AI Adoption, Outcome Driven Systems & The Future of Software | Tom Famularo

AI is moving faster than any technology before it but the real challenge is not the technology it is adoption.

In this episode of the AI Accelerator Podcast, host Matt Zembrowski sits down with Tom Famularo, CEO and Founder of Cap20AI, to explore how AI is fundamentally changing how businesses operate, build software, and make decisions.

With decades of experience building, scaling, and exiting technology companies, Tom shares what most organizations get wrong about AI, why adoption is a human problem not a technical one, and how leaders can rethink their entire business model in an AI driven world.

Together, they break down the shift from workflows to outcomes, why traditional SaaS models are under threat, and how companies can scale expert thinking using AI while keeping humans in the loop.

In this episode, Tom reveals:

◼️ Why AI adoption is more about people than technology
◼️ How AI is different from every previous tech wave
◼️ Why AI behaves like a self improving system
◼️ The real reason organizations resist AI adoption
◼️ The danger of relying too much on AI outputs
◼️ Why humans must stay in the loop
◼️ How to overcome organizational resistance to change
◼️ The importance of greenfield innovation inside companies
◼️ Why transformation should be business led not tech led
◼️ The shift from workflows to outcome driven systems
◼️ How AI can eliminate traditional software development cycles
◼️ Why scaling expert thinking is more valuable than automating tasks
◼️ Real world use cases in insurance and financial services
◼️ How AI improves consistency and decision quality
◼️ Why SaaS companies are at risk in the AI era
◼️ The concept of the SaaS apocalypse
◼️ Why companies must be willing to cannibalize their own business
◼️ How consulting and tech models will evolve with AI

Key Learnings

✔ AI adoption is primarily a human and cultural challenge
✔ Technology is no longer the bottleneck in transformation
✔ AI enables faster execution but still requires human judgment
✔ Organizations must rethink how they build and operate systems
✔ Outcome driven systems will replace traditional workflows
✔ Scaling expert thinking creates massive leverage
✔ AI improves consistency across large teams
✔ Companies must embrace change even if it disrupts current success
✔ SaaS and consulting models are being fundamentally reshaped
✔ Leaders must prioritize long term transformation over short term stability

💬 Tom’s Most Powerful Quotes

“AI is not the problem. Adoption is the problem.”
“The friction is organizational, not technological.”
“AI can get you to 90 percent fast, but the last 10 percent matters.”
“Scale the expert, not just the task.”
“Transformation should be driven by the business, not technology.”

Follow Tom Famularo

Website: https://cap20.ai/
LinkedIn: https://www.linkedin.com/in/tom-famularo-4b018311/

Follow Matt Zembrowski

Website: https://leadingaiagility.com
LinkedIn: https://www.linkedin.com/in/mattzembruski/
Email: matt@leadingaiagility.com
Phone/Text/WhatsApp: +1 978-618-5778
Facebook: https://www.facebook.com/mzembruski
Instagram: https://www.instagram.com/thelifeofmattz/

Transcript

SPEAKER_00 0:04

Welcome to the AI Accelerator Podcast. I'm your host, Matt Sembruski, founder and CEO of AI Agility, where we help organizations unlock the superhuman potential of their workforce through AI. Today I'm fired up about this conversation because my guest has done something that very few people in the AI space can claim. He's actually built, scaled, and sold a technology company. He's not just talking about enterprise enterprise transformation. He's actually lived it across multiple decades and multiple ventures. Tom Familaro is a CEO and founder of Cap20 AI and a three-time entrepreneur. Before Cap20 AI, Tom founded FAST, which is a leading insurance technology platform that was acquired by Verisk. He then led Verisk Life Solutions business, helping some of the largest insurers in the world modernize their core systems. Throughout his career, Tom has focused less on inventing technology and more on helping organizations actually adopt it. And if you've been listening to this podcast at all, you know that's exactly where the real challenge lives. That's where the change lives. And that's what I'm super passionate about as well. So, Tom, welcome to the AI Accelerator. It's great to have you here.

