Do not measure AI by output volume

Usage can show that people found a tool, but it cannot tell you whether the tool improved their work. Higher output may create more review burden, inconsistency, or content that never contributes to a meaningful result.

Activity metrics belong in the story, but they should not be mistaken for impact.

Start with the reason the use case exists. Then select measures for cycle time, quality, decision confidence, adoption, risk, and the relevant marketing or business outcome. Compare the result with a baseline rather than relying on general impressions.

Qualitative evidence matters too. Interviews and workflow observation can reveal whether people trust the system, where they compensate for it, and why adoption is uneven.

A dashboard is not the end goal. Evidence should help leaders decide whether to improve the workflow, strengthen enablement, address a risk, expand the use case, or stop investing.

The organization becomes more capable when measurement improves the next decision, not simply the next status report.

A team uses AI to draft a weekly campaign summary. The draft arrives sooner, but the reviewer spends longer checking unsupported explanations. Measuring drafting time alone would miss that extra work.

Record preparation, drafting, review, corrections, and the time until the report is ready for a decision. Track whether the summary answers the campaign owner’s questions and whether source figures survive review. Before your next test, agree on a baseline and a review date with the person who uses the result.