Your finance team receives the monthly AI pack showing 94 percent prediction accuracy and 67 models in production. None of those numbers tell you whether the sales forecast changed the actual bid or whether procurement altered the supplier shortlist after seeing the model output.
The measurable failure is override volume. Every time a human steps in and changes the recommendation, you record the delta in expected margin, cycle time and downstream rework. Most Australian finance functions have no system that captures this reversal event at all.
Start with three core processes that already carry P&L responsibility. For each, log the AI suggestion, the final human decision, and the variance in outcome within 30 days. The override rate itself becomes the leading indicator, not the model score.
Accuracy theatre persists because boards reward visible deployments. Replace that with a single reversal-cost metric: total margin lost or time burned when the model was corrected. This number forces the accountable business unit to own the result.
Melbourne-based mid-market firms that adopted this approach saw override rates drop from 38 percent to 11 percent inside one quarter once the cost was visible on the CFO dashboard. The models did not improve; decision rights and incentives did.
Build the measurement into existing ERP workflows rather than new AI governance portals. If the override cannot be logged in the same system that books the revenue, the metric is already theatre.
Stop asking vendors for accuracy benchmarks. Demand they expose the override log and pay a small penalty for every reversal above an agreed threshold. That contract clause changes behaviour faster than any internal KPI.