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Everyday AI Podcast – An AI and ChatGPT Podcast
Ep 780: Build, Buy, Partner, or Wait: The 4-Layer AI Stack Decision Framework for 2026 (Start Here Series, Vol 25)
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This is the Everyday AI show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business and everyday life.
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If you're still asking whether to buy or build AI, yeah, you're already behind. The question was a hot topic and made a ton of sense in the early days of generative AI in 2023, when rag pipelines and AI chatbots were cutting edge and your company had to make a choice to either build something fairly complex on new technology or try to take business advantage of a consumer chatbot. But the buy versus build question doesn't make a ton of sense in 2026. That's because AI agents take actions inside your business software, compile your data while you sleep, and complete real workflows and outputs without much input from you. So your company's AI decision is no longer a single choice. It's actually at least four. That's because AI lives in at least four different layers of your company. I mean, the model itself, the workflows it runs, your data in context, and the business software it plugs into. And for each layer, you have four choices. You could build it yourself, buy it from a vendor, partner with someone who has the expertise, or wait until the category settles down a bit. But get one wrong and you wind up being in technical debt. Get another wrong and you lock yourself into a vendor you can't escape or miss out on a new line of revenue that your competitors all seized. It's easier said than done. Yet I think this conversation is one of the most basic conversations being overlooked in boardrooms everywhere. That's why today we're walking through every layer, every choice, and exactly when to make each one so you can stop guessing and start owning the right pieces. So this is Everyday I, Everyday AI, and this is part of our Start Here series. But before we go any further, let me just paint the big picture. So AI is different than it was. And I think back in the, you know, 2023, 2024, when companies were still deciding if AI was for them, really boiled down to, okay, are we going to use a consumer chatbot? Right. Back then it was, you know, barred the early days of Claude, you know, copilot and Chat GPT, there was no business context. So you either had to say, okay, well, we're going to try this thing and maybe risk it, or we're going to build a pretty expensive, you know, custom rag setup, right? We're going to build this rag pipeline and bring in all of our data and a Lot of times that was a very expensive, long and tedious process. But this is all shifted because AI now off the shelf AI is made for businesses and it's not just giving answers via a chatbot, it's executing real work inside of the actual software that you use. So new McKinsey study actually said that 88% of organizations regularly use AI in business. I'm always, side note, I'm always like confused by these stats. I'm like, what are the other 12% people doing anyways? You know. But this is a big shift because it's completely changed the narrative versus the, you know, traditional buy versus build, which has always been a cornerstone of any tech innovation inside of any big companies. So now it's not just two choices. You have to decide when you're going to own, when you're going to rent, partner with someone, or if you're just going to wait. So on today's show, stick around and here's what you're going to learn. You're going to know why the 2023 builder buy question fundamentally broke in the 2026 economy. You're going to know the four AI stack layers you should own, rent, partner on or delay. You're going to know how to make the right call on each layer without those expensive mistakes. And I'm going to lay out at the very end the three week sprint plan to understand the build by partner or weight. All right, welcome to Everyday AI and this is the Start Here series. This is your essential podcast series to both learn the AI basics and to double down on your knowledge. That's because after literally 750 plus episodes of everyday AI, I never had a good answer when someone was like, I'm new here, where do I start? Well, you start with the Start Here series. I think these are best if you go about them in order. So make sure you go to start hereseries.com and you can do exactly that. That's going to give you free access to our inner circle community. And then you can go click on the Start Here series space there and go, listen, watch, read every single Start Here series episode in order as well as a Spotify playlist that we keep updated there. So this is our series in the volume number 24 in the start Here series. Or sorry, that was last episode was start here series volume 24. So we talked on that one. Open source AI 101 why local models, cheap APIs and AI agents change everything. And today we are going over volume 25 of the start Here series. So yeah, it's best if you listen to these in order. You know, you can knock them out in a long road trip, long weekend, something like that. But we're going over build by partner or weight the, the four