The #1 Reason AI Projects Fail (It's Not the Technology)
Most AI projects don't fail because of the technology. They fail because the people building them never understood the business. Here's what that means for your next initiative.
There’s a statistic that gets thrown around in AI circles: somewhere between 70% and 85% of AI projects fail.
The numbers vary depending on who’s measuring and how they define failure. But even the most optimistic estimates suggest that more AI initiatives disappoint than deliver. Millions invested, months of effort, and at the end — a tool nobody uses, or one that technically works but doesn’t actually solve anything.
When these failures get analyzed, the explanations usually focus on technology. The model wasn’t accurate enough. The data was messy. The integration was harder than expected. The platform had limitations.
These explanations aren’t wrong, exactly. But they’re not the real story.
The real story is simpler, and more uncomfortable: most AI projects fail because the people building them never actually understood the business they were building for.
The pattern we see over and over
Here’s how it typically goes.
A company decides they need AI. Maybe they’ve been hearing about it from the board. Maybe competitors are making moves. Maybe a vendor has been persuasive. Whatever the trigger, there’s momentum — and budget — behind the idea.
So they engage help. Maybe an internal team. Maybe an outside consultant or vendor. Whoever it is, they come in with technical expertise. They know machine learning. They know the platforms. They know how to build things.
What they don’t know is how the business actually works.
They don’t understand, in detail, how decisions really get made day to day. They don’t know where the workarounds are, where the tribal knowledge lives, where the data gets messy because of some quirk in how the team operates. They don’t know which pain points matter and which ones are just noise. They don’t know who will actually use this tool and whether those people even want it.
So they build something. It often works, in a technical sense. The demo is impressive. The accuracy metrics look good.
And then it sits there, unused. Because it doesn’t actually fit into how work happens.
Why this keeps happening
The fundamental problem is a mismatch of expertise.
Most AI implementations are led by people who are very good at AI and not particularly good at understanding operations. That’s not a criticism — it’s just a reflection of how specialization works. You spend years learning neural networks, you probably didn’t spend those same years learning how a finance team actually closes the books each month.
But here’s the thing: AI is a tool for solving business problems. And you can’t solve a problem you don’t understand.
If you’re automating a process, you need to know how that process actually works — not how it’s supposed to work according to the documentation, but how it actually works, with all its exceptions and edge cases and unofficial shortcuts.
If you’re building an AI that supports decision-making, you need to understand how those decisions get made today. What information do people look at? What do they trust? What would make them trust an AI’s recommendation — or ignore it?
If you’re deploying something that requires behavior change, you need to understand the humans involved. What motivates them? What frustrates them? What will make them adopt this tool versus work around it?
Technical teams rarely ask these questions. They’re focused on the technology because that’s their domain. But without the answers, they’re building in the dark.
The consultant problem
This pattern is especially pronounced when companies bring in outside help.
There’s nothing wrong with external expertise. Building AI is hard, and most companies don’t have the talent in-house. But the typical engagement model is broken.
Here’s how it usually works: the consultant comes in with a tool or platform they know well. They’ve built similar things for other clients. They have a methodology. They’re efficient because they’ve done this before.
But “this” isn’t the same across companies. Every business has its own context, its own quirks, its own way of operating. The consultant’s previous experience is only valuable if they take the time to understand those differences — and that’s often where the work gets cut short.
Discovery gets rushed. The consultants ask some questions, look at some data, and start building. They’re on the clock. They have a timeline. And frankly, deep operational understanding takes time that nobody budgeted for.
The result is a solution that would work great at some hypothetical company — but not at the actual company it was built for.
What “understanding the business” actually means
Let’s be specific about what we mean when we talk about understanding the business.
It means mapping the actual workflow, not the theoretical one. Every process has an official version and a real version. People create spreadsheets the system doesn’t know about. They send emails instead of using the ticketing system. They have judgment calls they make that never get written down. The AI needs to work with reality, not the org chart.
It means identifying who will actually use this, and what they care about. End users have their own goals, their own pressures, their own preferences. A tool that helps them will get adopted. A tool that feels like surveillance or extra work will be resisted — no matter how technically impressive it is.
