Structure Before Features with Evan J. Schwartz

There is a special kind of exhaustion that comes when you're building with AI. You've got the license, you've got the pilot, you've got a chat window that generates whatever you ask for, but then you hit the wall. The workflow that almost works, the dependency that loops back on itself, victories that only come after you've quietly moved the finish line. Today we're talking about how to get around the wall. Welcome back to Invisible Machines, I'm Josh Tyson, joined as always by Rob Wilson, co-founder and CEO of OneReach. Rob and I are co-authors of Age of Invisible Machines, the first best-selling book about orchestrating AI, and this is the podcast where we explore the future of intelligence through conversations with the people shaping it. Kevin J. Schwartz is chief innovation officer at AMCS Group and an adjunct professor at Jacksonville University, where he teaches a course in AI stewardship. He approaches the wall from three angles, inside the company, next to the customer and in the classroom. His advice, score a quick win by delegating necessary but low-value work to AI with humans providing the judgment, and then zoom out and ask how the whole cycle might run if AI had been there from day one. Along the way he names the trap that burns most teams, asking for features before you've articulated the structure, name the actors, and decided where change is allowed to live. The framing is simple once it clicks. Structure comes before features. Let's hear more from Evan J. Schwartz. Well, Evan, it's great to see you again, welcome to the show, you've been super busy since we caught up. I know I have it. We have, and one of the things we were busy with was thinking a lot about AI and what it's doing to doing, actually, because I think you made the point when we were talking before that we get a lot of identity out of doing things. Even if it's stuff that we don't necessarily like doing, we get some value out of doing it, and so now AI seems to be coming for the doing, and it feels like a lot of people who are having experiences with AI that they feel good about are people who are at first we're thinking they're making things, and that's probably true to a degree, but I think what they're really doing is directing. It's almost like the work of a film director, like marshalling all these different people and technologies together to do truly novel things. And we feel like that's an important moment, right, for a person to have, and particularly a person inside of an organization. You have experience seeing the light bulb go off for people, so maybe we can start there and just talk a little bit about that first tipping point where someone really starts to understand what they might be able to do with AI now that they don't have to do as many things. Sure, so where I came to this is across what I call the three swim lengths. So my role at AMCS professionally, cross companies, my ability to support our customers, so seeing it from the outside looking and seeing it from the inside living in it, and also as an educator, as an adjunct professor at Jackson University, we're seeing the impact on the way things are changing through education, particularly higher education of being able to graduate what would we graduate in today. And this has been about two years in making, in fact, my next course, which starts in August, is going to be the culmination of about two and a half years of living it, seeing it from the outside, and preparing the education. I call it AI stewardship, where exactly what you were talking about, the class is going to be around architecture and development, but you could take any particular vertical thing and you're now slicing it or bifurcating it from the top in, the design, the thought process, the outcome piece, and then the implementation details of the doing. And AI is taking across the board most of what I call output versus outcome type of strategy. We, the humans, are still today very much in control of the outcome while AI is becoming increasingly more in charge of the output. And it's how you as AI steward are driving that particular step in the process to produce this. To me, the most significant piece to really grasp is that you're no longer a specialist because you don't have to do the full vertical. You're broadening at the top end, like an inverse pyramid. So in the class I'm teaching, you're a little bit of an architect, you're a little bit of a product marketing or product owner or product manager, you're a little bit of a team lead and understanding how to communicate into the AI to get it to work. And what I'm proving in this course is that if you can describe what are the actors, what are the areas that are going to change together, right? You can make sure you give it the policy decisions on where dependencies need to point. Before you even talk about what it is you're asking it to do, you're describing the structure, then you're describing what it is to do. What it produces then is phenomenal. And we think about automating something. We usually immediately imagine something we already do, making machines do it in the same way we do it. I think that's just baked into it when people say, well, we should automate this process. They usually don't mean, oh, we should make it better. And