The Gap That Kills More AI Projects Than Anything Else

There's a gap that shows up in almost every AI project that struggles.

It's not a technology gap. It's a problem definition gap.

Someone saw a demo, got excited, and said "let's build that." And then the team went and built something. And it worked - technically. It did what they built it to do. But it didn't actually solve the original problem, because nobody ever wrote down what the original problem was in the first place.

They built the wrong thing perfectly.

This happens at companies with 5 employees and companies with 50,000. The scale doesn't protect you from it. The only thing that protects you from it is sitting down - before any technology decisions - and getting specific about the problem.

What exactly is broken? Why is it broken? What does "fixed" look like? Who owns the outcome? What data do we have? What would failure cost us?

If you can answer all of those questions clearly, in writing, before you touch a tool or talk to a vendor - you are already ahead of 80% of AI initiatives out there.

That document is what I call the Problem Definition Canvas. It's not complicated. It's just disciplined.

Full template in the book → https://www.amazon.com/AI-Enterprise-Lessons-Doing-Right/dp/B0H1WRNYRT

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