Build vs Buy in AI: Why Most Pilots Die Before Production (And What Makes the Survivors Different)

Last updated: June 15, 2026

AI build vs buy comes down to one question: is this capability your real competitive edge, or is it plumbing? Build the edge. Buy the plumbing. Teams that get that backwards watch the pilot die in the demo, which is exactly where most of them die.

Last year I watched a fraud-detection pilot get a standing ovation in a boardroom. It never shipped. The pattern barely changes. Someone runs a slick demo. The room nods. A budget gets approved, and then nothing ships. Six months later, the budget is quietly reallocated and the demo deck is deleted. The project is dead. Nobody who touched it will say the word failure out loud.

So before you greenlight anything, I want to walk you through how I actually make this call. Not the vendor version. Not the consultant version. The version I use when it is my name on the outcome.

Why Most AI Pilots Never Make It Out of the Demo

The numbers are worse than people think. Gartner expects at least 30% of generative AI projects to be abandoned after proof of concept by the end of 2025, blaming poor data quality, weak risk controls, runaway costs, and fuzzy business value. S&P Global Market Intelligence found that the share of companies scrapping most of their AI initiatives jumped to 42% in 2025, up from 17% the year before. The average org killed nearly half its proofs of concept before they ever reached production.

Then there is the MIT study everyone passed around last year. Researchers behind the NANDA initiative reported that 95% of enterprise generative AI pilots delivered no measurable impact on the P&L. About 5% actually moved the business. That is a brutal hit rate for something getting this much board attention.

Here is my own count, and it tracks. Out of every ten AI pilots I have personally watched get greenlit over the last two years, maybe two reached production. The other eight died in the demo phase. Eighty percent mortality. The technology was almost never the reason.

So what kills them? Usually it is the build vs buy call, made badly and made late. A team sinks a full quarter of its engineering time into building a slightly worse version of something a mature vendor already sells for a fraction of the price, then acts surprised when it stalls. Or it buys a black box to run the one process that was supposed to set the company apart. Either way the pilot looks great on a Tuesday and means nothing by Friday. If your answer turns out to be build, you are now hiring AI and machine learning engineering talent too, and that is a whole different problem most teams underestimate.

Build vs Buy in AI, Defined

Build vs buy in AI is the choice between developing a model or system in-house and licensing a vendor’s product. Build means you own the code, the data pipeline, and the maintenance forever. Buy means you rent the capability and the vendor carries the upkeep. Most real decisions land somewhere in the middle, and that middle is where the smart money has moved.

That last part matters. The MIT data showed something most people skipped past. Buying tools from specialized vendors and building partnerships succeeded about 67% of the time. Internal builds succeeded only one-third as often. Read that twice. The thing every engineer’s ego wants to do, build it ourselves, is the thing that fails most.

I love building things. So take this as a builder talking, not a skeptic. I have spent 25 years in regulated industries, mortgage tech and fintech, writing code and staying close to the stack on purpose, because the leaders who drift away from the actual work are the easiest ones in the room to fool. The most common thing I see when I come into an organization weighing a build is not a technology problem. It is a clarity problem. Nobody has actually decided what they are trying to win.

The Build vs Buy Decision, the Way I Run It

I run every AI initiative through three questions before I let anyone write a line of code or sign a contract. Show me the data on each, and the answer usually picks itself.

One. Is this our competitive edge, or is it plumbing? Fraud scoring on proprietary mortgage data is an edge. A chatbot that answers HR policy questions is plumbing. Build the first. Buy the second. The mistake is treating everything like an edge because AI feels strategic. Most of it is plumbing.

Two. Do we own data nobody else has? If your advantage is data a vendor can never touch, the kind of proprietary, messy, hard-won data that took your company a decade to accumulate, then building gets interesting fast, because a model is only ever as good as what you feed it. No proprietary data, no real reason to build. You are just paying a team to reinvent something you could rent off the shelf.

Three. How deep does this have to reach into our own systems? Some AI has to live inside the core, wired into the systems that run the business. When I led a Kafka pub/sub migration, we cut downstream processing latency by 45% precisely because the event platform was built to fit our pipelines, not bolted on. Other times you need a clean API and nothing more. Buy that.

There is a third path that has quietly become the right answer for most companies. Buy the foundation. Then build your own layer on top, the part that is genuinely yours. You take a mature vendor platform that gets you most of the way, then add the prompts, the retrieval, the integrations, and the human checks that turn a generic tool into something only your company could actually run. You get speed and a moat at the same time. The table below is roughly how I weigh the three.