then, and then let a machine do it, they usually think, oh, you mean have a machine do exactly what we're already doing. And I don't even know that people consciously think about that, you know, AI is so transformative. And the real opportunity is in doing things better than you do it. And that it's a different kind of intelligence. So even doing it the same way that humans do it is the wrong approach anyway, because it's not the same kind of intelligence. It's, we call it artificial intelligence as if it's like mimicking, you know, human intelligence. And I guess to some degree, mimicry is correct. But it really is a completely different kind of intelligence. If that's the pointed idea that automation works best when you adapt the way or what you do to optimize for the machine, then, then you start to get into the fact that the larger the organization is, the more change you're trying to create at one time, we have companies that come with like 55 use cases, right? And they'll have it on a sheet and they're like, okay, we want to automate all these, right? And you're like, so should we go through each one of these and redesign how each one of these should get done? Or do you just want to automate it the way it is? Because if we just take to 55 use cases, automate the way you're doing it today, you're going to be back at this because, hey, it's going to be harder to automate the way you're doing it in a lot of these cases, because it's not designed to do it exactly the way human does it. But also, we're going to be doing it because you've not taken advantage of the biggest value of AI, which is, you have an unlimited army of people now just sitting around waiting to do stuff at a fraction of the cost, like do more, do better. You're absolutely on the right track. I think you're old enough to remember the arrival of the internet. And there was a handful of, because this is what fueled the.com bubble, right? There was a handful of everybody myself included that just could see the vision of how great the internet was, and business after business through internet across their business, like a blanket, and so what? I still have the filing cabinets full of paper. I still have this rubber stamp. I'm still cutting my finger on, you know, paper cuts. Nothing has changed. And now you come to today, we couldn't imagine a business being a business without the internet. No one can even think of what that would be like without the internet. We're seeing the same kind of hallmarks with AI. People are trying to just lather AI on top of their business to your point. I want to just automate what I've got, or I want AI to do this work for me, and they're missing the point. But it's funny. They do think it's easier, too. And in a lot of cases, it's not because you're, because it is a different intelligence. Like doing it exactly the same way a human does it can make it even harder in a lot of cases, at least from my experience. But what I'm working with companies on is is making it a two step process and accepting that from the beginning. And in the first ways is not to take your processes as is and fully automate them. It's to delegate down to AI, the necessary but low value work, and compress the high value work into your humans. And what you're going to get is an immediate lift and gain, not, you're not going to get the asymmetric lift of rethinking your business. But you will have at least paid for the AI scene progress and built confidence. And they're, they're, look, we're humans, we're lazy, we're, we're messy, and we're greedy as just innate things. So we have to work with the things that we've got, and humans are just generally messy. So if we don't get them on board with it, they're not, they're going to be resistant to change very much so. And there is no change without pay. So this gives you a quick victory and it builds that confidence. And then you're right, the second wave is, how would I have done this if I had AI from the Gecko? Forget the process now. I've applied what I think is the less than multiples. I mean, a 30% efficiency, 40% efficiency gain here, just to cover my costs. Great. I can go to my board and I can go to everyone y'all about putting AI sticker on the outside of the logo and tell them I've got it. But then the follow up is we need to reevaluate this like businesses aren't today. And then you start to see the multiples in returns. And one of the success stories is a customer virus and we did exactly that. We just, we did the, we compressed the low value, but necessary, the stuff that they had to do. And they got about a 50% return on that investment. Then we went back and looked at the entire iterative cycle. And that's what you have to do. You must have to zoom out. You're not automating each one of these little cogs. You're zooming out and rethinking with AI. How could I do this whole thing? And they're now able to take in bids, process them, run them through their gauntlet and send back a fully quoted, ready to do business. And they're winning business and having a truck, heading that way to service that business. They just got to bid in by later that afternoon, which means that their competitor is still trying to get everyone's schedules aligned for the meeting to talk about the RFP. That is asymmetric growth. Now you're getting there. But it's, to