Launch metrics should measure whether the AI system changes the work, not whether it looks intelligent in isolation. Useful benchmarks include autonomous resolution share, reviewer acceptance rate, time-to-resolution, reopen rate, and cost per completed task.

Benchmarks need cohorting. A support workflow may improve dramatically for standard access requests while still requiring human ownership for policy exceptions.

Review benchmarks on a fixed cadence and tie them to decisions: expand scope, adjust thresholds, improve retrieval, add integrations, or stop a workflow that is not creating measurable value.