Diagnose and prioritise
Start with the operational problem and baseline. Identify a sponsor, a workflow owner and the systems involved. The audit should establish whether automation is feasible and which process or data issues need attention first.
Design and scope
Define the user journey, integration boundaries, human approvals and exception paths. Agree success measures and acceptance criteria. A proposal should state what the system will do, what it will not do and which dependencies remain outside the engineering team’s control.
Build and integrate
Deliver in manageable increments. Keep conventional engineering, data handling and model behaviour visible as separate concerns. Test with representative records and make failures understandable to operations staff.
Validate and hand over
Review the end-to-end process with the people who will use it. Confirm access, monitoring, support ownership and recovery procedures before launch. Record what must be measured after release.
Improve against the baseline
Review adoption, completion, errors and manual effort. Expand only when the evidence supports the next step. If the outcome points to a process change rather than more AI, the roadmap should reflect that.