me, it feels like still it's going to be a two step process until we get to where we are today with the internet is it's so apodetic that no one can imagine business without an AI. And I think one of the things that that people struggle with is it feels safer to not. It feels conservative, it feels like, you know what? If we don't use it, there's a last likely chance that something will go wrong or that we will waste money. And, and so let's not use it. And then we'll learn from everybody else's mistakes. And then, and then we'll use it when it's safe to use. So in my AI stewardship model, there's four cultures. There's the risk averse, the pragmatic, the innovator and the early adopter. And if you're going to adopt AI into your business, you should say, I mean, you are to the company. We're clearly in the innovator category, right? But our company is risk averse. I can't run an innovator adoption program in a risk averse company. Like we just described, I want to see it work. I'm going to implement known things. So typically risk averse companies will change when there's pain. Without pain, there is no change. Very few of us run after the pain. Where are they probably the few in the SATO masochistic category that run at change to see if we can make it better? Most would be in that risk averse category. The pain is, I'm looking at a competitor. I got that RFP. I'm struggling to get my schedule together and I realize by the time I've gotten my team together and even communicated with the proposal guy. Oh, we've already given an award to this to your competitor. Now you've created pain. Now I have to think differently. Like if we look at examples in the AI space, like automobiles, right? There's a velocity to this stuff. We're not using even a fraction of what's already available to us. Most companies are using less than 10% of what they could be doing every month. This grows what can be done, expands as models get better and we get better. I think it's a human thing personally. I don't think it's a technology thing. I think humans and their ability to adopt this technology. I have this very strong feeling that in the not so near future, we're going to realize that the real scarcity is AI talent. Not people who can pair it, clawed AI, but people who can actually use it with real experience and real success. It won't be about procuring the software or the technology. It'll be the people. And who's going to want to go to a company that's that's trailing the market. And now you have a full staff that's trailing because none of them have that experience. They haven't cut their teeth. When you talk to students and stuff, do you advise them? Like go somewhere that's adopting this stuff or you're hooking your trains or something that's eventually going to lead you to a catchup mode. Right. This this reminds me a lot of the, you know, 2018's where mobile units have come out. It was intuitive to that generation. And businesses were put on notice that if you don't have interfaces that these graduates can use, you're not going to have employees. They will not work there. I mean, I actually did some of this work in the 2015, 2016, where I put a, a, a GUI, a graphic user interface over top of an old DAW space, command line driven system, because there was literally nobody coming out of the universities that even knew what a C prompt was. So the company was like, I don't know how to get off of this mainframe yet. But I need something that's going to bridge the gap until I could get to the next generation tech. My children use AI intuitively for things that just feel superfluous, right, when next to what you're pointing out, what it can do and what you're using it for. But if you also look at Microsoft word, not one person, even the most astute user of Excel or word uses more than 15 to 20% of what those tools can do. And they have changed the business landscape forever. Nobody can go back. I think the next generation is going to demand this is too much work. In my last job, I didn't have to do any of this. I had agents and AI that I could speak to that produces, I was driven on metrics. I was driving the bottom line of the company where I came from here asking me to produce this tiny thing. Then that's not me. And you're right, the skill is the person that can look at that thing and is the ability to create the prompt, understand to source the data. And know how to measure it, not someone that can replicate someone who can look into the ether and go, if you frame your prompt this way, if you give it the structure first, then you give it the requirements, you make it validate your, your architectural constraints first, you get a much better coded product out the other side. So build on that. Though that's going to be the one that can create that not someone who can cut and paste prompts all these prompt libraries, if I get 5000, that's, that's not doing anything for anybody. It's the person who think through the constraints and capabilities of the AI and produce that. That's what businesses need because the reality is the world is is entropic. It's changing. It's chaos everywhere. So I mean someone capable of adapting the tools I put in front of them to cope with that with the, the scope and complexity of what you're describing, though, of getting some small wins and then starting