What to take back to your team
- Name the owner of the workflow and its output.
- Decide which errors are acceptable and how a person will handle work the AI cannot complete.
- Measure quality after launch and expand from what works.
Give the pilot an operating owner.
Choose one list of work, decision, or repeated task, such as sorting incoming support requests. Name the person who owns its result and the people who will use it.
Agree on the workflow change before you call the pilot a success. The team needs to know where the output goes and what happens next.
Use the pilot to test where data comes from, how the AI uses it, and what people do with the result.
Define the quality checks.
Set an acceptable error level for the task. Decide which results need human review and how users can report a problem.
Define how a person will take over when the AI cannot handle a task. Make that path clear enough for the support team to use.
Make repeated setup steps reliable before launch. For example, use a checked process to load new data and release software changes. Keep a record of the version and changes that affect the result.
Operate, learn, and expand.
Put the capability where people already work. Monitor output quality and usage once real work reaches the system.
Name who checks the results, responds to failures, and approves changes. These responsibilities must continue after the project team leaves.
Use the first stable workflow to guide a second use case. Let the evidence set the next scope and delivery schedule.
Plan for the day the pilot becomes someone's daily responsibility.
Apply it to your team
We can help the team responsible for an AI pilot define the quality checks, support roles, and steps needed for daily use.
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