Vernon Lees

Every automated answer carries your brand's name, and customers and regulators do not distinguish AI responses from human ones. Most organisations have pointed capable AI at knowledge that is scattered, stale and unowned, then been surprised by the output. This session looks at how leading organisations build a trusted knowledge foundation for customer-facing AI: which knowledge carries the most risk and value, who governs it, and how to keep proving answers are correct at scale.

Learning Objectives

• Identify which areas of operational knowledge carry the most risk and the most value for AI to use, rather than consolidating every source into one repository

• Define governance when AI is drawing on knowledge thousands of times a day, including ownership, approval and audit across markets and languages

• A method for evaluating answer quality continuously rather than by periodic sample

Key Takeaways

• Prioritise knowledge by risk and value, so the first phase of an AI programme targets the small proportion of content that answers the majority of customer questions

• A governance model with named owners, approval workflow and audit trail for AI-facing knowledge, so answers stay accurate and defensible in every market

• A test to evidence answer accuracy on an ongoing basis, and what to change if automation is still stalling at the handover