to think about how to apply AI in these bigger ways. Does it feel like midsize companies are actually at significant advantage over larger companies, what you're describing requires a lot of consensus around objectives, around how to organize knowledge. And what kind of things you want to use this technology to do because if you don't really have a clear idea of that, you could get stuck in that space where maybe you start automating some of this lower value work. Does it feel at all like smaller companies because they can literally get everyone in a room together and actually have a face to face discussion about what are our problems, what are our constraints, what are we trying to do with this stuff. They have a lot better shot of getting firm footing and having a consistent conversation. I'm going to say, first of all, school is not out. We haven't had enough runway for me to give you a definitive answer. The reality is that the smaller SMB companies because they already have broad, you know, master of none, jacks of all trade type of guys, the entrepreneur, they have to wear more flats, which is a strong fit for the model, they're going to have an early advantage. But they are going to hit a wall where they're going to have a struggle to maintain that and it's a discipline issue. So here's what I'm saying, they get greater returns earlier, but they tend to take on too much as a rule. They don't have the discipline from an organization yet because of that size. So they get off the ground much faster, but they tend to start to hit some kind of a drag. When you get to the mid size, those guys take a little bit longer to get up, but because they've got more discipline, the right of the crest, they spring forward in their ability to do more because they have larger resources, they already have a this structure, they can look in their organization and they can attack segments of it at a time. And then because what you're seeing is you're seeing a compression of the orchard, rather than this multiple things, it's coming very compressed and spreading out. When you get all the way up into the big tier ones, they are behemoths, they've always been that way, they it's like a battleship trying to get turned into the ocean, but they still can, depending on their structural approach can break off into pods and working business units, where they're running into most of the trouble. And that's why I'm saying the the jury's out because I haven't seen one run with a long enough runway to go. Does it in poorly or not? But they have a lot more resources to run at this. They're able to organize their business in a way, but where they get stuck because they're so structured is I optimize one and I'm now overrunning a piece of connective tissue upstream because I've now optimized this so much. Its output is exceeding the next feeder pieces ability to take it in or I'm draining the source of data that would go into that department so quickly that I'm now creating inefficiencies because I've made this so inefficient. So they're learning and this is fairly recent that they're having to eat themselves from the outside in the customer face until they can get down to the core. Otherwise they've invested heavily in a segment, gotten a super efficient neither it's downstream or upstream that's going to pay the price and because they're big business still they're not going to run quickly to the next step. They're going to take their time. It's problematic, but I think that's just a structural challenge. Again, this is a digital workforce challenge, not a tech challenge. We're getting really good at getting this thing to produce the output we need at the quality and repeatability that we need it to do. So that to me was the last technical hurdle. Now that we've learned how to do those, everything else is a digital workforce challenge and it's to Rob's point. How do I get everyone at the same level to be creators, orchestrators, stewards, pick a term here, director I think is what you guys started with that can produce new behaviors and understand the constraints to adapt and then the change management is the last big problem that we've got to overcome. There is some part of me that as I'm interacting with really super large organizations, I end up talking to people in these organizations that totally get it. Totally get it. We're finishing each other's sentences, but then within that mechanic that they're stuck in, they cannot get other people on board within their org. They're one voice at the table, there are two voices at a 10 and it's a vote. And they go around the table and what does everyone think, right? Well, since there's only two of you again that think that we should automate this stuff, we're going to stick with waiting. Yeah, I've seen it. And all I'm saying is right now the battlefield looks tilted asymmetric against the other side, but business has been like this for a long time. We could take AI out of the equation and you see the same battleship analogy with big business versus a smaller startup. And those businesses buy them or strategically pick them out of the equation or adopt that tech and kind of divide or spin off activities to adjust their size to make them more agile. Their resources are so vast they have options. I agree with you right now they look very slow. What