Customer service is one of the most natural places to start with generative AI. Agents handle large volumes of text, answer similar questions again and again, and rely on knowledge spread across many sources. Applied well, generative AI can make their work faster and more consistent.
Quick wins to try first
Case summaries. Summarising long email threads or chat histories when a case is escalated or reopened saves time and spares customers from repeating themselves.
Reply suggestions. Draft answers based on the case and knowledge articles, which agents review and adjust before sending.
Knowledge creation. Turning resolved cases into draft knowledge articles, reviewed by experts, to grow the knowledge base.
Classification and routing. Detecting the topic, language and urgency of incoming requests to send them to the right queue.
Pitfalls to avoid
Answers without grounding. A model that isn't connected to your own, up-to-date knowledge will invent plausible but wrong answers. Ground it on validated content and show agents its sources.
Removing the human too early. Start with agent assistance, where a person reviews every output, before letting AI answer customers directly.
Ignoring privacy. Customer conversations contain personal data. Check what is sent to which model, where it is processed and how long it is kept.
Measure what matters
Track handling time, first-contact resolution, customer satisfaction, and how often agents accept, edit or reject suggestions. These figures show where AI truly helps and where it needs adjusting.