SPEAKER_01 1:10

Thanks, Matt. I appreciate it. So I grew up on the as a tech guy. And based on some of the things that we'll talk about today, uh, you're probably going to question, does he really work in technology? Because I found out many years ago that people are at the center of the adoption of technology, and you really can't get to tech without the people involved there. As you mentioned, I started out um as an entrepreneur, small company, grew it pretty significantly, sold it to Accenture in 2006. I learned a lot there about just the human behavior. And then I started fast in 08 because I really wanted to go into a place where we could eliminate a lot of the unnecessary work that companies would do. And actually, interestingly enough, almost 20 years ago, what we built was similar to what you see with Claude Code today. Is the whole idea was to take business people and be able to let them speak in English in their business terms and let the code get generated as it was. Had a pretty successful exit there, sold in uh 2019, stayed on for a few years, uh, helped it grow significantly even more after that. But it was great to work for a company like Verisk, where I got to see how a data company who's been collecting data for 30 years, how they operate from that side of things, and it enabled me to really be able to connect everything together, which is everything that I've learned about technology and SaaS combined with data as well as AI to be able to launch what we've done with CAP20 AI.

SPEAKER_00 2:42

That's awesome. That's awesome. You've helped uh a lot of um, you've helped organizations adapt across a lot of these cycles, right? Like SOA and cloud and modern platforms today, and now AI is all all the buzz, right? And you've seen what's working, what's not working with these different technology cycles. So let me ask you directly, what what makes this this new AI wave that's going on right now? What makes AI adoption fundamentally different from every other technology that's that we've been through?

SPEAKER_01 3:10

So I yeah, I've been through all those different forms of change, and I can assure you that this one is very different. So obviously we've got it's faster, more powerful, and easier than most things that we've seen. Um, but the other side of it is that it improves itself. So it's the first technology that's more like a living organism that is improving itself, but it also has full visibility at the CEO levels and the board levels. Unlike things like the cloud, where it was like a cloud, it was pretty amorphous, it was ambiguous to people. This is something that you'll see CEOs just doing their own thing at night, and then they come in and say, Well, this is what we need to do. It obviously has it's on demand. There's no barrier to entry, no infrastructure required, no long development cycle. So adoption really the the is the friction of it is organizational, not technology. So from a speed perspective, things are moving faster. Things that you could do in used to take weeks, days, now are taking hours. And then the other aspect that makes this different is the autonomy. You're not as dependent on other people. So when you're going from uh into cloud, you were dependent on the cloud engineers. When you were going into from mainframe, you're required uh dependent on developers, so there's not that dependency. People could just take it on on their own.

SPEAKER_00 4:38

Yeah, it's very true. And you mentioned uh something important, like technology is moving super fast right now. Right now, it's a lot more, it's a lot more available and accessible to anybody, any level of the organization, right? With this new AI and technology and everything that's there. Um, and you also mentioned about how organizational and people are at the center. I totally agree with that, right? People are at the center. So if organizational uh and the people side of it is where the friction is, I want to unpack that a little bit more. What are your thoughts there about um how we get past that human resistance, right, that's in organizations? What do we do about that?

