Coffee With Digital Trailblazers

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.

This session discusses the “silent drag on productivity” caused by digital sprawl and the proliferation of SaaS applications in organizations. Key points include:

Isaac Sacolick:

Greetings everyone. Welcome to this week’s Coffee with Digital Trailblazers. We are started giving everybody a few seconds to join. I could see LinkedIn is having a little bit more of a delay than usual, so I’m just going to give this a few more seconds. I don’t see it on my screen. It says the event will start soon, but on the main screen it says it started. It’s really interesting how this works. I’m going to do a refresh here to see if that fixes it

Joe Puglisi:

On in and it’s got a count of six people.

Isaac Sacolick:

Yeah. Okay, so we got a quorum going. Greetings, everyone. Thank you for coming back. We skipped last week’s coffee hour for the US Memorial Day holiday, trying to give everybody a chance to kick off their summer, and it’s great to see some repeating folks here. Hello, Chris. Thank you for joining. John, it’s great to see you. David. Thank you for joining. There’s Joe saying hello to everybody. I will say hello to everybody too. We’re just going to give everybody a few seconds to join this episode and our discussion on the silent drag on productivity navigating digital sprawl. This is a recurring theme here where we talk about how do we bring technologies together? How do we help make our employees more efficient? How do we connect our data? How do we do things in a more secure way? Hello, Steve. Thank you for joining. Hello, Dennis and Heather.

Heather, you’re welcome to join us on stage. Thank you for joining this week and I’m going to give it a couple more minutes. I do want to introduce today our episode is sponsored by QuickBase. I’ll have a little bit more about QuickBase later in the show, but we’re talking a lot about productivity. It’s coming up a ton because of the entire AI story, how generative AI is letting us code better, how generative AI is letting us do content better, how agentic AI is bridging, prompting into our workflows and all that great capability is sitting on top of our technical mess, all of the different spreadsheets that exist out there, all the swivel chairs that people are doing to be able to bring workflow, connect workflow from one system to another. Every time I do an episode around this topic, I tried to get some research out there.

I’m going to paste this in the common stream in just a moment, but the latest research I have is companies, small companies are typically having around 32 SaaS applications, mid-size companies as many as 61, and companies with over a thousand employees have on average 200 SaaS applications. And so when people ask why am I entering data into tools? Why am I working across three different systems? Why do I have to export data into spreadsheets? Why do I have to do things in multiple places and why can’t I keep my employees informed? Along with all the other operational and security risks, we get into this conversation of is there a better way to do this? Is there a better way to connect our employees? Is there a better way to improve productivity and address the digital sprawl? Again, folks today my sponsor is QuickBase. I’ll have a little bit more about QuickBase today during the show and I do want to introduce Christian Pots who I’ve known for a few years now.

Christian Potts is the director of public relations over at QuickBase, but I know him from doing a ton of other things there at QuickBase and in general, and so I’m thrilled to have you join us as our special guest. Christian, I want to talk about, start off by just talking about the fact that AI is getting all the buzz, but when we talked about this is more than just ai, that’s helping productivity and there’s more than just issues, more than enough issues around how we’re using technology today. There are drags on productivity, so I’m just interested in your perspective. I know QuickBase calls this the gray work, so tell me a little bit about the gray work and what’s the impact particularly in industries like construction and manufacturing. Hello, Christian, welcome to the floor.

Christian Potts:

Thank you, Isaac. I’m really excited to be here. This has been a long time coming, so yeah, you introduced gray work, but first I want to address kind of I guess maybe the first part of your question about ai, which is it’s probably the easiest sort of takeaway from this is AI is being used and we’ve done research on this now three years running where we’ve been tracking how much AI is being used, not just by knowledge workers who are creatives or people who are developing software, but by every industry. And what we’ve seen is that AI is definitely up and it’s not just up in people using it every couple of weeks or every month or something like that. We’ve got data that shows over the last year it’s gone from being used by 21% of people in those sort of operations heavy industries to 52% are using it every single day to do something in their workflows.

So the cat’s out of the bag when it comes to ai. But the sort of operative part of what you’re talking about here, which I think is really instructive, is this great work piece. So for those folks who maybe aren’t familiar with the term, about three years ago, QuickBase started, we started to look into this topic of what we call great work. We kept hearing over and over from customers that there was a set of things that they were doing every single day that just seemed to be moving them further and further away from getting work done. And sometimes these were collaboration based things. So for example, they just couldn’t get ahold of someone in the field or they weren’t able to effectively communicate with the shop floor or sometimes these were software based things where let’s say they were using a particular project management tool or a particular data entry tool and they were the only ones who had access to it and therefore they weren’t able to take that tool and share that across the organization or across the project team or sometimes these were compliance things, particularly governance where they were finding themselves trying to reconcile multiple data sets from multiple spreadsheets and multiple other Google Sheets tools and things like that.

And each of these things were just sort of presenting these obstacles that they were having to find ways around every single day and that work, that is work, that is what great work is, and that’s something that people have been spending upwards of like a quarter of their work week on every single week we’re talking like 11, 12, 20 hours a week that they’re spending this stuff down. So this is really that silent dragon productivity that you’re talking about. It’s just lurking underneath everything and once you see it and once you understand what it is, you see it everywhere. And that’s been the real eyeopener I think for a lot of the customers we certainly work with, but I think a lot of the other folks, even like your cell eyes, when we first talked first started talking about this term, there was an aha moment that went on. I think for both of us. We were like, oh yeah, this is everywhere. This isn’t just a big company problem or a small company problem. This is an everybody problem and an everywhere problem.

Isaac Sacolick:

Yeah, I think it’s actually, it’s more easily seen in some smaller companies, but it’s a bigger and larger magnitude problem mid and larger companies. And what I find is when you get down into the depths of how people are working outside of common workflows, we know common workflows in accounts payable and accounts receivable and common workflows in hr, common workflows in marketing and sales. When you get down into what really makes the company operate, there’s a lot of business process there that cross teams that cross UIs and just an enormous supply of SaaS applications that have come up over the years to start filling in some of those gaps. And some of them do great jobs in terms of their experience and solving a particular problem, but they end up solving one step of a bigger piece of a problem. And then flash forward 2, 3, 4 years later, I’m going to hear a story from John next that just talks about exactly this problem of hundreds of applications in relatively small organizations that are just impossible to manage. John, I’d love to hear your story around this.

John Patrick Luethe:

Hi Isaac. Thank you for having me up here. And this is such a real problem and I’ve seen it at the last two companies I’ve been at with a proliferation of sa and it’s so easy for somebody to have a problem and get on and search and find a piece of software that they can sign up with their credit card to solve that problem that they’re working on. And what happens is people on different teams, each team will start using that piece of software that does the same exact thing as that one of the other teams in the company will use. And pretty soon we have hundreds of pieces of software that are being purchased individually on people’s credit cards and then expensed into the company. And so what happens is it causes so many problems because you can have hundreds of pieces of software and the teams can’t collaborate with the other teams. It costs a ton of money and people often stop using this software, but they continue to pay for it. And then there’s some real security issues because a lot of times since they haven’t gone through it, they’re not using single sign-on to access these things. So when people leave the company, they still have access to this stuff. And so it’s a real risk for data leaking and other bad things happening. And so I’ve seen this in my last two companies. It’s a real issue.

Isaac Sacolick:

John, do you want to go deeper into your story?

John Patrick Luethe:

So what happen is we get into an audit and they started asking us about what software do we use? And then so really answer this question, we start finding out that we have more software than we thought we had, and each time we started making a list of every piece of software we could find in the company. And then to do that, we really had to partner with the accounting team and start looking at what are people expensing in? And we would based off that and looking at the expense reports we would categorize every time somebody did an expense report saying that this is the software or this is an IT tool, we would build a list and then we’d find out who signed up for that, how much are we paying on a monthly basis and when’s a renewal date? And that would give us information on when we can start going to cancel these things.

And so then we would work with the different teams and we would say, okay, you use this tool and this other team uses that tool. How can we get all of the whole company onto one tool? And so then we would come up with a blessed tool for kind of each thing that we’re working on and have it be our corporate standard and then work through the renewal dates for each of these pieces of software to cancel as much as we can. And we found we were able to save a ton of money and we were actually able to make things a lot better people to have one common standard for project management, one common standard for a bunch of other things, creative and life was just a lot better after we simplified.

Isaac Sacolick:

Thank you, John. Derek, welcome to the floor. Derek, I love this statement. John said, and I’m sure you’ve seen this all the time, you asked the question, what software do you use? And you know that nobody has the comprehensive list of what’s being used, where is it being used, how well is it performing and who’s the owner and manages it? I’m sure you have some good stories around the risks around that particular type of problem. Absolutely. Good

Derrick Butts:

Morning. Yeah, some of the companies I’ve worked with in the past, as John mentioned, you do that audit before the audit. You ask them what applications are you working with, what do you have? And it’s like a deer, the headlights type of reaction you get back. But one of the companies working with, I see this more so in smaller companies, in the larger companies, but one of the nonprofits I was worked with in the past, we did initial audit that came back with 134 applications, and the question was, do you really need all these? And what we found out was this duplication of applications, there were applications they didn’t realize that they had, that they were using, and the problem was the organizations, departments were not talking to each other. So one department may have a software package and the other department didn’t know that, so they bought something similar, not realizing they could have utilized what they’ve already had.

At the end of the day, we found out from that 134 applications from initial assessment, we were able to get it down to actually 84 applications total that were useful productive applications that everybody could get their teeth into. There was one instance, we found 19 different versions of Adobe Acrobat, which was just crazy because now you’re creating this, John mentioned you’re creating a larger attack surface, so all these applications need to be managed, but the problem was, and John hit it on the head, the accounting department was not working with the IT department to make them accountable for what applications were actually coming into the organization. Everybody was kind of doing their own thing and they were running rogue. And until we streamlined it and had those services now vetted through the IT department and then approved by the accounting or the CFO, then people started to realize I can no longer just buy this.

So by saving them 30% of their operational spending budget, we were able to take that money and reinvest into more security technologies. They’ll keep those things from happening again. But again, the question is being asked what are the applications do it and how effective it’s going to be? In a lot of cases, we found the applications they had, they were only using 30% of the overall capability, which means they were wasting the money that they were spending because they’re not optimizing the full value and the full of what they’ve already purchased. So a lot of times it’s asking the questions, but really drilling down into and doing that assessment and then getting the people to talk to each other to help them understand how can we increase productivity not only between the communication between our branches, but also the productivity of the SaaS applications that we’re using across the board.

Isaac Sacolick:

I love this. So we have a productivity story, we have a cost saving story, we have a risk mitigation story. I think compounding this is I run a small business, I’ve seen pricing go up 10 to 20% a year on SaaS and when you’re only using a third of the capabilities, I think there’s a lot of really good question, we’re already jumping into consolidation opportunities. I want to stick on the problem statement, Joe, construction manufacturing, this isn’t an issue, is it?

Joe Puglisi:

Oh, no, no. Well, we could talk for hours about the disconnection between the field and the home office and all the subcontractors, construction managers and the owners and the financiers all using their own systems and mostly spreadsheets in the construction industry. But I wanted to come back to the issue that John started with the proliferation of my favorite application for solving my particular problem, and an issue that we haven’t surfaced yet, which I think is in a tremendous time sink and disruption to every organization, big and small, is a lack of the one truth principle. There’s this concept that something has to be the system of record for a piece of information. And when I have revenue in my spreadsheet and you have revenue in your SaaS application and someone else is using the revenue numbers from the financial platform, we’re going to spend a good portion of our time, our valuable time at the management table discussing who’s got the right number and boy, what a tremendous waste of time and energy that is. So the need to rationalize applications skinny down the list of things being used, everyone thinks that their application is the best suited, but you can’t all be right. So let’s come to a compromise and decide which one is truly the best for everyone to use. And then we have one place where any particular piece of information is the piece of information of record and we take away the debate.

Isaac Sacolick:

I want to bring in another use case here. Christian, if I can bring you back and then we’ll bring Joanne in. Christian, we’re talking a little bit about having too many existing tools overlaps between tools, unused tools, lack of integration between tools, even lack of a list of tools. I mean, the other part of gray work is the gaps between tools, right? The place where a department needs an application and never had one. I’m thinking about shop floor management. I’m thinking about tracking work in progress in construction where that’s typically done through every project manager’s favorite spreadsheet. In my case, I have a whole slew of applications that I’ve built up for StarCIO. Many of them are built on QuickBase. I’ve actually gotten some videos out around this that are unique to how I manage content. People ask me, Isaac, how do you write so much content out there every single month? And a lot of my process that you don’t see it would be hard to do without automation, without tools in place for being able to do it efficiently. So Christian, can you talk about the other side of gray work, which is lost productivity because departments don’t have the apps they actually need to do their work.

Christian Potts:

Yeah, I really appreciate you bringing this up, Isaac, because I think I want to sort of touch back to something Joe started to introduce, which is this idea of there are specific industries where I feel like this becomes less of a, it’s harder to get my job done thing and becomes more of a revenue or in some cases a safety issue. So those gaps that you’re talking about, they could be gaps between who has a single source of truth for getting a project kicked off. So you’re in the bid management process, how can you be sure that you have the right level of access to who’s available for when you’re bidding for work for a construction agency and making sure that you have the right HVAC installer available or that you’ve got the most recent certifications for your steel or your iron folks. But then there’s a whole other piece of this which ties into the safety issue, which is if there are gaps in what your field teams are telling you when they’re out in the field, that’s a real vulnerability that you’re going to have.

There’s one example that I sort of think of over and over again. There’s a large Australian based company that QuickBase works with. If you’ve driven around any sort of suburban area like where I live in New England, you’ve seen one of their trucks around, they do a lot of tree removal, brush removal, they take stuff off power lines, stuff like that after storms. And they actually pointed right at this issue, which was they had teams who were out in highly volatile areas by weather or by climate, even by wildlife, and they were running into situations where their teams were out there and they were trying to report back that, hey, that tree or that group of trees or that boulder or whatever that was in one place yesterday is now in an entirely different position today, and that has a real impact on our ability to get to this location so that we can do the disaster remediation work that it takes to get power lines back up or restore and water service or whatever.

So there’s two things that work there. Number one, it’s a productivity thing. Now your team is sitting there idling outside an area while they wait for another team to show up. We can get that out of the way. Or you’ve got a team that’s in that site already and now they’re looking at a completely different working condition that wasn’t there yesterday and maybe that working condition has an additional hazard that wasn’t captured because there’s no data flowing back and forth between what’s happening in the field and what the home office is seeing and then vice versa. So those are two really big places. I think that can be big points of vulnerability when you have those gaps, when you have those pockets, pockets of doubt or just pockets of no information between what’s happening out in the field, what’s happening on the front lines or what’s happening within your group of subcontractors when it comes to getting worked up.

Isaac Sacolick:

I think that’s a really good area for Joanne to comment on with her experiences around this. Also, Joanne, there’s a great interesting statement here from Dennis on the common stream that you might want to chime in on as well. He says it’s part of the lack of strategy development from the IT function and communicating that strategy to the broader business. In other words, saying things like, IT isn’t telling me that they have the tool, so I’m just going to go out and buy it myself. So we have this gap of information flow or gap of accurate real-time information flow from field to back office that Christian’s talking about and this communication lack of strategy from IT about how to use broad platforms to solve lots of different problems. Joanne, welcome to the floor.

Joanne Friedman:

Good morning and thank you. I think I want to address the strategy issue first because there’s a real dichotomy between context and situational awareness that affects both of these issues. One is IT strategy is to deliver to a specific set of requirements broadly, but where, and Joe May have a comment on this as well, where it drops the ball as an organization is making the business aware that just because the tool is designed to do X and Y does not mean it’s not also designed to do Zed. There’s this lack of awareness comes from a lack of contextuality and lack of situational awareness. People who are in the field who may be seeing a situation in real time, that contextual awareness, that situational awareness that’s not being fed back into a system of record. That’s where it needs to step up and the business also needs to step up to close that gap because, and this is where the semantic layer in some cases or contextualization from an LLM or an agentic AI agent, a true agentic agent that’s sensing and discovering and feeding back and learning into the system, that’s where those tools need to be brought to the forefront to say, Hey, by the way, if you’re designing and your strategy is to accomplish a particular goal with a system, don’t be constrained by a narrow perspective.

Apply a broader perspective to it as part of the IT strategy and communicate that to the business that just because you have this tool that you think is only designed to do a, B and whatever, it can also do a lot of other things. You have to apply a certain amount of what we would call critical thinking to it or contextual awareness. Don’t be afraid to change your hat for a different task, but use the same tool because the insights that you’re going to gain from it form a very valuable perspective that can be routed back to business units, but also back to it to say next time you’re doing a modification or we’re doing an upgrade or we’re choosing a tool, we’re not necessarily trying to enlarge the technology estate, we’re trying to pare it down for cost savings, but we’re going to be more creative in how we do those requirements to make sure that we don’t have hidden overlaps, which drag productivity down because you’re navigating through a bunch of stuff you don’t really need, but we’re becoming very purposeful about our new investments and how we’re pairing down the estate and recovering from sun cost and technical debt.

Isaac Sacolick:

Yeah, Joanne echoes of messages from Martin Davis who isn’t here today and Joe about it. Leaders getting out into the field and really seeing how things are operating. Then number two, bringing those stories back to their staff and giving them the time to learn what technologies are out there and what capabilities are out there, and more importantly to share what exists in the enterprise a little bit. So there’s a bit of reuse, and for me, the reuse comes from having platforms, right? Platforms that are extendable. I’m not picking a solution just for solving one particular step in a process just because they’ve got some really good user experience there around it and saying, look, I’m going to look at the whole end-to-end stakeholders. I’m going to look at all the end users. I’m going to look at field, I’m going to look at back office. I’m look at the data story and then to your point, Joanne, look at where agents are going to be capable of doing some of that work and partnering with on some of that work going into the future.

Joanne Friedman:

Isaac, if I may, I have a little anecdote to tell you, which is we took our lead architect and developer out into a factory and to give them the experience of this is what really happens on a manufacturing floor that neither of them had ever really been exposed to, and we let them walk around and talk to people and get a feel for you are designing for these users, you are designing for a company of this size or that size. These are the kinds of processes, this is what people go through every day. And the feedback was tremendous, not only from our folks, but from the people on the shop floor and the plant manager and the CFO who was there and the CIO who was there who never had put the connect the dots together in such a way to give that experience to the people who are actually writing the code.

And their perceptions and their perspective radically changed in a couple of hours. So we don’t live in a vacuum. We now live in the real world and we’re reflecting and taking in the tribal knowledge, if you will, lack of a better word, I apologize, or the institutional knowledge of that workforce and looking at how that colors their direction for design, for coding, for deployment, even. Oh, we have to change these things. And this is I think one of the things that has long been a drag on product releases. Nobody ever goes directly with a programmer in hand or with a code developer or a lead architect and says, see what this person is doing, see how they actually work over a period of a few hours. And it was a very enlightening experience for us. We had a good take on it, but those on the floor and the two individuals radically different perspective after the fact.

Joe Puglisi:

Joanne, I did the same exact thing with our EDI lead architect. He’d been with the firm seven years and it is never once climbed one flight of stairs to the manufacturing plant to see how the actual process works that he had been coding for seven years.

Isaac Sacolick:

Yeah, that’s crazy. That’s crazy. Let’s bring Liz in and then I’ll take my break after that. Thank you for these stories, Joanne, John and Joe. Hi Liz.

Liz Martinez:

Hey, how you doing? I love when we’re talking about linking this problem to the strategy. So part of the building a value realization office as I’ve been pumping out regularly recently, is understanding what the value is that you’re trying to bring to the business, to the strategy, to your end users, to your customer base, to your internal users. And as long as we’re maintaining that lens of looking at where we need to be adding value, this drag on productivity becomes a very clear problem that people will get on board with. Everybody likes to have their own car, but when we start thinking about global warming, maybe that’s not such a great idea. Maybe you don’t need six cars because one car will do, and a lot of people don’t think about software as buying another car or buying a bicycle or buying a motorcycle, but we do call our software assets.

