Everyone loves an AI-related PR disaster, as long as it isn't happening to them. Customer-facing AI carries real risk and even more perceived risk. Much of that risk starts with something ordinary. Two systems hold different answers, or one of them was never updated when the policy changed. A person spots the mismatch and knows which system to trust. An agent doesn't, and answers confidently anyway. When it gets that wrong, the damage is reputational, legal and financial, so plenty of leaders are still sceptical.

This session treats a wrong answer as a design problem, not an inevitability, and makes the case for a recovery layer built in at the start of a

deployment. A recovery layer catches failed interactions before the customer complains, shows which data or rule produced the answer, escalates with the context attached, puts the customer's outcome right quickly, and feeds what it learns back into the guardrails

so the same failure is less likely to happen twice.

Hemang Upadhyay has spent 16 years building enterprise AI, digital commerce and product data governance systems, and is currently Senior Manager for AI Product Management at LG Electronics as well as a member of CX Network's Advisory Board. Drawing from his experience across his career, he'll show how to design a recovery layer, and why CX teams can't do it alone.