layer AI stack decision framework for 2026. All right, so why are we tackling this? As crazy as it sounds, I, I, I talked to a pretty big company a couple of weeks ago, and I remember talking with them originally in 2024, and they weren't investing a lot of time, money and resources into the build side. And at the time, I nicely said, I don't think this is a good idea. The space is moving too quick, right? Because essentially you had chat GPT come out in November 2022, and then eventually as it got updated, as it got better, there started to get some business momentum, right? It was kind of weird. It was kind of like social media, right before Facebook had pages and it was just profiles, right? A lot of businesses were like, wait, should we just, you know, kind of in the gray area here and, you know, create a profile, right? Because we can connect with all these people. And I think that the very early days of like late 2022 and early 2023 were kind of like this for businesses, right? They saw these tools and at first it was just more of, you know, shadow it and, you know, employees were using it and, you know, a lot of decision makers didn't know what was going on. But eventually, right, in 2023 and 2024, businesses had a decision to make. Because at the time, large language models were not made for business, right? Even though businesses were using them. And the companies were starting to wake up to that to be like, wait, we can make a ton of money. Instead of selling one seat for $20, we can send one, we can sell 1,000 seats. But the infrastructure was not there. Now it is, right? If you look at one of the benchmarks I love most is gdp, Val, it's very simple. It's a model's ability to create economically valuable work going head to head against a domain expert, right? And where, you know, essentially the model and the domain, the humans get the exact same information and they have to produce an artifact. And then judges will look at those and say, which one's better. So right now, today's best large language model, GPT 5.5, can tie or do better than 85% of experts, right? So that's a big shift here. And that also changes those decisions, right? That's why the decision to build or buy made sense. And it was a binary decision back in 2023 and 2024, but not so much anymore because not only has agentic AI ushered in a completely new era of what's possible, it's actually made the decision much more difficult. And that capability puts some serious, you know, software creation power into almost everyone's hands. And that's one of the hardest parts because even building something two years ago was incredibly difficult. Building something now, something that your team, your department, your entire organization can use, is actually straightforward. It may not be simple in theory, but it sometimes looks straightforward enough that you're like, oh, let's do this. We don't even need to partner or wait because we can build this ourselves. But everything's kind of changed this month. You know, at least over the past six weeks there's been this big shift toward headless AI. We actually covered that in a recent Start Here episode. So make sure you go, go back a couple of episodes and listen to that one. But essentially things have really changed very recently. So yeah, right now, you know, it's May 2026, maybe you're listening to this in June or July or December. Right. So this could be not as timely, but there's been some, some recent happenings that I think are worth talking about. So one would be kind of this incumbent shift. So you've had these large enterprises such as SAP, they've launched an autonomous enterprise where they have 50 plus domain assistants orchestrating 200 plus specialized agents. You have now the lab as consultancy. So I would say anthropic and OpenAI made big plays, right? OpenAI a little bit more in terms of investment with Deploy company, but you know, in Google as well. So essentially these big AI companies are now putting out, you know, front end, I think they're calling them, you know, front end engineers into actual companies and they're creating also consulting companies at the same time. So even what your company, especially if you're at an enterprise company, there might have been things where you were trying to build or buy it, but it might make sense now to partner. Right. If you can have as an example a consulting company that is built or owned by Anthropic or OpenAI or Google come into your organization that might completely change what you thought was, oh well, we're going to build this or we're going to wait, well, maybe not anymore. And now there is the down market squeeze, right? And I think that this will become even more apparent probably toward the beginning of quarter three. I think Anthropic was first to the punch here. But Anthropic launched Claude for small businesses. This was late last week with 15 prepackaged agentic workflows. I think this is going to become the standard. I think you're going to see it across Microsoft, Copilot, Google, Gemini and across OpenAI as well. And you might be wondering what the heck is that? And well, why does it matter? So let's take finance as an example. You might have had to buy a completely, you know, separate finance product three to six months ago. Maybe