to be somewhere around 60 to 80% of IT spend was in the run operations and everything, just keeping the lights on. We have all the different phrases around it, all the infrastructure, all the network operation centers, all the help desk functions, and slowly we’ve been shifting those dollars and more importantly, our attention into the value added areas. We’ve been doing more with automation, we’ve been using more service providers as we’ve used cloud technologies and now AI is starting to take into that world. Things that we’re automating are now being AI oriented and identified and things like that. I’m finding advertisements for agents that will do root cause analysis, that will do dependency mapping that will in some cases actually create your cloud infrastructure for you based on a prompt. Joanne, how will itt SM cloud infrastructure end user computing evolve in the AI era?

Joanne Friedman:

Wow, that’s a lot to parse. Thanks for that one, Isaac. Not a Friday for a very long week. No, I’m just teasing. I think my rule of thumb, and I may get smacked for this one, own your AI because if you don’t, someone else will. And the idea of owning your AI will definitely impact ITSM. I think we’re seeing some repatriation. I think the trend is going in that direction not only as a result of AI but because of cybersecurity and also the cost of cloud. What does that mean for the organization or the individuals? I think we’re going to see a resurgence in on-prem data centers. I think colos are going to come back in a way that they’re not extensions of cloud that we’re going to get back to business in some more realistic format than the esoteric perspective people apply to cloud. The idea of lift and shift is over and I think AI is the key driver for that. So I would say some folks are going to end up doing two or three times their role for a short period of time until the workforce figures out where these new jobs are being created, what jobs have gone away as a result of ai both today and over the next 18 to 24 months.

Isaac Sacolick:

Joanne, I was getting ready for that net woven AI governance and updating a slide and had something along those lines as well as what’s the next two years of transformation look like? And I called it multi-Cloud 2.0 for some of the reasons that you’re suggesting I was a bear against multi-cloud two to three years ago because of the added complexity that’s required to do it. Virtually all organizations are multi-cloud now, but now there’s actually some good reasons to think about multi-cloud beyond just ownership and cost and just the flexibility of moving payloads around and things like that. Owning your AI is becoming a major theme. I want to get Martin’s viewpoint around this Martin controversial questions. I’m your board leader. I come to U-S-C-I-O and say I need a two year plan where we’re not going to have IT operations, I want the cost savings, whatever else you need to run our IT operation functions. Find really AI oriented low cost service providers. Is that a realistic demand?

Martin Davis:

Well, it depends on the company. Typical consulting answer, if you no,

Isaac Sacolick:

No, no, no, no. Pretending whatever company you want, I’m your board leader. You’re not going to say, well, it depends.

Martin Davis:

So I would put this in a couple of different frames. It depends on the type of company we’re talking about here. So if the company is a fairly modern company and has minimal legacy it, then it’s possible. Yes, you’re automating a lot of things. AI is going to drive a lot of the manual tasks and everything like that, the provisioning, whatever else, and I’m kind of generalizing here, it doesn’t matter whether it’s on-prem or it’s private cloud or it’s public cloud or multiple multi-cloud, whatever, you’re going to get AI to orchestrate a lot of that stuff. So that is all possible. It’s all feasible, it’s in a timely manner. We are already there in lots of ways. However, most companies have a massive legacy and a massive of cobalt and other things and as four hundreds and whatever else, kicking around and trying to actually get rid of all of that in a one or two year span without massive investments and massive redevelopment is not going to happen. So there you go. I’ll give you a definitive.

Isaac Sacolick:

So let’s take the other extreme around this. Martin, remember when cloud came out and there’s one sort of vernacular around DevOps that said, we don’t need ops anymore. Development’s going to run the infrastructure. We can dial up and dial down what we need and we’ll automate everything. We’ll put Terraform in there, we’ll put the ICD in there and we will be there as a public cloud start adding services.

Martin Davis:

OMG dev is going to run all everything else

Isaac Sacolick:

Even before the CISO got involved. The reality was you can do all those things in dev except then you become an ops department, right? All you’re doing is

Martin Davis:

Just moving the labels around. It’s not actually achieving anything.

