Design KPIs for AI Use
If you can't speak to "did adopting AI produce results," investment won't continue. Design so effects can be measured on three axes: quality, speed, cost.
Example Metrics per Axis
- Speed: work time, lead time, processed items per person
- Quality: error rate, rework rate, customer satisfaction, pass rate
- Cost: labor-reduction amount, AI usage cost, net effect after offset
Design Principles
- Take a baseline first: without pre-adoption values you can't speak of effect
- Don't speak by speed alone: faster but lower quality is counterproductive. Always pair with quality
- View net: subtract AI cost and operating labor from reduction effect
- Tie to work: meaningful units of that work, not abstract KPIs
Easy Traps
- Mistaking "number of uses" for results (activity ≠ results)
- Reporting only speed without measuring quality degradation
- Not accounting for hidden cost (verification/fix effort)
Key Point
Design KPIs as "baseline → net of quality/speed/cost → work units." Only once made measurable does AI investment ride continuous decision-making.
