Start with the work
AI creates value inside a real operating system: a customer journey, service workflow, reporting rhythm or decision. Map the work first—its inputs, exceptions, owners and consequences—so the technology has a clear job.
Make information trustworthy
Automation amplifies the quality of what it receives. Teams need to understand source quality, permissions, sensitivity, retention and which information can safely enter an intelligent workflow.
Design human accountability
Not every decision should be delegated. Define approval thresholds, escalation routes and named owners before release, with stronger human control where impact or uncertainty is higher.
Measure operating change
Model accuracy alone is not a business outcome. Track adoption, cycle time, exception rate, service quality and the capacity released for higher-value work.
Use this framework as a starting point. The right priorities depend on your operating context, data, risk and internal capability.