Isaac Sacolick:

It doesn’t, right? So you just move the responsibility. Maybe you shrunk the dollar a little bit, but it didn’t eliminate it. And so I’m wondering even those who don’t have legacy and that’s not a factor and you’re very cloud native, you’re cloud native in your applications, you’re very SaaS oriented, you’re very AI forward or becoming an AI organization, this IT operations completely disappear even in that situation. We’re going to give John a chance to answer that.

John Patrick Luethe:

I think IT operations are never going to disappear. And I think the other thing is that the bar used to be people had a technology bar before they could be adding technology to business. And with ai it’s made it so easy for people to use technology to build technology to integrate technology that I’m starting to see a lot of places, people now can do stuff that you used to have to be a developer. And so I just see the technology bar so much lower now that it is really going to have to step up their game on what they’re doing from a governance perspective and from a higher leadership perspective because now we have business people that can actually write code on things. I did want to make a comment back to the thing about DevOps, which is the one thing I absolutely loved about DevOps was it lowered the silos between those two different groups and it got the development team talking to the operations team, which in the past a lot of times they was just throwing stuff over the fence.

But that was what I thought was really, really neat about DevOps in that movement was more of a cultural shift and taking those barriers down between two different things. The last comment I was going to say is that I think so many, so much work in IT has kind of been taken over by SaaS over the years. And now what I’m starting to see in all the SaaS companies is they’re adding it to it. And so even if you’re not adding, I mean ai, so even if you’re not adding AI to your company, if you’re using third party software SaaS, they’re adding AI to all of that stuff. And so your company’s going to be infiltrated with AI one way or another, either by the business people or by the SaaS companies.

Isaac Sacolick:

So let’s put you in that situation, John, your CTO 200 people in your department, and let’s say you’re at the 30% ops side of DevOps or IT ops and five people come into your office who are on the ops side and say, how do I navigate my career through the AI era? What are the first three things you’re thinking of that will likely be things your organization’s going to need in IT operations going into the future?

John Patrick Luethe:

Well, I think the thing that people need more than anything is a list of what’s going on in their company. And so if we’re talking about what they need or the skills wise, starting with what they need, I think the most important thing for IT is always be aware what the business is using, what the technology is running, who’s providing that technology and what it’s doing. And that’s to me is always one of the absolutely most important things to have in a company. On the user side, I think with the rate of technology evolving, I think it’s important that we have a company of continuous learners, people that are always trying to understand what’s the most important problem to be working on? What are my options? What are people doing? What is the new thing that people are doing or what’s a tried and true tested thing and what are my options to solve problems?

Isaac Sacolick:

So I got two, I got one is around asset management and how things are operating, just being able to assess. And I got two on problem triage and really understanding what the right priorities to focus on. Do you have a third one before I go to Joanna Martin?

John Patrick Luethe:

I mean, it’s hard to choose. So in IT space, I think it’s really important to have some standards on some rules, regulations, standards on what is important. And this could go so many ways, but if people don’t have any guidelines or any boundaries, we’re going to have the wild west. And so I think it’s really important for us as IT to really have standards that everything has to adhere to so that we can say when something’s brought in that’s not treating customer data safely, that’s potentially putting things at risk. We can say, Hey, we have these standards for a reason. Let’s make sure that, let’s figure out a way to meet your business value your business needs in a way that is safe for the business and complies with our standards.

Isaac Sacolick:

Thank you, John. I’m going to put my AI hat on and think Joanne is going to talk about value and maybe around being closer to your end users. And I’m going to guess Martin’s going to talk about change management, but let’s see if I’m right about this. Go ahead, Joanne.

Joanne Friedman:

You hit the nail on the head. You do need to be closer to your customer. You do need, I think that there’s, and I’m already hearing about this from questions that I’m getting asked, how do I use AI to optimize my SaaS portfolio? Where can I get rid of stuff? Excuse me. Are there AI tools or can I build a GRC tool with AI to help me get over the hurdle of how much duplication, waste, whatever the three word acronym or phrase is about, not including corruption of course, because nobody is going to talk about that in IT ops, but generally speaking, how AI is going to drive that part of the market and what that means to it. I mean, think about the CTO or the CIO who’s got 2,800 SaaS apps to manage and wants to start paring it down not only for budgetary reasons, but just because two people using something that you’re paying for on a monthly basis that ops spend can be better spent as CapEx.

