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The Four-Layer AI Stack: Precise Build, Buy, Partner, or Wait Decisions for 2026
AI adoption in business has accelerated, but the familiar “build vs. buy” debate is becoming obsolete. By 2026, AI has become deeply integrated, agentic, and embedded across the business software stack, creating a new multi-layered decision matrix. Simple, binary choices mean risking technical debt, vendor lock-in, and missed opportunities. Instead, a four-layer, four-choice framework has emerged as essential for competitive businesses.
This article organizes actionable insights into the modern AI decision-making framework, based on the latest field observations, to support accurate decisions for every layer that matters.
AI Decision Framework: Four Layers, Four Choices
The classic “build or buy?” question is ill-suited for today’s AI landscape. AI now operates on four major layers in organizations:
Model Layer: The underlying AI models powering workflows.
Workflow Layer: How models are deployed in day-to-day operations.
Data Layer: Integration, structuring, and dynamic use of business data.
Business Software Layer: How AI interacts natively with existing business applications.
At each layer, there are four strategic choices:
Build custom solutions internally
Buy from external vendors
Partner with a specialist
Wait for the category/technology to stabilize
ROI, risk, and competitive advantage hinge on granular evaluation at each layer—not on blanket decisions.
Build vs. Buy: Why the 2023 Framework Fails in 2026 AI Environments
In early generative AI days, organizations chose between expensive custom “RAG” setups or adapting consumer chatbots with little business context. However, as of mid-2026, AI tools not only provide answers but execute workflows directly inside business applications, often bi-directionally—reading and writing data, creating files, and orchestrating agents for domain-specific tasks.
Off-the-shelf AI is now built for real business use, shifting the decision calculus toward multi-layer analysis. Rushed or outdated choices can create technical debt or lock organizations into restrictive vendor contracts, hindering competitive edge.
Building Custom AI: When Ownership Delivers Unique Competitive Advantage
Building in-house AI is now viable for organizations even outside the Fortune 500, thanks to streamlined agentic engineering and the emergence of local models. The most strategic use of “build” is when solutions must capitalize on proprietary data, internal workflows, and subject-matter expertise—elements unique to the organization and not available off the shelf.
Examples of build-worthy targets include:
Automation of proprietary processes documented in internal SOPs and meeting notes (not just spreadsheet data)
Durable workflows centered around confidential or IP-rich data
Modular assistants (e.g., AI meeting assistants) that link institutional knowledge across platforms
Building for these contexts delivers compounding value over time, while mitigating risk of vendor lock-in.
Buying AI Solutions: Focus on Common, Regulated, or Audit-Driven Workflows
The “buy” option is most effective where workflows are common across an industry and already embedded in existing platforms (ERP, CRM, communication apps like Slack or ClickUp). Buying is especially advantageous for heavily regulated or audit-heavy operations, where compliance and reliability outweigh customization.
Concrete scenarios for buying include:
Finance and HR workflows with audit requirements
Industry-specific regulatory tasks best handled by established vendors with proven track records
Existing enterprise application extensions where teams are already entrenched in provider ecosystems
Procurement in these zones protects operational continuity and ensures compliance, provided that contracts remain flexible for future data portability.
Partnering on AI: Critical When Failure Risk is High
Partnering is best reserved for business areas where the cost of failure is highest—cybersecurity, physical operations, or highly complex regulated domains. Here, external expertise helps share execution risk and keeps organizations abreast of rapid technological change.
Effective partnerships are:
Highly niche (e.g., legal AI agents tailored by legal technology providers)
Engagements where the domain complexity justifies shared investment and the need for continuous adaptation
Selecting the right partner is complex; misalignment can introduce new forms of dependency if vendors’ business models shift.
Waiting: Strategic Delay When Uncertainty or Instability Prevails
Waiting is an underrated but critical decision, especially when:
Vendor offerings change monthly or the market is highly unstable
ROI isn’t clear or organizational capabilities aren’t yet prepared for new workflows
Critical industry software is likely to launch native AI support soon
For example, early investment in open-source frameworks like OpenClaw sometimes left enterprises with steep learning curves and unclear returns. As mainstream providers rapidly caught up with similar but more robust and supported tools (e.g., Codex, workspace agents), waiting proved the smarter call for many.