you might have hired someone to come in. But maybe if you're a smaller organization, you know, I don't know, an SMB with, you know, a thousand employees, something like that. So maybe you didn't have the seven figure budget to go out and really build something two or three years ago, but now you might have something like a prepackaged skill inside of Anthropic. That's one area I think that they've just really have owned that vertical of having these high value agents, right, which are essentially a series of prepackaged skills and plugins that are essentially fine tuned for specific purposes, right? So as an example, small businesses or you know, if you're a medium sized enterprise trying to figure out the best, you know, finance aid use, maybe you were going through specific vendors that were not part of the big four, right, which is Microsoft, Google, Anthropic and OpenAI. But maybe now you are looking at those because I think they are going to start to verticalize and start to really essentially fine tune, right? It's kind of like I had this prediction, I think this was my 2023 predictions for 2024 maybe, right. But I talked about eventually there's going to be thousands or tens of thousands or even millions of small language models. And I think ultimately, at least now what that's going to be is skills and prepackaged agent packs essentially that are models which are more or less fine tuned via markdown files to very specifically carry out certain tasks. So the rules have all completely changed. So billing with AI also got a lot of easier. And I think that's also really changed the conversation over the past couple of quarters because you know, before when it was buy, build, partner or wait, it was hard to build, right? Unless you were a Fortune 500 ask level, right? Doing a couple billion dollars, 10, $20 billion in revenue. It wasn't always feasible or possible via a talent, per a talent perspective to build anything internally. And that's pretty much change because you look at different stats. But you know, some stats say that up to 51% so more than half of code that gets pushed out today is AI generated. But a recent Retool study found that only 8% of AI builders use AI generated code without making changes. So a lot of companies when they saw this, know this, you know, Vibe coding wave actually gets serious right now we call it agentic engineering, right? It's like when vibe coding went to college and in graduated. Now we call it agentic engineering or you know, agentic software engineering. The problem is a lot of companies thought that they could own something, right? You know, anthropics, Claude code really I, I, I guess exploded this, this sector in late 2025 and early 2026. And a lot of companies that were early adopters thought that they could, well they could build it and they could own it themselves. And then, well they realized that if they didn't actually know what they were doing and if they actually put these, you know, tools or systems into production, maybe they were, you know, dime smart dollar dumb and they maybe solved one problem but created a couple more with some expensive tech debt. So this is how we need to frame these decisions. Now. It's not a single choice, right? You don't just decide build by partner or wait for your entire organization. Because if you are implementing AI from the front office to the back admin to every employee in between, there's a lot of different layers. So we talked about that. There's the model. What model are you using? The workflow. All right. And that's not just the, it's not just the harness that a model uses, but it's how are you actually using that model in your day to day workflow. Then there's number three, your data, okay. That's bringing your dynamic data in and having a sound data strategy, making sure that you have clean structured data that is dynamic and then your business software or connecting it both on the input side so you can grab data from that software that you need, but also to write it. Right? Because I think even nine months ago most software integrations were one way streets. They're bidirectional now, right? It is most, it's kind of almost the status quo for most. Kind of. If you're looking at it as simple as, as connectors or apps, right? Different, different big four call them different things, but they're bi directional now, right? Read and write. It's no longer good enough to just be able to read your emails dynamically or to read your calendar and triage between your email and your calendar and your storage. Right now you need to be able to write, you need to be able to create a. A calendar out of your AI system, a calendar entry. You need to be able to create a new file agentically, right on demand, on a schedule, on a cron. But that's not, it's not happening because it's getting more and more complex and the updates are coming out faster. So you need to really be intentional. So if you are in the boardroom, if you are helping make these decisions on AI implementation, AI moves too fast to follow, but you're expected to keep up. Otherwise your career or company might lag behind while AI native competitors leap ahead. But you don't have 10 hours a day to understand it all. That's what I do for you. But after 700 plus episodes of