So I think we’re going to start a bit of a pendulum swing away from the OPEX argument to the CapEx argument as well. And well, to John’s point, we’ll never not have IT ops. Those ops jobs will take on a different form or rather they make still be called ops, but it could be ops in a different way because we’ll be looking at optimization. We’ll be looking at how do we run things not in a cloud or across the three clouds in some way that start setting up the notion of quantum because it’s not that far out and AI is pushing that side of the IT organization just as much as is having an impact on applications and what the business needs. Can we get farther faster? Because the other part of this is that our cost for infrastructure is going to continue to go up every time we’re using AI because of the way we’re in need of faster boards, more chips, all of the infrastructure that goes along with it. I mean there’s a lot of, there’s anything from building a server to using a cloud is going to change radically in the next little while because the costs are extraordinary to run AI in the cloud and people are very quickly realizing that the way in which AI is being tokenized and costed is not the same as cloud. Cloud is the add-on to the AI or the AI is the add-on to the cloud.

Isaac Sacolick:

Joan, you kind of gave me a theme here that’s worth just amplifying a little bit. First is this notion of consolidation. We actually covered that at last week’s coffee hour. I wrote a blog post around it. The link is in the chat if you want to do a recap. But then this entire theme of just continuous improvement, if you’re one of those companies, Martin had suggested that, hey, you’re not ready to be completely human free because of your tech debt or your architecture or any number of reasons that are just reality for most organizations. You’re in a loop of continuous improvement, right? Of finding ways to do things more efficiently. And quite frankly, it’s hard to find really good service providers who do that well, right? If you want to take something and say, I’m not touching tier one support. I’m going to do a playbook for the next 18 months, go find somebody to do tier one support for you, sounds great.

But if you have an infrastructure mess, if you have an application sprawl out there, you’re going to need some people to look at this and say, you know what? Where am I starting? Why am I investing in this change? And ask you all the right questions that suggest how to go out and improve things. So I like this idea of continuous improvement is a good focus of IT operation. And in your third area, Joanne, I don’t think we’re done with infrastructure disruption, whether it’s quantum or two things before that our data centers are going to continue to look and our cloud’s going to continue to look the way it does today. I just don’t see that as a stepwise improvement. There’s just too much power being consumed. There’s just too much compute being requested that we’re going to see some seismic steps and we’re going to need people in it who understand the trade-offs, the timing to make changes in their infrastructure model. Lemme bring Martin in next. Go ahead Joanne.

Joanne Friedman:

Sure. Sorry, I just want to finish with one thing. You’re going to see the emergence of the A ISP who is going to account for and accommodate for the extra compute on a lower price platform. The connectivity issues latency is rearing its ugly head already. If you use a GPT-4 0.5, it takes longer to think. So translate that into the amount of compute that’s going to be used. You’re going to look at new chips. Not everyone will be able to inform to afford high quality NVIDIA boards at 30,000 a pop or better. So there will be this new version of MSP that will take what they have, improve it enough, just enough to get you over the hurdles. And to your point, Isaac, there’s going to be two or three levels of disruption on the infrastructure side before quantum, but if we don’t think about quantum now, we’re going to get caught off guard later because it’s a completely different way of looking at the world.

Isaac Sacolick:

Thank you, Joanne. We’re going to go to Martin and then Heather, we’re going to hear from you around cloud and infrastructure, but I want to talk about org and leadership when you come on board. So we’ll shift gears after Martin too. Hey Martin,

Martin Davis:

So firstly you need to recalibrate your ai. I was not going to talk about change management. Oh no. Okay. I’m actually going to talk about the last part of this question, which is end user computing because we haven’t really talked about that at all. And I think we’re going to see dramatic changes in end user computing because why are we actually going to be having people defining some of these applications when you can give an AI tool to a business person, get them having a conversation with it and the identity AI tool is going to build the application for them. So I’m going to ask that question to you, Isaac, put you on the spot and ask you to answer that one.

