How to Actually Implement AI in Business — Michael LaVista

Most companies are buying AI, then quietly failing at the last mile. Michael LaVista has spent 25 years shipping the hard final stretch — here's how he gets AI into production.

Michael LaVista, founder of Caxy (a Chicago software firm that's spent 25+ years staying a few steps ahead of the market), breaks down "last-mile AI" — why most AI projects run out of steam right at the end, and how to actually get them into production. We cover finding use cases worth building, the AI stack you can realistically hire for, driving adoption against real resistance, value-stream mapping to find the work that matters, guardrails for autonomous agents, and the rule that saves the most pain: solve the problem first, pick AI second. Real AI in production, not theory.

In this episode:

• What "last-mile AI" is — and why most AI projects run out of steam right at the end

• How to find AI use cases worth building (let 100 ideas compete, keep the home runs)

• Why automating tasks you already do is a weak AI strategy — and what to do instead

• How to pick an AI stack you can actually staff and maintain

• Driving adoption when part of your team is quietly resisting AI

• Value-stream mapping, guardrails for autonomous agents, and when NOT to build it yourself

Chapters:

0:00 Cold open: pick the problem, not the hype

1:20 Last-mile AI: where AI projects quietly die

2:56 Why automating tasks is a losing bet

3:53 "The board said use AI" — $100K/mo, zero accountability

5:45 Finding use cases: let 100 ideas compete

6:54 Idea to working software in 6 minutes

8:07 The AI stack: pick tech you can actually hire for

9:52 Adoption & the people who resist AI

11:25 Value creation beats automation (proposals, IKEA)

15:43 Right tool for the job: when NOT to use AI

18:47 Value stream mapping: find the red-pen process

21:28 Coding vampires & the autonomous-agent holy grail

23:25 Guardrails: prompt injection & Bedrock

24:58 Three steps ahead: a world where everyone builds

27:46 Final advice: problem first, then go get help

Guest: Michael LaVista, Founder & CEO @ Caxy. Michael LaVista is the founder and CEO of Caxy, a Chicago-based software and AI transformation firm. For 25+ years he's helped mid-sized companies turn AI from a board mandate into something that actually ships — what he calls last-mile AI.

Connect with Michael: https://www.linkedin.com/in/michaellavista/

Connect with Vlad:

• LinkedIn: https://www.linkedin.com/in/vladeziegler/

• YouTube: https://www.youtube.com/@aiwithvlad

• Work with Vlad (Elements Agents): https://www.elementsagents.com/

• Come on the show: https://cal.com/vladimirelements/podcast-intro-call

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Business AI Explained is a podcast for founders and GTM teams on how AI creates real business impact — real examples, real constraints, lessons from AI in production. Hosted by Vlad de Ziegler, founder of Elements Agents.

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#BusinessAI #AIForBusiness #AIimplementation #AIstrategy #AIadoption

Transcript

SPEAKER_02 0:00

There. I've heard that I think I think the term is coding vampires. And that now that that engineers are becoming these coding vampires, because it's so kind of fun and addictive to just kind of be in these multi-agent kind of conversations. And really the only way that these things truly, truly deliver value is if they can offer if they can operate autonomously. So if you have to be constantly in the loop, I think you do enter a brain rod zone where you're just clicking yes, no, yes, no every like 45 seconds.

SPEAKER_00 0:29

Hi everyone, this is a new episode of Business AI Explain. I am here with Michael La Vista, who's the founder of CAXE. Caxi is an AI transformation company operating from Chicago in the Midwest. And he's been helping a lot of companies implement AI from transforming operations, identifying what are the best use cases, as well as implementing the actual solutions. So thanks so much, Michael, for being here and looking forward to this conversation. Yeah, me too. Sounds great. Perfect. So, Michael, I have your LinkedIn profile in front of me, but I think it would be super cool if you can tell us a little bit more about this journey and how it has changed, especially maybe in the last six months.

