Enterprise AI operating-model checklist.
Seven decisions to make before expanding an AI workflow.
Use this in an operating review
- Pick one workflow, rather than reviewing AI strategy in the abstract.
- Bring together the business owner, operations, policy, engineering, and people closest to the work.
- Leave with the next constraint to solve and the evidence needed before expansion.
Enterprise AI becomes real when an organization decides what work a system can own, what still requires human judgment, and how the operation learns from the result. These seven decisions make that conversation concrete.
1. Name the outcome
What customer, employee, or business outcome will improve? A workflow with no measurable outcome is hard to govern and impossible to learn from.
2. Set the scope
Is this a single task, an interaction, a workflow, or an outcome that unfolds across time? Scope determines the context, authority, and recovery the system needs.
3. Decide who owns each judgment
Allocate repeatable work to agents where it is observable and reversible. Keep employees and specialists close where trust, accountability, relationship context, or exceptions change the outcome.
4. Define the system boundary
List the knowledge, tools, permissions, policies, data, handoffs, and fallbacks required to complete the job. This is the operating system around the model.
5. Design the human role
Specify what people supervise, when they intervene, what they can correct, and how the system preserves context at a handoff.
6. Count the economics honestly
Measure rework, wait time, exception volume, quality loss, customer effort, and the oversight work created by automation, not only the labor that appears to disappear.
7. Create the learning loop
Capture outcomes and failures, assign ownership for change, and use evidence to decide whether to expand, redesign, or keep a human closer to the work.
How to use the result
A strong answer to every question does not mean a workflow is ready to scale. It means the organization knows what to test next. Use the Enterprise AI Readiness Diagnostic to examine the operating conditions around a use case, then use SupportBench to test system behavior inside its defined authority.