When a Metric Changes, Ask What Else Changed

A useful lesson in interpreting workplace results: a metric moving after a new tool is introduced does not, by itself, show that the tool caused the change.

Imagine an AI assistant is added to a support workflow and response times fall. That’s worth investigating—but perhaps the incoming requests also became simpler, or staffing changed during the same period. The result is an observation; the explanation is still a hypothesis.

Before turning it into a rollout decision, compare similar work under the old and new process, and note what changed alongside the tool. If a careful comparison isn’t possible, say so and make the next step a limited trial with a clear review point.

The practical question is not only “Did the metric improve?” but “What else could explain it—and what evidence would change our decision?”