How to engineer AI work people can review and trust

Start with a bounded job

AI is easier to evaluate when its role is specific. Summarising a document, classifying an incoming request, or drafting a first response can have clear inputs and a clear handoff. "Make the process intelligent" is not a testable requirement and usually hides the real responsibility.

Keep the source material available

An output should lead back to the information it used whenever that information matters. A reviewer needs to compare a recommendation with the source, spot a missing detail, and correct the result without rebuilding the whole request. Provenance is part of the interface, not only an engineering concern.

Design the review step

Review is not a button added after the model responds. Decide who reviews which outputs, what confidence means in that context, and what action they can take. A good review surface makes edits cheap, makes escalation obvious, and records the decision when accountability matters.

Measure failure in the real workflow

Model quality alone does not tell you whether an automation helps. Watch for rework, overrides, slow approvals, and cases that should have been routed elsewhere. Those signals reveal whether the system is saving time without quietly creating a different kind of risk.