They are assets, right? When you’re buying furniture, you don’t think, well, I don’t need 18 couches. Maybe I should find out how many chairs for an additional people I need in the existing living room that is already set. So thinking about that in terms of value to the organization and thinking about it in terms of value to your customers internally and externally, and ultimately your stock price is really where you start going with this, and that’s how you get the architect to get off his chair and go take a look at the manufacturing floor. That’s how you do it.

Isaac Sacolick:

Thank you, Liz. We’re going to take our quick break here folks. Thank you for joining this week’s Coffee with Digital Trailblazers. Thank you for sometimes

Isaac Sacolick:

Thank you for joining this week’s copy with Digital Trailblazers. Thank you for coming back. After last week’s break, we are in our interesting and thoughtful conversation around the silent drag on productivity navigating digital sprawl. I want to thank our sponsors today. Our sponsor is QuickBase. QuickBase is the AI powered operation platform used by more than 12,000 organizations worldwide to transform ordinary work into extraordinary impact, combined powerful AI capabilities and flexibility and ease of low-code, no-code technology. QuickBase boost productivity improves efficiency and enhances employee safety for organizations managing large scale projects and operations in industries like construction and manufacturing. Founded in 1999, QuickBase headquartered in Boston with teams in London and Bangalore. For more information, please visit http://www.quickbase.com. And for those of you interested, I’ve wet you a couple of my articles. I’ve written that around technology and I am also a quick base customer, so if you ever have a question about how to improve your gray work and address it, you can go through me, reach out, and I will show you demos of how I run my back office operation and eliminate my gray work.

Christian, I want to bring you back in. We’ve been talking about sort of the organizational impact, a little bit about having too many applications and what this looks like of having hundreds to two hundreds of applications. Let’s look at it from the employee perspective and what’s the impact on their workflow, how are they feeling about it? I think we need to get into that because a lot of the times when we’re either trying to get adoption on a new technology or we’re talking about consolidation or even if we’re empowering more citizen developed technologies, we have to look at it from the end user perspective first. What are you telling your end users about either the opportunity to use a platform to create a technology, use a platform to leverage a technology built on that platform and to consolidate technology, consolidate onto a platform to improve their productivity, happiness, and workflow? Welcome back, Christian.

Christian Potts:

Thanks. I really appreciate this question. I think it really does have a tie back to a lot of what’s been talked about definitely in the chat, but also in the conversation here about this idea of too many apps, too many solutions, too many things that only to use Joanne’s language that do a great job of A, B, and C, but maybe don’t do Z and that Zed might be the thing you really needed to do. And so now you’re just piling solution on top of solution. On top of solution. We’ve actually gone in and we’ve looked at this pretty closely, not only to the gray work research, but we also did survey in IT, consolidation. I want to put the IT consolidation research aside. That was mainly from the IT lead perspective, but from the ground floor, that everyday employee standpoint, there’s this real gap that’s developing between how much companies are investing in technology for productivity and how much that is actually making it harder to be productive.

The numbers are pretty stark when you look at it. So 80% of the companies that we’ve surveyed say that they are increasing their investment in productivity, work management, collaboration tools, but meanwhile, 59% of the employees are actually the ones who have to use those tools, say that those tools are making it harder for them to get their work done. And one of the big ones that jumps out, and this is something John, you and I were talking, but before we started the session here, our project management tools, I’m talking about your run of the mill Smartsheets, your Mondays or Asana. These are tools that were sort of heralded as being the unlocks for large teams to do these big projects. And what’s happening is that people are starting to bring these tools in and maybe because there are low license fees or no license fees, they’ve got a freeware version, they’re doing all, they’re sort of bringing these things on their own.

And what’s happening is now they have multiple project management solutions. Some of them have as many as six or seven they’re using daily just to find a piece of information to move a project forward. And that number, that high degree of number of project management tools they’re using, these are making it harder for them to do basic things like share information with teammates. You’re running a project, you’ve got to use the construction example. You’ve got a team of subs who are showing up on a day and maybe one of them is your HVAC sub. Maybe one of them is your concrete sub. Maybe one of them is there’s someone who’s dropping off a loader or dropping off a big piece of equipment. And each one of these is trying to figure out, okay, so they get the work order and now they’re trying to figure out, okay, do I need to drop off a hundred sets of PVC or do I need to drop off a thousand sets of PC?

But they can’t get that information because the work order lives in one platform and the supply base lives in another platform, or maybe it’s sitting on someone’s desk in a printout, and meanwhile, all of that information that they need to just answer that simple question is just not available to them. And that’s even harder because now if it’s not available to you, it means you can’t see it. If you can’t see it, how are you expected to act on it and how are you expected to act on, act on it? To get back to the point that Joseph made in the last portion of the conversation where there’s revenue in, how can you make a decision that’s going to move a project forward that helps you realize revenue and doesn’t instead lead to greater cost or things falling behind on a timeline because you can’t see it. I mean, these are the kinds of things that I think when you think about the employee effects, these are real effects that make people just feel really bad about going to work every day.

Isaac Sacolick:

Christian, let’s go. You mentioned safety earlier. Maybe go a little bit deeper into that because obviously if you’re in construction and manufacturing as an employee, that’s top concern for yourself. It’s certainly top concern for management, so there’s alignment there. What are some of the things companies are doing to sort of bridge the gap and say, you know what, this is how we put the right tools in people’s hands so that it has an impact on safety we lose Christian.

Christian Potts:

Yep. So I’ll give you a really quick example. So we work with Consigli Construction, they’re longtime customer of ours. We’ve done a lot of different work with ’em and I want to actually bring AI back into the conversation here because there’s a great story that they tell, and I think Isaac, you probably have heard this story firsthand from Anthony

Where he talks about that they had, they’re like every other construction company, they’ve get a number of different moving parts that are made up of people and equipment and they’re on job sites, they’re managing this large portfolio, and safety is a non-negotiable as it should be. It’s the number one thing that they’re concerned about. It’s the number one thing that gets them out of bed every day and make sure that they want their employees going to work and coming home the same condition every single day. So they saw that they were having not necessarily a safety problem, but just having a safety disconnect where there was information that they had on individual jobs that could help them plan out the next job or to plan a similar job in a different environment using the same kinds of subs and the same kind of workforce, same kind of equipment.

And there was a safety aspect to that that they were continually sort of having to redo over and over again. And so they started applying AI and LLMs to figure out, okay, what are the common factors that are showing up in these kinds of projects and these kinds of environments that these time of year and these geographies, and started to understand that that could be a way that they could advance their safety posture in a way that promoted that culture. And that does two things. Number one, I think obviously the most important thing, it keeps everybody safe, it keeps the workplace safe, it makes sure that you don’t have slip hazards or faulty equipment, whatever, but I think it also empowers employees to really take ownership of something like that. All of a sudden you’re saying to employees not only if you see something, say something, but if you see something, say something, it’s going to be now something that’s going to help someone else in a very profound way because we can apply a technology like AI to look for those common issues and to now start to put those common issues into the way that you are scoping projects or the way that you’re looking at job sites or the way that you’re looking at things that happen by the time of year or whatever.

One last little bit here, I heard a great story from one of our construction leaders here, guy by the name of Bob who’s former technology developer with electric, excuse me, with Lighthouse Electric, and he now works for us and he told this story to me because I thought was really indicative of that. So he was working on a job site with Lighthouse Electric where they had a job that was happening in somewhere in the, I can’t remember if it’s Arizona or Nevada, and they were doing a job where they were laying some PVC pipe, some conduit pipe that was going to be used to lay fiber to basically connect a couple of different locations and obviously the pipe, the trench has to be dug a certain depth and it has to be the pipe has to fit in and it has to be connected and this many feet and this much whatever.

There was one factor that I think was a big part of this safety story, and it was the fact that at the time of year the project was being done, temperatures down that close to the ground where the trench was being built and where the technician was going to have to go down and actually install the pipe, were going to be upwards of 120 to 140 degrees on the ground. And the person who was coming to do this was a big guy. He’s like picture in your head a sort of classic idea of what a construction person would look like. This was that kind of guy. And because they had the understanding gleaned through doing work in that area time and time again, that at this time of year in these conditions at this location, the temperature was going to be this high. They built a specialized cooling vest just for him.

So when he showed up on the day he wasn’t going to have some kind of heat stroke or some sort of catastrophic health event like that, and this was because they were able to use the data they had got, they had gathered from previous jobs at that site to put in place a protective element for this individual to do his job on that day that made sure that he was safe, that the project stayed on track and that they didn’t lose any money or any time because they had to wait for a cooler day or a different technician to show up. That to me is like a microcosm of exactly what you’re talking about. Safety is about the right data and the right place at the right time to make a decision that keeps people safe, keeps jobs moving forward and promotes the right kind of culture you want to have in your organization.

Isaac Sacolick:

Thank you, Christian. That’s just an amazing couple of stories there. Let’s bring Derek and then Joanne back in. Hi Derek. Hi. So Christian,

Derrick Butts:

The points you bring up are valid, especially when you’re talking about safety. I look at it more from a risk point of view. So the problem I see in looking at these organizations with all these different applications and trying to figure out who’s going to manage what is, again, we talk about the communication issues, they don’t talk to each other. I’m dealing with the customers even the past and even now where they’re dealing with several project management platforms and they’re not talking to each other. So when one part of the division has a risk in the other, I ask them, are you aware of the risk in project B? And the answer is no. So because of that, they don’t understand the ripple effect of the risk in one part of the organization coming to the other. So it’s going to impact everything moving forward when it comes to risk overall and how we can stay productive on that project or even maintain that particular schedule. So when you look at this across the board, I mean the communication is huge, it’s lacking, but it also goes back to the lack of leadership strategically working and foking through this, through either the IT department, the CISO or other departments that would work with this because they’re missing it. Everybody’s kind of doing their own thing because they’ve got all these applications or application overload and they can’t focus on the productivity or the safety or the risk at hand because they’re missing it altogether.

Isaac Sacolick:

Derek, there’s something subtle in what you just shared here that I’m going to share. I put it in a whiteboard, but the fact that when the organization finds it permissible to have hundreds of tools out there with every group or every department or workflow using their own tool for their own information, it also creates an amplification around communications that tools collect data, they create collaborations, they enable sharing of information. When you have all these tools that aren’t talking to each other, I think it makes it easier for people not to talk to each other. And when you centralize this information, when you have common experiences, when data has a central point of truth, as Joe called it out earlier, you end up with that transparent organization that most leaders are looking for. Go ahead, Derek.

Derrick Butts:

No. So when there’s lack of interoperability and communication and visibility between application, it definitely translates to lack of visibility and communication between those people who put those systems in place. Thank you, Derek. Hi,

Isaac Sacolick:

Joanne.

Joanne Friedman:

Hi. I would add perhaps a slightly different perspective. It’s not about the applications not communicating with each other. It’s more about the fact that until recently with AI, you were not capturing institutional knowledge and that the data that is storing it, for example, what is used to contextualize or create a semantic layer in technology terms is really part of the communication that goes between humans. And so human in the loop becomes even more important in this environment because to Christian’s story, the individual in question might be a very large human who needs a cooling vest, but in context, it might have been exacerbated by the fact that perhaps that person is diabetic, in which case they get hotter, faster or has another medical condition. My point is that all the various piece parts of data have value when they’re put together as a whole and aimed at a particular outcome or a problem.

Safety increases. What about when we’re in factories with robots? We have certain areas that we’re not supposed to traverse because that’s where the robot goes, but we can make the robot stop for a human. Well, what about the human stopping for the robot that goes to productivity and to yield and to cost and things like that, but it also actually makes us aware of our own surroundings which make us safer as employees. So we have to look at both sides of the equation, and it’s not so much a single source of truth, but how we go about contextualizing all the information when you go to an LLM, you’re asking a generic question. It uses time, it uses tokens, and it may not give you the answer that you expect simply because it’s pointing, its learning in a different direction than the way you’re asking the question.

So the perspective also has to be applied, and I think we’re at an inflection point with AI where these items are starting to spin out why we need specialized language models, why we need AgTech, why a foundation or a fundamental model. Frontier model isn’t always going to give us the best that we can get. We have to be more educated around how that communication with AI needs to work. Not in prompting, but it could be simple, it could be mathematical, and what kind of AI is really going to fill in that blank for us? I think as we’re looking at the sprawl of applications, some of those factors also come into risk mitigation, to costing, to stemming the sprawl and recuperating some cost in applications that we have we use, but we’re not using as much as we could have.

Isaac Sacolick:

Thank you. Joanne. I want to go to my third question at this point. We’re down to our last 12 minutes. I want to get into solutions because we’ve talked about the problem drags on productivity, impact on safety, inability to track contest. We have a context, we’re losing our knowledge, which is critically important in the industrial space. I want John to jump in here first. John, you’ve done the exercise of consolidation. I want to hear your perspective of where do you find the place to start? What’s the low hanging fruit and how do you find the big rocks, the big opportunities that are going to either improve productivity or improve cost or reduce risks? Where do you start with this problem? I’m hoping somebody will also chime in on the human factor, one of my guests, the folks who want to hold onto their tools. What do you say to those folks? And maybe even what do you say to the CFO where there’s probably an upfront investment to do the consolidation, but there’s probably a value at the end of the rainbow around this? Someone said 30% cost savings. So let’s look at this from several perspectives. John, let’s talk for you first. Where are you finding the first layer of opportunity when there’s a lot of tools out there?

John Patrick Luethe:

A lot of times what we want to do is try to look to see where we’re spending money and then what data is in places. And so the two areas that I’ve had really success looking in the past is one is SaaS applications, and then the other one is the cloud spend. And so what I’ve typically found is that we do one exercise to go through and understanding how much we’re spending on each of the SaaS applications that we’d have. And so we’d make that Excel spreadsheet, we’d understand how much is it a month or a year, how many users, what data’s in there and what are we using it for? And then that’s one. And then the other area I’ve actually had the bigger cost savings is finding out the digital sprawl in cloud spend. And so we would go to start pulling a report of basically how are people spending money in the cloud and based off the tags it has, and what we often find is a lot of people actually haven’t tagged their information.

And so then we have a report of these are our accounts with this much spend that’s not tagged, and that’s how we started. It’s just trying to identify where are we spending money and how much money are we spending? And from there we start looking at what do we want to go after is usually we want to go after what’s the most expensive one demo list sorted by value of money, and then on the cloud side, what’s the biggest spend that’s not tagged and who owns it or who do we think that owns it or what department owns it and what is it doing and it should it be there.

Isaac Sacolick:

So you’re looking for the biggest spend that’s underutilized is what you’re saying?

John Patrick Luethe:

Yeah, and we actually go the other way. We don’t just ask should it be there, flip it around, flip the script and try to saying, make the people justify spending the money for this thing. Are you really getting business value added? This $10,000 you spend here, it’s like, what does it add? Are you not able to use one of the other problems, the other pieces of software? We have to solve your problem and if we make people validate that if we give you some money, we find that more effective than just saying like, Hey, do you need this?

Isaac Sacolick:

Got it. John, let’s bring Joe in. Joe, which of my questions do you want to grab at?

Joe Puglisi:

Well, I’m going to talk about low hanging fruit, and I’ll give you a very concrete example as the CIO of a mid-size company. When I arrived, I mapped out all of the business processes that were in play and lo and behold discovered that they had not one, not two, but three BI platforms. And so one of the early low hanging fruit money savers and time savers was simply to mediate the discussion among the users of these three different tools to find out which one everyone could agree, could do 99% of the work or a third of the total cost. There’s so much of that in corporate America. I can’t imagine that there are savings out there in both time and money just in rationalizing tools. The other thing is in the chat, in the comments, in the live comments someone pointed out about it, strategy and communication, which is my favorite bail whip. People have to know what tools are available and the IT staff ought to know what people are trying to do and then map the tools we have into the processes that people are undertaking and let’s make sure we use the tools we have and that we don’t proliferate. As someone said, it is not telling me what tools we have, so I’ll just go out and buy my own. Well, you need to get in front of that and communication is key education.

Isaac Sacolick:

So Joe, let’s say we’re in that scenario. We’re now looking for consolidation. We find a low hanging fruit, whether it’s by unused or cost or a situation where there’s equivalents and now you’re bringing those folks in and saying, you know what? We think we can use an existing platform or we’re going to choose a platform here that allows us to do an end-to-end process in this area. How do you sell the stakeholders on that?

Joe Puglisi:

It is the usual characteristic stick, right? So the carrot is, Hey, look, I can train you on how to use this tool to do what you’re doing now, easier, faster, better, and you got to make code in the promise else you can say, look, we can’t have both tools. This one can be made to do what you’re doing. We’ll help you to get there, but the other tools going away. I used the latter by the way, so I’m speaking from experience. In one case, there was just no sense in having a second tool despite the fact that people said, oh, it was critical and it was really necessary and we really needed it. We didn’t and we proved it in the end.

Isaac Sacolick:

Interesting. Christian, what do you see on your end in terms of how organizations look to do consolidation or look to invest in addressing the great work?

Christian Potts:

There’s a big human component that happens here. We’ve now heard this I think three different times and three different questions about are you paying attention to, are you listening to or are you factoring in things like the way in which people want to work, decisions that people make that the example that Joanne gave that was really interesting was we teach the robots how to get around the shop floor or how to get around the job site and navigating around the people, but do we teach the people sort of how to work amongst those things? It’s very much kind of the same case with I think software. Do we actually, when you think about the software decisions that you made and the buying decisions that you made, has the strategic usefulness of that tool really ever sort of been considered? The example I sort of flashback to time and time again was years ago I started working at a market data research firm, and I was in the similar positions to 1:00 AM now and we were getting ready to launch a new brand.

And so I asked, okay, well, where does all our creative live? And it was part of it lived in one tool, and so the video part lived in one tool and the other part lived on a SharePoint and the other part lived in a Canva and all these different places. And I remember saying to my time, the CMO asking her just point blank, why isn’t in all these different places? And she said, well, it just seemed like that was the easiest place to put it. And I said, well, is it truly the easiest place or have we actually thought about the workflow that goes into this when someone like me who has to use all of these tools but isn’t necessarily the creator of any of them has to access them and then use those to put together some kind of cohesive brand story or some kind of cohesive campaign that goes into this.

I can’t be cycling through seven different tabs to find a logo here and a boilerplate there and an image there and whatever, just so I can get something into a place where I can start to drive leads off of it. That’s the kind of strategic decision making I think you have to have is what is the actual aim of what we’re trying to do here? And once you’ve got agreement amongst the business and agreement amongst not only from the ground level employees have to use the tool, but more importantly the people who have to make decision about buying the tool, then you can start to have that give and take process that goes along with that and decide, is this tool really, is it robust enough? Does it have enough of the features? Are we using it enough? Is it costing too much by a license? That’s when those decisions can happen. But first we have to have that elevated conversation where you say to yourself, is this getting us to the point where we want to go? Is this driving home the goal and the overall impact we’re trying to make?

Isaac Sacolick:

Interesting. Christian, thank you for joining us this week and thank you for having quickly sponsor our episode. We’ve got a couple minutes left. Joanne, a quick word before we close out,

Joanne Friedman:

Quick word to Christian’s point, what value does this application software or the data that it uses bring to the organization to move us to move the needle either to revenue, growth, resilience, all of those top line values, innovation or bottom line cost savings? And really that’s how we measure for consolidation because it’s not about the tool, it’s about the value that the tool brings from the data and from the individuals using the tool to the organization to move it forward. So it’s a value metric and return on data is one way to do it. Rhoda is another way to do it. You can use a Monte Carlo calculation, but companies need to start doing that more today than they ever have in the past, and I think that’s a big gap in their knowledge, how technology can be used to drive strategic direction.

