A team wants to automate how incoming support requests are routed. The model appears to work in a demo, but nobody knows what it will do with vague requests, urgent exceptions, or cases that belong to two teams.

Run it in shadow mode first. Let the existing process remain in charge while the automation records what it would have done. The comparison gives the team evidence before it grants permission to act.

Define one decision to observe

Start with a single output, such as the proposed support queue. Record the input available at decision time, the proposed queue, whether the automation abstained, and the route the team actually chose. Do not give the test access to the eventual resolution and then count the result as a prediction.

Use only data the team is authorized to process. If real inputs are not appropriate for the test environment, use a minimized, approved sample instead. Shadow mode prevents production writes only if the test is designed that way; it does not automatically make processing or logging safe.

Make it impossible for the test to act by accident

Use read-only access where needed, or send events to a separate test environment. Keep sending messages, changing records, and running downstream jobs disabled outside the model. A prompt telling the model to “only simulate” is not the same as taking away its ability to act.

In a routing pilot, the shadow result should go to a review log, not to the real assignment field. Make the distinction clear to anyone inspecting the results.

Review disagreement, not just agreement

A mismatch can mean the automation was wrong, the human chose a reasonable exception, or the original routing was inconsistent. Ask a reviewer to classify disagreements before turning them into a score.

For example, sending a routine billing question to the general queue may cost a few minutes. Missing an urgent access issue may be more serious. Track those outcomes separately. Also count cases where the automation made no recommendation; excluding them can make the result look more complete than it is.

Decide what earns a limited rollout

  • Define which cases are eligible to run automatically.
  • Agree on acceptable error levels and which errors block release.
  • Keep an exception queue with a named owner.
  • Start with a narrow rollout and retain a manual fallback.
  • Compare results again after the automation begins acting.

The point of shadow mode is not to prove the system is ready by itself. It is to learn which decisions are predictable enough to automate and which should remain with a person.

For the next step, see our AI workflow handover checklist or discuss a pilot.