However, strategic waiting is only viable when it does not introduce a capability gap competitors may close first. Delaying when peers are building successful new workflows can lead to organizational drift and disadvantage.
The Three-Week Blueprint: How to Audit, Pilot, and Govern AI Stack Decisions
A structured, repeatable decision process ensures alignment and mitigates risk across all four layers. The steps are:
Audit and Score AI Workflows
Identify and map at least 10 current or desired workflows.
Assign ownership and measure potential value or risk per workflow.
Pilot Build vs. Buy on Extremes
Build one low-risk, high-differentiation workflow internally.
Buy one workflow as a packaged option from existing vendors.
Evaluate by human and financial cost; terminate projects without ROI.
Govern for the Future
Compare build-vs.-buy performance in real deployment.
Renegotiate contracts for data export/model portability on purchased tools.
Establish ongoing weekly review cycles and training for continuous adaptation.
This method prevents expensive missteps and ensures the right layers are built, bought, or partnered for. It also helps identify when waiting is the wiser approach.
Conclusion: Specificity and Strategic Layering Are the New AI Mandate
2026 AI strategy requires moving beyond binary choices. Applying a structured four-layer, four-choice matrix—build, buy, partner, or wait—ensures organizations own what differentiates, protect what’s common, partner where failure hurts, and wait when uncertainty dictates. Detailed, workflow-level assessment yields durable value and guards against technical debt, vendor lock-in, and missed revenue opportunities in a rapidly advancing AI landscape.
Topics Covered in This Episode:
- Build vs. Buy vs. Partner vs. Wait in AI
- Four-Layer AI Stack Decision Framework
- Evolution of AI Agentic Workflows in 2026
- Buy vs. Build Decision Obsolescence
- When to Build Proprietary AI Solutions
- Prepackaged AI Workflows for Small Businesses
- Data Ownership and Integration Strategies
- Vendor Lock-In and Technical Debt Risks
- Partnering in Regulated or Critical Workflows
- Waiting for Stable AI Categories
- Three-Week AI Adoption Blueprint
- Capability Gap and ROI in AI Investments
Episode Transcript
Jordan Wilson [00:00:15]:
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 in the business software it plugs into.
Jordan Wilson [00:01:19]:
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.
Jordan Wilson [00:02:01]:
So this is everyday AI, 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, the decision 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 ChatGPT. There was no business context. So you either had to say, okay. Well, we're gonna try this thing and maybe risk it, or we're gonna build a pretty expensive, you know, custom rag setup. Right? We're gonna build this rag pipeline and bring in all of our data.
Jordan Wilson [00:02:49]:
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 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.
Jordan Wilson [00:03:41]:
You have to decide when you're going to own, when you're gonna rent, partner with someone, or if you're just gonna wait. So on today's show, stick around, and here's what you're gonna learn. You're gonna know why the 2023 builder buy question fundamentally broke, in the 2026 economy. You're gonna know the four AI stack layers you should own, rent, partner on, or delay. You're gonna know how to make the right call on each layer without those expensive mistakes. And I'm gonna lay out at the very end the three week sprint plan to understand the build by partner or wait. Alright. Welcome to Everyday AI, and this is the start here series.
Jordan Wilson [00:04:19]:
This is your essential podcast series to both learn the AI basics and to double down on your knowledge. That's because after literally seven hundred and fifty 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 starthereseries.com, and you can do exactly that. That's gonna 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.
Jordan Wilson [00:05:09]:
That was last episode was, start here series volume 24. So we talked on that one open source AI one zero one, 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 wait the four layer AI stack decision framework for 2026. Alright. 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 remembered talking with them originally in 2024. And they were investing a lot of time, money, and resources into the build side.
Jordan Wilson [00:06:03]:
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 ChatGPT 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 kinda weird. It was kinda 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 go in the gray area here and, you know, create a profile.