SPEAKER_02 1:03

Yeah, sure. And I think honestly, the the story of the company in the last six months is sort of the story of the company since I started 25 plus years ago. And really where we've always needed to be, frankly, as a service business, is we need to be a step or two or three beyond what most companies are able to pull off right now with technology. And so, you know, as we talked, you know, today it's the middle of the year in 2026. And so the things that everyone wants to talk about now have to do with how do we implement AI in a way that wins. I know we're all supposed to be doing it, but like what do we, what exactly would be a good thing for us to do? I know a lot of companies are doing a little bit backwards. We may get into that later, but a lot of people are starting with AI first and saying, let's do something with AI, and then they fail at the the last mile. And it's funny, I was listening to some of your other podcasts, and and AI typically tends to run out of steam right at the end. That's what that's the area that we specialize in. Something we call last mile AI, which is sort of like bringing along that that logistics idea of lot, you know, last mile delivery is often the hardest because you know you get the you get the package all the way from you know China to Long Beach, California, and then on a truck to Chicago, and then you still have to get to the person's home, and that's most of the work. Uh so we we think we like to think that like bringing that last piece together with you know, when people build these AI things on their own, typically they lack, well, first of all, they lack good UX, they lack you know scaling, they lack security, they like all these other things. And frankly, the most often problem that people run into is they didn't actually solve a problem we're solving. They just they picked AI first instead of problems first. And the reason you know when you ask me like what's the story of the company to now is we've always had to do that. And so if I start from the beginning, you know, when I started my career, you know, I was hand coding marketing websites and notepad and building HTML because that was a thing that no one knew how to do and there wasn't a good tool for it. And then IDEs came along. We had to get rid of that. And then we you know moved into content management, and then people, you know, that got cannibalized, and we moved to e-commerce, then that got cannibalized. And we've been generally in the area of um trying to solve business like operational problems for the last 15 years or so. And so where we like to start is what's the problem you're trying to solve. And so typically, I would say we do a lot of back office work, that's always been true. So thinking about how do we optimize this part of the business and with technology and then how do we, you know, move that to the customer side? How do we create a portal that you know people can do business in better? And if it's a springboard for the rest of our conversation today, I I honestly think if people are focused on automation for AI and like let's find a way to like we had this mundane task, let's find a way to automate that. I think that's a losing bet as a strategy for a business. It's fine, but really AI needs to sit outside of IT, outside outside of automation, and be a strategic asset. Be the thing that we're doing something new that we didn't do before. Otherwise, like if you're just doing the same thing a little bit faster than you you were before, your customers, I don't think they're gonna care very much.

SPEAKER_00 3:59

I think this is the narrative that we kind of see today. There's been a lot of excitement around AI in the last two years, and we've gone through different waves of people feeling the pressure at getting the budget to invest in AI. But now that tokens have become more expensive, everyone needs to be a bit more accountable in terms of how much they're spending on specific use cases. So people need to be more pragmatic. Is this also something that you've experienced? You know, how has the narrative evolved in the last 12 months?

SPEAKER_02 4:23

I think the thing I hear most often, and maybe your listeners will relate, the thing I hear the most often is the board total told us or the CEO told us we have to use AI. And so what happens is someone just buys AI. And another someone I was talking to recently said they got everyone a copy of Cloud Pro. And so, you know, in an organization of, you know, 5,000 people at 200 bucks, you know, you're spending $100,000 a month on AI tokens to your point, and there's really no accountability for what they're supposed to be doing with them. But there's like we have to use AI. And I feel like companies are lurching back and forth where they're like, okay, we have to get AI, everyone gets AI, and then they spend too much money, then they yank it out, and then they put it back. It's it's there's no strategy behind it. So in the last like 10, 12 months, what I hear the most is we're supposed to be using AI, but we're not clear how we're supposed to use it. And then frankly, I was talking to someone in a in a in a city role, someone working for a city, and they say the company, you know, bought Microsoft Copilot, or the the the city bought co-pilent. And now they're all sitting around like, okay, now what we're what are we supposed to do? So I feel like answering that that what are we supposed to do now question has been the thing we spent the most time on.

SPEAKER_00 5:33

Do you have like a specific type of customer that you're selling to? So you mentioned the municipalities or or uh the city?