Isaac Sacolick:

Thank you, Joanne. Thank you, Christian. I’m going to leave you one insight I find one of the best ways to do this is to get people, specific people in the organization excited about end-to-end workflow, end-to-end opportunity around data end-to-end value, and empowering them to be a part of the process for creating those capabilities in common platforms. That’s always been what’s excited me about using the QuickBase platform is I can find people who can actually solution across the organization in standardized ways, in ways that are connecting with data, in ways that are creating common user experiences. So thrilled to have QuickBase as a sponsor today. Quickb Base’s AI powered operation platform gives complex industries the data connectivity and visibility they need to keep large scale projects on time, in budget, and promote a culture of safety and compliance. For more information, please visit http://www.quickbase.com. Thank you again, Christian, for joining us and for sponsoring today’s episode.

Thank you for all my speakers, Elizabeth, Joanne, John, who else do we have here? Joe. Heather was here earlier. Derek, thank you for joining us. Next week’s coffee hour on the 13th. On the 6th of June will be AI era. Transformation is ai. The end of it as we know it. The 13th last year, we did an episode celebrating moms. This week, the year we’re going to do an episode on celebrating dads in tech, improved work-life balance for everyone. That will be on the 13th and on the 20th we’ll be talking about another user group preparing grads for the AI errors, opportunities, and disruptions. So I hope you’ll join all three of those episodes. I’ll announced the 27th in a couple weeks. Please join my newsletter for all my announcements. That’s at digital Starcio.com/driving. I think that gets you to the newsletter. Geez, I forgot my own URLs. Thank you for joining this week. Again, thank you for quick grace for sponsoring everybody. Have a nice weekend. We’ll see you here again soon.

You are unauthorized to view this page.

You are unauthorized to view this page.

The team discussed the potential and challenges of quantum computing, with a focus on its applications in various industries and its potential to revolutionize fields like cryptography, optimization, and drug discovery. They also explored the process of adopting new technology, the importance of educating oneself on quantum computing, and the need for C-suite buy-in. The conversation ended with a discussion on the potential of quantum computing in solving large-scale problems, the availability of quantum computers, and the potential applications of quantum computing in various industries.

[00:00:06] Speaker A: Greetings everyone. Welcome to this week’s coffee with digital trailblazers.

Interesting week of weather across the United States at least, especially for those of you in the Southeast. I’m really excited that you’re here.

We’re going to give our normal few minutes for everybody to join this topic that I’m super interested in.

I am nowhere near an expert. I invited a couple of experts to join us today, but we’re going to be talking about quantum computing and what digital leaders in particular should know about this technology.

We will certainly get into where it is on the horizon from science to engineering to application.

We will talk about some of the applications, some of the concerns around this technology.

And I am just super excited to learn as much as all of you are. We always say how important it is to be lifelong learners.

And you know, whenever there’s a new technology, we’re not quite sure when it will be ready for early adoption.

When should government and large enterprise really be investing time and energy into it?

When should the rest of us start getting more knowledgeable about it? And so we’re going to try to help all of you get onto that spectrum. This week we are going to use a common stream again. Please do say hello if you are here.

Super excited to see a bunch of people using it last week. So say hello there. I will be pasting in the questions there as they come up and I’m going to share some links with you. I did some research this week just to get slightly more knowledgeable today I have a link from the Wired Guide to Quantum Computing.

This is one of the Better 101 articles. This one I would say is a little bit more skeptical. There’s a quote I have in here. If you squint out the window on a flight to San Francisco right now you’ll see a haze of quantum hype in Silicon Valley. But the enormous potential of quantum computing is undeniable. I shared a link from Pascal.

Two of their leaders are here joining us today. They have an article on the essential guide for business leaders ready to innovate with quantum. So there’s an article I’ll share around that. I’ll talk about Google’s unveils unmind boggling quantum computing chip and what that chip is doing and how that’s potentially a game changer.

We’ll talk about the risk in terms of cryptocurrency, crypto, cryptography, sorry, and where that is. I have Some links that $49 billion in global quantum investments, that’s I believe in 2024 was the projection estimated to get to 200 billion by 2030?

And then I have a link for practical applications on quantum. But without any more delay, I want to introduce Michael Warren. Michael, thank you for joining us.

We’re just going to start with our first question.

We do a lot of discussions here, Michael, where we de jargon a new technology or a buzzword for our audience.

So I’m really interested in your opinion.

What should transformation and technology leaders understand about quantum computing? Hello, Michael, Good morning.

[00:03:47] Speaker B: Good morning, everyone. Thank you for inviting me.

Joining me on the call is Christian Beno. Christian is a senior technical advisor for Pascal with years of experience in this area.

So my background briefly is my eighth year in quantum computing, all on the commercial side. I spent 10 years, 12 years rather, at intel doing global innovation projects. So you can read into that as meaningful, introducing new and novel technologies that will drive innovation and ultimately drive earnings per share impact. So going way back and dating myself, you can think of that as virtualization, cloud computing, big data, software defined networking.

You have AI. Quantum is certainly one of those things.

So what I’d like to do and just give a quantum 101 and actually be about 2 minutes long is understanding what quantum computing is, how it’s different than traditional computing, really. Quantum computing is a revolutionary approach to computing that uses the principles of quantum mechanics to process information in fundamentally different ways than classical computers.

While traditional computers store and manipulate data in binary units called bits, either ones or zeros, quantum computers use quantum bits or what we call qubits. Qubits have the unique ability to exist in multiple states simultaneously, thanks to quantum phenomenon like superposition and entanglement.

Albert Einstein described this as spooky science, which it you can certainly look at it that way.

But in superposition, a qubit can represent both a 1 and 0 at the same time, vastly increasing the number of possible states a quantum computer can explore.

Entanglement, on the other hand, is when a qubit becomes linked such that the state of one qubit can instantaneously affect the state of another, no matter how far apart they are. Now, that is a difficult concept to get your head around, but absolutely that is the case.

These quantum effects enable quantum computers to perform complex calculations far faster than classical computers, particularly for certain types of problems like factoring large numbers or simulating molecular structures.

While quantum computers are still in the early stages of development, there is a robust ecosystem out there with no less than a half dozen different vendors having machines with different modalities. They hold the potential to revolutionize fields like cryptography optimization. So you can think supply chain optimization or financial optimization for derivative securities, drug discoveries.

However, building practical large scale quantum computers is a massive technical challenge due to issues like qubit stability or what’s called in the science quantum decoherence or error correction. But progress is being made.

Many researchers believe quantum will eventually offer capabilities far beyond what’s possible with today’s computers.

So a question I get all the time is, how do I get to be quantum ready? What does that mean?

And actually it’s no different than what your companies went through adopting any new technology. So let’s say that’s cloud computing.

Actually, it’s about a four step process. You need to learn, connect, you need to access, you need to get buy in. So the first thing when I meet with companies and I find out they’re just dipping their toe in the water for the first time is, you know, you have to go out and educate yourself on the technology. And there are a number of ways to do that. There are plenty of online courses.

My old company, Q Control, has a very good quantum learning process. It’s one of the better ones on the market.

That is for fee, but however, there are a lot of free resources out there that you can become smart on quantum computing and become conversant.

The second thing that really comes up, and I found this to be true, no matter how big your company is, if you’re in the Fortune 500 realm, you have scientists at your company that have been kicking around in Quantum for a number of years, but you don’t know who they are.

I have seen this firsthand, a very funny story of being at a company in Germany, actually standing in line at a commissary for lunch. And we were continuing our meeting and we start talking about Quantum again. And a voice behind us said, oh, I’ve been working on Quantum for 10 years now. We all kind of looked around and our host said, well, welcome to the Quantum team.

My point in this is there are scientists out there working for your company that are in the shadows and you need to bring them out. And you can do that by creating a special interest group at your company, sponsoring just a lunch and learn.

And you will quickly find out who at your company has been looking at this as a sort of a quantum hobbyist.

As things progress from there, a next logical step would be to identify those intractable use cases and when, I mean intractable problems that you cannot solve classically.

A good example would be the traveling salesman scenario.

If you went out to your line of business managers and had A conversation around what problem that you can’t solve now, but if solvable would yield a either high ROI or even an earnings per share impact to your company.

And if you kind of frame the question like that, I think you’ll find a lot of line of business managers do have these types of problems

[00:10:37] Speaker C: that

[00:10:37] Speaker B: they would be willing to talk about with you.

And sort of the last piece of becoming Quantum ready, you know, after you educate yourself and draw people out of the shadows, identify your use cases, the last step is really, really critical and it’s, it’s getting the buy in of the C suite.

So educating the CEO, educating the CFO or the chief operating officer as to the benefits of Quantum and actually being very upfront with the C suite. The ROI in Quantum is not measured in a week or a quarter or two quarters out.

AI, you can do that now, you can get immediate results.

This is going to be a long term play.

And what we’re finding, there’s no one industry that is really leading the way, but rather every single vertical out there, industry vertical, there are two or three leaders, trendsetters that are looking at that. So that could be big banks, big insurance companies, logistics companies, healthcare and the such.

So I know I went longer than two minutes there, but I hope that sets for everybody.

[00:12:00] Speaker A: Thank you. Michael, Christian, welcome to the floor.

I’m wondering if you can continue down with this and maybe dive into the time frames, the time horizon, Michael, saying we should learn about it and form our special interest groups for those of us who have reached into our executive committees. We should get them more knowledgeable about the opportunities.

But you know, I’ve heard everything from three to 30 years and if it’s three theaters I care about it and if it’s 10 to 30 years, I probably don’t care about it. So your perspective, Pascal’s perspective on the timeframe where large enterprise and government need to be stepping up and putting some resources against the opportunity around quantum computing.

[00:12:50] Speaker B: Yeah. So Christian, why don’t I take first swag at this and then I’ll let you jump in the. I think you all saw the announcement by Nvidia’s CEO last week saying, you know, we’re a long ways away, 15 to 30 years out from a universal quantum computer. And I think he was referring to a fault tolerant quantum computer and that’s probably pretty true.

That’s way out on the horizon. Interesting enough, Nvidia is partnering with every single company in the Quantum industry.

So it’s sort of interesting to hear that. But what we have now are computers which are in the NISQ era, which is the near term or near term intermediate scale devices.

And these are not fault tolerant, these have errors, there’s a large amount of error correction going on.

There are several different modalities from superconducting qubits to neutral atoms, which Pascal does, to ion trap to photonics, you name it. And we’re all kind of hustling every single day towards providing that. And we are seeing small incremental step ups in reliability and efficiency.

But Christiana, I’ll let you chime in from here.

[00:14:16] Speaker C: Yes, absolutely. Hello, Ariana.

So it’s, I would say it’s a multi layered question asking about timelines because there are multiple things to consider.

So indeed, as Michael has evoked, there is this distant horizon for reaching close to perfect or perfect ideal universal quantum computer, which is really the ultimate goal and holy grail for all of the currently existing hardware providers.

But that’s not the scale where quantum computing will become useful.

So for this to illustrate, I think one key information is that 2025 has officially been declared the year of Quantum by the United Nations.

The initiative behind this is really to bring around global awareness for this new emerging technology, especially due to the fact that it is of quite disruptive nature for quite a few different industries and types of problems that Michael has evoked. A couple of them.

What we are expecting in the next few years is that the current generation of quantum computers, so what we refer to as this NISK noisy intermediate scale quantum computers can already bring value to certain specific, well targeted use cases.

So this value can manifest in very many different forms. It is a function of your key performance indicators, your performance metrics, which can be dependent on the problem you are tackling as well as on your industry.

But this added value, this not necessarily leads to an advantage. What typically, typically people tend to refer to this so called quantum advantage. We try to be very prudent with this word.

We are not there yet. But that doesn’t mean that you cannot improve the existing processes. You cannot make the exploitation of your available computational resources better, more efficient by introducing this new way of computing into existing workflows. So it is an overall learning experience not just for us, but but for the end users as well. And the collaboration is key in better understanding. Therein lies the impact in a short term, because there can be impact in a short term in the coming years.

One famous example where we are expecting the most impact in the near future is material science related tasks which is mostly still on the R and D divisions from physics perspectives, but still can have a cumulative impact for a longer term for the materials we deploy, for coating, for paints, for constructions, etc. Etc.

There is also this longer timeline that a lot of the companies are aiming for as first main objective which is situated between 2028-2030 reaching fault torrent quantum computing, meaning the practical introduction of logical qubits instead of physical qubits. So switching to logical information units instead of the physical ones currently in the machine in order to reduce drastically error rates to approach better to what we have for instance in your everyday currently existing classical computer. This will enable much more diverse applications as well as new ways of handling information information, be it from a machine learning perspective or a simulation perspective.

And one final comment about your skepticism for looking at more long term, I think we will going to talk a little bit about the cyber security aspect.

Ever since the 90s, actually 1994, Peter Shor, mathematician, has devised an algorithm that is able to factor numbers much more efficiently than what we are capable of doing currently with classical computers by the use of a powerful enough quantum computer.

The key message with this algorithm, Shor’s algorithm, is that this is a risk for almost everyone because most of our current data sharing and cryptography processes relies on the fact that classically we are unable to perform this task, this factorization of integer numbers in an efficient way with the available computational resources.

So under this assumption, a lot of the cryptography keys currently existing are operational and quite robust.

However, the data that you’re sending now is not just for current usage. This is something that you want to keep secured potentially for 10, for 15, for 20 years. And herein lies one of the main risks and main points of interest is that okay, maybe a quantum computer cannot yet break existing cryptographic keys, but if in 20 years it will be available, people can already acquire encrypted data. Today they can keep it for 20 years.

Once the encryption method is operational, they can decrypt it.

So in terms of thinking about risks and potential impact, the time horizon needs to keep this also in account.

[00:20:23] Speaker A: Thank you Christian.

Before Joanne, we go to you, I want to get your opinion on this.

I’m hearing kind of a horizon of timeframes, but there’s a great common stream going on on LinkedIn. And I want to thank John, who’s shared a resource there that I’ve captured. He’s talked about an event in New York City for quantum computing and financial services.

Navid has shared a link for the Quantum Innovation Summit in Dubai.

So more information is Coming out on the common stream. This is obviously an area that we’re all sort of trying to learn from each other from. And I just point to you to go to that resource for that. Joanne, short term, long term, where’s your mindset around this? And is there an aspect of quantum computing that technology leaders and transformation leaders should know about that we haven’t covered yet?

[00:21:21] Speaker D: Okay, first of all, I’m in the NIST era too, the noisy intermediate scale.

I see a lot of work particularly coming out of universities that are aiming at commercialization potential of what they’re doing with quantum around materials, photovoltaic. Think about a car that’s painted with a new resin or a new capability that’s a liquefaction of something that allows it to generate its own energy as it’s driving.

Right? We see some of this in things like asphalt that’s used in certain countries that actually generates a capability for an ev.

So think about now, an EV that’s painted with a substance that allows it to capture sunlight and turn it into energy.

So there’s a lot of work in quantum computing around that. Things like materials, also drug discovery in the short term. So my time when I’m much more optimistic, and maybe not from a naive perspective, I would say, but optimistic that these niche areas are going to start to come to fruition much faster than people anticipate because the universities are already there in certain instances, niche problems. But they’re very complex niche problems that need to be solved. In drug discovery, it’s new chemical entities that perhaps will bring cancer drugs to market much faster than the 10 or 15 years they currently take.

So I see a timeline of in the next three years we are going to see companies not only looking to commercialize quantum computing, but that the results are going to be so dramatic that they will absolutely take the world by storm. Much in the same way as, you know, OpenAI did with, with LLMs.

We’re at an inflection point. And that inflection point is like crossing a chasm. We’re just getting to the other side. You know, I mean, there’s a lot of work that’s being done through the Quantum Economic Development Consortium. There’s defense stuff. There are, you know, Rigetti, IonQ, D Wave, all of these are specialized quantum startups. So we’re going to see them. And I think leaders have to be aware that if you have giant supply chain problems, if you have complexity in your engineering in particular, or in material sampling or materials development, that’s where you’re Going to need to be prepared very quickly. And I guess I would say as well, you know, it’s not just the risk of cryptography. There is a talent shortage around this.

These are problems that are not only going to be addressed in terms of very short timescales, but we need to start training people how to use Quantum. It’s a whole different development mindset than we’re used to.

And I think you have to start thinking about how you’re going to actually make this work in your own company.

[00:24:40] Speaker A: Thank you, Joanne. So Joanne’s sort of like a balanced approach and looking for opportunity, near term opportunities in some concrete areas. Martin, thank you for joining. I see your hand raised. I think you have a really good question that I see here in the comments. So go for it.

Well, I got first of all a statement.

[00:24:58] Speaker E: I think one practical opportunity of Quantum, but that obviously depends on quantum computing power being available in the mainstream is permutations and combinations. Anything that involves massive permutations and combinations and solving that, such as dynamic supply chains where you have the opportunities for routing and everything else. I think those types of problems are as well as things like material science and new drugs and things like that. Those types of problems are also very suited to Quantum, which is why the cyber security and cracking cyber security because it’s permutations and combinations.

But I had a really good question for Michael which is how do you explain this to the C suite? The C suite, normally fairly reticent. They’re reticent about spending money, whatever else. How do you sell this to the C Suite?

[00:25:50] Speaker B: That is a great question. And I’ll tell you what not to do. First, do not sit in front of the CEO, COO or CFO and start talking about quadratic unconstrained binary optimization, for instance.

Their eyes will glaze over and you’ll lose their interest.

[00:26:09] Speaker A: Michael, my eyes are glazing over.

[00:26:15] Speaker B: Yeah, not being a scientist, I was very proud of myself learning that acronym by the way.

But you know, seriously, the conversations you have with the C suite are, you know, concentrate on the ROI of solving those intractable problems. And, and I’ll go back to a conversation I had with a CFO at a company who had a multi billion dollar supply chain and we asked what would it mean to your company if we optimize your supply chain by 3/4 of 1% to 1% year over year. And his eyes lit up and he said, well, I would be the next CEO of my company if I could do that.

So outlining a intractable problem and then Doing your homework and finding out if solvable, what would that mean? Because C suites, they’re concerned with earnings per share. They’re, you know, if I invest money, when am I getting it back and what could be the possible reward for that? So I have always found and been successful in my career in Quantum over the last last eight years of having those types of conversations and kind of pushing the science to the side.

I hope that answers your question.

[00:27:34] Speaker E: Thank you, Michael.

[00:27:36] Speaker A: I think we have another question from John or a comic. Hi, John. Welcome to the floor.

[00:27:40] Speaker F: Hey, good morning. Yeah, thank you for having me here. So Warren and Christian, I was at IBM in 2016 when they kind of had their press release and then they had that version that you could play with on the web of their quantum computer. But, but how real are these things today? Like, what are, what do companies have? Like, what are they doing? Like, I would love to hear, because I haven’t really heard anything about them for, for eight years. And so, like, what do we have today?

[00:28:05] Speaker A: Yeah, that’s, I don’t know who could comment that. It’s very similar to my question about, like, if I had access to one of these things and I was a developer and, you know, maybe I know some things about this, what does the experience look like today in throwing a problem at a quantum computer?

[00:28:24] Speaker B: Okay, I’ll take a stab at that and we’ll let Christiane add the technical aspects of it.

So right now you’re looking at a couple of major buckets for quantum computing.

There’s the chemistry and material science for pharmaceutical drug discoveries, or companies like BASF or Dow Chemical.

Optimization is another big bucket around supply chain and financial optimization.

Machine learning is another one crossing over into AI and large language models. So each company will have very specific use cases. And it all deals with developing the algorithm to address that. There are some algorithms out there that will certainly further the cause. And Christiana, I’ll let you take over from here.

[00:29:20] Speaker C: Yes, definitely. So actually with Quantum in particular, even more so than other emerging technologies, the very nice thing is that it’s still very much academia and science driven.

And a particular consequence of this is that the community itself is very open, open and sharing results, open and sharing resources, open and sharing materials. So actually online you can find a lot of introductory materials and a lot of highly technical materials.

The problem is really trying to go through which one is more adapted to what type of audience.

But in terms of resources available, resources, already everyone can pick their preferences.

