What an AI workflow audit actually includes

Start with the operating reality

A practical look at the work, evidence, risks, and decisions required before implementation starts.

Document the current inputs, systems, decisions, handoffs, delays, rework, and exceptions. Establish a baseline before choosing a tool or model.

Design the controls with the workflow

Agree on approved sources, access limits, human review points, ownership, escalation, logging, and acceptance criteria. Test against real historical examples before expanding.

Measure usefulness after launch

Track turnaround time, quality, exceptions, adoption, and user confidence. Keep what works, repair what does not, and avoid automating judgment that should remain with a person.