There's a dashboard-shaped hole in most early-stage AI companies, and the usual instinct is to fill it with everything. Latency percentiles, token counts, cache hit rates, prompt versions, retrieval scores, a funnel, an NPS widget. Within a quarter you have forty charts and no idea which one to look at when something feels off.
The problem isn't too little measurement. It's that measurement without a decision attached to it is just decoration.
A metric earns its place on the dashboard only if a specific number would make you do a specific thing.
By that test, an early-stage AI startup needs about four. Here they are, in the order they'll save you.
1. Time-to-diagnosis