everyday AI, the most common questions I get is, where do I start? That's why we created the Start Here series, an ongoing podcast series of more than a dozen episodes you can listen to in order. It covers the AI basics for beginning beginners and sharpens the skills of AI champions pushing their companies forward. In the ongoing series, we explain complex trends in simple language that you can turn into action. There's three ways to jump in. Number one, go scroll back to the first one in episode 691. Number two, tap the link in your show notes at any time for the Start Here series. Or you can just go to start here series.com, which also gives you free access to our inner circle community where you can connect with other business leaders doing the same. The Start Here series will slow down the pace of AI so you can get ahead. This isn't one size fits all, right. You don't say, we're going to build this, we're going to buy it, and that's it. You do have to look at it, at those four separate layers. But obviously in the long run, you want to, if you can, if you have the foundation, if you have the talent, if you have the data, always want to skew toward owning if you can, right? There's, there's no doubt about that, but it's not always feasible or possible to own. But when we talk about owning or building something, right? So kind of using those two terms interchangeably, you should be building for proprietary processes, for internal context and workflows that are unique to how you operate, right? So you shouldn't be, as a lot of companies were wasting a lot of money in 2023, you know, trying to essentially build their own large language model, which. Go back and listen, I said all along, I'm like, that's not smart. You know, maybe you got some nice gains in, you know, in 2023, but then you were left with tech debt or you had to kill the project. So that same retool study found that 35% of AI builders replace a purchase tool with a custom one. So this shift is happening, right? I was even talking about this recently. I had a lot of software, you know, I'm a small business, but I just replaced it, right? I. If I'm using a tool and I'm like, ah, some things are good, but some things I don't like, I just build it, right? That's not going to be the case. And I'm not saying that, you know, you're going to have a bunch of SMBs, you know, being like, all right, I'm out Zoho CRM, I'm building my own. It's not feasible or realistic, although it is possible. But I think you have to look at building those common workflows yourself where you can actually have a long term competitive advantage. So when you're thinking about the things you should be building, when you are thinking about the things you should have ownership on, think about your proprietary processes. That internal, right? I always call it first company, first company data. So it's not just what shows up in the spreadsheets, it's what shows up in your subject matter experts, minds, what's in your, your SOPs, what's in your meeting notes. Those are the things that you want to build ownership around, right? And a lot of times you'll be porting those off to other places. But those processes, even something as simple, something as simple as having a, a meeting assistant and being able to take those transcripts and instantly share that knowledge with your organization, that shouldn't be locked up behind a certain vendor. That should be a process you can own. And yes, there are local models that are easy enough, right? I built this for myself. Not terribly hard, right? So your organization should be looking at those, those processes are ones that you should own, right? Your proprietary data. And I have a little graphic here on my screen. You know, you should be building what compounds. So not just the proprietary data, but something that can help you create a durable moat and something where lock in would destroy the value, right? Think of something like this, Think of something like, you know, if you're trying to use, you know, Zoom and Fireflies, AI and Google Meet all at the same time in your organization, well, that's not good. You either, well, need to build a process at least where you can have complete ownership of that and it's not segmented and fragmented across these different buckets or just build your own solution because that at the baseline think of what compounds and I think it's people's time, it's your subject matter expertise and it is that ip, it is that first company data that is essentially I think the fuel for reasoning models. That's what you should be building around. Next, buy when the workflow is common and already in your software. Right. So you should buy common regulated, audit heavy workflows that already sit inside of your existing platforms. Right. So this is obviously going to be different from sector to sector, but outside of the big four there's probably good reasons to buy certain soft, you know, AI powered software that fits within those workflows. Right. And maybe that SAP is a great example, maybe something, if you're a startup, you know, on the entrepreneur scene, maybe it's something like Slack. Right. Just because when you're talking about buying workflows inside of regulated or audit heavy or existing platforms, it doesn't mean that you have