Put the human review where a mistake would matter.
A practical starting point for designing useful AI workflows.
Identify the consequential step
Look for the point where information leaves the team, changes a record, commits money, or shapes an important decision. That is a useful place to start designing an approval boundary.
Make the evidence reviewable
Give the reviewer the source, the proposed output, the checks performed, and any unresolved exceptions. Asking someone to approve a polished answer without its evidence is asking them to guess.
Define the failure path
Specify what happens when an input is missing, a source changes, or a validation fails. Route the exception to an owner. Keep a manual fallback and a record of the decision.
Earn a wider scope
Start with a bounded workflow. Evaluate representative inputs and difficult cases, agree acceptance criteria, and review performance before expanding permissions or distribution.
Bring this discipline to your own workflows.
Explore the AI Operating Leverage Audit →