I'm waiting for is the pain threshold to reach that point to four section. I believe that there is no change without pain, but pain tolerance is different between the small business, the medium and the large. I think that once the big boys start to see these smaller five and twelve man startups hitting billion dollar valuations and starting to eat into their market share, then it becomes a top down initiative, board start going guys do something or I'm going to invest my money somewhere else to do something. We're also seeing a world right now where we're still very subsidized in the AI pricing. And we're seeing a lot of missteps where the cost of AI is not where we thought it would be. Now you and I both know it's a lot of that's due to the misuse, mishandling of AI or the way that you implemented it. But there is a cost factor of AI that's going to come to fruition that I think is going to either price out the little guy if he's not careful. And which will take a lot of that. And then the mid tiers and the big businesses are going to have to figure out how to bring this to market. So in our company, our number one criteria for lead qualification is have you done it and failed because if you failed, you appreciate what we've done. If you haven't failed, then you don't. What that tells me fundamentally is that failure has moved them forward or or else I wouldn't be saying that. I'll say I'd be saying it doesn't matter if you tried or not. You'll still appreciate what we've created. If we see this, it means that all that failure hasn't been for not, right? It means that they have learned something significant. They know that it's way more complicated than they thought. They know that pilots to production is not something you should take for granted. They know that somebody vibe coding a solution and showing it in a meeting is far from something that's actually going to move the needle. And that random acts of AI in your company is not a strategy, right? So they, they kind of, this failure seems to have created within an organization this crisis essentially or this moment where people get together and say, okay, how do we fix this problem? Which creates a level of consensus is remarkable how different the mentality is from we're in front of trying to implement this stuff, which they think would be fast, easy to, okay, we've tried and we're humbled. Yeah. So you're just describing in a different way that there is no change without paint. There has to be some paint. And we're also talking about something that is a paradigm shift, just like the arrival of the internet was a paradigm shift. And when the internet came, there just wasn't enough there for new minds to see really how to use it. But there were visionaries everywhere that were confident in today. They would have to go, I told you exactly today was going to happen. You would not have an a business without a internet. And there are people sitting the same thing with AI to get business to that same conclusion is going to take time. It is a journey. And it's a journey that's going to have to be thought with paint because so many of the POCs I've seen and when you quietly talk to the person who built it, they move the goalpost. They hit walls that they couldn't figure out how to get AI across. So they just changed the goal and called it victory and then presented it. And now when you present it with that light, what was built appears miraculous. Anything could be done. But they've completely hidden all of the pain and suffering that, you know, what a friend of mine was trying to do a music video. And one of the things that he couldn't get the AI to put the nozzle into the tank. So in the video, it was just spraying the car with gas. So he just took that piece out. Right. So he moved the goalpost to get to and it was still a beautiful video in outcome. But that wasn't the original one that he drew on the board. And there is lies the gap of AI is that when business comes to you with a requirement, I don't get to move the goalpost. 99% of the time, this is the requirement. This is what we have to produce. I don't get to say we're not going to do governance or I'm not going to put, I don't get to do all of that. So you're right. It's so easy to do a POC because I'm presenting you finish work as though I intended to make it when 99% of the time that was not the goal I was going for when the internet first came out and received them on being able to do error correction for shifting files. We had internet, but we couldn't send a file without it being corrupted on the other. There were things that needed to happen to get it to the point where we take for granted today that we're having a video conversation right here. And there's no problems, no mistakes, no nothing. That takes time. So this is going to be the same kind of adoption program. It's a paradigm shift. It seems like if you're like a director or steward, this might be a somewhat frustrating time in a sense because you might be in an organization and you might be the one person who gets it and then everyone so now you meet a rob who gets you all excited, but you still can't get anyone else in your company to buy in. It's almost like the strategy might be to like keep speaking up, but also keep sharpening your tools and wait because having failed with this stuff means