What we have noticed as a trend is that hyperscalers Crowd providers are also positioning in terms of providing access to quantum computers and building this as part of their programs and offerings for people to be able to test it out, to get a feel for it. Because that’s the first thing that you need to establish when you are trying to get into the technologies. Is it actually useful for me? How can I make it useful for me?

And part of the answer is really as it was already mentioned by John as well, is trying to tackle the right problems.

But part of it is also getting a better idea, better understanding of it. And for that there are some very, very nice tools and resources already there and available for anyone to use them.

[00:31:17] Speaker A: So what does a tool look like? Christian? If I, if I were trying to do an experiment and had access to something, what would it look like? Would it. I don’t, I’m not, I’m picturing an ide, but I don’t think it’s an ide, is there?

[00:31:33] Speaker C: So the tools, at least the tools I know of are from a programming perspective are very much Python based.

So from a developer, like a programming developer aspect, the Python based libraries, but still open source dedicated libraries are predominant in the industry and the level of abstraction and how high level you can get from these basic elements is sort of dependent on the technology, on the company, on the provider.

You can get access to interactive jupyter notebooks, you can get access to graphics user interfaces that work in a no code environment.

If I may cite for instance there is IBM Qiskit composer that allows you to just play around with quantum logic gates and literally without any coding you can just move around floating virtual boxes or on Pascal side we have Pulsar Studio which allows you to play around with small atomic systems. All of these tools run on your web browser with your web browsers available resources so they are quite user friendly in this sense.

[00:32:53] Speaker A: Thank you.

[00:32:53] Speaker C: You can also find some more much more deeper and in depth resources. It really depends on your level of maturity and how at ease you are with either the mathematics, the physics or the informatics side of it.

[00:33:12] Speaker A: Thank you Christian. I’m going to go to Joanne next. Joanne. Let me take my quick break folks who have joined us for the first time you are at the weekly coffee with digital trailblazers. We meet on LinkedIn every week to speak about topics for digital transformation leaders, often leadership practice, governance. And today we’re talking about emerging technology with quantum computing and what digital leaders need to know. I want to thank again my special speakers Michael Warren and Christian Benio from Pascal who are here to explain to us what quantum computing is all about.

I’ve done some reshuffling of the updating calendar. Next week is Data Privacy week and so I decided to switch around the calendar a little bit. Next week we’ll talk about how to take control of your data, which is the Data Privacy week theme this year. And I’m hoping to have a couple special guests for that one.

On the 7th we’re going to talk about establishing product management in non tech industries. This has come up a few times, but in terms of building up your skill sets as digital leaders, most organizations have agile going on, have design thinking going on, but product management is still a work in progress. I’ll talk about on the 7th. On the 14th we’ll talk about workplace transformation in the AI era, embracing new roles in skills. And then on the 21st we’ll be talking about building smarter organizations, transforming to intelligent operations. So those are the four that are upcoming and as you all know, use the URL starcio.com Coffee Next event. You can see it in the top right hand side of your screen. That will always Redirect to the LinkedIn page where we’re hosting our next event. I’ll switch that over later today. So Joanne, before I get to my next question, I think you either have a comment or a question for the group.

[00:35:14] Speaker D: It’s, it’s both actually.

The question is for Michael, would you agree perhaps that the way to get to the C suite in terms of early education and then later investment in Quantum is a value based argument. I’m, I’m hearing this over and over again from those people who are very early adopters, let’s say, and, and have commercialized capability out of university research.

That their argument, their sales perspective is we, this will add tremendous value not to your top line or your bottom line, but to both and your overall resiliency as a company.

Do you agree with that?

[00:35:59] Speaker B: Yeah, absolutely.

Yeah, I absolutely agree with that and that’s an excellent point.

We’re seeing a lot of collaboration between Fortune 500 companies, government and the university sector on jointly developing solutions. But your point about value based selling to the C suite is absolutely spot on. That is definitely the way to go and that’s something that resonates, resonates with them and that will certainly help create, buy in for any Quantum project at organization

[00:36:34] Speaker D: because I’m looking at this from the point of view of training. Also like somebody asked me not that long ago, maybe a month and a half, you know, they’re starting to hear resonance about Quantum and there this is a company in the life sciences industry and they’re looking at how do they begin to retrain, not augment the training of, but retrain some of their IT folk to gear up to use Quantum. And that to me is not only a value based argument to the C suite, but it’s also value add to the individuals. Like, you know, we’re seeing a lot of companies having to retrain or augment the learning of their staff for AI.

To me this is akin to the same thing.

[00:37:25] Speaker B: Yes, I certainly agree with that as well.

If you look at, well, stick to the pharmaceutical industry. If you have a computational chemist that is, you know, doing drug discovery work, for instance, and he or she has the interest in Quantum, continuing education would certainly make them more valuable than organization.

A number of years ago I would, I had chemists working for me when I was at Zapata Computing in Boston. We were working on several projects and to upskill them, I sent them to Will Oliver’s course at MIT.

And that’s specifically designed for PhDs.

Very, very heavy on the math, very heavy on linear algebra. But that is certainly a great way to upskill your existing workforce and create value within your enterprise.

[00:38:21] Speaker A: Yeah, if I’m going to chime in here, Michael, I like this, you brought up linear algebra and that when I finally made the connection over what types of large scale problems Quantum is going to be good at, that was sort of the first skill set that I was thinking about. I was thinking about, you know, all that, all that algorithm that goes into mapping technology and how do you plot a course across and optimize it for certain variables and how hard it is to think about the programming around that, because there’s a bunch of different paths that you can go down. But if I think about programming that linearly, I need to go doing a lot of sequencing to get down to an optimal path. And I think this is a big part of what Quantum is trying to solve for, is using the probabilistic nature of cubits to be able to do that more efficiently. Do I have that right?

[00:39:18] Speaker D: I would say yes, because if you look at what’s going on with agentic AI and also symbology being used to do, instead of writing a long, long, long, long prompt with many variables, there are math oriented and math specific large language models that could be used. I know that sounds antithetic, but the notion of symbology is to convert the very long natural language prompt with all the variables into algebra.

And it’s amazing the difference that it makes and I think that might be the intermediate step between those working on AI or aspiring to be digital trailblazers in quantum. That that would be an area of focus and I’m not sure if that’s correct from, from Kristen’s point of view or from Michael’s point of view, but that’s how I see the world.

[00:40:13] Speaker A: Let’s go to Joe. Joe, I think you have a question here. I see in the comments that I think is a good sort of variable to bring into this discussion. Yes. We haven’t talked yet about the potential

[00:40:25] Speaker C: cost of using this technology.

[00:40:29] Speaker B: At least in my limited vision of

[00:40:33] Speaker A: what these things are.

[00:40:34] Speaker B: You know, there are these multi billion

[00:40:36] Speaker A: dollar installations with a priesthood of technicians around it and I just wonder about the cost structure.

What does it cost to use these things?

Go ahead, Michael.

[00:40:49] Speaker B: Yeah, I can take, I’m sorry, I was on mute there. I’ll take a run at this.

There are two ways to access quantum machines right now in the NIST care and that is, is you know, over the, over the cloud. With you know, you can do that with Pascal, you can do that with IBM, you can do that with a host of others. And the other is actually having a machine on Prem, which we’ve seen mostly governments around the world. Pascal has a number of on PREM machines that are sitting in data centers right now. And you know that can be you know, 20, 20 million euros and up up. So they’re, they’re very expensive.

But it all really comes down to the algorithm. In the use case that you are trying to run, it’ll dictate what your costs are. Christian, what’s been your, your experience in that area?

[00:41:44] Speaker C: So I would say that it is, yeah, the cost is, is one thing but with that this is something that’s as I mentioned, still scarce resource.

So it is being off balanced by that fact, the current cost schemes.

But on top of that there are many alternatives.

And at the whole, I would say development process itself when you are designing quantum solutions is tailored for the fact that you do not immediately need your the access for a quantum computer. Indeed, when you’re developing methods and you’re developing solutions, a lot of the implementation and a lot of the development task is happening via what we call emulators.

So you use conventional classical computational resources like CPUs or GPUs in order to simulate the behavior of a quantum computer or emulate the behavior of a quantum computer.

You can use classical resources that are available in a very, very inexpensive way in order to get a first feeling of how problem Formulation problem implementation works in a quantum framework without actually having to rely on quantum computer. Of course if you use classical computational resources for this, you will never be able to actually showcase what is the added value, what is the advantage of it. However, just getting a better idea of it, drawing some conclusions, reaching initial consensus of what I can expect if I were to rely on quantum computers in this or that specific task is already helpful to get an idea of.

Does it worth to go to the next step and actually rent out a quantum computer or eventually to buy an actual quantum computer for the usage itself?

I hope that Replies to a question

[00:44:08] Speaker A: I know Joe has a follow up question, but when you talk about 20 million euro machines, are these actual quantum computers that I can buy and put in a data center?

I didn’t think that was available just yet, but can you clarify what that machine can do?

Is it a simulator or is it an actual quantum computer?

[00:44:29] Speaker C: Michael Christian it is the machine. So in Pascal’s case we have our first generation analog type neutral atoms quantum computer available for purchase and it is a machine that has already been deployed here in Europe where I’m based in two high performance computing sites.

One is currently being subjected to its final operational tests before putting it on the actual run as part of the French Atomic Agency site and the second machine has recently been installed in the ULI supercomputing center in Germany. So these are analog quantum computers.

So not just an emulator, not a simulator in this sense, but actual Quantum computers with 100 physical Qubit available at their disposal.

[00:45:31] Speaker A: And what does the word analog mean in this case?

[00:45:34] Speaker C: So analog in this case means that the computation process does not rely on quantum logic gates.

So it is not what we typically call a digital quantum computer. The computation procedure is happening in a continuous in time fashion.

In our case it is with laser impulses. Depending on the technology, it’s done with different methods. But what it means being analog is that you do not have discretized sets of instructions that you you transfer to your units of information to carry out the computation, but you do a single continuous in time evolution of the information evolution of the quantum system in this case.

So it is akin to what has existed in the 70s.

So there was something called an analog computer that worked with specific impulse signals and that also relied on this continuous in time manipulation to carry out computations.

However, in a classical computational framework, given that data itself is binary, it is something discrete, it is digital in this sense, using quantum using logic gates for a classical computer made much more sense. Therefore this analog way of computing in a classical computer has died out since then.

However, since for quantum computers information is much more complex, as it was highlighted in the beginning, it’s not just a zero and a one. It can be, in a sense, everything in between.

Information itself is continuous, so why not make the computation process itself a continuous action?

[00:47:23] Speaker A: I’m just wowed. I didn’t know we were at that stage yet. This is not an area I follow much. And Joe, I think you have a follow up question. Martin is raising his hand. Go ahead, Joe.

[00:47:34] Speaker C: Well, you got into it.

My question really was answered quite extensively there.

[00:47:42] Speaker A: I was also curious about whether I could go and buy a quantum computer next week. But it’s been answered, so. Thank you, Isaac. Thanks Joe. Martin.

[00:47:52] Speaker E: So I’ve got two questions.

First one is to what extent is the lack of off the shelf applications to run on a quantum computer holding things back? You know, I kind of almost align it to when we were learning C back in the 80s and we didn’t have lots of libraries to call on.

[00:48:12] Speaker B: Almost, yeah, that’s a, that’s an excellent point. And if you leave scientists to their own devices, they will do science all day long. But the adoption of any technology really depends on developing those applications for very specific use cases that solve problems.

And that’s where we are right now. And that, that’s really something that Pascal is doing is kind of taking this out of the spooky science world into creating different applications for, you know, credit risk scoring or optimization of electrical grids.

But that’s a good point. Until you have applications that solve problems, it’s just a science experiment.

[00:48:59] Speaker A: So, so let’s follow up on that. I had a question here. What are the applications that, you know, you mentioned material science before, financial services, insurance, you know, those that should have people looking into these problems. What are some of the early problems that you see people working on right now?

[00:49:22] Speaker B: Some of the early problems you can sort of go by, you know, we’ll stick with pharmaceutical, you know, everyone’s interested in protein folding. Well, that’s an extremely complex problem and it’s not going to be solved for years even with a quantum computer right now in its current state. But what people are doing is they’re looking at, if I can, I’ll butcher the science by saying kind of less intractable if you will, not, not trying to hit the grand slam, but pick problems that maybe don’t have a lot of variables that are solvable in the near term. And then as the technology gets better and the hardware gets better, certainly the algorithms need to get better as well. So it’s sort of a whole ecosystem of quantum from the hardware, the software use cases and talent all coming together to drive towards a common goal. But Christian, what has been your experience there, especially on the algorithmic side?

[00:50:25] Speaker C: So recently what we have noticed is an increase in interest from especially transportation and mobility industry as well.

And this also ties back to some of their increased needs in terms of either logistics, scheduling or in general optimization type tasks that lend well for a quantum computing based approach to propose solutions.

So I would say the question is not just what is the right industry or which industry can benefit from it most, because all industries can find the specific parts of them that can have lasting impact for this new intelligence, for this new technology to be included.

If I may mention, a particular example that we have worked on recently is coming from the energy industry.

So energy and utilities sector, they tackle a lot of not just inventory management, but resource management type problems. So they tend to work with large scale networks, problems that are heavily constrained.

These are typically good indicating factors that the problem itself becomes quite complicated to resolve relatively quickly.

So the good point here is that we are not limited by the problem size itself very quickly. We can find ourselves in a regime where the problem is too complex to be solved with existing exact methods.

So what we employ are what we call heuristic or approximative methods. They are widely used, have been widely used for the past decades in various fields, but they do not provide, or they do not necessarily provide perfect solutions, hence the name. They provide good quality solution, but there is typically room for improvement.

And an additional message that lends well for quantum computing based implementation is that they typically provide a limited number of good solutions.

With quantum computer you can expand this sort of portfolio of solution candidates. Therefore you can improve the efficiency by not necessarily improving the solution quality of your particular problem, but you can improve the overall workflow.

You can think of it as you’re tasked at allocating your resources for a specific delivery, but behind that, that delivery will be processed by a specific site.

There might need to be another delivery with a different type of van, boat with plane. So for your specific problem of just allocating your parsers, your packages to specific delivery sites, you might not know the whole picture.

So for these unknowns, getting access to many solution scenarios can be beneficial in improving the overall efficiency of the procedure.

[00:53:57] Speaker A: Thank you. Christian. We are down to about our last five minutes and I know we have two quick questions, so let’s try to squeeze them in. John, your quick questions.

[00:54:08] Speaker F: Yeah, first question is, is when you, when you build one of these quantum computers, are they specific to a single algorithm or a type of algorithm, or are they general purpose?

[00:54:20] Speaker C: They are general purpose machines.

So they are not yet what we refer to as universal quantum computers, but they are general purpose capable of executing a large quantity of algorithm of quantum algorithms.

[00:54:37] Speaker F: And the other question I had is if you have 100 qubits and you look at like the size of the asymmetric encryption keys, has your company started switching over to the NIST approved kind of quantum safe encryption algorithms?

[00:54:56] Speaker C: So I will be very, very brief on this.

So quantum safe encryptions do not necessarily imply using a quantum computer to protect yourself.

So it is part of the field, what we call post quantum cryptography, which relies on cryptographic methods that can be executed on classical machines that are proven to be safe against potential cyber attacks of even future robust quantum computers.

So here you do not necessarily need quantum computer to protect against quantum cyber attacks.

The whole normalization process is still ongoing.

So there’s a lot of attention for the NIST analysis. I think that should end later this year or early next year on finally identifying which algorithms are have their label of being quantum safe. But it’s still an ongoing process of investigation. What encryption methods are truly considered quantum safer?

[00:56:09] Speaker A: Thank you, Christian.

Let’s move on to Joanne. Quick question from you.

[00:56:15] Speaker D: Yeah, quick question.

If you were going to give advice to an enterprise that’s looking at this eagerly, anticipating the future and thinking very future forward, how would you create the bridge between.

Let’s assume that they’re not at a huge maturity level of AI, but mid level of AI. How would you bridge between AI and quantum?

[00:56:44] Speaker A: What’s the AI use case, I think

[00:56:46] Speaker D: is a, well, the AI use a large supply chain or materials or drug discovery or you know, any of the more complex engineering challenges that companies have.

Cold chain might be a good example.

[00:57:02] Speaker B: Yeah, I think the gap there, it’s, it’s not one of the other.

There is significant overlap between AI and quantum.

Yeah, you know, that’s sort of the best way to describe it is just that complementary. You’ll have AI jobs that can come up and be run on the cpu, GPU and then some of the data aspects, especially when you’re looking at large language models using, you know, machine learning techniques to be run on a quantum computer, then bringing them back into AI. So I think they work very much together.

[00:57:38] Speaker D: I’m, I’m glad you said that because I see it the same way. I don’t think it’s A one or the other. And I, I think that as AI progresses, quantum will be just added on and evolve with the two together.

[00:57:54] Speaker B: Yeah, a common misconception, I know we’re kind of bumping up on time is quantum computing is not going to replace cpu, gpu, FPGA or anything else for that matter. It’s just a continuation of the high performance computing stack. It’ll be suitable for some jobs, but not all jobs. So it’ll just be another, you know, silver bullet in the arsenal, if you will, of enterprises to drive results and earnings.

[00:58:22] Speaker A: Thank you, Mike. I mean, that’s a great closing statement. Just great way to think about this. I learned a lot today from you guys. Thank you. Michael and Christian from joining us and for Joanne, John, Martin and Joe’s questions. Michael, maybe just give us a very brief overview. What does Pascal do in the quantum space?

[00:58:40] Speaker B: Yeah, so Pascal, briefly. We’re based in Paris, we have 300 worldwide employees. And what we do is we harness the power of neutral atoms to build scalable high performance Quantum processors, or QPUs.

Our goal is to bring quantum computing out of the laboratory, away from the spooky science, into real world applications that can accelerate innovation across the different verticals like finance, materials science, healthcare, any one number of verticals there.

This, this approach has led us into a number. We have about 40 customers right now around the globe.

Very cost efficient, I may add. We’re not talking about. You always hear about chilling qubits to, you know, you know, minus 270 degrees Kelvin. The neutral atoms that Pascal cells are QPU and our cloud solutions run at room temperature. They drop into the traditional HPC footprint. So that’s something that’s been very appealing to companies.

[00:59:49] Speaker A: Wow. We’re getting a lot of applause here from Keith, David, Heather, AJ, for just a good session.

I learned a lot. You know, as you probably could tell, I came in a little bit skeptical, but quite frankly, if I’m hearing I could buy a machine for $20 million. If you work in an enterprise with an R and D budget north of $100 million, this is probably something you need to be looking at. If you’re working in industries with large scale computing problems that are already investing in high performance computing, you probably need to be looking at this.

You know, when Heather is on here and Joanna and I are talking about this, we always talk about hiring diverse candidates into our IT organizations. Maybe it’s time we start going back and hiring some, some physics students coming out and saying, look, you know, I want you to learn, learn and be our internal expert on quantum computing. So a lot that can be digested from here. I really like this, these use cases that we shared that if you’re working in any of these industries and you’re working in this space, do let me know.

I’m in sort of incentive now to do my own homework and see if I want to cover more around quantum computing in my writing and my research. So thank you again Michael Christian for joining us. Thank you Martin, Joe, Joanne and John for our questions. We’ll be back next week for the coffee with Digital Trailblazers. We’ll talk about Data Privacy week and we’ll be talking about how you should take control of your data, lining up some new experts for that one. On the 7th we’ll be talking about establishing product management for non in non tech industries. On the 14th we’ll be talking about embracing new roles and skills to support your AI error transformation. And then on the 21st we’ll be getting into intelligent operations. Folks, thank you for joining this week. I look forward to all of you being here next week. You can watch the recording on LinkedIn. I’ll have it up on my website. And for those of you who haven’t found this yet, Please just visit drive.starcio.com community I launched the Star CIO Digital Trailblazer community last year. We launched three new advisory Connect programs just over the last few weeks. And if you’d like to know more about it and have questions about it, do reach out to me on LinkedIn. Everybody.

You are unauthorized to view this page.

