A clear method for making AI fit the work.

Capture how the work happens today: volumes, time, delays, rework, exceptions, cost and the people or systems involved.

Output: Current-state workflow and evidence baseline.

Talk with the people who own and perform the work. Review real examples and identify causes of friction, constraints and failure points.

Output: Validated problem statement and opportunity register.

Compare process change, existing software, automation, AI assistance and no change. Consider costs, risks, data, human review and success measures.

Output: Recommended option, alternatives and a scoped pilot plan.

Configure or create only what the approved scope requires. Keep the work small enough to test and avoid technology without a defined job.

Output: Working pilot or agreed implementation.

Fit the solution to relevant systems, information flows and permissions. Define how errors, exceptions and handoffs reach a person.

Output: Tested connections and documented data flow.

Test realistic cases with the people who will use the workflow. Train users, gather feedback and adjust before wider use.

Output: User-tested workflow, guidance and acceptance decision.

Review performance against the baseline. Fix problems, measure outcomes and decide whether to maintain, extend, simplify or retire the change.

Output: Outcome review and next-step decision.