Jordan Wilson [00:06:32]:
Right? Because we can connect with all these people. And I think that the very early days of, like, late twenty twenty two and early twenty twenty three 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.
Jordan Wilson [00:07:09]:
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 valve. 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 in 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, GBT 5.5, can tie or do better than 85% of experts.
Jordan Wilson [00:07:49]:
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.
Jordan Wilson [00:08:45]:
We don't even need to partner or wait because we can build this ourselves. But everything's kinda 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.
Jordan Wilson [00:09:14]:
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 assistance 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 Deployco, but, you know, in Google as well.
Jordan Wilson [00:09:52]:
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 in Propic or OpenAI or Google come into your organization, that might completely change what you thought was, oh, well, we're gonna build this or we're gonna 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.
Jordan Wilson [00:10:49]:
This was late last week with 15 prepackaged agentic workflows. I think this is going to become the standard. I think you're gonna see it across Microsoft Copilot, Google Gemini, and, across OpenAI as well. And you might be wondering what 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 you know, I don't know. An SMB with, you know, a thousand employees, something like that.
Jordan Wilson [00:11:27]:
So, maybe you didn't have the 7 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 AI to use. Maybe you were going through specific vendors that were not part of the big four, right, which is, Microsoft, Google, Infropic, and OpenAI. But maybe now you are looking at those, because I think they are gonna start to, verticalize and start to really essentially fine tune. Right? It's kinda like I had this prediction. I think this was my 2023 predictions for 2024 maybe.
Jordan Wilson [00:12:32]:
Right. But I talked about, eventually, there's gonna 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 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 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, $1,020,000,000,000 dollars in revenue.
Jordan Wilson [00:13:24]:
It wasn't always feasible, or possible via a talent, per a a talent perspective to build anything internally, and that's pretty much changed, 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, you 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 it 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.
Jordan Wilson [00:14:19]:
Right? You know, anthropics, Claude Code really, I I I guess exploded this, this sector in late twenty twenty five and early twenty twenty six. 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 dump. 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 There's a lot of different layers. So we talked about that.
Jordan Wilson [00:15:16]:
There's the model. What model are you using? The workflow. Alright. 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.
Jordan Wilson [00:16:03]:
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 bidirectional 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 a calendar out of your AI system, a a a calendar entry.
Jordan Wilson [00:16:36]:
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, 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, you always wanna skew toward owning if you can.
Jordan Wilson [00:17:26]:
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've 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 thirty five percent of AI builders replace a purchase tool with a custom one. So this shift is happening.
Jordan Wilson [00:18:22]:
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, some things are good, but some things I don't like, I just build it. Right? That's not gonna be the case, and I'm not saying that, you know, you're gonna have a bunch of SMBs, you know, being like, alright. I'm out, Zoho CRM. I'm building my own. It's not feasible, or or realistic, although it is possible.
Jordan Wilson [00:18:53]:
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 wanna 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 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.
Jordan Wilson [00:19:53]:
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 note and something where lock in would destroy the value. Right? Think of something like this.
Jordan Wilson [00:20:28]:
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 gonna 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.
Jordan Wilson [00:21:40]:
Right? And maybe that SAP, is a great, example. Maybe something if you're a a startup, you 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 or sorry, via the the big four providers. But if your workflows are so ingrained, right, in this enterprise, insert enterprise, ecosystems. Well, maybe that's where you buy.
Jordan Wilson [00:22:29]:
Maybe that's where you, yes, spend that extra money on, you know, the the the Slack AI. Spend that extra money on the ClickUp AI, second grade, 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 by what protects. Right? So those workflows that are, like I said, common regulated mission critical in or require that strict audit log.
Jordan Wilson [00:23:07]:
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 by what protects. Number three, partner when the cost of failure is too high to go alone. Alright. So, you know, if this is something in cybersecurity, 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.
Jordan Wilson [00:23:48]:
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, Claude did come out with some, you you know, some impressive, legal agents. Same thing with, Microsoft Copilot. They just rolled out some supports, for some legal agents as well. But when you think about zone three here, partnering where failure is too expensive, if the workflow touches physical operations specifically, right, or highly complex domain data that is extremely valuable, maybe, you you know, this is kind of the like, the thing I think of this, you know, when you partner when failure is expensive. I kinda think of this as the on prem versus off prem. Right? So if there's 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.