This episode of the Coffee With Digital Trailblazers was sponsored by Appian.

The event is hosted by Isaac Sacolick on LinkedIn Fridays at 11 am ET. The event attacts digitial transformation leaders, from CXOs to team leaders, who learn from experts on driving change in their organizations. Every week we explore a topic and share lessons learned, and all are welcom to attend. Visit https://starcio.com/coffee/next-event which will redirect to the next event.

Recordings of the events are available to StarCIO Digital Trailblazer Community members .Review the community’s advisory, leadernng, and leadership programs.

Isaac Sacolick:

Mike, I’m glad you’re here. We can get started now. Welcome everyone to this week’s Coffee with Digital Trailblazers. We are excited to have this week a session on why AI is boring in 2025 and how all of you can learn a little bit about how to accelerate your transformation programs. Today’s session is brought to you by Appian, a software company that orchestrates business processes, and I want to thank Mike Beckley for joining us today and being our guest speaker. Welcome John, Joe and Joanne to the floor. And Mike, we can give it a few more seconds just to get some more people to join. It’s usually like 1104 or five where I really get started with everybody, so everybody should be getting ready for in the US getting ready for your Thanksgiving weekends. I’m really excited. I’ll be doing some travel this week to go visit my son in Arizona, so if any of you’re in Arizona and want to meet up with me, I’ll be in Phoenix for a day and I will be in Tucson for a few days and happy to say hello to anybody who is around again. Today we’re talking about why AI is boring in 2025 and how to accelerate transformation. Again, today’s session is brought to you by Appian, a software company that orchestrates business processes. Mike, it’s so good to have you on stage despite our few hiccups. I want to give you a form first, tell us why you think AI is going to be boring in 2025.

Mike Beckley:

Yeah, thank you Isaac. So glad to be here and to get past our firewalls. Finally. So what’s boring about AI is that it’s going mainstream, but it’s how it’s going mainstream that’s uniquely boring. The most powerful and effective use cases for generative AI are simply not flashy and exciting the way they have been in the past year. Regenerative ai, we’ve all gotten to see how amazing it is at drawing pictures, at painting, at generating photos and images, and it’s incredible at that. It’s also incredible at writing stories for us, and anytime your nephew wants to hear a scary story, you can just go to generative AI and create one. And it’s amazing at those types of tasks. But where it has fallen down completely has been in real operational workflows. And now what’s coming in 2025 is the where generativeAI is going to be delivering the most value. Where we’re seeing already where it’s been making incredible breakthroughs in business systems is in the most boring part of them. And that’s OCR, that is scanning documents, that’s extracting data from PDFs that has completely revolutionized the economics of scanning and digitizing paper, and it’s the most incredible thing to see dramatic multimillion dollar savings, but oh my God, is it boring?

Isaac Sacolick:

Mike, maybe go a little bit deeper than that. We’ve seen document processing before, it’s been around for a long time. OCR and then can scanning in my invoices and pick out dates and numbers out of it. They can scan my contracts and again, pick up dates and numbers around it. What is generative AI doing above and beyond that?

Mike Beckley:

Yeah, that’s really the whole point is that OCR, we’ve taken it for granted because well, it kind of works, but also that it kind of doesn’t. And so to make traditional AI using deep learning techniques made it a little better. But it still was a consulting engagement where humans had to train the AI on every document and had to give enough samples. And if there was variability even within that, the AI was a little better than the old template during models. But the fact is it was still cost prohibitive to actually automate most of your documents because you can only focus that kind of attention and AI engineering on the most high volume, most repetitive documents. And so that left hundreds of document types out. So if you’re an insurance company and you want to be able to quote fast because the faster quote gets the business, most of the time you’re dealing with hundreds of possible agents out there all submitting their own unique formats.

And with traditional AI models, you might get maybe 60% straight through processing. And now with generative ai, it’s solving all of the edge cases. And I don’t mean that without being specific. I mean all is all. So all the things that made AI fail before, tables that span multiple pages, tables without lines between the words and numbers, the documents got too large. Whatever it is. Handwriting generative AI brings the context to the document that makes this really error prone issue. No more error prone at all. Your strength through processing rates go up to like 97, 98, 99, 99 0.9%. It it’s insane. And syns insanely great and how different it is when you apply general, it just obsoleted the entire OCR industry virtually overnight.

Isaac Sacolick:

And Mike, you mentioned insurance. What other industries and types of companies should really be taking advantage of this?

Mike Beckley:

Well, it’s the most regulated, most paper intensive industries because the regulations require you to submit so many forms. So banking, financial services of all kinds, investment banking, asset management, private equity, retail banking. These are massively paper intensive transactions. Insurance of course, property and casualty life, all of it. And then the life sciences world, if you are trying to conduct clinical trials and working with many different outsource labs and facilities that the paperwork requirements are massive and putting drugs through trials, it is just a very document heavy compliance heavy sort of process because human lives are at stake and the documentation is never going away. But now with generative ai, we can actually capture that whole long tail of unstructured data and tructure it, then the government. This is really going to be hugely transformative in government. You want to make a dent in how much manual labor still is required for the government bureaucracy to operate generative ai.

Attacking the paper problem in a new way is going to be really, really impactful. But also, I can’t emphasize enough, it’s the paperwork that matters. It’s not talking about generative AI replacing the human agent. It’s not generative AI that’s going to somehow make the decisions. It’s generative AI that in 2025, it’s going to actually be able to finally fulfill the dream of the paperless office, and that’s going to transform government. It’s going to transform banking, it’s going to transform insurance and life sciences to start, but wherever the regulations are heaviest, therefore the paper’s the heaviest, that’s where the impact is exponentially greater.

Isaac Sacolick:

I’m going to go around the horn. Joe, you’re giving us a thumbs up to the paperless office. I’m sure you have a lot of examples of that.

Joe Puglisi:

I saw this week, Isaac, an example of where exactly what Michael is talking about has come to fruition. It was a startup company showing an RPA solution, and we know RPA is good as long as nothing changes, right? So version 1.0 of RPA was very, very rigid. Well, when you bake in an AI underpinning and the RPA instructions can be delivered in context, suddenly you’ve supercharged your RPA agent. And even when the forms change a little bit, whether it’s a new column or something else that would ordinarily derail the RPA script, it can be more adaptive and maybe get 98 or 99% accuracy as opposed to 60 to 70%.

Isaac Sacolick:

Yeah, I like the term Mike uses around exceptions because, and that’s why I throw out the invoice processing example. RPA has the same issue, which is you sort of design the flow to the ideal path and cheer success, and then all of a sudden you start putting in real world examples. It just doesn’t work. And I agree with Mike, the ability for it to not only just handle the parsing issues but handle a lot of the interpretation issues that come up when you start using long form content. It is pretty amazing. Joanne, what are you seeing that’s boring that we can really deliver value out in 2025 around ai?

Joanne Friedman:

Well, to the point about RPA Gen AI is not the only way to do this, but agentic AI will blow the RPA out of the water because it does both. We designed the flow to take the standard, and to Michael’s point, all of the exceptions, and you can actually, because agents are multi-layers and they sense and detect their environment before they do anything else, they can immediately discern whether it’s the exception or the norm to speed up the process. And you can actually let them go off and do their thing in an even faster way than Jenny and I can do it because it’s pre-programmed to not only sense and detect the anomalies and the environment, but just the way the frameworks for Gen KI are being created. That is, I wouldn’t say RPA on steroids. I would say more like this is a new class of technology that has the capability, particularly to the point about paper intensive industries and sectors.

Think about a hospital and the amount of paperwork that you have to sign, even if it’s on a tablet, now you can contextualize the same piece of data in different flavors. So your chocolate, vanilla, strawberry around the same is the different contexts that can be applied in agent ai. This becomes that much faster as quickly as something like Claude or Hitachi, BT can do the tasks in the RPA, the agents can do them even 30 times faster with less power, less compute, et cetera, et cetera, because it’s a framework that tells ’em what to do and go off and execute without human necessarily having to be involved. Do you trust it? You will right off the bat, but after 10,000 mes, absolutely you will.

Isaac Sacolick:

Thank you, Joanne. So Joe brought up RPA, you bring up a agentic ai, let’s let John, how do we take boring use cases and make them value in 2025? And then Mike, I’ll come right back to you.

John Patrick Luethe:

Yeah, and thanks for having me up here. I completely agree with Mike that generative AI has been so powerful for translation for information retrieval. I think the things that are holding it back right now and make it boring, two things are, one is that we’re using general purpose AI often for very specific use cases and well, that can be helped by augmenting it with information. I think as we get towards more custom built language models and gendered AI that are built just specifically for the use cases we’re in, I think it’s going to get a lot more interesting. The other one is that I think a lot of the people using gendered AI right now are interfacing it through a website. They’re pacing stuff in and they’re getting things back or they’re using Microsoft Copilot or one of the other ones where it’s like the humans are the interface to it. And I think once you start stitching generative AI into land of business applications, that’s when management happens. And so I know Mike here at Appian, just the ability to have users build stuff with low code or build stuff with you have the development team built up with regular development tools and start stitching generative AI into really into the workflows. That’s when it gets really, really neat. You start saving time,

Isaac Sacolick:

Mike, they’re throwing you the softballs because I was at your conference earlier this year and I saw some great examples of that convergence between what you used to be able to do with RPA and maybe it fell off and you get into intelligent automation, you get into low-code capabilities, plugging it into machine learning capabilities. Now Joanne is introducing agents which you introduced back in June. Tell me about this convergence, right, this convergence of all these capabilities. What are you seeing businesses being able to benefit from all these capabilities when they’re brought under together in a platform like Appian?

Mike Beckley:

Well, the important thing is with the new technology like generative ai, can you actually use it in these highly regulated industries? And can you answer that question for a regulator? Can you answer it for your customer that you’re keeping their data secret and private and you’re not training an LLM on their private information and therefore risking leaking it and sharing it in some unanticipated way because the LM is out of your control, you can only predict what it will do. You can’t necessarily stop it from doing something unexpected. And so what Appian has done for the last year, and I think some of our competitors as well is really focused on this concept of private AI and making sure that generative AI can be installed directly into a process or a workflow and kept walled off from, you don’t have to train it on your data.

You are keeping everything within your security perimeter, the data space of your control, and you’re getting the power and benefits of generative AI and gen AI to make your RPA to make your processes, to make your low-code applications to make them actually intelligent. And so that’s again, the most boring part of this is now we have the security guarantees in government we call the FedRAMP guidelines. We got to comply with, we have FedRAMP approved generative AI in banking, PCI compliance and healthcare HIPAA compliance. We have these compliance regimes that we’ve been able to get approval for generative AI in and a deployment mechanism through our partnership with AWS bedrock to actually deliver generative AI and agent AI directly into all these workflows that have previously thousands and thousands of people inside of banks are still doing manual work just because there wasn’t legal compliance approval to use generative ai. And that’s all those barriers are falling down in 2025.

Isaac Sacolick:

So I’ve got private LLMs, I’ve got compliance for the major industries. Can you comment on what agentic AI is going to do for these industries?

Mike Beckley:

Yeah, so agen AI is the new trendy term and it’s being used in wildly different ways. What Joanne was talking about is interesting in some ways different from what I see in that’s that most of what’s going on with Egen is the AI companies are trying to chain together multiple actions within the ai. So having philanthropic demonstrated plug itself as an RPA bot, if you will, going and driving a web browser. So when people say eTech ai, they usually mean generative AI driving a web browser as opposed to a script in a bot driving a web browser. And that is very primitive and slow right now, and it’ll get a lot better. I don’t know how fast it will evolve in 2025, but instead the effective agenda AI right now is using a workflow engine like an Appian to provide a tool chain to the generative AI and therefore having the chain of reasoning governed by the process that the humans have built or designed. And then AI can be so much more powerful because it can perform whole activities like an underwriting action within the constraints and the common sense guardrails and the goals that have been set by humans and within the compliance boundaries of the process. But it is not just a chat and discussion where I ask a question, I get an answer, it’s do something for me, and it goes through multistep, it invokes many different data systems and comes back with an answer. For me,

Isaac Sacolick:

Mike, you’re being consistent with your boring use case because basically my version of an agent AI is bring the generative AI capability to the workflow people are doing in the platforms that people are doing them with the data in that platform, but also the data outside of that platform that we make accessible and bring that human in the loop to make them more productive, more smarter, bring capabilities to them that they didn’t see before because it’s buried behind a lot of data ahead. Joanna, I know you have some thoughts around this as well

Joanne Friedman:

To both of the points that were made. The beauty of agen AI is that it actually can do not only the process calls, but the actual programmatic functions features. You can build it. There’s a variety of different kinds of frameworks. So think about taking something like a microservice or a container and saying to using an agenda framework. And as I said, there are plenty of them where you can actually tell the agent, go look at all my back office systems that would normally be used in this workflow, an ERP or PLM or a CRM package, whatever it is, and now also fetch data or go and query against a different kind of database, call it a time series and put all of these pieces of data together in a context that makes it very usable for the user. So you’re getting the right answer in the right context on demand and you’re getting the opportunity to either add to it, which means it could be used for a training purpose or it could be used for a knowledge capture.

And over time as it learns all of the pieces of information, it then becomes sophisticated enough that you can say, okay, now do this for me. Do this for me. Go and execute and do it with the assurity that all of the data from all the different systems when they’re choreographed together in the right way are going to give you the right results. And it’s not about using things like mag around the gen AI to make it more less hallucinatory. It’s about actually go do this for me. So all of the mundane tasks that don’t require a tremendous amount of strategic or real thinking around problem solving can be done by agents and you can actually have one agent collaborate with another. So if you do have complex problems that we do in manufacturing, I can put my agents together to get an answer like here’s the root cause and do it in an incredibly fast way.

So depending on the role I have in my workforce, if it’s in a financial services business, if I’m on mortgage approver for example, as opposed to someone doing another part of the process like validating and verify, I can have agents do the validation and verification. I gave another piece of paper called my T four, my income tax return to make sure that I’m qualified. I can do the same thing in manufacturing with a bill of materials. Both those workflows and both those processes could be done automagically or autonomously by the agents. So what it’s doing is it’s freeing up workforce time to work on the more complex issues that really drive business value. And that’s what fascinates me about it. And to see these kind of capabilities embedded in pieces of software that will then be sort of smart out of the box, that’s going to change the face of business, not just the face of technology,

Isaac Sacolick:

This discussion around how boring AI is going to change the face of business. And Joan, you’re bringing up another key point around where agents are going to be very interesting, but what you’re describing is role-based agents and looking at workflow and saying, I’m a security expert, I’m a privacy expert, or I’m not, and how can I get some help around what to do here given the circumstance or where we are in our flow? Mike, I’m wondering if you can comment on that, on that role-based agent ai and then also, again, coming back from being at the Appian conference back in June, I think it was, I lose track of time. What intrigues me about your offering is the intersection of these capabilities with your data fabric. So maybe talk a little bit about that as well.

Mike Beckley:

Yeah, well, lemme just say this. What I love about Joan’s talking about is how important the workflow is, and it’s becoming more important than the generative AI models and the lms, the underlying large language models themselves. Because what’s happening is that the models are becoming commoditized. They’ve all become so good that even the open source alternatives are catching up. And we saw China release a model, an LM model, which may or may not be nearly as good as the American versions in this past week. This is a kind of rapid convergence on LLMs that is devaluing the innovation of the LLM and instead shifting where the value will lie to the application layer, to the workflows themselves and therefore to get value to create these different role-based agents you’re talking about, it’s all about do you have the right workflow, do you have the right tools that the LM can act on?

And then what you just asked about data fabric, this is our approach. This is, I mean, app is not the data fabric now they’re becoming data fabrics are becoming quite trend and popular. Most of our low-code process automation competitors have recently announced new data fabric technology. Just because we were one of beginning early adopters, it’s really become common to see because what’s a data fabric? A data fabric is a way to work with all these different microservices, all these different remote data and systems that you don’t control because today’s business is done through an ecosystem of suppliers and it’s more distributed than ever. You don’t do your own payroll, you outsource that, you don’t do your own all kinds of things in financial services transaction. The clearing is done by clearing a broker. There’s all kinds of different interactions throughout the economy, and so to bring all that data together so that the AI and the agenda, AI knows what it’s working on, data, fabric technology is how we make that simple. And so combining data fabric with the Appian process engine, the workflows, and then having an interface to that to the humans because you still want humans in control, humans setting the goals, humans supervising and orchestrating. That’s how it all comes together with the data fabric, making sure that you have the right data.

Isaac Sacolick:

John, want to go to you for a second before I go to the break? You got all these capabilities that are coming to fruition. What are some of the use cases that you think about?

John Patrick Luethe:

Well, I did an activity based costing exercise for the insurance companies and we’re looking at TV agents and it was a workforce of a couple thousand people and we looked at how they spent some time and we divided it into add value, doesn’t add value, and we looked at the stuff that obviously things that don’t add value, want to get rid of the things that add value. We looked at is it automated or is it not automated and how much you can do for the time savings. And when I see the capabilities come out to Stitch during the AI into business applications and workflows, and then I look at how much time people are doing really manual things, the opportunity is out there and that’s what really, really excites me. It’s just being able to go back and find something that a thousand people do on a daily basis and how do we not do that task because, not because it’s not required, it’s an absolute required step, but how can we automate that thing and make it go a whole lot faster?

Isaac Sacolick:

Thanks, John. Thanks everybody for joining this week’s Coffee with Digital Trailblazers episode 1 0 4. Today we’re talking about accelerating transformation, why AI is boring in 2025, talking about the intersection of document processing, RPA intelligent automation, low code machine learning, and now generative ai, and now bringing Ag Agentic AI to our organization. Folks, everybody, this week’s episode is brought to you by Appian processes to find your business, make them better. With Appian, the leading platform for process orchestration, automation, and intelligence, the Appian platform empowers leaders to design, automate, and optimize important processes from start to finish. With our industry leading platform and commitment to customer success, Appian is trusted by top organizations to drive transformational process change for over 25 years. Amazing, Mike, I love that picture of all of the founders at the conference up on stage, all working together for 25 years around this mission that you’re describing as boring.

I want to start my next question with Joe and then we’ll get back to you, Mike after this. Joe, we’re creating a paradox here, right? We’re saying AI is boring, but for the last year and a half, it’s the excitement that boards and CEOs and quite frankly a lot of technology companies and even those of us in media have been shining a light on all these exciting, sexy, amazing areas, whether it’s autonomous vehicles, whether it’s robotics, whether it’s AI breaching into generative ai, that’s what’s getting everybody excited and a little bit fear of falling behind. And we’re coming here today and saying, AI is boring in 2025. Take these amazing capabilities and plug it into the most pragmatic areas of your business to drive value and lean on the intersection of all these different capabilities that have been around for a while. Lean on them because generative is adding an extra flavor of capability to be able to bring workflow to an amazing set of productivity. Connect people, connect data. Joe, how do we excite leadership around the business opportunities when we’re saying these are boring areas for the business to invest in?

Joe Puglisi:

I think there are a couple of things, Isaac that come to mind. We’ve held out the promise of the single pane of glass for management to be able to know what’s going on in their organization sort of across the board. And despite lots of efforts of BI tools and integration tools without the ability to orchestrate the flow of information among disparate systems, rationalize, align, and truly present that information in a cohesive way, which I think AI is going to have the capability to really do, we haven’t been able to deliver on that promise. And so it’s boring in the sense that we’re talking about the same old thing, really understanding what’s happening in your business. But I believe for the first time with tools like Appian and other workflow and orchestration kinds of tools, we can really do it. We can go to all these different systems and understand the data and build that true perspective of what’s going on and even aid in the decision making about who, what, when and where to move the parts around.

Isaac Sacolick:

So Joey, you’re going to have to go deeper from me here, right? We’ve been selling this for a while. The CEO wants to be wowed, the board wants to be wowed, and we’re going to come back to them with a use case and a set of use cases that on the surface seems boring but have a tremendous opportunity. How do you sell that in? How does a digital trailblazer sell that in as a priority going into 2025?

