Garbage In, Trusted Out: Fixing the Knowledge Layer That Instructs CX AI

The rush to deploy AI in customer experience has surfaced an uncomfortable truth: most organizations are pointing powerful models at organizational knowledge that is fragmented, inconsistent, and ungoverned. But direct a brilliant model to broken knowledge and it only leads to disaster.

Across self-service, agent assist, and automation, teams are learning that AI does not fix bad knowledge, it amplifies it. Conflicting policies, stale procedures, duplicate content, and undocumented expertise turn into wrong answers to customers, broken automations, compliance exposure, and a steady erosion of trust in both the AI and the brand behind it.

This session examines how leading CX organizations are building a trusted instruction layer for enterprise AI: the foundation that lets these systems deliver instant, trusted answers across every channel.

Drawing on lessons from hyper-scale AI deployments, we will look at how to move beyond traditional knowledge management toward AI-ready infrastructure that reliably powers self-service, agent assist, and agentic automation, so customers get answers they can rely on and AI earns the trust it needs to scale.