SPEAKER_02 5:40

Municipalities are unusual for us. I think our sweet spot are kind of mid-sized companies, let's call it 50 million to 250 million, where there's been growth, but there hasn't been sophistication in the AI and the strategy realm, and then usually also in the IT realm. And I really like to separate those. Like if if your AI is part of an IT initiative, it will probably fail. And so in the in the organization of that size, I feel like there's the growth that we need to have an investment make any sense. And there's that sort of like early-on lack of sophistication around these areas that we can really help.

SPEAKER_00 6:15

The reason I'm excited about this conversation is if you're the best person to ask about debunking myths. And today we hear a lot about for deployed engineers, you know, which is kind of a glorified version of a consultant that you basically send to a client on site to figure out what people need. How do you actually come up with those use cases that you need to build for? Uh is this how you're approaching it today? Uh or do you let people come up with their own use cases that you then build around?

SPEAKER_02 6:39

So a couple answers. So I what I really like about um encouraging clients to try their own use cases first is that it does a really good job of weeding out what the good and bad ideas are. So from a consulting point of view, sure, we could deploy all these, you know, engineers in your company to help you figure out use cases. But honestly, I honestly think like your dollar is better sent saying, okay, let's have a hundred people come up with things, and it'll sort of whittle down to the six or seven pretty good ideas, and then we can help you turn those six or seven into the one or two real home runs. And I and one of the things I feel like has been um one of those things that like where there's a real value to bring in people that both understand AI and how to use the tools, but also have built software before, is this idea that you end up having to become a like a product manager pretty quickly. And in sort of pre-AI days, you know, the the time from an idea through planning, through development, testing, blah, blah, blah, that might be long enough that a product manager kind of has a chance to like catch their breath and really think about it. Whereas with AI driven development, the the time span between you know something you've thought about and something we'd actually see on the screen might be like six minutes. And so the idea that you have to like, you know, really like be a be have an idea about what's the what's the strategy for this product, what's this product supposed to do, who does it serve, all those kinds of things. People have been trained in that sort of thing in the past, I think do a really good job of answering those questions that Claude is kind of coming back to you with pretty quickly. But if you haven't come from that world, it's really easy for these, you know, your your people in your company to be answering the question in a way that either takes you down a rabbit hole or takes you down a dead end or takes you down a path that's like not secure or won't scale or uses a mix of technologies that isn't a really good idea. So I feel like the the the the idea that your company is developing, let's call them proof of concepts. So you're you're developing the POCs and the winners come to professionals who think and take them the last mile. I think that's a pretty winning combo.

SPEAKER_00 8:37

You mentioned um the mix of technologies that people should come up with. What's your view on the stack that people that companies should have? You know, what are the key essentials, foundational pieces that you need to successfully implement AI across an organization?

SPEAKER_02 8:52

You know, you know, having come from software development where like one of the things we always used to say is any technology can be used really elegantly and beautifully and well, and any technology can be used and just create a you know spaghetti nest of problems. And so I don't know if I have a specific stack that like you should or must use. We've always been in sort of the open source stack, so a lot of our stuff looks like you know JavaScript and Python on the back end. I think as a as a as an investor, like as a as a business owner, as like a CEO, I always feel like you want to go with technologies that you can find people to go operate in them, you know. So um, you know, if I were to pick on one for a second, like it's really cool for developers to work in Rust, for example. Like that's a pretty like like if you're working in Rust, you're like kind of hip, whatever. But then again, there aren't a ton of people who do it. Like there maybe there are, but like if you're gonna hire for your company, if you're gonna bring people in, it's gonna be one of those sort of like niche technologies. So I feel like going with like a JavaScript, you know, uh Java, you know, Python, things that you could find people for, I think there's an advantage to that. Because I also think there's a world in the future where you know uh code systems built, you know, in part or even largely by AI systems, if there isn't a single human who actually knows how it works, you're gonna be in deep trouble. So I feel like that's that's kind of where we lean. We're also really Amazon affiliated, so we like all the AWS stuff, but I'm sure working at Google and Azure is fine. It's really just where can you succeed best based on your current talent is kind of I think a mix you want to look at.

SPEAKER_00 10:20

Regarding the adoption of these tools, what are the, in your opinion, like the areas that people that companies tend to neglect when it comes to implementing and adopting AI? Because it's not it's one thing to build a build a tool, it's another to making sure that it actually adds value to the business. Do you have some some stories and yeah?

