Measurement should help leaders learn whether AI adoption is producing useful behavior change. It should not be used to inflate claims.
Measurement Principles
- Baseline before introducing the example workflow.
- Measure time, quality, learning, and risk awareness.
- Report underperformance honestly.
- Keep examples low-risk and synthetic or de-identified.
Example Metrics
| Category | Example metric | Frequency |
|---|---|---|
| Adoption | Active participants using the example pattern | Weekly |
| Productivity | Average time from input to reviewed draft | Weekly |
| Quality | Missing information or rework rate | Weekly |
| Learning | Self-rated confidence on named use cases | Start and end |
| Risk awareness | Correct classification of example scenarios | Training checkpoints |
Weekly Summary Template
- What changed this week?
- What improved?
- What did not improve?
- What questions or risks need local review?
- What should change next week?
End-Of-Proof Questions
- Did the example workflow save time after review effort?
- Did output quality improve, decline, or stay the same?
- Did participants learn when not to use AI?
- Which support model would be needed for expansion?
- Which workflows should remain out of scope?