SPEAKER_01 5:12

So uh first, the hype cycle that people go through. And I I would say everybody goes through this, which is they start using a basic uh gen AI and it's the greatest thing in the world. Look how much it could produce, and then they pretty quickly realize that they could get to 90% of the way there in no time, and then that last 10% takes forever, or they rely on it too much and they just pass on work that's just wrong. So then they get burned by it, so then they shift from this is the greatest thing in the world to what doesn't work, and it creates this tension. Most companies in general, for every tech transformation I've been through, they companies resist change, right? So they're always going, people, individuals, most people they really don't want things different. If you've uh I got a kick out of when I was watching the show Yellowstone, right? You were he he ran on the premise that he wants to stand against progress, and it got me thinking, why would anybody want to stand against progress? The reality is because the people that are making decisions are people in pretty good positions, they've got a good job, they make a good living, they like what they do. Why would they want anything to be different? However, the reality is the world is changing around them, so you have no choice but to adapt to be able to re-maintain what you have. So that tech resistance is there every single time. Um, so it applies to pretty much everything. And there you have the usual obstacles or that people throw up, uh, compliance and legal, and somebody else that uh in a different group doesn't want it, and then there's too much legacy to be able to actually uh integrate with in my ecosystem to actually make it work. So you've got all these things stacked up against you. There are really two simple ways. One, I would advise all of the suppliers out there, vendors, to just create things that actually add real value so that people can adopt that. And then customers go greenfield, start over, create small working groups, small departments so that you're not encumbered by all the legacy of people as well as technology. And then the the second side of this is for all of us, recognize it's not magic. It seems pretty close to magic and it does a lot, but realistically, AI is only going to get you so far. We still need humans in the loop there. We can't think that it's gonna automate everything we do. We use this uh catchphrase around here all the time, uh, which is most people don't recognize that the human and the chimp have a 98.8% overlap in DNA. That 1.2% is a big difference. So I think of AI as the 98.8%, that's the chimp. It can do almost everything, but without the human there, it's gonna get it wrong, and people aren't gonna be able to embrace it and adopt it because of that. So if you combine the two and do it the right way, then you can get the results, and then people can adopt the technologies.

SPEAKER_00 8:07

That's awesome. That's awesome. I love that. Yeah, human in the loop is so important, and you and I are both have both seen this in a lot of uh a lot of organizations. And I want to get um a little bit more tactical and unpack that a little bit more when you're working with a Fortune 500 company or a mid-sized organization. You know, what are the top adoption challenges that you see that's that's out there right now? Like I guess what what's your playbook for pushing through some of those challenges that you're starting to describe?

SPEAKER_01 8:33

Well, first you're seeing companies at different levels where some are forbidding their people to use AI, which I think is absolutely insane. Um, and then you've got others who are firing people for not using it enough. And so, in reality, there there is there's not a real good strategy there. Um, when you think about it, is and this is just like all other tech transformations, it's always been driven by the tech people. So, tech transformations shouldn't be driven by tech people, it should be driven by business with the tech there to support it. And unfortunately, because the history of technology has always been you need the tech people there to be able to uh do it, they're the early adopters, and they get out there and they set the agenda, and then you're never gonna get people aligned. Um so most companies don't recognize that the first thing is it's it's really a culture and a change management issue, it's a strategy that is I've got to change my culture, I've got to have this embrace this change. And the other problem here, especially with AI, is gonna be that people are used to change cult changing culture and change management taking years, decades. It has to be really rapid, it has to happen very quickly, otherwise, you're not gonna know what hit you. And so, this is where I I keep coming back to go greenfield, start over. The best thing I could have ever done was leave a large uh data and tech company and start over because it I was completely unencumbered and we could do things so much faster. Well, you could do that same thing in a large organization, whether you're a startup or a large organization. I've worked with some of the biggest companies out there on transformation and change management, and it always comes down to I I used to have this uh visual. I there was this uh outside my old office, I would watch out the window, there was this bridge being built, and you had this river and this old decrepit bridge. Well, they didn't knock down the bridge, they built a bridge next to it, got it up and running, then they put some traffic onto it. Once it got up and it was good, then they blew up the other bridge. Well, that's the same thing that has to happen. And I've worked with some really good companies who have taught me a lot about how to do it from the other side, from the big company side, and I've always done it from the vendor side. And partnering with those types of companies has made a difference. It can be done.

