Start by describing the task in plain language. Is the AI extracting fields from a document,
classifying a request, drafting text or proposing a next step? Define what a good result looks like
and identify the kinds of error that could cause harm, rework or a poor customer outcome. Keep the
AI task narrow enough that the reviewer can assess it.

Next, decide which outputs can proceed automatically and which require review. A low-impact
formatting suggestion may need a different control from a customer classification, a financial
record or a recommendation affecting a person. Your organisation should set these thresholds with
the relevant process, risk, data protection and security owners.

Give the reviewer useful context. Depending on the task, this may include the source document or
record, extracted values, the AI response, validation warnings, a confidence indicator only if it is
meaningful and tested, and a clear explanation of the available actions. The reviewer should be able
to correct, reject, request more information or escalate—not just approve.

Define the exception path before launch. Plan for missing data, conflicting values, unreadable files,
model or service failure, unexpected content and cases that do not fit the intended workflow. Make
it clear who owns the exception, what happens to downstream steps and how the process resumes after
resolution.

Finally, test the process with realistic examples, including difficult and failure cases. Record who
reviewed or approved an item, the outcome and any changes, to the extent supported and required by
the deployed system. Review the workflow periodically and after material changes to the model,
prompt, data source, integration or business process.

Human review is one control within a wider design. It does not guarantee accuracy or compliance, and
it cannot compensate for unclear ownership or unsuitable data. The organisation deploying the
workflow remains responsible for setting its policy and deciding what level of automation is
acceptable.