Designing AI-Enabled Customer Service Workflows

Choose the service task carefully

Identify a repeated enquiry with clear source information and a manageable consequence of error. Decide whether AI will help staff prepare a response or interact directly with customers.

A staff-assisted draft is a useful way to learn before considering greater autonomy. Keep complaints, disputed commitments and sensitive cases within defined escalation routes.

Prepare the knowledge and boundaries

Assign owners to service policies, product details and process instructions. Remove obsolete versions and state when the system should say it cannot answer.

Use the data readiness guide before connecting a knowledge baseA managed collection of information used to answer questions. Do not expose information simply because it exists in a shared folder.

Design the handoff

Tell staff what to verify and how to escalate. Customers should have a practical route to a person when the workflow cannot resolve the request.

Google’s People + AI Guidebook offers a human-centred design reference. Test whether people understand the assistance and can recover from an unsuitable answer.

Test realistic exceptions

An illustrative delivery-enquiry pilot should include a missing order, conflicting dates and a request for information the user is not authorised to access. Check that the workflow does not invent a status or disclose restricted details.

Have staff compare answers with the source and log corrections. Our human review guide helps define checkpoints.

Measure service quality and operating effort

Track resolution quality, repeat contacts, escalation, review time and unresolved requests. Faster responses are valuable only when they help customers reach a correct outcome.

Use the pilot evaluation guide before increasing scope. Train service owners to maintain the process after launch.

Sources and further reading

• Google People + AI Guidebook