Start with a Smaller Problem

Most AI projects fail because they aim too high from day one.“Let’s automate everything.” “Let’s reinvent the business.”That’s how you burn time and budget without shipping anything useful.

The better approach? Find one narrow, well-defined problem — and solve it properly.

A friend at a logistics company did this right. Instead of trying to optimize the whole supply chain, he built a simple classifier to flag delivery issues. Their team used to review every exception manually. Now half of that work is automated. It’s not fancy, but it saves real time every day.

Another example: a PM I know at a fintech firm added a small model to predict which users might miss a recurring payment. The system nudges them early. Fewer support calls. Fewer failed charges. A minor change, measurable impact.

These aren’t breakthrough models. No deep research. Just clean data, clear goals, and useful output.

If you're working on AI, don’t start with vague ideas or “transformational” roadmaps. Look for things your team does manually, repeatedly, or too slowly. Then fix that. If it works, do the next one.

Good AI work scales — but only if the foundation is solid. And the foundation is solving one real problem at a time.

Invest your energy into finding a small problem to solve, not a big one that you won't have enough resources to deal with. Then solve it, because you can.