Test the Exceptions

The demonstration goes well. AI reads a customer request, finds the right information, and prepares a useful response. The steps are clear. The result looks ready to use.

Then comes a customer whose order has already been partly refunded. Or a supplier with two conflicting price lists. Or a request that combines an ordinary purchase with an unusual delivery condition.

This is where a workflow earns its place in the business.

Bring Three Difficult Cases

Before adopting an AI workflow, choose three examples from your own work that required extra attention. Use cases with known outcomes so you can compare the result with what should have happened.

For a sales assistant, that might mean a discontinued product, a negotiated discount, and an order below the usual minimum. For a document workflow, try an incomplete file, contradictory versions, and an important detail buried in an attachment.

Decide what a good result looks like before running the test. Depending on the case, success might mean completing the task, requesting clarification, or passing it to a person with the relevant information and a clear explanation.

Watch the Review Work

Look beyond whether the final answer sounds right. Did the workflow notice the complication? Did it preserve the conditions that mattered? How much checking and correction did someone need to do?

A process that saves ten minutes on a routine case but creates thirty minutes of repair on an exception needs adjustment. That might mean improving the source information, changing the instructions, or narrowing the range of tasks it handles.

Keep those difficult examples and repeat them when the workflow changes. Add new ones as real work reveals them. Three cases are a useful starting point; broader use requires broader testing.

This week’s SIGNAL: Before making an AI workflow part of the routine, test three cases that have complicated the job before.