AI Adoption: Change Management for Commercial Teams

The first failure mode is quiet refusal: the system is deployed, nobody uses it, and reporting quietly reverts to the old process. The second is uncritical acceptance: outputs are trusted without review until a visible error damages confidence irreparably.

Both are cultural failures, and both are preventable with the same intervention, visible, structured review in the early period.

Run the automated and manual process in parallel for a defined period and publish the comparison. Nothing establishes confidence faster than a team seeing the system agree with their own judgement over a hundred real cases.

Decide in advance what recovered hours will be used for and say so publicly. Ambiguity here is read as a headcount question, and the programme acquires opposition it did not need.

Successful programmes create two roles: a system owner accountable for output quality, and a reviewer who handles exceptions. Both are usually existing team members with redefined responsibilities rather than new hires.

Weekly for the first month, fortnightly to month three, then monthly. Each review examines exception rate, error patterns and the commercial metric, in that order.

The goal is an internal team that can extend the system without external help. Document every workflow, train two people per system, and schedule a formal handover date at the outset.