to think these, you know, large enterprise. Right. It could be something as simple as ClickUp, it could be something as, you know, simple as Slack. Right. So you could be doing a lot of these things via third party tools but if, or sorry, via the, the big four providers. But if your workflows are so ingrained right in this enterprise, in certain enterprise ecosystems, well maybe that's where you buy, maybe that's where you, yes, spend that extra money on, you know, the, the Slack AI. Spend that extra money on the ClickUp AI, second braid, whatever they're calling it nowadays, the same thing. Maybe you spend that extra money on, on the salesforce, whatever it is, wherever your team's data is locked into your day to day processes. So when you think about, I'm kind of saying zone two. So zone one is build what compounds, Zone two is buy what protects. Right. So those workflows that are like I said, common regulated, mission critical in or require that strict audit log. So yeah, if you're somewhere in financial something HIPAA that needs to be HIPAA compliant, maybe you can't get that specific workflow off the shelf from some of the big four. So buy what protects number three partner when the cost of failure is too high to go alone. All right, so you know, if this is something in cyber security, regulated decisions, physical operations, critical infrastructure, that's when you should be looking at partnering. Okay. A good partner also needs to share the execution, execution risk. But some AI categories maybe are still a little bit Too unstable to commit to. So partnering is important. So I'm not just saying, oh, like on the legal side, we need to go get, you know, Harvey or whatever. Right? Or, you know, Quad did come out with some, you know, some impressive legal agents. Same thing with Microsoft Copilot. They just rolled out some support for some legal agents as well. But when you think about zone three here, partnering where failure is too expensive and if the workflow touches physical operations specifically, right. Or highly complex domain data that is extremely valuable, maybe, you know, this is kind of the, like the thing. I think of this, you know, when you partner, when failure is expensive, I kind of think of this as the on prem versus off prem. Right? So if there are certain things that you would just do on prem, whether it's, you know, from a data storage or whether just, you know, physical operations, right. We can't move this operation, you know, somewhere else. Well, because it needs to be physically located here. I think that's where partnering is a big reality. So last but not least, wait. All right? And I will preface this by saying waiting can be both the best decision your company makes and the absolute worst decision your company makes. Let me explain that again. Not to keep beating the dead horse, all right, But I'm gonna go pick on the, the initial, you know, rag pipelines, right? Don't get me wrong, rag's not dead. Right. It's just, it's. It's just changed. Right? But if you go back to quarter one of January 2023 and probably quarter two as well, right, There was a lot of companies who were on the edge, right? Not edge devices, but on the cutting edge of AI. And they were, in theory, making good decisions by saying, hey, this large language model, this generative AI thing, we're going to get on the train, right? We're going to invest heavily instead of using a consumer chatbot, which at the time, right, you didn't have the ability to connect your, you know, in five clicks, you couldn't bring in your emails, you couldn't bring in your, your SharePoint, your OneDrive, your box, right? All these things. It was impossible. You couldn't, it wasn't an option in the off the shelf consumer chatbot. So, you know, a lot of bigger companies had to make this investment. And at the time, it might have seemed like a smart idea. Even though, go back and listen, I was saying, don't do it. This is one of those times when you could have waited because even to do something like that correctly and to train your entire organization on taking advantage of okay, now all of a sudden we, you know, ragged up our data to, you know, I don't know, at the time, what was it? GPT 35 or GPT 4 or the original Quad 2 or Claude 3. Right. Number one, the models weren't good enough to use in production anyways without some great prompt engineering. But number two, I think there's a big divide between non agentic AI and agentic AI, right? So when the, you know, the AI was essentially just super smart, next token completion. You know, the fact that these companies really wanted to go all in, it's brave, it's courageous. And I think you have to make some of those decisions. But sometimes the brave and courageous decision is to wait. Especially now, knowing the pace and the rate of innovation, right? So as an example, maybe you're just waiting for a certain industry specific software that everyone in your industry uses. Let's say you're in construction and everyone uses, you know, something to, you know, create bid shells for RFPs, right? And you're just waiting because there's two pieces of software everyone uses. So you're like, okay, do we wait or do we build? You have to understand the risk versus the reward versus the rate of innovation, right? So