Scaling an AI pilot is a workflow decision. The model must fit a complete task, with reliable inputs, accountable ownership and a way to evaluate what changes.
Start with a task and an owner
Define the decision or task the pilot supports. Record who performs it, what information they use and where the result goes next.
A named process owner should agree the baseline, the intended improvement and what would make the use case unsuitable. This creates a decision to test rather than a demonstration to admire.
Design the process around the output
An AI result is useful only if the next step is clear. Map inputs, reviews, approvals, system updates and the route for exceptions.
If an agent can act on the result, specify the actions it is allowed to take. Agree which decisions need approval and when the work should return to a person.
Evaluate the whole task
Include ambiguous inputs and unusual cases in the evaluation. Check the time spent on human review, corrections and rework alongside the quality of the output.
A pilot should have criteria for proceeding, changing the design or stopping. A technically successful demonstration may still be unsuitable for wider use.
Make the foundation repeatable
Clarify data ownership, access, monitoring and support before expanding the scope. Decide what can be reused and what needs to remain specific to the workflow.
Give users a clear explanation of the new process and a route to report problems. Wider use depends on both operational readiness and the ability to learn from what happens.
- Name the task, its owner and its baseline.
- Test normal work and exception cases together.
- Agree the evidence needed before expanding the pilot.
