AI operating model
AI operating model design defines who owns the business outcome, data, engineering, security, human review, support, change, and evidence when an AI capability becomes part of a service.
Name the service owner, product or process owner, delivery owner, platform owner, and decision authority. Describe who can approve a use case, change a workflow, pause an action, and accept the result.
The AI consulting and implementation route provides an internal path when the model must become a delivery plan.
Record data authority, access, permitted use, quality review, security controls, evaluation ownership, and escalation conditions.
Define entry criteria, evaluation evidence, approval, deployment, monitoring, support, disablement, and post-release review.
State how users report a problem, who investigates an output, how a change is reviewed, and what happens when a key person is unavailable.
Use incidents, exceptions, reviewer questions, usage patterns, quality checks, and service-owner decisions to refine the model.
Central coordination does not remove domain accountability. Business, data, security, delivery, and service responsibilities still need named decision rights.
Bring the pilot, service boundary, or ownership question that needs a supported operating model.
Discuss AI operating models