A careful starting point for an AI or automation project

Choose a repeated task with clear boundaries

Map the trigger, input, expected output, and exceptions. Prefer a workflow where the team can explain what a correct result looks like and what should happen when the system is uncertain.

Decide where people stay in control

Some outputs can be prepared automatically; others need review before they affect customers, money, or business records. Decide who checks the result and how they can correct or stop the process.

Review data and failure handling

Identify sensitive information, access limits, retention needs, third-party services, and the consequences of an incorrect result. Provide a manual fallback and a way to notice failed or incomplete runs.

• • Use only data approved for the workflow
• • Test normal cases and likely exceptions
• • Record failures without exposing unnecessary personal data
• • Review outcomes before widening usage