as an example, now with not a lot of technical expertise, companies can spin up a model context protocol server. And even if there wasn't a bridge between enterprise software that is crucial for your day to day versus the AI operating system that your company or department uses, well, now with model context protocol, you can build that, you know, maybe in an hour, right? So this concept of waiting, there's no one size fits all, but you have to be able to wait when vendors change monthly, you have to be able to wait if the ROI is unclear or you might have to wait until the category is a little more stable. A good example. Yeah, here's another one. Y' all not saying I was right, maybe I was wrong, but I'd say I was right right? Now you notice don't talk a lot about openclaw on this show, right? I got a lot of emails, people being like, oh Jordan, you could be making so much money, you know, teaching people openclaw. And I said it, I'm like, hey, openclaw is great, right? It is the most successful open source project of all time. Nvidia CEO Jensen Huang, right, said it's the best thing since sliced bread isn't what he actually said, but he called it the best piece of open source software ever. And a lot of companies and a lot of very visible Leaders have invested into OpenCloud. And I've been saying since the beginning, do I have an open clock? Sure. Do I use it? Not much, right? I use it enough to know how it works and to, you know, see the struggles and the successes other people are having. But I knew at the time, I'm like, this is one of those times when it's better to wait. Because I knew that the Big four are eventually going to have their versions, right? And I'm not talking like Nvidia's version of openclaw, Nemo Claw, where it's essentially openclaw in a more secure environment. I'm saying, no, they're going to have chat. GPT is going to have its own version of openclaw, right? Claude is going to have its own version of openclaw. And that's obviously where all of the big players have been trending. So I don't know, is it worth it to get, I don't know, 500 employees on an open claw, and all of a sudden they set all these things up, but their heartbeat not working, their soul MD files corrupted. Cron jobs aren't croning right now, all of a sudden. I don't know. By being on the cutting edge, maybe you were able to take advantage of it. Maybe you were able to squeeze, you know, the juice while the lemons were, were ripe and no one had lemonade and everyone was thirsty. Maybe you had that opportunity. But probably I would venture to say that 90% of, you know, enterprises that invested heavily in OpenClaw, the learning curve is so steep with just large language models. Right. I think this is one of those where, well, the ROI was kind of unclear. Yes, it's exciting. And that's one of the problems with, with AI. Everything you see is exciting. Everything you see, it's like mad fomo. Oh, my gosh, if we're not using this right away, we're going to go out of business. That's not the right way to look at it. You have to understand what's possible today, what's coming next, and then where do we build? By partner or wait. And sometimes the bold and courageous thing might be being first or it might be waiting, but there's no, you know, one size fits all answer on that. But waiting while competitors redesign their workflows, though, that's drift. All right, so if you see everyone else in your space is doing something, yeah, maybe your strategy is to zig while everyone else zags, but if you can see that there's a product market fit with a Certain early AI technology in your space. Maybe that's not the time you wait then, right? Maybe that's the time. You know, as an example for openclaw, there's probably great enterprise use cases for it until everything else caught up. And now, I mean, I don't know. Codex is essentially a way more stable openclaw. Right. Obviously, you know, OpenAI kind of aqua hired, you know, the, the sole creator of openclaw. So we're seeing a lot of the best pieces of it get implemented in a much more secure way, robust and enterprise friendly way. All right, so, so this is kind of zone four is the danger of weight because it's also the capabilities gap. Right. So you need to look at the choice to wait on a case by case basis because it's not only the capability of the technology, it's also the learning curve of the people who have to use it. So I think the Open Claw is a good example there. Now it's much easier to go inside of something like Codex which if your company uses Chat GPT, Codex is so easy to see and understand or you know, the, the workspace agents inside of, you know, ChatGPT business plans which are essentially like simple little open clause. Right. But these are kind of the same things. They're things you can, you know, message from your phone and connect to all the data sources and you can run them on a schedule. Right. But sometimes there is the danger of waiting. But you do have to look first. The capabilities gap and the, the knowledge gap and the training gap. Right. So is your company still in the chatbot era? If so, you have to understand that capability gap is going to be a steep hill to climb. But if you are using, for the most part, if your company is using models agentically, if