SPEAKER_00 10:56

Yeah, that's awesome. That's really cool. I totally agree with you. There's so much uh innovation that can happen with AI and and with as you're saying, sort of building. I I love the bridge analogy, right? It's building, it's building the new bridge on the edge with a different team, same team, what have you, but you're building it out. You can build it out quickly now with technology. And um, and then when you're ready, then then you make the move as an organization. And I think there's um, well, I've seen a lot of this happen quickly. So one of one of the biggest questions that that I'm I'm hearing a lot from executives, Tom, is that you know, where do we even start? I find that there's a lot of companies, as you said, some are like, oh, don't use it at all, or you you're gonna get fired if you don't use it enough. Right. There's there's a whole mixed bag of um uh perspectives out there from leadership, and it definitely starts with leadership. Um, but I I feel like a lot of leadership that are out there, um, a lot of the folks, um, they want to do the right thing, but they're not sure exactly what that is. They know AI is big, they know it's gonna impact their organization, their industry in a big way, their role, their career, everything, but they're sort of asking themselves the question, where do we even start? And when they get clear on that, then they can start to lead their organization in that direction. How are you seeing companies start to unpack that? Are they engaging with um um experts like you that are like helping them understand what that is, what their options are? Um, you know, how are they figuring out um, you know, what they should focus on first? How do they solve that?

SPEAKER_01 12:22

Well, it is a big struggle because similar to the past, they're looking to technology to make these decisions for them. And tech people are going to be more likely to do things that are for tech's sake versus for from a business perspective. Um, when you think about it, you you really just have to take a step back and rethink everything and how you approach things that that you do. So, just kind of give you an example of something, uh, from an implementation standpoint. Let's I'll give you two examples. One is more implementation, and the other one is more end use. So, from an implementation standpoint, we think in terms of um being able to take a business idea and turn it into technology. And so, to give you an example of this, is at many different companies and industries I've seen, you'll you'll give somebody, a product actuary or a business uh analyst, this 50-page, 100-page document that they're gonna read through and they're gonna try to figure out all of what the customer really needs. They're gonna iterate for weeks trying to figure this out, break it down. You need to be an expert in that business domain. Then you need to be an expert in what the system is gonna do and how that's gonna line itself up, and you break it down into like a flight plan and figure out what you need to do. Two, three months later, you know what you need to do, and then you send it over to somebody to either configure it or develop it or whatever to get it done. Well, we look at it and say, just use the source. So the data itself, the artifact that creates that product specification or that uh commission contract or whatever it is, that becomes the source. So the business people work with that source document and that source information and then automatically turn that into the working software. So instead of cut having these big configuration processes and projects, you can eliminate all that on the business side of things, and I think this is kind of where the future of all software is gonna be going. Same similar, using that same source of data. We think in terms of use cases and user stories, and I I want to get rid of that because in reality, I think the future is gonna be it's outcome driven. So you think of something like Waze, which I think is a phenomenal simple application. What do you do? You put your destination in, and what happens? It figures out the software figures out how to get there. You don't have humans. I wrote this blog a while ago, the death of Visio. You don't have humans deciding what the workflow is in each flight map, is flight plan is gonna be to get me from point A to point B. I put in my destination, and in real time it's constantly updating itself. Well, that's the future of software. So if I put in my outcome that I want as a business and as an end user, I want to process a transaction, I want to take a loan, I want to create a blog, I want to do whatever I'm gonna be able to do. I don't tell it all the steps, I don't pre-define that, I give it the destination and let the AI guide me along the way and continuously go get the data that it needs from wherever, from data sources automatically. And what if some of the data needs a user? Hey, we'll ask the user for a couple questions. But you just get till you get good enough data to be able to process the next thing, and then ultimately you get to your destination. So that's gonna be how things are gonna be done in the future. So then when you look at your use cases and your priorities, it's really the priorities of your business. So whatever your business priorities are, that's what you look at, and you tackle some of those discrete problems one at a time and say, How do I get this start starting point data? What's my destination? So I start at point A, I end at point Z, and then you just let the software and the AI help you get there.

SPEAKER_00 16:07

Yeah, that makes a lot of sense. I love the Waze Waze analogy as well, too, because I remember back in the AAA trip ticks, you used to have the whole thing printed out. If you took one wrong turn or using Google Maps with a printout, you're done. You're like lost, and there's no re there's no recalculating until you got to GPS and now with the AI and everything woven into that. It's um, you know, we can we can lean a lot more as humans who are imperfect with our memories and with everything else, right? We can lean a lot more on technology to do that. And that makes a lot of sense. I'd like to, I'd love to share, you know, our audience loves like different use cases and things. You shared, you shared a couple different ones, and I know you have a lot of background in the in the financial industries and and so forth. Um, what what are some with with AI and with some of the adoption that you're talking about? And you're, you know, um uh, you know, we think about like what you just shared in my words, was like leaning out processes. Like what what what are we trying to do? We're trying to go from A to B. How can we do it in the most effective way possible? And and we as the human are not going to go in there and figure all that out. We're gonna let AI do a lot of that work for us and it also helped guide us through that. Um, what are some uh like real company examples that you've seen as far as like uh uh a use case where like an AI workflow transformation uh was really effective? Like, and we're able to like measure anything about that.