you kind of understand the scope. Oh, you understand the analogy, even if the scope's not fully well. Yeah, you at least have a better idea of what you're trying to accomplish. And it does feel similar to how conversations around UX were like 15 years ago, right? Like the people who really understood UX and saw the potential of what it could bring to a brand or a company like they were thinking holistically, they were thinking about information architecture, they were thinking about so many different aspects of an organization that could change in order to bring about this advantageous shift, but there wasn't much appetite for it. And it feels like the appetite arrived, like you said, Evan, when there was pain or there was like competition that was suddenly threatening your very existence. So the apple, the Android fight that's still going on today, if you're an Android guy, you love to config it. Most Android guys that are taking advantage of the Android are deep into config, know how to make that thing work and make fun of Apple people that love their Apple, but those Apple people didn't have to do anything, but doing it on and it works. It does everything they needed to do. it's intuitive, they can just naturally interact with it. And those are kind of the two worlds that have been flowed. Before the internet, there were dial up Bolton board systems, right? Those guys were the elite. They worked with command prompts. They knew ATT codes into a modem. You know, they were like little digital gods that could do things with computers. And they didn't like the internet because it killed their precious Bolton board system. Right here. You're going to have that level of resistance based on skill sets and age groups. But it will come. And my advice to someone who has frustrated at work who has maybe a leadership who isn't really that forward thinking is you have the ability in your hand as long as you can stick within the governance of that company. And you've got an AI that's not leak in IP to produce output faster than anybody else in that room. So keep doing it. Yeah, I agree. When we were working on the Dreamliner, it was just probably $2 billion worth of RMD to create the automation. And a lot of people don't know this, but it's like the first airplane that had orchestration. Every airplane prior had very dedicated closed systems like everything was singular, right? You didn't have a place that fed data into a centralized brain, let's call it. The one decision that was made very early on was there will be two orchestration systems. There will be one for nonessential and one for essential. The nonessential is Wi-Fi entertainment, sure, right? And then the essential is all of the plane mechanics, right? And a lot of that had to do with essentially the compression of data into relevant pieces. So more data is not better in orchestration. The right amount of data is this feature reduction is key. So that was like number one for why to have these two systems. When Wi-Fi goes down, if that was the same system, and it's going down all the time on these plates, when company is deploy AI in the organization, which our whole premise was if you can automate a 787, you can automate a company. If you mix these systems together, if you have critical systems that are orchestrating running alongside one orchestration system or disparate systems, which is what a lot have. They have different AI point solutions that purchase, right, which is the old airplane. There's no way you're getting to automation. Like that plane doesn't fly in the old architecture. It also doesn't fly if you treat all systems as the critical or critical systems, like they're not critical. So you have to have these very two distinct concepts. One that takes a lot more planning and one that takes a lot less planning. And we see that if you're on an airplane and somehow the entertainment system goes down for a second, it's inconvenient but fine. - Right. - When I say orchestration, I mean knowledge plus orchestration, right? - Yeah. - But in that system, it was knowledge of how to keep the plane level. You had to understand optimal boundaries, right? It's like a containment envelope. If the pilot pulls really fast on the yoke, the plane doesn't do what he said with his hands. The plane safely accelerates and climbs at the safest max rate. It's not a direct connection to the system now. You're talking about, okay, got it. You want to go up as fast as we can. Like these systems are designed to work within envelopes. And that's how you want your company, right? You want your company that if someone's there and they suddenly jerk the yoke that, oh no, no, we know what you mean. Safely climb, it's quickly as possible. So they go out and they take a use case that's more appropriate to writing an email to someone where a human's gonna review it, edit it, right? And they're like, look, we automated this, right? Which is really the non-essential systems. - Sure. - Which is quite easy to automate. And then they drop into the use case, that's critical. Right, into the same apparatus. And then that fails. And then they shook in the whole thing because they just fundamentally didn't separate mission critical workloads, which take a lot of human oversight, reading code, validating, testing against non-critical use cases, which