The reversal log from the claims system shows 18% of AI-accepted decisions overturned within the same week. Boards never see that figure because the success dashboard only records the initial accept click.
That single omission turns every adoption chart into fiction. The model appears to reduce manual work while the actual exception queue grows from the reversals that follow.
Australian insurers that adopted early claims triage tools discovered the pattern first. Their vendor reports celebrated 85% straight-through processing until finance noticed the spike in re-opened files and manual overrides.
The correct metric is not adoption volume but the delta in downstream correction hours. Track the exact cost of every reversal, including the staff time spent re-entering data and explaining the error to customers.
Procurement contracts that tie bonuses to model accuracy alone guarantee this distortion. Replace them with clauses that require reversal rates below a verified baseline measured in production, not the vendor lab.
Integration teams must export the reversal log weekly and feed it into the same board pack that carries the usage numbers. Without that link, the reported ROI remains an artefact of the logging rules rather than an operating result.
Smaller APAC operators without dedicated data teams can start with one narrow process. Pick the highest-volume AI-assisted workflow, export its reversal timestamps for thirty days, then calculate the net hours saved after corrections.
Boards that accept usage counts without reversal context are signing off on a measurement system that rewards the appearance of automation over actual throughput gains.