SPEAKER_01 17:26

Yeah, so another aspect of this is when you look at what I said before about the human and the chimp and that extra uh piece that the human does. If you most people are looking at scaling and automating mundane tasks and the lower level things that people do, and that's low-hanging fruit. It's easy enough to do, people are tackling that, but in reality, it's not going to get you what you really need, and it doesn't cover that 1.2% of where we really need the humans. So we're looking at things and saying, let's scale the experts, let's take what's in the experts' head and figure out how to automate their decision making. So no more rules engines, no more, right? Spending all thousands of hours codifying things in in all these different rules engines and workflow tools, and let's extract out of the human so we can scale their expertise. So, an example might be um in uh banking or an insurance, you have an underwriter who's making a decision on whether or not a case could be submitted. So rather than coming up with all these different imperfect rules of every possible scenario out there, you use AI to capture what that chief underwriter think? How does he think? You combine it with, again, the source data, their underwriting manuals that they've written. They've got these underwriting manuals that they've written for 30 years that are pretty rock solid. You combine that data source, take the expert, can consume that in a millisecond to be able to take that big manual in and process it the way that person would think about it. So now your use case is as simple as an application is coming in from an agent, from a portal, from a piece of paper, from anywhere. That in data just comes in. The process would go in such a way that it figures out, well, what data do I need to be able to make this decision? And go get me more data. So it recursively would go through getting more data until that chief underwriter has enough to be able to make that decision. And then that decision process is just automated, and now your case can get issued. That same process can work with a claim, it could work with uh compliance, it works pretty much with anything that has experts making decisions and source data of what that company's rules are, and as well as industry and regulations. You combine all of that together, and now you do that. So, pretty much any business process that people do today, if you rethink it to the outcome, and the example I gave, what's the outcome? Is you want to determine the right level of risk. Well, why do I need thousands of workflows and systems and people involved to do that when I could just have this simple process? Just take data recursively augment my data till that decision can be made, and then just dismake that decision.

SPEAKER_00 20:22

Yeah, that makes a lot of sense. And and it's um and to uh you brought up a lot of good points there, Tom. Number one is the more data that's involved in the decision making, the more applicable the a new AI transformation would be, right? Because if there's a lot of data, what you're talking about there um is also about number one, processing all the data without taking on that that human mental capacity effort, because that takes an expert to do that, and it's mentally taxing. It tires that person out to try to process all that on their own without AI. So um you mentioned you mentioned that, but also I'm guessing, and I this is a question for you, did when when you do those transformations, do you see that the quality of like the risk assessment and the quality of the approval levels and all these things, like the quality goes up? Obviously, the time goes down because you what you just shared shrinks the time down significantly to go from um here's the information to let's make a strategic decision about it. So that time gets compressed significantly. Um and and with it with all the data, with all the numbers. And I'm I'm guessing that there's some measurements that you've seen and probably put some KPIs or some type of ROI um involvement in there. So can you speak at all to that? Have you noticed like that that the quality goes up significantly with with this type of um AI assistance through the process?

