Why Most AI Projects Die Before Production (And It's Not a Tech Problem)

You've probably seen the stat: "87% of ML models never make it to production." It gets cited everywhere. Conference talks, LinkedIn posts, vendor pitch decks. There's just one problem. It's made up.

That number traces back to an unsourced 2017 guest blog post. No survey. No methodology. No data. It got picked up by VentureBeat in a sponsored article, and the internet did the rest.

The real numbers aren't much better, though.

RAND Corporation published a major study in 2024 looking at why AI projects fail. Their finding: more than 80% of AI projects don't deliver, roughly double the failure rate of regular IT projects. Gartner's latest data shows only about half of AI prototypes ever make it to production, and the average journey takes eight months. BCG surveyed a thousand C-suite executives and found that only 1 in 4 companies have figured out how to generate real value from AI.

So the problem is real. But the reason is almost never what people think.

It's Not a Model Problem

BCG quantified this in a way that should be on every AI leader's wall: roughly 70% of AI implementation challenges come down to people and processes. Twenty percent are technology issues. Only 10% are about the algorithms.

After shipping AI across finance, defense, and healthcare, I've seen this play out the same way every time. The pattern has two parts.

Nobody Talks to the People Who Do the Work

On a recent government project, I inherited a scope that was shaped more by what stakeholders had heard AI could do than by what they actually needed. The gap between expectations and reality was massive.

So I did something that shouldn't be unusual but somehow is: I sat down with the front-line users. The people who live in the system every day. I asked them to walk me through their actual workflows. What their days look like, where the bottlenecks are, what's painful, what's manual. Not what leadership thought the problem was. What the problem actually was.

That changed everything. I was able to match what I learned from those conversations with what was technically feasible given our constraints: the timeline, the budget, the infrastructure we had to work within. The result was a system that was both buildable and actually useful. Not a showcase demo. A tool people would use.

This isn't a hack. It's supposed to be the baseline. But most AI teams skip it. They build what they think is cool, or what the executive sponsor asked for in a slide deck three months ago. They don't talk to the person who will use it on a Tuesday afternoon.

RAND identified this exact pattern, the disconnect between technical teams and domain experts, as the single most common reason AI projects fail. It's not close.

No Eval, No Safety Net

The second killer is subtler and arguably more dangerous: teams ship AI without a real evaluation framework. Or they build one for the initial model and then forget about it.

This is one of the biggest risks when moving AI to production. Any change to the data, the model, or the prompt can shift the outcome. A system that passed evaluation last month might be producing garbage today, and without continuous eval, nobody knows until a user complains or something breaks visibly.

What "no eval" looks like in practice: someone runs the model, looks at the output, and says "yeah, that looks right." That's it. That's the evaluation framework. Maybe it gets reviewed by a manager who doesn't know what to look for. Maybe there's a dashboard that nobody checks after the first week.

Production-grade AI means evaluation never stops. It runs continuously. It catches drift. It flags anomalies. It's the thing that lets you sleep at night after you've deployed a model that makes real decisions for real people.

And yet fewer than 1 in 5 organizations even track KPIs for their AI solutions, according to McKinsey's latest survey. That's not a maturity problem. That's a blindfold.

The Bottom Line

Most AI projects don't fail because the model was bad. They fail because nobody asked the right people the right questions, and nobody built the system to know when it was wrong.

The fix isn't more compute or a better framework. It's spending time with the people who know the domain, designing within real constraints, and building evaluation that doesn't end at deployment.

That's the work that gets AI from prototype to production. Everything else is a demo.