Lesson 3 of 6
Build for a problem, not for AI
6 min read
A team spends six months shipping a brilliant AI feature. Usage is near zero. Nothing was broken about the tech. What went wrong?
The 'because AI' trap
The most common way an AI product fails has nothing to do with the model. It's starting from the technology — 'we have an LLM, what can we build?' — instead of from a problem someone actually has. AI makes it dangerously easy to build something impressive that no one needs. A demo that wows in a meeting can still solve a problem nobody was losing sleep over.
'We have AI, now what?' is backwards. The winning question is 'whose painful problem could this solve better than today's options?'
Problem-first, AI-second
Flip the order. Start with a real, painful, frequent problem. Ask what a great solution looks like — and only then ask whether AI is the best tool for it. Sometimes it is; sometimes a spreadsheet, a rule, or a better form wins. When AI is the answer, you'll know exactly what job it's doing and how you'll measure it. 'It uses AI' is never the value; solving the problem is.
AI is a means, not the product. Lead with the problem and the outcome; let the technology be an implementation detail the user never has to think about.
Beware the impressive demo. 'Wow' in a boardroom is not the same as 'I'd use this every day'. Validate the problem with real users before you fall in love with the capability.
The shape of it
- —AI products fail most often from starting with the tech, not a real problem.
- —Start problem-first: painful, frequent, poorly served today — then ask if AI fits.
- —'It uses AI' is not value; solving the problem is. Measure the outcome, not the novelty.
Which pitch is the healthier starting point for an AI product?
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