SPEAKER_01 21:44

Well, you you certainly get consistency, right? And that's what ends up happening uh traditionally with regular systems out there is you still end up with people making decisions along the way, inputting data. And they'll no matter how much the system Tries to cover everything, it's there are going to be some mistakes. So, like we we always ask people, wouldn't you rather scale that expert in the best person you have? And to your point, I've seen situations where and operationally from a scaling perspective, where you'll see across 500 users where the top 10 people are doing 95% of the work. And I don't care whether that's an operation back office operation or a sales force, that's it's the same pattern. So when you're able to scale the expertise, and knowing the other thing is the human still exists. So we're not saying get rid of the expert, we're saying augment that expert, make them superhuman so that they can scale infinitely, but they're still there to catch any kind of flaws. So what's going to happen is while you're getting ready for this, they're validating that everything is good. So they actually the accuracy goes up. And one of the most interesting things, dilemmas that I had was what happens when the human would have made a mistake? Do we replicate their mistake? And obviously the answer is no, you don't want to replicate the mistake, but at the same time, you want to make sure that you're not just turning this into a robotic process. So that's an iterative process there of getting there. So that human is still going to exist. So in an ex in the example where with 500 uh users, a handful of them doing all the work, even going back five, 10 years of technology and unrelated to AI, the answer to the that question is not enhance the system to make it more efficient. It's to take those top five producing people and have them train the rest of them because so they're doing something right and the others are doing something wrong. Well, that same principle now exists, except that expert, those experts, those best people that do it, are training the data and training the models so that it can all get automated. So, yes, the your scaling goes up immensely, which cuts costs significantly, obviously, and it makes things way more consistent.

SPEAKER_00 24:04

Very, very good. No, that's that's fascinating. There's there's so much going on, and uh, I really appreciate the uh direct examples there. I want to I want to shift for a second and talk about something that I know it's uh uh near and dear to your heart, but in the SaaS industry, there's been a lot in the news media lately, like there's a lot going on, AI versus SaaS, and a lot of traditional SaaS data companies have been taking big hits uh with their stock valuations. What what are your thoughts, being someone who's an expert in the industry? You know, what's happening there? How is AI reshaping the landscape? Is it the end of SaaS? What's going on?

SPEAKER_01 24:37

Well, it's the end of SaaS for a lot of companies. So we're gonna see whoever who adapts. Um, so yeah, I wrote a paper on this about two months ago before I ever heard the term SaaS apocalypse, and now that's all I see. Um, whether it's the financial institutions uh or the tech people, everybody's now talking about this. And to your point, if you look at a lot of the major, like the best SaaS companies, SaaS and data, and even consulting companies, over the last year, smart money was betting against them a year ago. Why? Because they saw what was coming, that these technology companies are really at risk. And and what are some of the things that we're seeing? First, and I've been on this side where I've scaled a very large company. It's really hard to cannibalize your own business. I remember a situation back in 2017. My board was putting pressure on me to create a model where we would get better financial results, having more recurring revenue, less services, less people dependent. And it was absolutely the right thing to do. We did it, and it was really difficult to do. And obviously, what happened was our revenue flattened out for a little while while we were exchanging bad revenue for good, but we flattened for a couple of years. And the same people on my board were panicking over, but our revenue's flat. I know, but the quality of the revenue was so much better, the sustainability of it, we're not as dependent on people. So we were able to make that transition, but it's it's hard and it's scary. If you're a large public company out there from a who's dominating a SaaS market where you've got the best technology two, three years ago, the best functions, best features, so such great market share, it's really difficult to then say, all right, you know what, we're gonna gut our business here. We're gonna transition everybody down to a much lower cost model. It's just not something that large companies really have the appetite to do. Um, so it's hard to catalyze. At the same time, another trend that I'm seeing is a lot of the large companies out there, their customers of those SAS are saying, Oh, I could build this myself. I could just get some developers out there and replicate these systems with cloud code. And and you know what? Their early results look great because in weeks they're replicating 90 something percent of what that functionality does. The problem is now I'll give you a different bridge analogy. You're building two bridges across the river, and they're not gonna come together. It's not gonna work because you're building not based on understanding the real techniques for software and what where the future is going, you're building these capabilities rapidly with a Claud code or something else, and it looks great and it gives you a lot of false sense of uh security, but that's not there. So you're you're kind of seeing that happening at once, and you also have this feeling with startups that oh, we could just get into the market and go build something again, impressive technology. I was able to replicate some pretty large-scale SAS uh companies inside of weeks. Now, I've it it doesn't actually process the business that the customer is going to need, but it looks good, right? So you have to get to the the approach, kind of like I said before, for the technology companies to who embrace the outcome-driven architecture with the approach that we were talking, taking that step back. I would encourage all tech companies to do what I've always encouraged other large companies to do with their go greenfield, start over, build, take build that new bridge, yeah, bring your assets over. Your IP is really your strength of your data, your knowledge of an industry, but the and your people, but really you have to rethink everything. So the tech companies that are willing to cannibalize a little bit, and I see a couple uh really good consulting companies out there, I won't name them, who have taken that their lumps in the market, and I think they're gonna emerge really well. So when you think about consulting companies, they're based on pyramids of thousands and thousands of people. Well, that those thousands of people are gonna be automated and not needed anymore. But you're seeing some of the uh consulting firms who were smart enough a few years ago, they took their lumps, their revenue is dropping, they announced their revenue would drop as they were starting to shift into AI. I think those companies are gonna thrive. We'll see from the SaaS perspective and the data companies which ones actually took those measures a few years ago. So they're all taking their hits right now. Some are gonna emerge and they're gonna be stronger and better and sustainable for the next 10 years, and others I think are just gonna die out.