you're already there, maybe waiting doesn't make as much sense because it could only take you, you know, I don't know, a quick, you know, three hour training and you know, weekly check ins to kind of close the capability gap so your team can actually take advantage of the latest and greatest in AI. All right, so here's the four wrong calls before we get to the one, two, three week plan. So wrong call number one, being wrong in the build creates technical debt that your team might have to maintain forever. Right? Yeah, I know there's still companies out there maintaining their, their rag chat bot from 2023 wrong buy that can create vendor lock in that you can't exit without a major cost. All right, don't. There's too much competition up there. Don't get locked into a contract for two or three years, right? These vendors are going to sell you something like there's no other option. There's always option, all right? The wrong partner number three there that can create dependency on someone whose business is also changing. Working with the right partner is sometimes trickier than the the buy versus build because you have to then understand a third party's complete front to back. But you know, your partners should be extremely niche and like we talked about earlier, you know, in those certain areas. And then last but not least, the wrong weight decision that can create a capability gap that competitors can quietly close first. So here is your two, three week blueprint, all right? To go through and understand your build by partner or weight decisions. So week number one, you're going to audit and score. Week number two, you're going to execute the extremes or the edges. And then week number three, you're going, you're going to govern and hedge. Here's what I mean. So do have this on a screen here, but I'm just going to read it all off. But you can always go watch the video version on our website at your everyday AI.com so week one, audit and score, you're going to pick 10 workflows you can, you're going to score them on an ownership map and then you're going to name one accountable human owner for each. All right? That's where you always need to start. Before you even go down the four different layers and the build by partner weight, you have to have an accurate inventory of what AI is actually being used for. All right? And then week two, you need to execute the extremes. So you need to build one low risk or high differentiation, high differentiation internal workflow. Then you need to buy one packaged workflow inside your existing, you know, erp, CRM, whatever the case, and then kill projects with no roi. So essentially first you're going to audit and score. Then you're going to fit. You're going to find a low risk, high, high value internal workflow and you're essentially going to ab test it, right? And you're going to say, okay, what does this look like if we build it ourself and what does it look like if we build buy something else? You need to measure your human costs and your external costs for the buy side. And then last but not least week three, you need to compare the build versus buy performance. And then for the buy side you need to renegotiate contracts for data export and model portability. Because again, think of something like skills Right. Skills are modular. They're open source. You can take those, you know, as an example from vendor to vendor. So you know, if you are doing something on the buy side, you need to make sure that, number one, you're not locked in. But number two, that whatever you are investing, you know, aside from the money, but your resources, your data, your education, you have to make sure that it's modular before you get into deep. And then last but not least, in week three, make sure you establish a weekly review cycle and ongoing learning as well. All right, that is a wrap for the Build Buy, Partner or wait, the four layer AI stack decision framework for 2026. And y', all, this just like everything else, it all moves quickly. So this is not a. You know, if you're listening to this, sorry, if somehow you're listening to this in 2027, you might just want to forget everything I said, FYI, and instead just keep up with the daily podcast. Because like everything, sometimes advice, sometimes the latest and greatest has a shelf like has a shelf life just like the models that we all use. So I hope this was helpful. If so, do me a favor. Go to start here series.com that is going to get you free access to our community. That's the only way you can get in right now. Then make sure you go straight to the Start Here series and check out the Spotify playlist that has every single volume of the Start Here series ready for you to consume. Whether it's in text, reading the email newsletter that went with it, watching the video, listening to the podcast. It's all there in one place. And you can also network and connect and ask questions with other people who are going through the same process. So thank you for tuning in. I hope to see you back tomorrow and every day for more Everyday AI. Thanks y'. All.
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And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going for a little more AI magic. Visit youreverydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.