How to run an AI pilot alongside your existing team

A shadow pilot gives the proposed workflow a copy of the inputs people already handle. The team keeps doing its normal job while the new system produces a separate recommendation. This makes it possible to see what the system would have done without changing a customer’s order, sending a message or interrupting a shift.

Compare decisions before revealing the answer

Imagine a service desk testing help with incoming requests. The coordinator records the destination team and any missing details, then closes the usual intake step. Separately, the pilot records its suggested destination, its reason and a link to the request. Keep the pilot’s answer out of the coordinator’s view until both decisions are saved. That preserves a more useful comparison.

Sample across busy periods, new customers and unusual request types. A hundred routine password requests reveal little about an urgent building access issue. Include enough meaningful exceptions to find out where a human needs to intervene. Use approved examples stripped of unnecessary sensitive information.

Treat disagreement as a question

Have a reviewer classify each disagreement. Was the request ambiguous? Was the team directory out of date? Did the person know a special arrangement the system could not see? If the rules conflict, fix the rules before counting either answer as wrong.

Set the promotion criteria before looking at the scores. For instance, a team might require every urgent request in its test set to reach a human and every routine recommendation to include its source. If performance meets the agreed standard only for one department, open a limited draft workflow for that department. Leave other traffic in shadow until its own examples support expansion. Record the decision, the cases reviewed and who can stop the next phase.

Put this into practice

Bring a sample of the work and the tools your team uses. We can help define a first project, who reviews it, and how to tell if it works.

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