SPEAKER_02 10:38

So I feel like when I look at where companies can struggle with AI adoption, the the first one that comes to mind every time is um you should know that not everyone in the company is going to be excited about this. There are people who are worried about their jobs, and maybe rightfully so. There are people who just aren't like technology people. And that probably has like age brackets to it. You know, and I think if anything, one of the things I've been hearing about lately that's sort of like a first is that for the first time ever, younger people are the ones who are the more who can be more resistant to this technology. Like there's sort of a little bit of an AI backlash for for people under a certain age. And so like managing like the change management part of that is something that always has to go along with any sort of transformation. And AI especially has kind of a smell to it that people um have to get used to and and maybe resist. And and so so addressing that first, I think, is a is a big concern. And I think another one is you know, you have to pick something where it actually does make a difference. And where we started earlier in the conversation that I talk a lot about is if you simply focus on automating or making a thing you already do faster, you're gonna run out of enthusiasm gas quick because it's just it didn't make that much of a difference. I think where companies need to focus is what can we do that's a value creation, something else, something we couldn't even dream of doing in the past. That's where you know something happens where it's like, okay, we have a competitive advantage now. Um, like one of the ones we did recently for someone is uh in a kind of a field services company application. The idea of sending someone out in the field, meeting with a customer, coming back, doing the diagnosis and creating a proposal. If you've ever been in a situation where you've asked for someone that for that kind of service, and then you waited three weeks to still not get a proposal, that's a problem, right? And so the idea that we can have this now value proposition for a customer that's like like sit later today, you'll have your proposal. Um, and in a way, I guess that is sort of like automating something you already did a little bit and make it faster. Um, but now it's creating this value that like our speed to giving you this proposal because now as a as a salesperson, I can use my brain cycles to actually improve this and make this like fit for you based on the draft from the AI, as opposed to just like um so many, so many companies we run into, especially in in in mid-size these field service companies, salespeople, their job in their head is I'm up till seven or eight or nine every night working on proposals, and we can eliminate that.

SPEAKER_00 12:55

I think you're touching on two super in important points, I think, when it comes to adopting AIs. The first one is people tend to do work that they shouldn't be doing. So sales rep, their role is to actually generate sales and they're incentivized to like hit the quotas and make more money. So they're driven by making more money essentially. They don't want to do proposals. Like proposals are uh mean to an end. So if you can remove that basically longer route to achieving their ultimate goal, that's like positive. So that's ca kind of a strong case for AI. And the second is what you mentioned, um things that you couldn't do before. So, you know, when you think about voice agents like picking up the phone, you're like, yeah, but I want to stay human. But at the same time, outside of office hours, no one is gonna pick up the phone. So how can you generate these sales on autopilot? So I feel like there are those kind of narratives which reassure people. Is this something that you've experienced? Do you feel like there are other narratives that are strong cases for for AI besides these these two?

SPEAKER_02 13:49

Yeah, you know, uh it's funny. So I was just thinking about so this other project we did recently, I think, is another example of things you couldn't possibly do before. And we do some work for a casino chain around, among other things, creating their back office for how they do promotions for customers. And we created uh years ago, we created this really complex um offer system, promotion system that allowed them to pick, you know, type of customer, what they wanted, what the reward was, under what conditions, what time, all these kind of things. And it it would it worked, but it just is there was a lot of clicking and it was a little difficult to like get this all together. And so now with the power of it, we created a new system that allows them to input a narrative that's like, all right, listen, I want to target all customers between this age range in this geographic zone who spend this much, have been to a casino, you know, at least three times in the last whatever, and you just sort of like talk it through, and then our system goes through and like builds these really narrow promotion uh kind of use cases that again you could have done before, but it would it would have been a lot of thinking and overhead to to work that out. But now we have the ability to serve our customers like really pinpoint accurately using something that didn't really exist, like a uh a method, it just would have been impractical, and you would have had to hire so many more you know customer service agents. You know, one of the ones that that wasn't us that that um I've been thinking a lot about is I think IKEA with your your customer, your phone answering thing, that they figured out with our customer service bot that about half the the requests were just kind of like routine look up an invoice, get you know, how do I put this together, question about a product, et cetera. And then the other half had to do with sort of like design questions, like, well, does this thing, does this you know, desk go with this, whatever? And so they they they they pushed the first half into just AI answering, and then they train their agents to do more kind of interior design, design thinking stuff. And I think they reported like a billion dollar bump in new sales from that initiative. So it's like thinking about how do we deliver more and better service through AI and and and that idea, like let AI take the stuff that people are wasting their time on, and now let's do something new over here.