Jordan Wilson [00:24:48]:
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.
Jordan Wilson [00:25:20]:
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 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 gonna get on the train.
Jordan Wilson [00:25:57]:
Right? We're gonna 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.
Jordan Wilson [00:26:29]:
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 three five or GPT four or, the original quad two or quad three. 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.
Jordan Wilson [00:27:34]:
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.
Jordan Wilson [00:28:28]:
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 would say I was right right now.
Jordan Wilson [00:29:12]:
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 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 Wong, right, said it's the best thing since sliced bread is is 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 OpenClub.
Jordan Wilson [00:29:44]:
And I've been saying since the beginning, do I have an OpenClub? 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 Open Claw Nemo Claw where it's essentially Open Claw in a more secure environment. I'm saying no.
Jordan Wilson [00:30:16]:
They're gonna have ChatTivity is gonna have its own version of Open Claw. Right? Clawed is gonna have its own version of Open Claw, 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 is not working. Their sole MD files corrupted. Cron jobs aren't crawling. 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.
Jordan Wilson [00:30:45]:
Maybe you were able to squeeze, you 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 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.
Jordan Wilson [00:31:15]:
Everything you see, it's like, mad FOMO. Oh my gosh. If we're not using this right away, we're gonna 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 But waiting while competitors redesign their workflows though, that's drift.
Jordan Wilson [00:31:44]:
Alright? 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. Codecs is essentially a way more stable OpenClaw. Right? Obviously, you know, OpenAI kind of aqua hired, you 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, robust, and enterprise friendly way.
Jordan Wilson [00:32:26]:
Alright. So, so this is kind of zone four is the danger of wait 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 ChatGPT, codex is so easy to see and understand, or, you know, the, the workspace agents inside of, you 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 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.
Jordan Wilson [00:33:28]:
Right? So is your company still in the chatbot era? If so, you have to understand that capability gap is gonna be a steep hill decline. 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, 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. Alright. 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 chatbot from 2023. Wrong buy.
Jordan Wilson [00:34:24]:
That can create vendor lock in that you can't exit without a major cost. Alright? Don't. There's too much competition out there. Don't get locked into a contract for two or three years. Right? These vendors are gonna sell you something like there's no other option. There's always option. Alright. The wrong partner, number three there, that can create dependency on someone whose business is also changing.
Jordan Wilson [00:34:46]:
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 one, two, three week blueprint, Alright. To go through and understand your build by partner or weight decisions. So we can number one, you're gonna audit and score. Week number two, you're gonna execute the extremes or the edges. And then week number three, you're gonna you're gonna govern and hedge.
Jordan Wilson [00:35:33]:
Here's what I mean. So, do have this on a screen here, but I'm just gonna read it all off. But you can always go watch the video version on our website at youreverydayai.com. So week one audit and score. You're gonna pick 10 workflows. You're gonna score them on an ownership map, and then you're gonna name one accountable human, owner for each. Alright. That's where you always need to start before you even go down the four different layers and the build by a partner weight.
Jordan Wilson [00:35:59]:
You have to have an accurate inventory of what AI is actually being used for. Alright. And then week two, you need to execute the extremes. So you need to build one low risk or high differentiate, 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 gonna audit and score, then you're gonna fit, you're gonna find a low risk, high, high value internal workflow, and you're essentially gonna AB test it. Right? And you're gonna say, okay. What does this look like if we build it ourself, and what does it look like if we buy something else? You need to measure your human cost and your external cost for the buy side. And then last but not least, week three, you need to compare the build versus buy performance.
Jordan Wilson [00:36:53]:
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, that 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 in ongoing learning as well.
Jordan Wilson [00:37:32]:
Alright. That is a wrap for the build by 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 wanna 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 life has a shelf life just like the models that we all use. So I hope this was helpful. If so, do me a favor.
Jordan Wilson [00:38:09]:
Go to starthereseries.com. That is gonna 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.