AI ROI is not only cost savings. A useful scorecard also measures quality, risk, adoption, and whether the workflow actually improved.
ROI Scorecard
| Metric | Question | Evidence |
|---|---|---|
| Time saved | How many minutes per task changed? | Before/after timing |
| Quality | Did errors or rework change? | Review samples |
| Cost | What did tools and usage cost? | Invoices and usage logs |
| Risk | Did incidents or sensitive outputs occur? | Risk log |
| Adoption | Are users still using it after novelty fades? | Active usage |
| Business value | Did the workflow improve outcomes? | Revenue, backlog, response time |
Simple Formula
Net value = time value + quality gains + revenue or capacity gains – tool cost – review cost – risk cost.
The formula does not need to be perfect. It needs to be consistent enough to compare pilots.
Decision Thresholds
| Result | Decision |
|---|---|
| High value, low risk | Scale |
| Medium value, manageable risk | Improve and retest |
| Low value, high review burden | Stop |
| Unclear value | Measure again with better baseline |
How to Use This
Use the same scorecard for every AI pilot. That makes results comparable across teams.
Bottom Line
AI ROI becomes clearer when teams measure more than usage. The real question is whether work became faster, better, safer, or more valuable.