It means understanding how success will be measured. Not in abstract terms like “efficiency” — in concrete terms that the business tracks. The metrics need to connect to how the organization actually evaluates performance.
It means knowing the political landscape. Who benefits from the current way of doing things? Who might feel threatened? Where is there appetite for change, and where will there be resistance? These aren’t technical questions, but they determine whether a project succeeds.
It means going deep on the data. Not just what data exists, but how it’s actually captured, who enters it, what’s reliable and what’s not, what’s missing, and why. Data problems are often people problems in disguise.
This kind of understanding doesn’t come from a few discovery meetings. It comes from genuine curiosity, from observation, from asking “why” multiple times, from spending time with the people who actually do the work.
The uncomfortable truth for AI consultants
Here’s something that a lot of AI consultants won’t tell you: they don’t actually want to understand your business that deeply.
Not because they’re lazy or dishonest. Because deep understanding is slow. It doesn’t scale. It’s not what their business model is optimized for.
Most consulting firms make money by applying repeatable solutions efficiently. The more clients they can serve with the same approach, the more profitable they are. Taking the time to truly understand each client’s unique context — spending weeks embedded in operations before writing a single line of code — that’s not efficient. That’s expensive.
So they skip it. Or they go through the motions without really absorbing it. They deliver something that looks like a solution, move on to the next client, and hope it works out.
Sometimes it does. Often it doesn’t.
What this means for your AI initiatives
If you’re thinking about AI — or if you’ve already had a project underwhelm — here’s what this means practically.
Beware of consultants who lead with technology. If the first thing out of their mouth is a platform, a methodology, or a case study from a different industry, be cautious. The first thing should be questions about your business. (We’ve written more about how to spot problematic AI partners.)
Look for genuine curiosity. The right partner should want to spend time understanding your operations before proposing anything. They should ask questions that go beyond the surface. They should be interested in the exceptions and edge cases, not just the happy path.
Expect discovery to take time. A good AI engagement doesn’t jump straight to building. It starts with real discovery — observing processes, interviewing stakeholders, understanding how things actually work. If this phase is rushed or skipped, the project is already at risk. (Curious what that discovery looks like? See what happens in an AI readiness assessment.)
Involve the people who do the work. Your frontline employees know things about operations that nobody else knows. If they’re not part of the process, important context is getting lost. And if they’re not bought in, adoption is going to struggle.
Define success in business terms, not technical terms. “95% accuracy” doesn’t mean anything if nobody uses the tool. Define what success looks like in terms of outcomes the business actually cares about — time saved, errors reduced, decisions improved, revenue generated. (For more on asking the right questions upfront, see 5 questions to ask before starting any AI project.)
The right way to approach AI
At the risk of being self-serving, let me describe what we believe the right approach looks like.
Before we write any code or recommend any solution, we try to understand your business like we work there. Not just the processes — the people, the politics, the pressure points, the metrics that actually matter.
We talk to the employees who will use the tools. We dig into the data at a level that often surprises clients. We look for the places where the official process diverges from reality. We map the real workflow, not the idealized one.
Only after we have that understanding do we start talking about technology. And when we do, the technology fits the context — because we’ve taken the time to understand what the context is.
This is slower than how a lot of AI work gets done. It requires more upfront investment. But it dramatically increases the odds that what we build will actually work — not just technically, but in the real world, with real people, solving real problems.
The real differentiator
Here’s the takeaway: AI success isn’t primarily a technology challenge. It’s an understanding challenge.
The companies that succeed with AI aren’t necessarily the ones with the best data scientists or the most sophisticated models. They’re the ones where someone — internal or external — took the time to deeply understand the business before building anything.
That understanding is what separates the 15% of AI projects that deliver real value from the 85% that don’t.
If you’re evaluating AI consultants, don’t just ask about their technical capabilities. Ask about their process for understanding your business. Ask what discovery looks like. Ask how long they spend learning before they start building. (Here’s how our process works, if you’re curious.)
Those questions might tell you more than any demo ever could.