Joe Puglisi:

I think you highlight the pitfalls that the previous attempts have fallen into. What have been the wrinkles misaligning of data, the lack of a true understanding of implications of certain decisions. AI has the ability like people to look at the numbers and say, well, wait a minute. There’s something a little askew here. Let’s figure this out. It can integrate more, it can integrate faster, it can have many, many more rules to follow. So I think you can paint a much broader picture of what you’re able to present and the quality of what you’ll be able to present to management. I truly believe in it,

Isaac Sacolick:

Mike, help our audience here, right? We’re really excited by this idea of document processing. I think that’s probably the area that every major company has struggled with over the last 10 years, and now we’re bringing all this gen AI capability to it. We’re bringing low code capability to it, but how do we sell this in so that the executive committee and the board see the value out of it and get over this, I need to put my eggs into the hike basket.

Mike Beckley:

Well, I think this is really easy. It’s budget times and when you look at your traditional OCR and it’s costing you $14 million a year and it’s got straight through processing rates that are still only in the high 60 percentage points, maybe 70 if you’re lucky, and you’re able to say, well, now generative AI directly, like Joe was saying, addresses the edge cases. It addresses the exceptions that we’ve been talking about. That means that we can dramatically cut our spend upfront on the software. It’s going to be less expensive to use generative AI to process that paper. And because there’s going to be far fewer errors, we’re going to get higher straight through processing. That’s a lot less manual labor and that’s more indirect savings that will translate into millions more. And so that’s the simple part, but ghost reporting right behind handling all that paper is the creation of new paper.

That single pane glass is really another way of saying people need to create dashboards and reports and spend the white color workers spend a whole lot of their time, maybe 20, 30% minimum on generating reports for their bosses, and generative AI is helping solve that problem. Providing ways to automate those data reporting pipelines and gather information more efficiently through data fabrics and attaching a data fabric to a generative AI engine in a secure way allows us to not have to know where the information lives, the generative AI can go find it for us and show us its chain of reasoning and how it got to create the queries, and then automating that pipeline so that we don’t have to go create a manual report every time we’re asked for one by the executives on how that new product is doing and how’s that product launch going and how’s the adoption go? All of that can be generated for us and let people go back to thinking and reasoning over what to do about the results rather than trying to find out what the truth is.

Isaac Sacolick:

Yeah, Michael, I think that’s a good way of showcasing that. The intention was always there. It was actually a lot harder for us to implement a lot of the things we were discussing, and then we got to the point where we had all the data integrated, we had all the capability there, and we fell short because of all the exceptions that you were highlighting at the beginning. There’s a lot of messiness in human decisions when it comes to complex processes, and that’s what we’re trying to bring to the table today. Go ahead, Joanne. You wanted to jump in on this?

Joanne Friedman:

Yeah, I did because in addition to cost savings and value creation on the value creation side, whether it’s agentic or just generative, and you can’t necessarily use generative AI for everything. It’s not a one size fits all, but in the cases where you are using it as part of angen capability, you have something different than a single pane of glass. You have the opportunity to create context around each of the panes in the single pane and go deeper, go broader, go wider. That gives you a different kind of perspective. And that perspective is really what seems to be resonating with the c-suite around it because if they’re purposeful about what they want to accomplish using the shiny new tool and it’s presented to them as an opportunity to broaden the way they make decisions or incorporate other factors into those decisions, then they’re out of the silo of the process of decision making. They not even think bigger and by thinking bigger, they get to more cost effective value creating decisions, so revenue, growth, innovation, resiliency, all those top line things now become doable. We’re not just looking at process optimization for costings, we’re leveraging the same data to be able to do those top line values just as much.

Isaac Sacolick:

Excellent. John, top line values, what do you want to bridge off of that on

John Patrick Luethe:

Going you? Just to respond back to the other one about the democratization of data, if I can, you can comment on that really quickly.

Isaac Sacolick:

Yeah, of course.

John Patrick Luethe:

And I think over the last, I’d say 10 years, the people have been a lot more willing to consolidate all the data to give in data lakes and the generative AI has been such amazing informational retrieval tool, but we’ve also had people using, I would say power BI and things like that. It’s been so nice to be able to actually treat the dashboards you want, but there’s a whole lot of people, they can’t create the dashboards because they don’t have the skills for it or they get a dashboard and it doesn’t quite have what they want. I think power generative AI is ability to write code or change things. I think is also going to just help really close lot of the information that people want to see on the dashboards. I think if somebody’s able to get the data consolidated into a data lake and somebody’s able to get a report and it doesn’t happen what they want, it’s going to be so much easier to say, have the generative AI tailor the report to what you need to get exactly the data you want or create a new report for things or query that you need for the data.

And so I think from surfacing data for executives for reports to get the information people need to make decisions, I see a lot really good stuff in that space on it.

Isaac Sacolick:

Yeah, I think John, to your point, I mean the fact that so many people on our staff in the employee base have jumped on being able to test and evaluate and use LLMs, that will be a bridging point for them to work with these other capabilities and maybe just lower the change management barrier that we’ve seen when we brought new capabilities into our platforms. Liz, welcome to the floor. How do we make a boring use case? Sound exciting? Oh, Liz, can

Liz Martinez:

You hear me? We can hear you hear me. Excellent.

Yeah, so first of all, I don’t think there’s anything boring about making money, so let’s just take that right off the table. The idea about these boring use cases allows us to take something that we know is a huge cost of doing business either operationally or potentially cost of good sold just in terms of the labor intensity and reduce that down so that we can then invest in things that make a much higher value add to the company strategy. Now, the hard part is actually doing the business case around that. Everybody likes to say how, oh, this is going to be so great. We’re going to set up these internal l lm, it’s going to be private lm, and we’re going to do all this great AI stuff and blah, blah, blah. That’s really expensive. I’m sorry. It’s really expensive. And so you actually have to do the hard work of doing the business case, building out those AI tools, those agents, whatever it’s that you’re going to do actually has to be offset with the value that you’re going to get so that you can, and maybe it’s an ROI over multiple years, or maybe it’s within a year, I don’t know, but putting pen to paper and demonstrating the value add in of dollars and when those dollars can be then redirected to something that’s more strategic for the company.

That’s how you get UR Csuite engaged.

Isaac Sacolick:

So start with the money, which is usually a place that we’re all fearful of and yet we have some pretty good interesting use cases. Mike, you brought up document processing earlier and Joanne brought how do we show growth and potentially long-term value out of this? What I love about document processing is being able to bring my corpus of documents, my 2, 3, 4 years worth of documents back into an environment that’s intelligent, and then being able to ask questions around it and saying, how do we get smarter as a business around this? Joe and I have seen this in the construction industry. How do we bring all of our bid documents, all of our planning documents into a single environment and ask a very simple question, what projects are we more profitable on? Which ones should we bid more aggressively on, and which ones do we have to get more efficient in our operations around before we start bid around? I really like this idea of now I have a way of bringing all this intelligence from 3, 4, 5, 7 years of documentation that we have and using that to some kind of competitive advantage. Joanne brought up healthcare. That could be a really exciting place for building efficiencies and also developing smarter and more personalized healthcare by looking through all that documentation that we have there,

Liz Martinez:

Michael, and that’d be part of the business case, right? That actually you estimate a percentage of your business that you can actually create some efficiencies on and you include that in the business case.

Isaac Sacolick:

Absolutely. Michael, tell us some more of these exciting use cases that come from the boring side of gen ai.

Mike Beckley:

Yeah, so let’s say the most boring one is you mentioned earlier about software development lifecycle. People have this dream and this vision that generative AI will suddenly replace all software development and on its way to achieve singularity and placing all humans, the first thing that’s going to do is write all the code for us. And so we don’t need to worry about it. I don’t mean to be negative, but that doesn’t matter. It’s not going to happen in 2025. Don’t put it in your forecast or your savings plan unless you really want to look silly. What is happening though is the most text heavy part of the software development life cycles requirements, and so we can automate the heck out of requirements, and that’s where it works today. We can ingest all those requirements documents for the most complex applications and create very detailed plans for how to maximize the reuse of existing components and systems and data integrations and minimize the redundancy and the cost and the risk of building and modernizing your ERP systems.

So this is what everyone’s doing. Everyone’s trying to modernize their systems so they can take advantage of ai, but AI itself, where it’s doing it is not automatically replacing all software developers. It is automating the most text heavy part of the SVLC, and that is upfront requirements management and planning for these application build. And then so that’s another great boring use case for you. But I do want to emphasize when I talked about pushing paper and OCR in these business cases, I’m saying it for really specific reasons. I don’t think you need to get esoteric with the future value when you’re talking to executives because they get jaded quickly. They’ve been promised and over promise what new technology is going to deliver in cost savings. But when you start very specific on OCR, you can prove it in days and weeks, and that is what people need.

They need to see proof and real value from generative AI applications, and that is one way we can sit down and run away, feed in the documents like you were talking about Isaac with construction documents, planning documents, whatever they are underwriting documents and show executives absolute knock down amazing results virtually overnight. And so that’s what’s going to get funded. That’s what’s going to be effective. And yes, of course that will have value in terms of better customer experience. They’re getting their insurance quote in seconds and not in weeks. They’re better employee engagement because they don’t have to sit around doing all this manual, be keying and twin systems, but don’t promise that. Just promise what you can show and demonstrate, which is you can scan a whole lot of different paper than you ever could at much higher quality than you ever could before, and that leads the foundation for transforming all of these human workflows. And so no, it’s not as sexy as exciting as the singularity. It’s not as cool as worrying about whether or not generative AI is going to cause nuclear war, but why will it be a massive improvement in your bottom line?

Isaac Sacolick:

Mike, you’re highlighting a piece that I think is incredibly valuable for anybody who’s worked in the low-code and no-code space, the ability to take a concept and show results pretty quickly that we’re heading down the direct track, that we’re demonstrating value around it. I mean, I think going back to the question I put Joe on the spot, how do you get the board excited over this? Tell me if I’m sitting on top of all this information, if I’m all the underwriting data that we have access to all the tax documentation that the government has, what can we do with it quickly and start showing and getting people excited about this use case of looking through this documentation? What can I do in a short amount of time?

Mike Beckley:

Well, whatever you’re doing with it today, what you’re doing with today is leaving most of it behind, but you’re automating some of it with maybe 70% breakthrough processing investing. And so to be able to take that data and now feed it through generative AI and invent AI powered pipeline into your workflows that you already have, don’t create, invent a new workflow that takes too much time. Use your existing workflow and pilot using new generative AI techniques, and you can overnight improve this rates. You’re processing 20, 30%. You can actually get much better accuracy on more fields, on more data types, on more document types and show those results right away. That’s what you do. Don’t invent the future. Just reinvent what you have right in front of you. With this new technology.

Isaac Sacolick:

Saji has been trying to raise his hand. He asked me a question over a message. We start bringing all these capability and we start exposing it to our employees. How can we then get creativity using AI now that we have access to all this information? Mike, I don’t dunno if you want to take that or if somebody else wants to take that, but I think it’s an interesting question. We keep bringing more intelligence and more capability, and now we’re doing it inside people’s workflows and we’re saying, Hey, we’re picking away the difficult work that you were doing before and we’re bringing more creativity capabilities to you, Joe, what are some of the creativity that we can bring to them? Thank you, Joe.

Joe Puglisi:

Look, nobody likes to open a spreadsheet, copy a couple of columns, open another spreadsheet and paste them. Then go to the ERP run report, seven dash a copy, a few numbers off there, but this work is mind blowingly boring, and if we can introduce tools, low-code tools like Appian or other tools that we can teach our employees how to do their functions, just what they’re doing today, just do it faster, smarter, and with a higher degree of quality and free up their time, they’re going to embrace that. I’ve long been an advocate. You see me post all the time about how corporate America needs to invest in its existing employees, and this is one of the best ways that you can take your current works out and elevate them and give them the ability to do the mundane work, hand that off to an agent, teach ’em how to hand that stuff off to an agent and let ’em add more value in ways that AI isn’t capable of yet.

Isaac Sacolick:

Go ahead, John.

John Patrick Luethe:

I was at Stanford for 10 years and I remember being a new analyst and Joseph gave my be nightmares work I used to do back in the days. Yeah, yeah. So that totally resonates with me. The other thing I was going to say is that I have seen that the jury’s out on how much generated AI helps on development, productivity, know pretty large company, they went all in on it talking a year later on, how much of efficiency gains did you get? And I think you guys study that more. I do know that anything that you code you do get from generative ai, man, the amount of testing you have to do, it goes massively up. And so that’s an area I think that it’s, you almost have to look at how much you have to increase in testing for whenever you bring in this third party technology or third party stuff, and with generated ai, it can give you different results at different times, indeterminate nature of it. So yeah, I remember being so much, so much busy body work than I said just starting off in my career. It was hours of the days of it.

Isaac Sacolick:

Look, I subscribe and I’ll go to Joanne. Joanne, you remember the days when mobile first came out and cloud first came out and we kind of went after the low hanging fruit. What does it help us optimize that we haven’t done before? But the real exciting part of mobile was when we built mobile first interfaces and extended people were doing from things that were doing in the office to things they were doing out of the office, that became a whole new set of capabilities. Same thing with cloud. What it enabled us to do is scale things that we couldn’t do before and get access to capabilities that we couldn’t do before. I think when you start putting together RPA intelligent automation low code, you put all this together document processing, and you start bringing into the workflow people and say, start using this and start thinking differently about what you’re doing. Get off of trying to do copy paste spreadsheets and start asking this thing questions. We don’t know exactly what people are going to start using this for. We want them to work with us and partner with us to figure that out. Go ahead, Joan.

Joanne Friedman:

Well, we definitely want them to work with us and partner with us, but we’re freeing up the workforce to be innovative, to experiment, to have the time to think that they didn’t have before. But really, if you look at it from a data fabric perspective or a data perspective, there are common data elements across different workflows or different process streams, and this is where agen AI really comes in because once you have those common denominators and you start adding different contexts, I like to think of it like a diamond, right? When you look at the diamond and you look at the light hitting the stone in one way and then turn the stone a different way, you get a completely different perspective and it makes you think about things in a way that starts adding value. I mean, the creativity that one can use with generative AI is one thing.

When you start combining the mls, the machine learning and specialized process controls, if you’re in manufacturing or you’re making things and all the different kinds of other kinds of ai, that’s when the world changes because the mundane tasks that you started with ends up being, wow, I just took all that paperwork, got rid of it out of my sort of day. It is all done. Now I can think about how can I make a better product? How can I take people’s feedback on a product, whether it’s an insurance policy or a cup, and say, how do I make this better, faster, cheaper, more enticing to my customer, more enticing to my customer’s customer? That’s where we’re giving knowledge workers actually the ability to become knowledgeable because we bring in all the different streams of data in new ways. It’s kind of like Lego, right? If you only work with white blocks, all you’ve got is something with white blocks. If you start mixing and matching those and make them light, microservice light or containerized light, you can build whatever you want, and that is, I think, the greatest value of this inflection point. As AI is getting more sophisticated, we have the ability to create

Isaac Sacolick:

Mike to tee up for you. What are we creating? You were going to chime in on this.

Mike Beckley:

Yeah. Well, what I was going to say was how do you spark that and scale it that creativity, and the way we’re doing that with our clients today is we’re able to run hackathons where business leaders, business users, 50, 60 of ’em at a time, who have not previously built Appian applications. They’re not experienced low code developers. You give ’em a two hour enablement session and then they game out with workflows that they already deal with. They want to automate and using low code generative AI and agent building techniques on the existing workflow in a single day. They can innovate and create, but what comes out of that is on platform, which is already designed to scale and be governed by it and is already regulatory approved and compliance, so they can not just have creativity, but that creativity can then actually be put into the most direct way in operations and workflows and scale that across an enterprise. And that is the real magic of what we’re talking about here by saying, look at the workflows you have, use these low-code, generative AI agent technologies in combination with your existing workflows, and that’s where people can all be part of the process of change. People can all be involved in the process of operationalizing how AI is going to actually transform the most boring parts of the work.

Isaac Sacolick:

Mike, both John and I had the same question. What types of applications are coming out of these hackathons that your business users are able to spark out that quickly?

Mike Beckley:

Yeah, so it’s operations. It is the middle office and back office operations of how do you actually perform a treasury financing operation? How do you move money from one account to another? How do you actually onboard a customer more efficiently when you have to reconcile many different systems to accomplish that today? How do you move from just onboarding a customer to then updating the regulatory reporting for that customer? It is in the operations groups where you have the most pent up demand to try and tackle those customer and retail operations. I think that is the immediate benefit from holding those types of hackathons. As long as it’s not just about the ai, it’s about how do you look that through a workflow perspective.

Isaac Sacolick:

Mike, I’m going to let you end with that statement. I think that’s incredible advice to all the digital trailblazers listening here in terms of bringing boring use cases. Bring the capability to your operations teams. They know what the steps in their process are. They know where they’re struggling with too much work in the boring areas and give them the ability to do a hackathon, bring the capability to them where they can experiment with these technologies. The fascinating thing about this now is we have data fabrics to bring data in. We have low-code capabilities to enable building out the workflows. We have document processing to load in all this documentation. We have all these different capabilities, and the reality is, let’s bring it to a workflow. Let’s set up our agents where people are actually working and let’s let them feel empowered. Mike, any last words for the group today before we close out?

Mike Beckley:

I think you’ve closed out. Well, thank you, Isaac, and thank you to the panelists. I think it’s been a great conversation. I know we’re going to have incredibly boring in 2025 by the time of real actual practical value from Generat ai.

Isaac Sacolick:

Thank you, Mike, and thank you, Joanne, Joe, Liz, John for joining me today in this discussion on how AI will be boring in 2025. I want to thank our sponsor today, Appian Business and Organizations run on processes, make your process better with Appian, the process company. Visit appian.com to learn more. Thanks again, Mike, and to the Appian team for sponsoring today. I just want to let you know about our upcoming episodes next week, holiday week. We’re going to skip November 29th. Here are your three episodes for December. I just announced this yesterday, December 6th. We’ll be doing culture transformation, evolving, diversity, inclusion, hybrid working and global collaboration. A lot of changes I expect happening in companies over the next few years, so we’ll be talking about that on December 6th. On December 13th, AI area transformation, gen AI guardrails, and how to implement safe gen ai, and then on the 20th shaping tomorrow moral courage on taking the right path and doing the right thing. All three of those we’re recommended by listeners. So if you’re listening here and would like to share an idea for a topic to message me, again, thank you, Mike, and thank you Appian. Thank you, Joanne, Joe, Liz, and John for joining me today. Happy Thanksgiving to all of us who are celebrated here in the United States and everybody have a safe and happy weekend. Have a good one.

You are unauthorized to view this page.

This episode of Coffee with Digital Trailblazers focuses on how to communicate bad news to executives in ways that are calm, constructive, and aligned with organizational culture.

Host Isaac Sacolick frames the discussion around recent outages and crises, asking when issues should be escalated, how to avoid over-escalation, and what executives actually need to hear. Guest expert and former global CHRO/board advisor emphasizes knowing your executive’s preferences, sharing updates early and often, being transparent about what is known and unknown, and always focusing communications on solutions, next steps, and long-term impact rather than drama. She and others stress managing personal anxiety, “playing cool jazz in the background,” and avoiding “Chicken Little syndrome” by classifying issues (true crises vs. bad news vs. everyday incidents), so not everything becomes a fire drill.

Additional panelists expand on practical guidelines: define clear escalation criteria (e.g., brand damage and lawsuits), decide whether you’re informing, asking for help, or seeking a decision, and never let executives hear bad news from outside your team first. They also highlight security-specific nuances, the need to lead with facts while acknowledging uncertainty, and the importance of finding the “upside” or learning opportunity in crises so organizations improve rather than just react. The episode closes with reminders to thank messengers of bad news instead of “shooting” them, and to respect that seasoned executives have heard bad news many times before and can handle it when given clear context and options

[00:00:00] Speaker A: Bad news to executives. And we’re just going to give a few more minutes for everybody to join. I see some old friends here.