are lots of human interaction, lots of human review and if it goes wrong, no big deal. I see this like as being one of the biggest issues companies have right now is not separating critical systems with non-critical systems. - Yeah, look, I think you're probably very deep in the weeds getting it into that particular bucket at a higher level, what is missing is, we'll typically get a vision, we'll have a high-level AI vision statement, no architecture at all, whether it's solution or technical-- - Right, that's point solution, that's it. - And then just take the hill at the bottom. Some of you may die, but that is a sacrifice, and we'll run, go do it. Hope being to find some sort of kismet in all of it, and that's not how reality works. It's not going to weave itself together, naturally, it just won't. What is missing, and it's the reason why I'm pushing so hard for the AI stewardship model is that it demands structure first. So almost gets all the way back to the start of this, is that if you're going to write a piece of code, before you even talk about the feature, what are the constraints, what are the policies, what are the dependencies, what are the structure, describe that, make sure that there's no interdependencies, there are circular dependencies to your point. I don't have a critical system dependent on a non-critical system. If it's a critical system, I better have a way to continue to operate this, if the system goes down. Someone better be able to get a pedal out there and start making the propellers go around without it. So those are the things that require orchestration to your point. I need an architecture around this that allows me to run my business. We've got similar crums of this in business on the manufacturing side. When you look at those assembly plants, those stations, and they're called operators, rather than stewards, running a machine, looking at inputs, making sure that the recipe has been plugged in and at each stage, we're hitting that. And then if we're off conformance, is there anything else in the recipe guide I can still make? Can I salvage this production run or not? There's a lot to learn from the discipline of that as we bring it into the back office, but it has never been designed that way. It's been loosely coupled, almost not even, I wouldn't even say Pub/Sub data contract model between contracts and HR and legal and accounting. It's more of, I need this, can you get it for me? It's almost on-demand service-oriented architecture, and then we're trying to build some sort of automation around something that's event-driven on-demand. There's no way to predict what you're gonna need or what changes are gonna come at you. So that is a missing piece, and that I'm trying to push it into the stewardship model is structure first to define your output properly, but you can only build that structure if you know what your outcome is. Otherwise, what is your architecture meant to do? To your point, if I don't know I'm building a plane and it's gonna have critical and non-critical, what is the structure I'm putting in place? I don't know. So there is, there's a new breed of solution architect that it needs to come into being. There's a new breed of application architect that is the steward commanding the AI that's producing that output. So someone has to be zooming up, and then you're gonna see the organizational architecture. So the stewardship model I'm putting together is AI Stuart, the guy running the thing. It's the operating model, what is the whole structure, and then there's the adoption model. How do you get all the stinky humans to adopt this thing and in what order is your plan to get the adoption, to get the best most efficient use out of it? Architecture is first and foremost to alleviate effort. That's what it's there for. It will change and when it changes how do you reduce and make the most efficient effort out of it? And you brought this out of the beginning is I think that people are confusing reduction of effort to automation and those are two different things. It's easy to automate something that never changes and is repeatable. But if you've got sort of an end-to-end relationship going into the system and you're expecting this automation to cope with any number of inputs, with any number of potential radical outputs, I don't know that you're gonna get there the way you think you are. - Yeah, that's like flying a plane and imagining perfect conditions all the time. - That's exactly how I build a plane for no wind, no storm, no lightning. - Yes. I still can't get over the fact that I think that we're on the precipice of a huge, huge deficit of human capital. - Yeah. - And the reason I say that is because it's not about, you know, learning the very specific things about how this technology works. But it's the rethinking of how businesses are run along with how technology is made and how businesses are structured and how they deliver their services. And all of this is a lot of learning. And the most important thing I think is that it seems very clear to me in my life. many years of doing this that as much as I try to communicate with words on the page or in a podcast, nothing is a substitute for getting your hands on it. Right. So now you have to find someone with a big budget that's willing to let you burn it so that