Isaac Sacolick:

I think the view of the world through a screen that’s application oriented is going to change dramatically over the next five years. I think it’s probably a five year horizon when you look at how enterprises change cycle out their infrastructure and the costs around it. But I think end user computing may have the equivalent of an iPhone moment because I think you’re right. I don’t think clicking into apps and connecting to browser windows is going to be how we’re working in the future when the main window of what we’re looking for is either working with agents or prompting or even using voice. I think it’s going to change a lot. So I agree with you. And what does that mean? If you’re into these devices and this is what you do, I think the devices will change, but I don’t think the job function specking out learning how to deploy it, figuring out how to manage it, figuring out who gets how to configure it securely make it easier, but I don’t think it’s going to just completely disappear. So I hope that answered your question, Martin, because it’s a good one. Heather, I’d love to hear talk about cloud, but I want to shift gears and also talk about the org and how the role the CIO is changing. Welcome to the floor, Heather.

Heather May:

Thank you. I want to just touch point on something that John mentioned earlier, and that was some of the three things that he thought was so important and talk about the priorities and understanding what the IT departments already have. I think taking what the business unit’s needs are and connecting that to it as a business unit, that’s where the priorities will come. They’ll bubble up that you can’t, just because we have this, whatever this is, doesn’t mean that it’s going to work for the business unit, doesn’t mean it’s going to have the result and the impact that is necessary for the business unit that’s going to be using it. So let’s not lose sight of what the business needs are. Also, there was some discussion in the comments section about the whole new jobs, and I think what people are often afraid of is that my job is going away and I’m not going to have this new jobs. Doesn’t mean bad, it just means different. And I think if we keep that perspective, the panic will be less and the ability to adjust will be greater.

Isaac Sacolick:

I’m writing that quote down. New jobs doesn’t mean bad, it means different. Okay, so now you’re advising leadership around their recruiting strategy and they’re hiring strategy. They’re sort of confiding with you, Heather, about how the org is changing. What are you seeing around this in terms of how CIOs are thinking about hiring and who they’re hiring for and more importantly how the org is going to change over the next few years Because of ai,

Heather May:

One of the biggest issues irrespective of what department or what type of company or industry is the consolidation, having one person wear many hats. So to the extent that someone can bring into an organization a lot of perspective from either other roles that they had, creativity, innovation, just because you’re not in an innovation role doesn’t mean you shouldn’t bring innovation and being able to share those experiences and make them relate. I was talking to someone yesterday about a role that I was thinking of that they have to fill, and he came as a referral and looking at his background, I said, you know what? This person really is not employable in this particular role. But because he came as a referral, I wanted to have the conversation. And as I was talking to him, I said, look at your role. Look at yourself rather as the generic potato chip package, black and white product X, think about what the company needs from a bare minimum, not how it’s going to be applied, but it needs to do this, it needs to do that.

And then you can figure out how you can translate what people can do to bring that on. I think all too often people are saying, oh, I need someone who’s in SaaS and they only look for SaaS salespeople or only look for cloud administrators. You got to look for people who have an array of skills that can bring those different perspectives to the role. And yes, the department will consolidate, not because it’s it, but because everyone’s consolidating. There’s expense reduction and the salaries is going to be one of the largest expense lines. So if you don’t have that mindset, then you’re not going to be able to find the one person that does what you need them to do. You just need to be open-minded and look for that range of people. And I think to that end, be mindful that sometimes more senior people that are usually impacted or more often impacted by ageism are excluded, but they’re bringing all those different perspectives that could impact an organization positively.

Isaac Sacolick:

Thank you, Heather. I also put a different quote down here, look for becoming multi-skilled and look for the multi-skilled. I think that’s just general good advice. And related to that is wearing many, many hats, but also if you wear many hats, being able to collaborate with others and don’t become a silo of knowledge. I hear from John and Joe, I’m pulling you back on stage. We’re talking about leadership. You’re getting a seat at the table to tell us how you’re refit the IT organization in the AI era. John went off. John, do you have anything to add?

John Patrick Luethe:

One of my favorite things to do is to look at what is the top programming language. Three years and every year a different programming language emerges as the most important one, and then you start looking at programming books from five years ago is 10 years ago, and they’re almost worthless. So I quickly come to realize is that programming and technology and those skills, they’re very shortlived because they’re always being replaced with something else. But you look at leadership books and business books from 50 years ago, a hundred years ago, they’re still very applicable. And so I think it’s really important for people in the company to always, you won’t have strong technology people, but having the business background and thinking like a business person is so critical. Trying to understand what is the most important thing for the company? How is my role in this? What can I do to move the company forward? What can I do to move my portion of the company’s goals forward? I think the mixture of those skills are what is really, really important from our it, and it’s going to be more important going forward when we have ai, which makes it easier to do things with technology.

