How to Prevent AI Hallucinations in Customer Service

This editorial guide examines the growing risk of AI hallucinations in customer service applications, outlining common causes such as low-quality training data, generative limitations, and weak retrieval mechanisms. It cites a 2025 McKinsey report finding that 50% of U.S. employees consider inaccuracy a top risk of GenAI, and references real-world examples like Cursor's chatbot inventing a nonexistent subscription policy. The article highlights mitigation strategies used by companies including CVS Health, DoorDash, and NICE Ltd., such as human-in-the-loop reviews, Retrieval-Augmented Generation (RAG), and AI systems that flag uncertain responses. It concludes with actionable recommendations for CX leaders: prioritizing real-time data quality, defining clear AI guardrails, conducting thorough testing, and maintaining transparent escalation paths to human agents.