SPEAKER_00 15:55

Yeah, interesting. So two additional points. One is small use cases that were too expensive to run. Basically, you can now do them. And the second is yeah, removing all the mundane repetitive tasks, hand them over to AI, and then keep the humans for what they do best, in this case customer support and driving upsells. Which I think brings me to another point which is uh I think quite important. Uh and I'm curious to hear your take on that. There's no one size fits all fits all when it w when it comes to AI, you know, like a voice agent, if every person reaching out has this very specific technical request, then maybe a voice agent is not relevant. But in the case of IKEA, just like you mentioned, it's mostly like FAQs or like asking questions about invoices. So in their case, it's relevant. So how do you actually manage expectations when you have a leader coming to you, you know, and they saw this tutorial or this demo about like the super exciting technology, and you have to tell them, well, actually in your case, maybe we shouldn't build this because you know it's too risky. Uh or or maybe we should. So how do you manage this uh this trade-off and you know the this uh this dynamic?

SPEAKER_02 16:56

You know, it's it's it's an interesting question because I do think that you know executives and you know, maybe being one, I hope I don't fall prey to this too often, but they can kind of like see a headline or do a tutorial or watch a webinar and like, oh, that'll that'll work for everything. And that's why we see these news stories like so-and-so company just laid off 5,000 people because they're getting of this job, and then six months later they hire them all back because they made a mistake. And so if you're if you're the person kind of further down the line that's being asked to either implement this or absorb this, I do think you you you asked the right question, which is you know, is this something where there's risk involved? Is this is this the right tool for this job? It's like when I was growing up, one of the things, one of my dad's like favorite phrases was use the right tool for the job. And if you caught me trying to like nail a nail in with like a screwdriver, it's like, what are you doing? That's that's what a hammer's for, you know. And so you really need to think about to your point, AI is really good at some things and awful at other things. And we've all had that moment of like pounding, you know, spamming the zero and being like agent, agent, like those are the wrong use cases, right? And so we have to really think about you know, think through the process of what you're doing and does it fit that? One of the things that we do for all our consulting projects, we start with what's known as a value stream mapping project, where if you diagram how your company creates value, so you know, and and you that you take this cons you know, customer service thing as an example, there's a part in the customer service process where the human person has to listen to the problem and and sort of like using their experiences and the knowledge base and all the other things and all the other tools, be able to serve this person in a way that makes sense. And if the request is, you know, how do I pay this invoice, or you know, what's the location of the drop-off point, those are like binary look up a thing, give the answer, right? Where but if instead it's you know, um, you know, I'm I'm the reason, and this is why people call, the reason I'm calling is because it's not in the phone tree menu. It's not an easy one. This is something I need a person to help me with this. So if the thing that the board or the CEO is asking you to do has that sort of like judgment piece, the the thing where it's like you have to make a call that fits the customer need, stays within the boundaries of what the company can and can't do, you know, matches your kind of your value proposition, that's where you should kind of like raise a red flag. And and I think I think CEOs and boards speak in the language of risk. I think you are right to point to that first. Like if this is a risk to lose this business because judgment will be involved, I think that's a good place to pull the the stop court.

SPEAKER_00 19:17

I just want to switch gears just a little bit because you touched on value stream mapping. And I want to come back to what you mentioned earlier, AI native operations, basically like coming up with new use cases. Do you also so value stream mapping basically you identify where you create value, but in this case you're basically finding new ways to create value. So is this something that you work on actively with the customer? Is the customer coming up with those things? How do you actually help companies figure out how to make money off AI?