Isaac Sacolick:

Thank you, John, for participating today. Joe, I’m dragging you up here. What are you doing as a CIO differently to prepare your org and to continue to prepare your org as the technology changes so quickly?

Joe Puglisi:

I’m not changing my discipline, Isaac. I have always and will continue to value most enthusiasm, excitement, interest, thi for knowledge, willingness to learn, adaptability, flexibility, all the ease. We always talk about empathy and there are so many personal qualities that I look for because I think to John’s point, knowledge has become a commodity. If I don’t know how to do something in Excel, my computer can actually talk me through it. At this point, if I don’t know enough about the business or my competitors, there’s so much information that I can get publicly available and digest it through an AI engine. These things can be done. What you need is the drive, the personal excitement to get into these things. So I’ve always looked for that. I will continue to look for that. I think that’s how the department evolves, bring in fresh new talent that’s excited and willing to learn.

Isaac Sacolick:

Joanne, I’m guessing you wrote scaled and I bolded it, and now Joe mentioned it. Are you going there?

Joanne Friedman:

I was going there and I think those skills are even more important today than they were when I wrote about them. But the other part of this is I think we’re going to see CIOs are becoming more and more strategic, and I think we’re going to see a blending of corporate strategy with technical skills being applied, and it’s the application of the technology that will be the standout skill if you can connect the dots between A, B and P or F or whatever enumerator you want to use. It’s that ability to not see so far in the future that you’re a futurist, but rather to see the end goal that may not be clearly defined today, but rather nuanced and shoot for it and put your steps in place from a very pragmatic but strategic role, I see a lot of CIOs picking up more and more, not so much MBA type stuff, but leadership skills that are around particular areas of interest in their industry or their organization, and there’s an elevation process happening. Heather May disagree with me, but I see a lot of CIOs that are trying to gear themselves up to become CEOs

Isaac Sacolick:

If the job is disappearing. I’m going to give Martin the last word here. This is a great topic, Martin. I’ll give you the final word, is ai, the end of it as we know it, and if it’s not going to be the end, is that even possible or there’ll always be an IT department?

Martin Davis:

It is not the end of it. It is the end of the beginning and now is the beginning of the next phase. AI is changing a lot about how we are doing things, but so many things have over the years, I’ve always pushed that it should become more and more strategic, should be more and more looking at the business, not looking at the bits and bites, and I think AI is just driving that to become even more true. We have to actually look at how it’s going to drive the business forward, how all of these things are going to be used to deliver business value.

Isaac Sacolick:

That’s a really strong ending point. I’m hearing a resounding theme of moving App Stack, not just understanding the business, but a lot of what vendors, again deliver today are horizontal capabilities and where businesses that have been lagging in technology capabilities, part of it is just complexity of their industry. Part of it is lagging industries that have underinvested in, but if you look at the places where AI is going to be the most disruptive and also has the most potential impact, these are opportunities for all of us who are working in it. I’m speaking about healthcare, I’m speaking about manufacturing, I’m speaking about government, I’m speaking about higher education, even areas of retail. Are we going to bring an immersive experience back to in-store type of capabilities in retail through ar, vr, AI experiences? So I’m pretty excited about the future of technology. I think that every time we’ve gotten a disruptive technology land on our shoes, we’ve asked this question, is this the end of it as we know it?

And the answer is yes. Move on time to do the next thing. Time to be innovative. Time to understand your end users time to continuously improve what you’re doing so you can make room for the next new thing that will drive impact in your business. Folks, thank you for joining this week’s Coffee with Digital Trailblazers. Really fun a conversation. Thank you, Martin for introducing the topic. Joe, John, Heather, Joanne for joining us. Some great comments this week and questions we’ll be back next week, our conversation on celebrating DAGS in Tech, improve work life for everyone, and our conversation on June 20th, preparing grads for ai eras, opportunities and disruptions. Everybody have a great weekend. I’ll see you here next week.