The Error a Single AI Score Can Hide

A useful lesson in AI evaluation: a strong overall score can still leave the decision-maker asking the wrong question.

Imagine an AI tool sorting incoming support requests. Counting correct labels is a start—but a mistake that sends a routine question to the wrong team may matter less than one that delays an urgent case. The same total score could conceal that difference.

Before testing, define the errors that would change the decision. Compare the tool with the current process, examine important case types separately, and decide which cases need human review. Then try a bounded pilot and check whether the results match those expectations.

The aim isn’t to collect more metrics for their own sake. It’s to find out what evidence would make us deploy, revise, or stop.