AI impact measurement
How to measure AI impact beyond token spend
Token spend is easy to count, which makes it tempting to treat as the primary measurement of AI adoption. The problem is that consumption is not impact. A team can spend more and get worse. Another team can spend less and unlock a critical bottleneck. The measurement model has to connect AI activity to the work and outcomes it changed.
Key takeaways
- AI ROI should be measured through work outcomes, not only provider invoices.
- Useful metrics include acceptance rate, rework, cycle time, throughput per pound, and quality signals.
- Every metric should have an evidence path back to source events.
Consumption is not the same as value
Provider dashboards can show requests, tokens, and cost. They rarely show whether a suggestion was accepted, whether review quality improved, or whether a workflow created hidden rework. That is why AI impact measurement needs source-level evidence from the systems where work happens.
Start with outcome metrics
A better measurement set includes accepted AI-assisted work, time-to-PR, review rework, false-positive rate, incident resolution movement, documentation gaps closed, and throughput per pound spent. These metrics are more meaningful because they connect AI to organisational work.
- Acceptance rate
- Time-to-PR
- Rework rate
- False-positive rate
- Throughput per pound
Keep the evidence inspectable
Measurement only builds trust when teams can inspect the evidence. Leaders need rollups; teams need the ability to trace a number back to repositories, work items, agent runs, decisions, provider calls, and review events.