Make oversight part of how an AI service operates. We help define what people review, what evidence they need and how they can intervene when the service needs attention.
The right approach depends on the task and its consequences. A draft answer, an automated update and a recommendation affecting a person need different checks. We help define where review should happen, which actions need approval and what should trigger escalation. The person responsible needs a clear purpose, not simply a place in a workflow.
Useful review requires enough context to judge the result and a practical way to challenge it. We consider source information, confidence limits, permissions, workload and available actions. This includes what happens when someone disagrees, when information is missing and when the system should pause or hand over.
We help establish measures that reflect the service, such as answer quality, successful handovers, unresolved exceptions and changes in source information. Regular review should lead to an action, an owner and a record of what changed. Supplier updates, new uses and staff feedback can all prompt a fresh look at the controls.
The output can include review responsibilities, approval points, escalation routes, monitoring measures and a change-review process. We connect these to the team operating the service, so oversight remains part of everyday delivery. The model should develop as evidence from real use becomes available.
Bring the current or proposed workflow, examples of outputs and the people who would own review. We agree which questions need to be resolved and identify any specialist input. The aim is a practical system of responsibility that people can understand and exercise.
Discuss AI oversight
