Time-to-outcome measures how long it takes to reach a business result, not just to finish a technical step. In enterprise systems, a task can be “done” long before the real outcome appears, such as an approved payment, a closed ticket, or a completed investigation.
In cyber security, this metric matters because attackers and defenders both operate on outcomes. An attacker may only need enough time to move from access to impact, while a defender may need to detect and stop that path before the outcome is reached. For agentic AI, time-to-outcome helps show whether automation is actually useful and safely governed: a faster workflow is not a win if it increases unauthorized actions, rework, or audit gaps. Measuring time-to-outcome alongside approvals, exceptions, and evidence gives a clearer view of both productivity and control.