Answer first
Choose one primary business outcome and protect it with quality and risk guardrails. Compare the same unit of work before and after AI, including human review time and exception handling.
NIST's AI Risk Management Framework treats measurement as part of ongoing risk management, not a one-time accuracy test. The business case should therefore include performance, controls, monitoring, and ownership.
Build a balanced scorecard
The first dashboard should answer whether the workflow is better, controllable, used, and economically sustainable.
| Dimension | Useful KPI | Common mistake |
|---|---|---|
| Outcome | Cycle time, qualified volume, resolution, or conversion | Measuring activity with no business result |
| Quality | Task pass rate and material reviewer corrections | Using an average score that hides severe failures |
| Risk | Unsupported output, privacy, access, and escalation exceptions | Counting only successful responses |
| Adoption | Eligible cases completed in the intended workflow | Counting logins instead of completed work |
| Cost | Model, integration, review, support, and change-management cost | Comparing API cost with full labor cost |
Calculate ROI from an observable baseline
Use a defined period and comparable cases. Estimate benefit from measurable time, throughput, error, or revenue changes, then subtract model, platform, engineering, review, support, and governance costs.
Treat assumptions separately from observed results. A range is usually more useful than a single precise forecast until a representative PoC has produced evidence.
Test whether your AI theme is measurable
Use the diagnostic to identify whether the baseline, data, and decision threshold are ready.