SPEAKER_00 29:15

That's a that's a really, really great perspective. And I appreciate you sharing that. And um, I'm excited to take a look at your paper that you put together on that too, because it's been all over the news media. And what I've found is that there's a lot of uh disconnect between the the simplicity of the news stories that come out there versus the accurate versus the the real story that like comes out in white papers like that you put together. So, Thomas as we wind things down, um, if you could give one piece of advice to a CEO or business leader executive out there who knows they need to make some moves on AI, but they haven't really started yet or feel like they're behind, you know, what would you tell them?

SPEAKER_01 29:53

I would tell them that they need to get involved. So they, I think one, you've got to find I know um companies are starting to do this, find the right change management person that's gonna lead you into the future, be hands-on with that person as the CEO, partner with that person directly, very involved in that, and almost treat that new green field, new operation like the most important thing for your sustainability. Unfortunately, as you know, a lot of large public companies, they're living quarter to quarter, they can't afford that. And so unfortunately, comp structures are typically structured around that short-term win and not the long-term sustainability. So if you're building for the long-term sustainability, you're gonna have to accept the fact that the short term is gonna be bumpy. Get involved, have the right change agent there to drive into the future, and it's a cultural thing, organizational thing, and a mindset, and not a technology thing. The technology is there gonna be to be so to support us eventually. Tech is gonna be like electricity, flip on the switch and it's there. We've got to be able to change that culture and mindset so that we can embrace that and use that.

SPEAKER_00 31:06

Yeah, I fully agree. It's um it there's there's so much to be done with the greatest asset of the companies that are out there, which is the people, right? Like let's work, let's help the culture, let's let's leverage the technology to do that. And and Tom, this has been an incredible conversation. I really appreciate your time, your effort, your insights, all the all the energy that you shared and uh and everything today. Where can people find out more about you, learn more about Cap20 AI? Where can they go to learn more?

SPEAKER_01 31:33

So they uh just go to our website at cap20.ai.

SPEAKER_00 31:38

That's easy. All right. We'll make sure we include that in the show notes. And I think people can even remember that listening, even if they're driving in the car or they're uh zooming down some um some uh slope uh on their uh on their their skis or mountain bike right now, cap2020 uh.ai, which is uh which is awesome. Well, that's great. Well, for everybody listening, if this conversation got you thinking about how to actually get your team trained on AI, not just talking about it, but doing it, that's exactly what we do at AI Agility. Head over to leadingaiagility.com, find me on LinkedIn. Definitely dig in more into Tom and his background and everything he's doing at CAP20 AI. Lots of great insights were shared in this conversation. And if you enjoyed this episode, share it with someone who needs to hear it. Every share reaches more people. That's what this is all about helping change people's lives in a positive way with AI. And until next time, I'm Matt Zimbruski. Keep accelerating.