Know what works before work depends on it.

Define acceptance before implementation.

Agree on the inputs, expected outputs and evidence for each critical path. Scope quality assurance around your actual application and workflow; testing coverage and responsibilities are agreed per engagement.

Test beyond the successful example.

Review representative records, missing fields, duplicate inputs, access restrictions, interrupted connections and recovery. For dashboards, check definitions and data freshness. For web applications, include relevant mobile and keyboard journeys.

Review AI output where it matters.

If AI extracts fields or prepares text, compare representative outputs with the agreed requirements. Keep uncertainty visible and define the human review route. A plausible response is separate from a verified record or completed business action.

Leave evidence your team can use.

Deliver the agreed test cases, results, defect notes and handover checks. Resolve or explicitly accept outstanding issues before launch. Testing reduces uncertainty within its coverage; it cannot guarantee the absence of every defect. Ongoing regression checks and support belong in the management scope.

Useful next steps.

Write testable acceptance criteria

Plan ongoing management

Start with a workflow.

Tell us what repeats, which tools are involved and what an employee should receive. We can discuss a clear scope, acceptance criteria and the next practical step.