Your internal audit lead at a 400-person Brisbane resources group opens the latest AI update pack. The slide deck lists coverage across procurement, maintenance scheduling and invoice processing, yet the only figure that moves month to month is the count of processes that touched the model at least once.
The exception queue tells a different story. Every week the same twelve contractors still receive manual price overrides because the model flags their invoices for review and then defaults to the prior quarter’s rate. Those overrides consume 180 analyst hours that never appear in the adoption metric.
Coverage numbers reward breadth. They count a model that scans 4,000 purchase orders and correctly routes 3,200 while ignoring that the remaining 800 still require the same three-person team that handled the lot before the pilot began.
The finance controller who signs the quarterly variance report sees only the headline saving. She never receives the line item that records extra contractor days spent reconciling the model’s conservative output against actual site costs.
Shift the metric to exception hours closed. Track the reduction in analyst time spent on cases the model escalated rather than the number of models that reached production status.
When the board next asks for AI success, present the exception log from the last ninety days. Show which rules were added after human review, which vendor contracts were renegotiated because the model repeatedly under-priced risk, and the cumulative hours removed from the queue.
Adoption rates hide the work that still travels around the model. Exception hours make that leakage visible and force the next investment decision onto actual cost displacement instead of slideware reach.