Why most AI projects fail (and what actually works)

The companies that get real value from AI aren't the ones with the biggest budgets. They're the ones who started with the right question.

Most AI projects fail not because the technology doesn't work. They fail because the project was defined backwards — starting with a tool, then looking for a problem to justify it.

The right question

Instead of "how can we use AI?", the question that actually leads somewhere is: "where does time or quality quietly disappear in this business?"

The answer is almost always surprising. It's rarely where leadership thinks it is.

What we look for

When we come into a business, we look for:

• Tasks that are repetitive but require context (perfect for AI)
• Knowledge that's locked in someone's head or buried in documents
• Decisions that take too long because information is hard to find

These aren't headline-worthy. But fixing them compounds quickly.

Start small, on purpose

The best AI implementations we've seen started as experiments. Small scope, clear question, measurable result. If it works, you have proof and momentum. If it doesn't, you've learned something cheap.

One workshop to find the right problem is worth more than six months building the wrong solution.