One workflow and a shared definition of success
Name the process owner, the trigger, the expected output and the part of the workflow the pilot covers. Record what is outside the scope.
Agree how the team will judge usefulness. Compare against the current process using your own baseline; avoid treating an impressive demonstration as a measured business result.
Inputs, integrations and access
List the data and tools involved, the permissions required and who will provide access. Check API availability and usage limits before treating a connection as a fixed commitment.
Use representative examples for testing. Public demonstrations should use synthetic information unless there is permission to show the real data.
- Source and destination for information
- Data quality and content ownership
- Access permissions and account responsibilities
- Platform, hosting, messaging and AI usage costs
Human review and exception handling
Decide which outputs need approval and which events should stop the workflow. Give the person responsible enough context to inspect the request.
Test missing inputs, duplicate submissions, unavailable systems, uncertain classifications and unsupported answers. Agree how a failure becomes visible and how work resumes.
A handover the team can use
A pilot should leave a clear description of the workflow, its limitations, the accounts it depends on and how to report a problem.
Agree who maintains source content, monitors the system, authorises changes and pays third-party charges. Training and ongoing support need their own defined scope.
A decision at the end
Review the evidence with the process owner. Did the pilot meet the agreed checks? Is the review effort manageable? Are usage and support costs understood?
Then decide whether to extend the work, revise it, maintain it as it is or stop. The next commitment should follow the evidence.