Define the result before the saving.
An answer, an accepted handover and a completed action are different results. Agree which one the business owes, then count the full effort of delivering it—including repeat contact, investigation and recovery.
Let results earn more responsibility.
Then look at the work left with people. A low exception rate can still overwhelm a team as volume grows. Expand AI's role when results and the team's capacity support the additional responsibility.
Keep what the business has learned.
As AI takes on more work, each rollout should build on what still holds. Keep the rules, reasons and owners that remain valid. When a model changes, carry unfinished work forward and retest what changed.
Follow the promise across the business.
The same thinking must extend across teams. Local targets can all be met while a customer still waits. Follow the shared promise to improve the process, allocate capacity and reconsider commitments the business cannot meet.