1. Data scope
Start by deciding what information can and cannot be entered into AI systems. Customer data, contracts, personal information, and source code should be classified before broad adoption.
2. First operations to automate
Begin with frequent, structured work where human judgment remains part of the flow, such as request triage, application review, or record updates.
3. Approval and exception handling
Even when AI drafts or classifies work, teams need to define who makes final decisions and when a process should stop for review.
4. Logs and improvement
Production workflows should make it clear who requested work, what AI executed, and who approved the result. Logs support both audits and continuous improvement.