Hello Kristen. Hello Dave.

Hi Ronan. Hi Jay. Let’s see some new folks. Oh hi Roman. Roman, my wife and daughter are in your area today and tomorrow they’ve been visiting colleges up in the Chicago area.

I think they’ve been by your university, Roman.

They told me they were going by. I don’t know if that’s her, one of her top choices, but they’re out in the area looking at universities. It’s been an interesting week. They’ve been out there. I was in Las Vegas earlier this week at the workday conference.

I should have a blog post around that on Monday.

Just some of the learnings and findings I’ve had out there.

Very interesting conference and I did do a couple of posts on LinkedIn around it. So if you want like my day one and day two learnings, I did do some photos and posting around that.

But I will have a recap blog post on Monday around it.

For those of you who joined us last week we announced that the beta launch of the Star CIO Digital Trailblazer community.

A good number of you did use the coupon code and joined.

If you have not done so yet, I’m leaving that coupon code available for at least the next couple of weeks and it is a free access to the community.

So please do take advantage of it in the comments for today. If you click on the URL for this event, you can get there by going to starcio.comcoffee/next-event do click on the comments tab, scroll down, you’ll see the link which is drive.starcio.com and you will see that coupon code to join for one year free. The Star CIO Digital Trailblazer community.

In the last week I’ve got a couple of new experts who have joined me.

I don’t see Liz Martinez here. I don’t know if she’s joining yet, but she has joined us as one of our program and portfolio management and governance experts. We are getting a Advisory Connect program up for her shortly. I just finished reviewing it today so it should be up soon.

And I want to welcome Jennifer Krevitt who has listened here a number of times. This is her first time on stage. She’s going to be here for about half of the session today.

She is one of our Star CIO Digital Trailblazer experts. She has a long career of being a global chro at financial services companies like Invesco and Goldman Sachs. She’s a board advisor and board member. She does executive coaching.

She’s also a good friend of mine. Jennifer, welcome to the stage today. And today we’re talking about community communicating bad news to executives. And just to set the preface of how this got on our agenda, I added a number of sessions after the crowdstrike outage. We talked about being ready for outages. We talked about DevOps and some of the things that you should have in place to make sure that you’re defensible against outages. And I thought this one was going to be an interesting one because this, it’s all kinds of different crises, right? It’s not just a security or technology outage. Anytime there’s something that’s happening in the organization that falls into the category of bad news.

And now you have to decide as a, as an executive, as a digital trailblazer, is this worth sharing? Is this something that we need to talk about? Do I need to call up the CEO at 4 in the morning to tell them this? Does this need to be on the agenda for our next SLT meeting?

And I find different organizations and different leaders have different perceptions of how much to escalate, how much to communicate, how to go about communicating it. It’s probably the more important question. And so, Jennifer, we’re going to start with you. I want to ask the question, you know, what really constitutes bad news?

What should be escalated? What really should be at a lower priority? And maybe answer this. As a board leader, right, you’re usually one of the people, as a CDC chro, one of the confidence of the board and one of the confidence of this, of the CEO. You know, what constitutes something that really needs to be escalated because it’s important.

And what are some things that can be managed outside of, you know, a call on the bat phone and escalating to your executive?

You’ll need to go off, on, off, mute. It’s that button on the. There you go. Jennifer, welcome to the stage.

[00:05:06] Speaker B: Thank you so much. It’s so nice to be here.

So thank you very much for the question. And this is obviously something we all think about. No matter where you are in the organization, how do you tell more senior people what’s going on and keeping them in the loop and really ensuring that you are able to meet the needs of the organization as well as the needs of your teams and leadership.

The most important thing I think here is to know your executive, to know you’re bored. And so some of us have different appetites for where we want to be brought in. And I think always that each of us has particular preferences and each of us prefers to ingest information differently.

Some like to know the data and analysis perspective of it, some are very task oriented the how of it and others just want to know the big picture and assume you are progressing along as you should be with respect to bad news or I would say unexpected news. I as a leader and find as a person who reports to leaders that more postings are more important than less. And so that is a difficult balance of managing timeliness versus having everything buttoned up.

But if you really do understand your role in the organization, your team’s role in the organization, management’s role and leadership, it’s much more helpful.

And so I think when we think about digital trailblazers, these are folks who are really charged with much more than just the deliverable on the particular tech project or any other kind of project. They’re people who are trying to move the organization and help the organization shift. And so my bias is more information is better than less. And I never like to be the only person that knows anything. I see an old colleague of mine on the call today and I am certain he’s heard me say that before, which is never be the only one that knows anything. And so when I think about bad news or missing milestones or things not progressing the way you would expect them to do, I think about the importance of assessing the situation to really determine the urgency.

Always be prepared when you post, indicate that this is just an update to let you know what we’re working on, but really try to keep people involved.

Consider the stakeholders perspectives, know your executive, know your leader and know what is going to drive them.

Be transparent and have direct straightforward communications and always be purpose driven. Focus on solutions and focus on steps to mitigate.

The issue of timeliness is one that I spend a lot of time on because I as a person, as a manager, as a leader, as a worker and someone that likes to be brought in early and often but not, not everybody is like that. And so know your executive and manage the importance of the long term impact on the project. How commercial is the project, how commercial is the missed milestone and really take it from there with respect to using your judgment, knowing the organization, knowing how important it is to your team, management and leaders and proceed from there. And always see yourself as someone who is committed to the organization and working through all of the organizational shifts and the cultural shifts and trying to model all that behavior as you proceed in situations such as this. So we Call it bad news, but it is really just the day to day of organizations missed milestones, unforeseen errors, and so that there are no surprises at the end of the day. So you are minimizing risks which always exist and you are continuing to discuss and evaluate as you proceed.

[00:09:23] Speaker A: Jennifer, I have a couple of follow ups. I know you’re only here with us for the first 20 or so minutes, so want to get as much wisdom out of you. You’ve been on so many big companies, ones that are very risk driven and I think you have a lot to share. So I have two questions for you that are follow ups.

First, you know, how formalized in the, you know, executive groups that you’ve been in the boards, how formalized around what should be escalated?

Is there a policy, is there something that’s been communicated? I unfortunately, many of the companies I’ve worked with and I’m going to ask Joe the same question when we ask him to speak. Many of the companies I work with tend to want to know too much, want to get too much escalated. Every little thing that can impact particular customers or operations, they want that escalated. And it sort of turns into a culture of firefighting when almost everything gets escalated into. The first thing that you’re talking about is all the things that are going wrong and what people are doing about it. So I’m wondering, number one, is it formalized in these companies that you’ve worked with, have they been more leaning toward only tell us the most important things are more leaning toward, you know, give us a real status update of all the major risks. And the second thing is like, what do you advise, you know, leaders who are not generally at the leadership team, but maybe managing a very big program or a very important operation? What are you advising them to come with when they’re escalating? What’s the message so that they don’t create undue panic? They provide facts.

What are some of the things you’re looking for as somebody sitting on that committee saying, look, something is wrong here and you’re getting the information that you need as an executive.

[00:11:20] Speaker B: Excellent. Two very important questions. So first, I think different cultures, different organizations have different cultures. And I think it is very important to know your culture.

One of the organizations I’ve worked with was a very risk oriented organization. It was in the blood of everyone. And so not to fight the hypo, Isaac, but the notion that risk is only bad news is one way of seeing risk, which is I know I’m a lawyer by training and so everything I Do and see and say is through the framing of risk.

And so I choose not to think of risk as negative. I choose to think of risk and driving the strategies as intertwined and really important as you move forward.

So all of us are at different levels in our career and at different levels in the organization, yet all of us are really important to, to the job that we are actually doing.

And so people are more junior to us and we are their senior leader. And then of course we are others workers in service to the broader organization and the most senior leaders. And the way I think about balancing is that no discussion should happen one day knock on the door and just blurted out with no context and no prior conversations. And so, for example, if there is a real issue with something, a missed milestone, there is a hierarchy that goes up. And hopefully everyone in the organization, as we try to transform organizations, has had open conversations and dialogue about how to move in an agile and effective transformational culture. And what that does is it leads to continual discussions and evaluation of where we are, where we think we’ll be next week, and how we think about managing, as I say, the risks and the drive to move forward. So on that I think it is really important to know the culture, to never give up on managing risk and communications and to know your executive.

Some folks really do not want to be part of the details and some folks do. And I think it is incumbent upon each of us to have that inner compass to make sure that we are doing what we think is the right thing to do for the organization, not, not just what you think may annoy someone who’s more senior than you.

And you could say that about the board as well. So on that, that’s the first question.

And then when we think about leaders, different folks have different appetites for what kind of data and information they want. And so when you speak to a leader, and again, a leader can be at every level in the organization and if we’re talking about the most senior leaders, they’re going to really want to know context and where it fits in to the broader organizational priorities. And so the way I always prepare for those conversations and expect people to prepare for conversations with me is to really have an assessment of the situation, to really determine the urgency of what is going on and to really understand where I am in the cycle of the quote, bad news? Is this an early post, a middle post, a follow up post, to really understand that and to really understand if it significantly impacts the priorities of the organization, priorities of time, you know, time, deadlines and Milestones that are broader than the organization. So, so first, assess the situation.

Second, always come prepared. Collect data and insights to understand the full context and communicate clearly and confidently. It’s so obvious. But so many of us, even if you’re posting on something you found out a half an hour ago, spend the 15 minutes to ask yourself, what questions would you ask if you were the one getting this information?

And almost always those are the questions that are asked. And so often I used to tell people I move desks or I stand up or I sit somewhere else to figure out what I would ask if I were hearing this information.

[00:16:12] Speaker A: I think that’s really great advice, Jennifer. I mean, I think that’s, you know, that’s an easy sort of takeaway for folks listening, is to put yourself in the, in the shoes of the, the people who are executives. They need to know, do I need to do something different today, this week, this month, based on the information that you’re, you’re giving us? And you’re exactly right, you know, providing some context about what’s a material risk versus what’s, what’s an actual impact, you know, revenue brand are the two that come to my mind that we need to be able to give some specificity around.

And that’s really good advice for people who are listening. I want to, before you sign off, I want to give the mic over to Joe and then maybe Joanne to see if they have any questions for you before you drop off. Joe, we’re going to all comment on this, but if you have a question for Jennifer, go for it.

[00:17:12] Speaker C: I think we sometimes when I say we organizations suffer from severe cls, Chicken Little syndrome.

You know, the sky is falling when something goes awry.

And I wonder, Jennifer, how do you dampen down the. This sort of, you know, it’s the end of the world message that comes either from people that report to you or conversely, a reaction when something is reported to senior management.

You know, I often cite my rule here of remain calm at all times, rcat and I live by that, but others don’t. So how do you deal with that?

[00:17:57] Speaker B: Yeah, it’s funny because my husband happens to be on this call and he knows that at home I’m a lunatic at work. Many, many, many years ago, I made the decision that I was going to. The more anxious I got inside, the calmer and the slow, slower I was going to walk at work because otherwise I wouldn’t make it through a career. And so the reality is, if you’re all rowing in the same direction and you Assume best intentions and you trust is given that leads to a very, very, I think a fe effective organization focused on impact, excellence and accountability.

And so there are always times that people get very anxious about coming in.

I think most people have never been afraid to tell me bad news, but they themselves may be anxious. And if you just say that we’re going to figure it out, we all know that this is, you know, the mantra of this isn’t heart surgery type thing, which is we can figure it out. We can focus very clearly on what the next steps are. As Isaac said, what are we going to do today, what are we going to do tomorrow and what are we going to do next month?

It’s a much more productive way of living your life. And the truth is that has to be embedded deeply in cultures. And so some cultures not only want to know what the problems are, but they really expect all workers to be anticipating what’s around the corner. And so the sky is falling is never helpful. And if you walk into someone with the sky is falling perspective, they are going to think you are a lunatic and don’t have things under control.

And so as I’ve always said, never let them see a sweet and let’s play cool jazz in the background. And it doesn’t mean you don’t feel anxious and crazy inside. It just permits you and your team to be collaborative, to be focused on what’s next and to really continue to support an organization that Isaac discusses very importantly and calls the people like those of us who try to focus on this digital trailblazers.

[00:20:28] Speaker A: Thank you, Joe and Jennifer. Joe, I’m going to try to give the mic over to Joanne, see if she has a quick question for Jennifer before she has to drop off. Hi, Joanne.

[00:20:38] Speaker C: Good morning.

[00:20:39] Speaker D: I do have a question and my question is what I didn’t hear in a lot of what you were saying, which is very good advice by the way, is to present some form of an upside.

Because regardless of what the crisis is, there is always a bit of an upside. And I found from being on boards, talking to executives on a daily basis that if you can find any kind of an upside to the bad news, like it gives us an opportunity to do X at the same time that they begin, the calmness tends to return, the ire tends to deplete and the wisdom starts to come out. And I’m wondering if you agree with that.

[00:21:28] Speaker B: Yeah, so I think that’s very good advice. The way I would frame that in my own head is really to focus on the solutions. The next steps the where we are and focus on the forward. And then as you proceed, you follow up on the meeting with a summary, you include next steps, you reinforce the accountability and you keep the focus on moving forward. And that is what essentially the upside is. I’m a rather cynical person, so if someone comes in and says, you know, the sky is falling, but the good news is we can paint it purple, not blue. Now I, you know, my, as I described before, my preference for ingesting information is not generally that, but I think your, the advice or the suggestion you’re giving is a really important one, which is to focus on the forward and the opportunity we have in front of us. And that’s really important. And then you continue to build a culture of learning and reflecting throughout the project. Not just, and I say project, it could be really anything, but not just at the beginning or the end, but you continue to learn through the process and always do after action reviews very, very periodically, which are structured ways to reflect on the situation, evaluate responses, identify lessons learned and to focus on the upside.

[00:22:59] Speaker A: Jennifer, I just want to say thank you for joining us today and sharing all of this. I mean, Joe has our cat remain calm at all times and Joanne brings chocolate to diffus the situation. And we’re going to remember, you know, you’re the cool jazz lady. You’re, you’re playing in the background and keeping us calm through no matter what is happening to us. And some really good advice. I think the, you know, the one thing I’ll remember is how important continual communications is, you know, and then, you know, when you’re escalating something that’s really bad news, you have a benchmark to compare that off of something is really awry off of what we’ve normally been communicating and therefore the executives need to know about it. And therefore here are the things we really need to consider based on what just happened or what we just learned. Jennifer, thanks for joining us today and thank you for joining me as a star CIO digital trailblazer expert. And we’ll have you back on here soon.

[00:23:58] Speaker B: Excellent. Isaac, can I say one thing?

[00:24:01] Speaker C: Sure.

[00:24:01] Speaker B: Okay, I’m going to add one thing. It’s about the cool jazz. So years ago I went into a very important meeting and I was presenting and sort of the staffers, very senior people who were in the next room from the very important meeting had papers flying, like literally papers all over the place, papers flying. We’re two minutes late. And it engendered no confidence in me that I was going into a meeting that knew what was going on and it didn’t make me feel like they were any more important in this side meeting and in the side room. And I always thought that the drama of it all sometimes makes people feel in the know, in the middle and empowered. And the output of that, the response to that, the energy that that sends back is exactly the opposite. So it isn’t that you’re less crazy inside, it’s just that in control, facile with the data and the insights. And to Joanne’s point, always focusing on the forward and the upside is really critical. And I so look forward to continuing to work together and I’ll speak to many of you soon.

[00:25:16] Speaker A: Thank you again, Jennifer. Folks, you’re listening Today to the 96th episode of Culture Transformation. Today we’re talking or 96th episode of the Coffee with Digital Trailblazers Today. Today we’re talking about Culture Transformation communicating bad news to executives. Next week we have our episode on Geez, I lost it. I had it right in front of me and I lost it. Next week we’re talking about shaping tomorrow green tech platforms and initiatives. I’ve left you two links to my articles that I published this week. This week I did a article for CIOs for those or not just CIOs, but if you’re not reading it, the tea leaves are changing.

You know, we had an interesting announcement this week about interest rates, but I’ve been posting around this the 6% unemployment in it.

Amazon made some announcements about people coming back to work five days a week. We are definitely at an inflection point in terms of the IT and technology and transformation economy. That most likely will be an episode that we cover in October. But I left you five things to think about for your digital transformation budgets going into budget season. That’s on blogs, that’s star cio.com and then an article got a lot of lot of feedback on around the emerging role, the next gen roles of enterprise and solutions Architects. That’s on CIO.com I have a few more weeks. I’m doing my free coupon to the Star CIO Digital Trailblazer community. The link is drive.starcio.com the coupon code is in the comments. Several of you did take advantage of that last week. I’m keeping that open for a couple weeks. You’re getting free access for a full year and you’re going to be able to sign up for advisory connect programs with some of the experts that you see here.

Joe has one on Ask the Expert. Liz has joined Us as an expert. She is now on the website. I’ll be working with her on an advisory Connect program very soon. Jennifer is one of our experts, so we will be doing one with her as well. So and of course, John, Joanne, Heather, Martin, they’re all experts on the program. So again, look at the comments.

You’ll see the link, you’ll see the coupon code. You get free access for a year. I’m keeping this open for the next couple of weeks. And thank you for joining this week’s session on communicating bad news to executives. I want to bring John and Liz in first. Joe and Joanne, thank you for your question questions there, John.

You know, I just feel like we haven’t really nailed how to establish what needs gets escalated. I know you wanted to talk about that earlier. So how do you set principles not just with the executive group, but how do you do it with your boss?

[00:28:06] Speaker E: Yeah, what I really like to do is have a conversation about what type of items get escalated, who they get escalated to and how fast. And for example, at my company, the things that I know that if I get any whiff of these, I immediately have to go and notify people. One is if we have a brand issue, like if we’re doing something that’s going to impact our brand, I know I immediately got to get a hold of and notify the people that I work for. The other one is a lawsuit. Right. And so to me, I have really prescriptive guidance on if you see these two things, let us know immediately what’s happening. And the thing about bad news is that it doesn’t get better with time. And then there’s a quote from Rand Fiskin. He says bad news is like kimchi. If you bury it, it only gets worse. Right. And so he’s the founder of CEO Moz seomoz, and I just love that quote.

But the thing is, when you’re going to go talk to the executives, it’s like there’s or the people above you, there’s some things you have to figure out. Do I have this covered or am I asking for help?

If you need help going to your leadership team, absolutely. They can help you out. And then you have to think through what do I want to tell them, what do I want to ask them, what help do I need and what do I need them to do? You think through those things so that when you go tell them what’s going on, you can get whatever help you need, you can get whatever guidance you need. You can get them to take actions for you or you can get them to go enable other people to help you respond. And the one thing that I’ve seen in bad news that really kills me, though, is I’ve seen when there’s peers and toxic companies that really weaponize it, they’ll hear bad news in kind of like my area. And then they wait until we’re in a leadership group and they say, hey, I just got this bad news. Sorry I didn’t give a chance to give you a heads up. And then they tell the people that run the company or that own the company.

And so you’re hearing the bad news at the same time as them.

And so that’s what you absolutely want to avoid doing because that just is bad. It’s like it’s a toxic culture trait. And so it’s like figuring out who you talk to in what order is really, really helpful. And that’s a good trait that you should have so you can collaborate good with your peers and people around you. The very last thing I was going to say is sometimes when you go, you actually don’t have a solve for it.

And I really tell people, if you have bad news and you don’t have a solve for it, just go tell people so that you can get help on solving it.

[00:30:37] Speaker A: John, really good advice there. I mean, being sabotaged by bringing up bad news when you’re not aware of it. And that surprised me. That’s just awful. And I’ve experienced is a culture issue, but that idea that, yes, you’re going to come up and escalate bad news, whether it’s legal or brand, what your guidelines are, and, and even if you don’t know what the solution is, being able to communicate the issue and the potential impact is fairly important.

Liz, I’m hoping, you know, running PMOs and, you know, we’ve talked about governance here, even though we don’t like to use that word, the G word. The G word. Liz, what’s your best case scenario where there were clear guidelines about how to escalate and what to escalate?

