Evaluate the whole assisted workflow
Course overview · 4 min reading + 12 min practice, estimated
Principles and method
Measure the task from input preparation through final approval, including correction and exception handling. Compare with a baseline using sufficiently similar work and record differences. Test ordinary cases and difficult cases such as contradictory dates or incomplete records. Define a quality guardrail and a stop condition before expanding use. Supplier or model updates can change behaviour, so retain a regression set and an owner. AI use is successful only when the overall service improves under the required quality and handling constraints.
Worked example
Drafting falls from twenty minutes to five, but review rises from three to fifteen. Net effort falls from twenty-three to twenty minutes, a smaller gain than the drafting metric suggests. The team also checks whether error severity changed.
Put it into practice
Design a five-case pilot and calculate net effort using fictional timings.
Use fictional information and keep your work in your own notes.
Compare your approach: self-review guidance
Include preparation, review and corrections. State the sample’s limitations and the condition under which you would stop. Do not report only the fastest generated output.
Sources and further reading
Original Academy teaching and fictional examples. These references provide context, not endorsement. Edition 2026.09; updated 2026-09-24.
- GOV.UK: Responsible AI in recruitment
UK guidance on procuring and deploying recruitment AI.
- NIST: AI Risk Management Framework
Voluntary framework for organising AI risks and controls.