Why Most AI Projects Fail Before They Start

About four years ago, I was working as a project leader for a client in Northern California. One afternoon, a team member walked in and said he had something to show us. He'd asked ChatGPT to write a song about our company, set to the tune of something like "Row, Row, Row Your Boat." It was cute. We all laughed.

Then somebody asked the question that started everything: "Well, what else can this thing do? How do we actually use it in our business?"

Almost four years later, millions of business owners are asking the exact same question. And that's the problem.

The Tool Came First

Here's what I've watched happen, over and over. A company discovers an AI tool. Someone on the team starts using it. They get real value out of it; emails are written faster, research takes less time, and presentations come together more easily. Management notices. The question becomes: "How do we make a bigger impact?"

So they buy more tools. They bring in someone who shows them what the tools can do. They built a few automations. They spend months trying things. And at the end of it, they still can't point to a clear return on their investment.

The problem isn't the tools. The tools are fine. The problem is where they started.

Most AI projects begin with a technology and then go looking for a problem to justify it. That is exactly backward.

Problem First. Always.

Think about what changes when a project starts off right. Someone in the business asks: Where are we losing time? Where are we losing money? Where do things keep breaking down in ways that cost us customers or opportunities? What would it actually be worth to fix that?

Those questions, asked first and answered honestly, change everything about what follows. They determine whether AI is even the right answer. Sometimes it is. Sometimes a simpler process change does the job. And when AI is the right answer, those questions determine which tool, which approach, and what success actually looks like.

They also tell you what you're measuring. A project without a clear metric isn't a project — it's an experiment with an open budget. Before anything gets built, you should be able to say: if this works, here's what the return looks like. Fewer hours spent here. More revenue from there. Faster decisions on this. Specific, measurable, connected to something the business cares about.

A Lesson From Michelin

In the 1920s, Michelin had a great tire and an awkward problem: people didn't have anywhere to go. Cars were new, roads were sparse, and nobody was driving far enough to wear out their tires. So Michelin invented a reason to drive. They started publishing a guide to restaurants in the French countryside, destinations worth the trip. People drove to them. Tires wore out. Sales followed. The Michelin Guide eventually became one of the most respected restaurant rating systems in the world.

It's a great story, but Michelin was the exception. Most businesses aren't in a position to create the problem their solution solves. Most need to find the problem that's already there, costing them something, waiting to be fixed.

AI is the same. It is a powerful capability in search of the right application. Finding that application is not the tool's job. It's yours, and it should be the first conversation you have with anyone who claims they can help you use it.

What Changes When You Start Right

When a business identifies the right problem first, the rest of the process gets dramatically simpler. The right tool becomes more obvious. The scope stays manageable. The people who need to support the change understand why it matters. And the return is measurable from day one, not something you try to calculate six months after the fact.

Problem first. Tools follow.

It sounds simple. Most good principles do. But the number of businesses that skip straight to the tools and wonder later why nothing stuck is higher than you'd think.

If you're not sure which problem is right for your business, that's actually a good place to start. Knowing you don't know is more useful than assuming you do. The AI Readiness Scorecard takes about two minutes and gives you a clearer picture of where you stand and where the opportunity is most likely hiding.