1. Start with the operational pain
Identify recurring work that consumes capacity, creates delays or leads to inconsistent execution. Define the responsibility in business language, not a list of technology features.
2. Name the accountable owner
Choose a business owner who can define the work, approve authority, judge exceptions and measure outcomes. Operational AI needs ownership after deployment, not just during the pilot.
3. Check feasibility and trusted context
Map existing systems, records, knowledge, communications and access requirements. Confirm the data is useful and the operating rules can be made explicit.
4. Make the boundaries measurable
Define permitted actions, human approval thresholds, escalation paths and the metrics that demonstrate completed work and business impact.
5. Start supervised and expand with evidence
Run a bounded role with visible evidence and human feedback. Evaluate quality and outcomes before releasing more authority or adding new responsibilities.