[00:31:26] Speaker F: Of course, yes, fantastic question.

So often the communications charge is underneath the pmo and communications usually includes, you know, risk communications to leadership, especially because the pmo, if you’re in charge of any kind of complex program, you’re typically the group that knows about it first and foremost.

Some of the stuff that she shared about knowing your executives and understanding, you know, your culture and the context of what’s going on is extremely important, like how much an executive likes to know detail, but also understanding your own role in the process is really important because, you know, it could be that you’re accountable for fixing the problem, or it could be that you’re just the person who’s supposed to be informing and making sure that the communications.

Or maybe you’re just the risk person who’s supposed to be evaluating the impact or possible options. Right. You have to understand there’s so much more than just knowing your executives and as well as your culture and also understanding how much you personally can influence that solutions and how bad is bad. Listen, if you find out that all of a sudden your subcontractor is going

[00:32:46] Speaker A: belly up, like, we’ve been through that.

[00:32:48] Speaker F: Right.

[00:32:49] Speaker A: We’ve been through one of those together.

[00:32:51] Speaker F: Right?

[00:32:51] Speaker D: Correct.

[00:32:52] Speaker A: Yeah.

[00:32:53] Speaker F: And. But you also know that you thought it was coming down the pike. You had already lined up some other subcontractors. You had already told your executive leadership that this is a possibility, and you had already, you know, that you’re, you know, kind of primed for that to happen.

Then you can, you know, it’s a different situation than something coming out of the blue where you had a subcontractor that had two people, they were on the same plane and then went down, God forbid. Right. There’s. Those are two different, very different situations, you know, with, with all their ip.

So clarifying the information that you have, and I love what Patrick said, if you don’t have the information, say so.

Right. You use the kimchi example. I use the fine wine example. Bad news does not age well. It is not a fine wine.

So basically it’s more like, you know, cheese. It gets stinkier and stinkier. But we’re gonna, we’re gonna run out of analogies.

Cheese. Wine. I like wine and cheese. You know me.

But anyway, remaining solution focused. But if you don’t have the information and you feel the need to share the information, share that it’s happening, making sure that you’re clear on when you’re getting back with data. Right.

This is what happened. I know it’s impactful. I’m going to find out the impact. I’m going to find some options for you. And regardless of what information I have, I will be getting back to you within two hours.

Right. Or whatever. Right.

So.

And the impact, the impact of the business, especially the impact because it could be, in the end, the impact very low.

[00:34:39] Speaker A: So sometimes I want to bring Joe back in here. Maybe we’ll do a little scenario planning with Joe and, and maybe even David. Joe, I’m sure you’ve been in that this situation, you know, we’re talking about, you know, legal and brand crisis. We’re talking, you know, we’re crowdstrike is still in the back of our minds. That’s a, you know, completely hemorrhaging customer outage. What, you know, long term issues as a cio, you know, you’re getting, you know, escalations that are a few, you know, rungs down the ladder. You know, a system is out, the ERP didn’t take last night’s data feed.

You know, the project is got some material risk and you know, you’ve led long enough, right? You know, big enough teams, they’re coming to you with these material risks at your level. What do you want the team, what’s your temperament? What do you want to know as a CIO about a material issue? And what should the team come to you with when they have to escalate something like that?

[00:35:43] Speaker C: That’s a great question, Isaac. And first, I want to cite the first of my three golden rules. If you work for me, you learn my three golden rules and you can read about them in, in my blog. But for purposes here, I’ll just tell you. Rule number one is I want to be the first to know if there’s an issue. I never want to hear about the issue from anyone outside of my department.

And, and that’s critical. And I make sure that my staff and their staffs understand that.

So often people will come to me with a problem, sometimes sheepishly and sometimes with their arms flailing in the air.

As I’ve already stated, I’m unflappable, so I will listen intently and I would try to put things in context.

Several people have used the term context, and I think it’s so important to really put it in context, to put it in perspective.

Just exactly how big of a problem are we talking about?

Is it something that’s going to have a broad impact across the entire organization, or are we talking about a router that failed in, you know, Muskogee, Wisconsin, and easily dealt with just within the department?

So put it in context.

What do I want to know? Well, I want to know everything you know about the problem. And if you don’t know much, then I’m probably going to send you back to get more information.

And the last thing I’ll say is in the conveyance of any messaging, whether it’s bad news or anything else, you have an objective in mind. If you come to me to deliver a message, in this case some bad news, what is your objective? Are you merely letting me know and you’ve got it under control.

Are you letting me know because, oh my God, this could have a tremendous impact and you need to take this upstairs or are you coming to me because you found something and you’re not sure what to do about it and you want advice?

So tell me, you know, what’s the purpose for you conveying this information to me and then we’ll deal with it appropriately.

[00:37:57] Speaker A: Joe, thanks for that. I hope you can leave the link to the any posts that you’re referencing in the comments for everybody.

You’re giving us examples of what Jennifer and John had recommended. Set expectations with your teams in terms of how you want to be notified and what to come with.

Joanne, I’m going to bring you back in a second. I want you to think about culture for a second, but I want Dave to answer the same question.

Security issue is, you know, I think it’s a little bit different. You don’t always have the full context.

You might have had one desktop have a ransomware notice around it. You might have had passwords exposed and in the public. No.

Usually when the SoC gets some kind of alert about a problem, they really don’t always know the magnitude and they almost always don’t know the resolution.

What’s the best way to handle it to avoid, you know, the sky is falling reaction, which unfortunately a lot of organizations feel when they feel like they’ve been attacked and there’s an issue out of their control.

[00:39:02] Speaker G: Yeah, that’s a very good question. But let’s just back up a little bit.

So the security organization is often, you know, the house of no. Right. You know, so you bring a question there, you expect to know, well, let’s turn the security organization over and say, find a way to. Yes, okay, so how do you do that? It’s fact based. In a security organization, opinions hold no water at all because security tooling brings you a lot of facts. That doesn’t mean, however, that in the course of doing business you don’t follow your intuition in order to get the facts associated with a sense that’s developing.

Bad news can be the. There can be a leading sense of bad news is imminent. Right. You have a pattern that’s coming out of your sock, as you suggested. You don’t have enough information to know what’s going on there, but you suddenly have a sense of the sky is falling. So, you know, the first thing is to get the facts right. And it could be a fact that you cannot get the facts right.

So what do you do with that?

You bring it to leadership that we have a gap in our metrics. Our ability to measure in this particular sense isn’t bringing us facts because, you know, when it comes to bad news, facts trump everything else.

Another point I’ll bring up about bad news in the security world.

Whenever security speaks up, it’s like a, you know, a hundred pound hammer, right? It just trumps all other activities.

You know, the crowd strike, for example. All our systems are going down.

Oh my gosh, we’re going to put all our resources there, right?

So security has to be cognizant of how the bad news is communicated, such that overreaction is not the result. Right.

Let’s see, one other thing.

Security is an organization that brings a lot of bad news.

But I like the way it was mentioned. It’s all relative, right? So if the entire company has a metric that’s below industry standard, that could be our operational context.

If one particular part of our company is exceptionally poor relative to the rest, that’s a different thing entirely.

And I would like to close this little contribution with a mantra that I picked up, you know, more than 30 years ago. Let no crisis go unexploited. Right?

So as bad news is developing, look for the opportunity in it, right? Is this an opportunity to inform a future investment, to inform an organizational shift?

So as I’ve gathered information, I mean, you look for the big picture, what’s the pattern that the bad news is telling you the negative trending data is leading you towards, and then anticipate that as an opportunity to improve things in the future.

[00:42:39] Speaker A: Thank you, David. I mean, that’s just a good solid takeaway is always coming with the facts that you have. But leaving off what you don’t know, being clear that, hey, we don’t know the magnitude of this, we don’t know if it’s taking everything down and then always going back and saying, you know, where are we going to button things up for the future? Joanne, thank you for being patient today. I know you probably have a lot of comments on here, but I’m really interested, beyond your comments, if we can talk a little bit about culture.

You know, we’re, we’re focusing on bad news and we’re also sort of talking about extreme bad news.

And that’s why I try to bring it back down to Joe and David. Every day there’s something going wrong in a company, some days worse than others. And many of us have seen organizations where they’re just, you know, always in firefighting mode and you can’t drive transformation. You can’t try, you know, have a healthy culture. You can’t get employees excited if every day your day is disrupted because of the latest risk or issue that somebody has escalates. I really want to hear your thoughts around that, but start with your general comments. Thank you for being patient, Joanne.

[00:43:56] Speaker D: No worries.

First of all, to your, to your comment about firefighting mode.

One of the things that I’m a little disturbed by in this discussion is that we’re viewing bad news as one bucket, if you will.

And I think particularly for digital trends, Transformers, what’s required is to set up a level to level set on what is the criteria that makes up a crisis, what is the criteria that makes up bad news, what is criteria that sets up for.

We’re all having a Monday today versus it’s an incident. It might have some impact later, but we can mitigate both the risk and the cost and the impact.

And that’s, I think the first thing that people really need to look at is how do you classify, quantify, clarify the difference between those buckets? Because the strategy for each will be different. The communications up, down, sideways will be different.

You know, when I asked the question before about what about the upside?

One of the things that I’ve always tried to do, and yes, I do it with chocolate because it’s an endorphin releaser, it makes people feel better, it calms their mood and it distracts them just enough to not, you know, go to full outrage if it’s something that’s really horrible.

To me, a security breach is a crisis.

To me, bad news is we didn’t close that deal with so and so that’s going to impact cash flow. Our share price might go down, we’ll see a bit of fluctuation, etc.

Those type of events. Then there’s the mitigatable risk and, and, or, or the event that makes you say, okay, we can mitigate this in each of those cases to something that David said as well. There is always an upside. And so I try to balance, you know, my delivery of the chocolate to the executive along with the news in whatever bucket it’s coming from with. And by the way, the silver lining or the opportunity that I can see from this, maybe in the near term, maybe in the midterm, maybe in the long term, is blank.

So I’m trying to convey the notion that you have to think holistically and you have to think about not only the sort of steps that you would take in the how to do the retrospectives, but to deliver the news with both the bad and the good, and in a way that the context that you’re creating fits one of those buckets. It’s really a crisis.

It’s really bad news.

Somewhat manageable, or we’re all having a bad day today. And don’t let it color your judgment going forward, because unless we quantify and clarify that that bad news does color people’s judgment for a lot more than just the next hour or the next 12 hours or two days, it lasts, they remember it, and they immediately go to, well, maybe the sky didn’t fall then, but maybe, maybe it is actually falling down.

[00:47:33] Speaker A: Joanne, I love your definition. I just love your definitions. Because we come from a world where these things get classified as SEV1s and SEB2s or P zeros and P1s. And only the folks who created the system that manages that have any clue what that actually means.

Crisis is a word.

You know, bad news means something different.

Liz could talk all day about how to, you know, get a one sheeter in front of the organization that starts putting some language around that so that John, John knows how to deal with this and Joe knows how to deal with this at a, you know, at an escalating down level.

Joanne, you know, I, I do want you to comment about, you know, let’s put you in a scenario like I asked Joe and David, but let’s do something different. What happens if you’re the CIO or the CMO and your CFO is one of these panic people and they’re a ladder up or diagonal up the ladder, and they’re creating a culture around firefighting, around everything is a crisis and you don’t have a lot of room to change the culture directly with that.

How do you manage to that. How do you live through that? How do. How do you not let that affect you and assume that your CFO doesn’t want chocolate?

[00:49:00] Speaker D: I’ll say he’s chocolate allergic or she is on a diet, whatever, whatever frame you want to put around that. How I’m. How I have always managed around that is I have something to tell. And I always start with this in one form of wording or another. I have something not great to share with you.

Sit down, please. Take a deep breath.

And what I found is some people Bach, because they find it a little bit pejorative, but I do it in a, In. In Joe’s version of an arat way.

But really try and make them understand that if you look at everything as a firefighter, you are instilling Fear you are losing productivity. And to a CFO in particular, that translates into you’re increasing your cost. I don’t think that’s what you want to do.

And really, in a very brass tax sort of way, because if you allow that, it’s not a FOMO culture, but it’s a firefighting culture to permeate.

You’re constantly behind the eight ball. You have higher attrition rate, you have lower productivity, you have a longer period of time to get back to productivity. And, you know, it’s like my skip the dip kind of comment, which is basically when you’re doing a changeover on anything, whether it’s a light in a factory or a piece of software, timing is everything.

And so if you start creating that kind of culture where if it took five minutes longer, but nobody suffered a victimless crime, let’s say, so what? Who cares?

It’s not a crisis. And that’s where I started learning to really classify these things into these kind of bucketed areas to prevent that from happening, because I lived in environments where it was a constant firefighter, even in. At sea level, even in some board levels. I was in a meeting the other day, and it was getting into a firefight, and I basically said, look, this is costing a lot of money to have bickering and firefighter when the issue is really not that significant. If you kind of go to the end state, reverse engineer it, and break it down, how really significant is this today?

And at the end of it, the response I got was, yeah, okay, not everything is a firefight. It doesn’t have to be a firefight.

So we need to learn to sort of clarify and classify around that.

[00:51:42] Speaker A: Joanne, I love how you just unpacked someone’s deposition, brought them to a different space, and then started rebuilding them up in their context. Particularly when you start talking to a CFO about cost and revenue impact. I think it’s a really smart way to coach folks here, listening, because maybe you’re not talking to the cfo, you know, maybe you’re talking to just a director of marketing.

And, you know, as much as we try to have a healthy culture, we have individuals that we’re working with, and not everybody, you know, works in this calm RC mode. John, I think you’re going to bring us a funny story. This will be a good way to potentially end us.

[00:52:28] Speaker E: Yeah. I have a friend that leads manufacturing and assembly at a company, and people come to him with problems, right? And they’ve had some funny problems, like a truck full of their product once Drove into a plane. Right. And they have all sorts of crazy stuff, and every time somebody comes to them completely frantic, he always asks, okay, has somebody died in this situation?

And every single time, the person always responded, no, no one’s died. He said, okay, great. So it’s a problem. No one’s died. So we can work through this. And it’s just the concept of putting it into perspective about how serious the problem is and so that your response can be as appropriate as the situation is one thing. I found it just super helpful. And so he just, like Joanne said, he frames every issue on how serious it is, and then the org responds to a level that’s appropriate.

[00:53:19] Speaker D: Well, you know, to your point, John, and I know you have children. We all have kids. But those. Those in the listening audience and. And even amongst us where there are younger children, I always did this as a mom, and I think Liz might agree with me on this. The first thing you say to a child who’s like, screaming at the top of their lungs is, are you bleeding?

Are you hurt?

If you’re not bleeding and you’re not hurt, then calm down.

And I think the same may be true of executives, but in clearly different words.

[00:54:02] Speaker A: Go ahead, Liz. Let’s just. We have five minutes left. I’ll give everybody.

[00:54:06] Speaker F: No, no, no. I just was gonna say that’s.

[00:54:07] Speaker D: Well said.

[00:54:08] Speaker F: Well said, Liz.

[00:54:10] Speaker A: If you. Let’s give everybody one minute.

Last comments on our topic today. It’s been a really good episode communicating bad news to executives. Go ahead, Liz. One minute. Final, final thoughts.

[00:54:21] Speaker F: It really is. And I loved how Joanne put this in context. And. And each. Each one of us as leaders in the space, have given our take in our own way, our own leadership way of putting this in context of the executive that you’re speaking to, understanding their world and how you’re going to land in the context of that person and how they take bad news and figuring out the best way, whether you can, like, get them to breathe or just, you know, if they’ve already coached you on how they like to receive information or whatever. But that is extremely important to understand the person that you’re speaking to and how they receive that information and be prepared. But I just.

I just love working with you leaders. You’re fantastic. Fabulous.

[00:55:09] Speaker A: Thank you, Liz. We’ll go, Dave. And then Joe.

[00:55:13] Speaker G: Yeah. So I like the way this topic has been presented, but let’s point out two different ways of messaging. So I have a lot of data that could be acted on individually, but will that drive a whack A mole response.

[00:55:30] Speaker D: Right.

[00:55:31] Speaker G: Because, you know, going after the details. A lot of leaders like details because they can always win the argument at the detailed level, or you can aggregate it, as I was suggesting before, big picture. And my best big picture summary that I’ve ever provided anyone was there’s nobody at the cybersecurity helm. And they were so excited because they’d been playing whack a mole for two years.

And they said, can you prove that? And I said, you’ve been proving it for two years. And I put all the two years worth of evidence in this report.

Go get somebody in charge of the cybersecurity helm.

[00:56:13] Speaker A: Thank you, Dave. I just made a note. I mean, there’s a side topic here we might want to bring up about what level of information to share, not just in a crisis.

There, you know, it’s very easy to see too little information, but most of us who are very data driven tend to fall victim of sharing too much information.

And that could not only be overwhelming, but it can also lead people down what I call the rabbit holes, particularly in security and operations, where, you know, the culture is everybody’s all hands on deck. Well, maybe you don’t need all hands on deck. You know, maybe we just need this one group to really do a deep dive into what’s going on. What’s the root cause? Joe, last thoughts for today.

[00:56:58] Speaker C: I think it’s been touched on already, but let me reiterate that when communicating bad news or any kind of news, it’s good to put yourself in the other person’s shoes. As I said earlier, someone comes to me, they know that I want to know why they’re coming to me. Is it. Is it because it’s so urgent I need to push it upstairs? Is it something they’re just letting me know they got it under control or are they looking for help? Well, let’s turn that around. If I’m communicating to the board some bad news, I either want to tell them because they need to know, but I’ve got it under control and I can share that, or it may be, as I believe was alluded to earlier. It may be that I’ve got to bring this up because, hey, you guys have to help me figure out what’s the right strategy to, you know, to deal with with this impending crisis.

So I think seeing it from the other person’s perspective and then lining up, as David said, the relevant facts, the relevant information is key.

[00:58:02] Speaker A: Thank you, Joe, John and Joanne. We have one minute left. John, you left a really important comment Here, I think it’s worth saying, yeah,

[00:58:09] Speaker E: I’ve, I’ve been coached to just really thank people for bringing bad news, being the messenger that brings the bad news and communicates it. And, and what I’ve seen is, is that when you’re in a toxic environment, the messenger gets shot and then if the messengers are getting shot, they don’t bring the bad news. And so it’s just like really bad stuff happens if people don’t, don’t know what’s bad out there. And so it’s like if you can, a healthy organization, it’ll, it’ll really let people know that they’re not going to get shot for being the messenger.

[00:58:38] Speaker A: Thank you, John. I think that it was just really important to share that. Joanne, we’re at our close. Last thought for everybody.

[00:58:45] Speaker D: Last thought for everybody. Just remember this, the executive that you’re delivering the bad news to, this is not the first time that they’ve heard bad news and give them the credit for being seasoned leaders and understanding. So if you give them the out of understanding and being empathetic to them just as much as they may be to you, you stop the firefighter or fear of flight mentality that tends to permeate some cultures.

[00:59:17] Speaker A: Thank you, Joanne. Thank you Jennifer. Earlier who joined us, one of our Star CIO Digital Trailblazer experts along with Liz and Joanne.

John and Joe David, thank you for joining us as a speaker this important topic of communicating bad news to executives. Our session next week shaping tomorrow green technology platforms and initiatives. I’ll announce October’s lineup next week.

Just remember I have the link for the Star CIO Digital Trailblazer community drive.starcio.com in the comments. You have a free coupon to join for a free year.

One of the things you’ll get are the recordings from the coffee hours and this one particularly I think is really important one. Ronan is here listening. He will be putting up that recording sometime in the next week.

So tell your friends we’re launching this slowly but for those of you listening, I do have a coupon code for you to join for a free year. Thanks everybody for joining our next week episode again for green tech platforms and we’ll see you all here next week. Have a great weekend. Thank you again Joe, Liz, David, Jennifer and John for joining me on stage.

[01:00:36] Speaker E: It.