A reliable AI workflow is defined as much by what it must not do as by the task it should complete. Make the boundaries visible before the workflow receives access to business data or takes action.
Describe one repeatable job
Start with a task that happens often and has a clear input and output—for example, sorting incoming requests into a human-reviewed queue. Write down exceptions, sensitive information, and the existing tools involved.
Make the stop conditions explicit
- Ask for review when the request is ambiguous or outside the approved examples.
- Do not expose data the workflow does not need.
- Require approval before sending messages, changing records, or making commitments.
- Log the handoff and make it easy for a person to correct the result.
Test the difficult cases
Test incomplete requests, conflicting details, unexpected attachments, and service failures—not just the ideal example. Keep the workflow in a limited pilot until the team can see how it behaves and has a clear way to pause it.
A person should remain responsible for decisions that carry legal, financial, safety, or meaningful customer consequences. Automation can prepare useful context without taking that responsibility away.
← Back to insights