When a Better Metric Could Mean a Worse Outcome

Before settling on an AI-workflow metric, try a metric inversion check:

1. State the outcome the metric is meant to represent.
2. Imagine the metric improves while the real workflow gets worse. What could explain the gap?
3. Ask someone to suggest a different explanation or a signal that would distinguish between the two.
4. Make clear who owns the next decision and what finding would prompt a review.

For example, faster processing might look like progress—but could also reflect a review step being skipped. The point isn’t to distrust every metric. It’s to test what it might be missing before the metric starts steering the decision.