During a training exercise, an AI assistant recommends a refund. The trainee can see the order and the proposed response. Everything looks ready to send.

One detail changes the decision: the item was purchased through a third-party seller, and the refund process follows a different route.

The exercise is useful precisely because the suggested answer is plausible. An employee who simply learns where to click may accept it. An employee who understands the policy can identify the missing condition, check the source and explain the correct next step.

Preparing a team for AI starts with that difference.

Practise the decisions that remain with people

Tool training should cover more than navigation and feature demonstrations. Employees need practice deciding whether a recommendation fits the facts in front of them.

Use realistic cases with incomplete records, conflicting information and requests outside standard policy. Ask the trainee to explain the decision before taking the action. Include a customer who challenges the answer or asks for an option the employee cannot authorize.

The aim is not to turn every agent into a technical specialist. It is to make the boundaries of the job clear: what the employee can decide, what needs verification and when another person must take over.

Show how to report an unreliable suggestion. If the tool repeats the same error, the knowledge or product owner needs usable evidence, not a general complaint that the AI is unhelpful.

Change coaching when the work changes

A supervisor reviewing the refund exercise should look beyond whether the trainee used the recommended wording. Did the employee notice the seller, check the applicable rule and set an accurate expectation?

Those behaviours deserve attention in live quality reviews too. Otherwise, training teaches judgment while the scorecard rewards unquestioning acceptance or speed.

Coaching can then target the actual gap. One employee may need help finding the relevant evidence. Another may understand the policy but struggle to explain an unwelcome answer. A third may need practice recovering a conversation after an incorrect suggestion.

This takes supervisor time. Plan for it rather than assuming a new tool removes the need for coaching.

Prepare for a different mix of contacts

If automation completes routine tasks, the remaining workload may contain more exceptions. Review that mix before changing staffing or performance expectations.

Agents dealing with complex cases may need longer conversations, more specialist access and time to recover between difficult interactions. Capacity plans should include those demands, alongside the effort required to review and correct automated work.

Also rehearse an outage. The team should know how to find essential guidance and continue the service when assistance disappears.

Return to the refund exercise after training. The test is whether the employee can reach and explain the right decision with appropriate support. That capability should survive beyond the next software release.