Human-in-the-loop is an operating model, not a disclaimer_

Teams often say a person will review AI output, but provide no queue, priority, evidence, ownership, or feedback capture. The reviewer becomes an invisible dependency.

A useful review path tells the operator why a case was escalated, what evidence the system used, what decision is permitted, and where the correction goes.

What changes in practice_

Define confidence and policy triggers, assign service levels, preserve source context, and feed recurring corrections back into tests or rules.

• Make exceptions first-class records.
• Measure review volume and overturn rate.
• Do not hide uncertainty from the operator.

Our take_

The durable advantage is not adopting the newest tool first. It is building the identity, state, evidence, and operating boundaries that let a real team own the system after launch.