AI hallucination - where generative AI produces confident but factually incorrect information - poses significant risk in customer-facing contexts because customers may act on inaccurate guidance about policies, pricing, or procedures, leading to disputes, financial harm, or compliance violations. Hallucinated information that contradicts actual company policy can create legal exposure if customers reasonably relied on what they were told by an AI system. Repeated incidents of hallucination, even if individually minor, erode customer trust in AI-powered channels broadly, undermining adoption of otherwise valuable automation. Detecting hallucination after the fact is often difficult since the AI's confident tone does not reliably signal lower accuracy. Mitigating this risk requires grounding AI responses in verified knowledge sources through retrieval-augmented generation, implementing confidence thresholds that flag uncertain responses, and maintaining ongoing auditing of AI output against actual outcomes.
Risk & Compliance