Which customer service tasks should AI take on first?

In the distributor example in The AI Playbook for Mid-Market Leaders, the customer wants a new delivery date. The service team can check two records to prepare its answer. The judgement involved changes when the customer asks for compensation.

Start with the order update

The order system shows what the customer bought. The carrier’s record shows what happened to the shipment. AI can bring those details together and draft a reply for the adviser to check and send. The team can test that change on one kind of enquiry before extending it to others.

A useful early test is a conflict between the records. If the order system still shows the original delivery date and the carrier has recorded a delay, the adviser needs to see both. A confident reply that repeats the original date makes the problem worse.

Keep the credit decision with its owner

A credit above the service team’s limit needs a manager’s approval. AI can prepare the order history and the amount requested, leaving the manager to decide whether the circumstances justify a payment. The customer receives a decision from someone authorised to make it.

Zenith calls this use of AI Assist. The distinction matters when estimating savings. Drafting a reply saves part of an adviser’s time. It does not remove the time needed to review an exception or approve a credit.

What would justify a wider rollout?

Before changing the process, record how long a late-order enquiry takes to resolve and how often an adviser has to correct the answer. Compare the same measures during the pilot. If replies go out faster but customers call again to correct the date, the team has moved work into a second call.

If response time improves and the answers remain reliable, management can decide what to do with the capacity released. Handling more orders with the same team is one possible benefit. Reducing overtime is another. The measures should reflect the outcome management intends to fund.