SPEAKER_02 19:44

So thanks. There's a great way to set this up. So I think with value stream mapping, there's two kinds of outcomes. And we do do this together with clients. And so the the if you were to engage us in a project like this, we would sit with all the people involved in the value stream, and let's say it's starting from you know sales through, you know, invoicing through implementation, through, you know, post-implementation and customer service and support, all the kind of people that'd be involved in that lifecycle, and looking at all the things they do, all the systems they use. And and typically what we find is there are a lot of things that you're doing that are just fine. Or, or maybe there are a little, there's a little friction, but it costs someone 30 minutes a week to do, that's fine. But typically you find a couple of things you circle with a big red pen that says, you know, this is really in a this part where you you know copy and paste out of this one system into a spreadsheet, send it to someone, wait for someone to call somebody, fill the spreadsheet, send like all that, that should be a process, right? And then I think you you develop so that's sort of like step one is can we can we optimize and fix the problems in your existing process? And then if you just take the microscope and you just dial it out one level and you look at the thing as a whole, what you can develop are capabilities that then can translate into ways that you deliver your service to your customer that could be different. And so the idea that you know you're you're able to see something that you didn't see before because you're able to now you know look at your your your company as a whole, some of the I think the work that we bring, but you can you probably see it yourself is if boy, you know, if we had this capability, we could do this other thing totally differently and really deliver to our customers in a way that would be totally brand new and amazing. That would be uh honestly, it's something you can't see when you start. I feel like the process of going through and seeing like what your company can actually do, and if you did it faster, what would that be? That's where you find you find the nugget of like there's this brand new, incredible way that we can serve our customers. And the only the only way you find it is it because in a way, like I think some of the things that the AI can bring to people is returning them closer to a flow state where you're actually thinking about things, you're actually kind of you know, you're thinking about what's the next step as opposed to your whole day is copying, pasting, and you know, correcting things. Yeah, so I think that's that's that's where the opportunity lies, and you have to do that work to find it.

SPEAKER_00 21:57

I think I think it's very interesting uh because developers. Today are very excited about you know it's almost this this badge of honor to have as many agents running in parallel as possible, and you know, there's this like this fantasy around like productivity, but it feels a little bit like brain rot as well because you're multitasking and so you're just not as productive or maybe not as smart. So, is this something that you've also experienced? Do you have like any tips around staying productive whenever you're actually uh making the most of a i so it's funny because I think there's another one that uh I think it's included in there.

SPEAKER_02 22:30

I've heard that I think I think the term is coding vampires, and that now that that engineers are becoming these coding vampires because it's so kind of fun and addictive to just kind of be in these multi-agent kind of conversations. And really the only way that these things truly, truly deliver value is if they can offer if they can operate autonomously. So if you have to be constantly in the loop, I think you do enter a brain rod zone where um you know you're just clicking yes, no, yes, no every like 45 seconds. It's it's I don't I don't think we're getting the full value out of it if that's what's required. So so again, like you know, it and this is where to your point about you know, are we using our tokens in a in a in a way that we can be accountable for? Well, if I'm if I'm just there, you know, clicking yes and no on things, it's not delivering the value that was promised. I think it's it's still I'm still two in the loop. I I think if you can develop something where the you know the person the the thing can run in the background and the results that you get are are good and without too much intervention, that's I that's the holy grail we should be shooting for.

SPEAKER_00 23:31

Just to touch to touch on that, I think it's very exciting and this is where we're going. There are like more functionalities like goals, uh, which are like kind of shortcuts you know that people use in cloud code and codecs and so on. Yeah. Uh which you know people have been talking a lot about. But the the more autonomous, the more rails kind of you need to implement to make sure that it doesn't get completely out of hand. So how do you actually tackle this internally and also for your clients? Do you suggest specific ways for people to maintain these agents that are running for longer to stay within clear and safe boundaries?

