A simple test for telling the AI features worth building apart from the ones that only look good in a demo.
Founders ask us this every week. Our answer is always the same: add AI when it removes a step your users already find slow, not when it adds a new one they never asked for.
Three questions we ask first
- Is there a repetitive task users do by hand today?
- Do you have the data to do it well, or can you get it?
- What happens when the model is wrong — and can the user recover?
If the answer to the last question is "nothing good", we look for a narrower version of the feature, or put a human review step in front of it.
Start with a prototype against real data
A week spent prototyping against your own data tells you more than a month of planning. It shows where the model shines, where it fails, and what it will cost to run at scale.