you can learn and and that's hard to find. As it should be, I guess. And so these people are going to be really rare and I think at some point we're going to be like, I mean, they're already very rare in in the narrow AI space, you know, if you look at what anthropics paying. But this is going to become more and more rare and more and more valuable to larger companies. And they're going to stop looking at what technology do I need. And they're going to stop looking at technology vendors to solve their problem and they're going to have to start looking at HR and recruiting. And I wonder what your thoughts are as you see these students coming out, they're like, okay, we want to know this stuff. But getting that that hands-on experience, they're like so critical. So I guess the advantage of a university today is they do have a slight larger budget able to allow the students to play. And I am getting across civil universities now. It's not just Jackson University, UNF is catching on to this and Atlantic Technical Institute in Galaway. So we're starting to get the higher education folks that are going to write that check to involve and give that opportunity to play and learn they're they're getting on board with it, right? Because they need to, right? The whole point of their university is to output what is the next productive citizen going to be? What are businesses need, right? So that's great. And it's giving us an opportunity to learn and what we're finding and we're getting closer to it. The other center of the equation is how can we take what we learn and do a feedback loop back into business today? And I'll leave you with this because this is my biggest concern and I'm seeing it everywhere. The our best producers, human producers, right? Are the best suited to be AI stewards? Because they already understand deeply how to produce the thing that they're now delegating the AI to produce. And the problem is is their identity is so tied to that output that they're offended by it or they can't make the leap to look outcomes. I'm not going to say it's 100% right now. I mean, we're going to lose about 30% of our highest producing workforce because they're going to go, I'm not doing this. Not that they couldn't. It's because they won't. And we have nothing to blame but our own educational systems for that because we create an education system that creates dogs. Yeah. And there's also a big component of them being asked to do their day job and also automate their day job. The wrinkle there too is some people will find loopholes with that, right? Like, oh, okay, I'll automate this part of my job. I'm just not necessarily going to let you know that I did it. Well, I think enterprise systems are going to make that difficult because businesses have incredible visibility. The transparency and the observability rules are going to make it impossible to hide. But if the business, and this is how I advise the companies I work with, I like your vision on what you're trying to do. But your vision needs to explain to the people you're asking them to do it, what their world looks like too. What will they be doing as this AI steward now focused on outcomes? When do you decide to write side your business? I'm a person plus AI strategist. My view is if your first rule of thumb is to try to cut head count, you're doing it wrong. That should be your, I'm not saying you're not allowed to resize your business, but that should be the provider of last resort. If you've invested all this money to train all these people to know how to command AI and produce that asymmetric outcome, why would you send them into the workforce for your competitor, grow your business asymmetrically and just don't have to put any more in the head count. You're, you're almost nirping yourself by doing it. Now at the end of that, if your empire has grown as big as you can grow it, by all means, write side your business and be generous and send all these remained people into the workforce that know how to now compete with you and fill your business by all means. Do that. But that, that needs to be very clearly stated and not just put in a vision statement, lived because the second you break that contract when you get out there, it's exactly what you're saying, Josh. And I've seen some companies go, I'm not participating in this. I'm not going to participate in my own extinct. Yeah. Go, we're big fans of service design. And I think the key is not the verb, but like the asset, because you end up with this very easy to read, very easy objective that's oriented towards either a customer or employee experience. Right. And it's something that organizations could budget tokens for and measure success on and say like this was the objective. It was experiential. And now we can say, did we meet this objective where the ROI is just meeting the service design spec? Um, so anyway, this was great. I love talking to you as you do, man. Enjoy it. I get educated. Everything that's up back at you. Yeah. Thanks, Evan. It's a blast. Appreciate it, guys. Thanks for hanging out with us on invisible machines. Remember to like, subscribe and or follow us wherever you get your podcast media. Thanks to one reach.ai for their support and much gratitude to the many people working behind the scenes to make this podcast great. Until next time, a waggy.