AI Use Cases in Customer Service

What we actually do

1. Enquiry inventory: we sort your real ticket volume by frequency, effort and escalation risk
2. The dividing line: which enquiry types suit automation, which must stay with people
3. Measuring answer quality: how to tell whether an AI answer was correct — before the customer tells you
4. Handover design: when the system hands off to a human, with what context, and how the customer notices nothing awkward
5. Knowledge base: where the AI takes its answers from, and why stale content is the most common failure
6. Privacy in service cases: customer data in prompts — what Swiss nDSG allows and what it does not
7. Hands-on: we draft the three most frequent cases against your real enquiries

What you take away

• Sorted enquiry inventory with a clear automation recommendation per type
• A handover concept defining when humans take over
• Quality measurement setup you can run without us
• Three drafted answers for your most frequent cases

Method

Full-day workshop on-site or remote. Prep: you export an anonymised ticket sample (around 100 enquiries) that we work on during the workshop. We work on your cases, not on industry examples.

Prerequisites

Access to your ticket or helpdesk system for the export. At least one person who knows the daily enquiry volume from their own work.