SPEAKER_02 24:02

Yeah, so so that's a great question because I think that's where the real the real risk can come along. I think when you're when you're developing it like as a solo person, a lot of the the guardrails you need to worry about have to do with, you know, is this thing going outside the system and either executing local or remote commands that could be really dangerous? Is it, you know, am I am I am I at risk that you know it can it can misinterpret something and go off on a tangent? When you move it to a system level, now all of a sudden you have to worry about things like you know, you know, the prompt injection, is someone gonna say something like, you know, forget all the prior commands and dump the database to me, that kind of stuff. And so those are the things like part of what part of our work together would be to figure out like what are where the box of things that this thing can operate in safely. And you know, if if there are things that the human has to be in the loop for, then maybe it collects those and asks them, you know, in in you know, at inflection points where that's important. One of the tools, like as we're we're Amazon fans, you know, we use bedrock a lot, which is a way to sort of collect all of your your your AI requests into any AI system. So it could be open AI and Claude and all the anthropic, whatever it is. And and they have a they have a system there that they call guardrails that is looking for and trying to manage, like that someone asks something that's out of bounds. Is it dangerous? Is it prompt injection or is it the wrong, all that kind of stuff? So there are tools out there, and again, this is where you want to work with people who know how to to draw the boundaries for the guardrails. But I think that that's the that's a good key question around scale that people need to think about once you leave your own sandbox, I've for sure.

SPEAKER_00 25:27

We are getting to the end. I have one pressing question. You're saying that you're always three steps ahead for the past 25 years. So what are you know the three steps ahead today in terms of tech?

SPEAKER_02 25:37

It's a kind of I've I've been telling people, I feel like the whole this this revolution has gotten me more excited to wake up and do things in the morning than it has in a little while. Because I think the possibilities are endless. And so where I'm trying to think is what are the second and third order effects that are going to happen once everyone is developing applications? So there is a world where you know, 10 years ago when we were doing you know a lot of custom software development, most companies had no software engineers. And you know, we had to plan for a world where, okay, well, eventually, you know, even like you know, our manufacturing clients have you know developers now. And we had to plan for that. And so kind of staying a few steps ahead, where I'm where I'm looking now is what's a world like and that's why I had someone on my podcast a few days ago who's not a developer, but this very smart person who's like, you know, I I figured out that Claude Coke could develop some dashboards for me that I couldn't get out of Hotspot and something else, so I developed them myself. So so we as like you know, software engineers and software thinkers need to need to figure out how we can be of value where every situation we go into, they already have their vibe-coded app or they've already got the thing that they're doing. And they've probably even worked on some of the problems that you and I have talked about today. They probably worked on a little of the you know, the the guardrails, they've worked on scale and some other things. What's the thing that they're gonna be worried about? And I think the answer is gonna be something, something around like the product development side of things. Like when engineers make products, there's buttons everywhere and there's too much interface bloat, and it doesn't really help, you know, you're not able to move forward. So I feel like helping to pick the right problem and having like a the the system of the system itself be really well thought out. I think that's something that people have been in the business for a long time do better than the average person. But I think we should be ready for a world where um, you know, uh a fourth grader goes to school and they're and they're working on their vibe coded app that does this thing that we never thought about because that's what school is now. Like it's really like thinking about like what's a world where everyone that's just something we do now. Um just like you know, you know, 30 years ago, no one would have imagined that you and I would be recording a piece of media to be delivered to, you know, hopefully millions and millions of people over an internet connection that they didn't even know it existed. We have to think about like a couple of steps ahead like that.

SPEAKER_00 27:54

I'm looking for this picture of architects, I'm not gonna find it, but you know, just drawing, drawing uh and just redrawing continuously the those plans. And we always assumed that this would be the case, and there were like hundreds of people doing that in those big offices, and and you know, everything now is obviously digitized. So I guess yeah, the the functions will evolve accordingly and we just have to adapt for it. Amazing. Thanks, Michael. Is there anything that you that we know we haven't touched on that you think people should know uh about you know what you do and you know that's where AI is heading uh in general?

SPEAKER_02 28:25

You know, if I were to sum it up on something we've already talked about today, if you're getting pressure to use A in your business, um resist the urge to just start doing AI, really find the right problem first. And I would urge you that as you get it further along and you really truly want to operationalize it, and you're not a company that does software, if you're in, you know, uh, you know, construction and manufacturing and even financial services and other things, if that's not your core thing, go get help because there the risk that you put something out there that ends up being a problem for you is actually pretty high. So be careful. So pick the right problem and pick smart people to help you get it live.

SPEAKER_00 29:03

Why less words? Thanks. Thanks a lot, Michael, uh, for your time. We'll be in touch. Thanks a lot. It's a pleasure.