The Build Trap Nobody Warns You About

Building is seductive because the first demo is easy. A sharp engineer wires up an API over a weekend and the thing dazzles. Then reality shows up.

Now you own it. The model drifts. The data pipeline breaks at 2 a.m. A vendor you leaned on changes its API, and your duct tape gives way. You touch one thing. Two others break. It becomes load-bearing spaghetti, the kind nobody planned, that just grew quietly over a few release cycles until everything was holding everything else up and no one on the team wanted to be the one to touch it.

That is the part the weekend demo never shows. The cost of a build is not the build. It is the decade of maintenance after the launch party, and the senior people you have to recruit, pay, and actually retain to keep the thing breathing once the engineer who built it moves on to another company. If you are going to build, you are committing to a team, which is a direct hire decision long before it is a technical one. A lot of companies bring in a senior engineer on a contract basis first, ship a real production slice, and only then decide whether to staff it permanently. That is a smart way to de-risk.

What Makes the Survivors Different

The 5% that work do not have better models. They have better discipline. That is the whole secret. I have watched enough builds succeed and enough of them die that the difference stopped looking like luck a long time ago, and it almost always traces back to how the work was framed before anyone opened an editor or signed a contract.

• They scoped the pilot to one real workflow with a number attached. Not “explore AI.” Cut claims processing time by 30%. Specific enough to fail or win.
• Somebody senior actually owned it, with the authority to kill it. No owner, no shipping.
• They measured by business outcome, not demo wow. Did it move the metric, yes or no?
• The build vs buy call got made early, on paper, with the three questions above answered honestly.
• And the boring one that matters most: they planned for who keeps it alive after launch, before they launched.

Notice how ordinary all of that is. I have said for years that the operating system around the technology matters more than the technology itself, and AI did not change that rule so much as raise the price of breaking it. The stakes are higher now. A failed AI bet burns more cash and a lot more executive credibility than a failed project did five years ago, and these days it tends to do it in public, on LinkedIn, in front of the same board that approved the budget. Most of what stalls a team is the same thing that always stalls a team, which I wrote about in why engineering velocity stalls. It is rarely the code.

One more thing the survivors share. They do not confuse buying with outsourcing the thinking. You still need people who understand what is happening under the hood, even when you buy, or you cannot tell a good vendor from a confident one.

Questions Executives Actually Ask Me About Build vs Buy

So should we just buy everything and skip the build?

No, and that is the opposite mistake. Buy the plumbing, build the one or two things that are genuinely your edge. If you buy everything, you have no moat, and you will wake up one day renting your entire advantage from a vendor who just raised prices.

How do I know if a use case is actually our competitive edge?

Ask whether a competitor could buy the same result off the shelf tomorrow. If they could, it is plumbing. An edge usually rests on data only you have, or a workflow nobody outside your company understands. Everything else is a buy, even when it feels strategic in the meeting.

Why do so many AI pilots die after such a strong demo?

Because a demo proves the easy 20% and hides the hard 80%. Integration, data quality, drift, maintenance, and ownership are what actually decide whether it ships. Gartner and MIT both put the failure rate north of 90% on real business impact, and almost none of that wreckage is the model itself, but rather the integration, the data quality, the drift, and the simple fact that nobody owned the thing past launch. It is everything around the model.

What is the fastest way to de-risk a build before we commit real headcount?

Ship one production slice, not a demo. Pick the smallest real workflow, put it in front of actual users, and measure exactly one business number that someone in finance already cares about. Bring in a senior contractor to do it if you have to. You will learn more from three weeks in production, with real users hitting real edge cases at the worst possible times, than from three months of pilots that never leave the sandbox.

Do we need a whole AI team to build in-house?

Not a whole team to start, but more than you think to sustain. Building is a long-term staffing commitment, not a project. You need senior people who can own the model, the pipeline, and the integration, and you need to keep them. That retention piece is where most build plans quietly fall apart.

Where I Would Start

If you are staring at a build vs buy decision right now, do not start with the model. Start with the three questions. Be honest about whether this is your edge or just plumbing. Most of it is plumbing, and admitting that will save you a fortune. Plumbing is not failure.

And if the answer is build, get the people right before you get the architecture right. A model with nobody to maintain it is a liability with good marketing. The recruiters I work with at KORE1 fill IT and engineering roles in about 17 days on average, with 92% of those hires still in the seat a year later, which is the part that actually keeps a build alive. If you are trying to staff one, talk to a recruiter who has placed this kind of team before.

Want to argue with my framework or tell me where I am wrong? Connect with me on LinkedIn. I answer the build vs buy question more than any other, and I am always sharpening how I answer it.