Measure AI like a business outcome, not a toy metric
Efficiency slides get polite nods. They rarely unlock more mandate.
If your AI story is only “hours saved,” finance will discount it and product leaders will ignore it. Tie AI to outcomes they already track.
Better measures
- Cycle time on a customer or release path
- Error or rework rate
- Conversion, retention, or risk metrics where AI touches the funnel
- Time-to-decision for managers using AI-assisted insights
- Cost to serve and quality together, not cost alone
How to report without noise
- One primary metric per use case
- A baseline from before go-live
- A short note on what the model did not change
- Quarterly kill/keep decisions in writing
30 / 60 / 90
- 30: Baseline and primary metric for the live (or soon-live) use case.
- 60: First measured month with narrative for HQ.
- 90: Keep, fix, or kill decision based on evidence.
Takeaway
Dashboards are not strategy.
Pick outcomes that matter to the business, then let AI earn its place in the budget.