An AI feature can feel impressive and still be a bad product decision. The demo is fast. The answer sounds useful. The team is excited. Then usage grows and nobody can answer the basic questions: Is it accurate enough? Is it saving time? Which customers trust it? Why did costs spike? Should we scale it, fix it, or kill it?
That is the trap an AI metrics baseline prevents.
A baseline is not a dashboard full of vanity charts. It is a small set of before-and-after measurements that tells you whether an AI workflow is getting better, getting worse, or merely getting more expensive.
Why AI features fail without a baseline
Most software teams already track uptime, errors, and conversion. AI features need those too, but they also need new signals because model behavior is probabilistic.
