In this session, Heather Roth and Logan Patterson explore how AI is transforming both sides of the customer experience equation; shifting customer interactions toward conversational, AI-mediated channels while fundamentally evolving internal enterprise workflows. They demonstrate how uniting Tealium (the real-time customer signal and activation layer) with Snowflake (the governed enterprise context and reasoning layer) creates a shared, trusted context layer. By bridging system-of-action interactions with warehouse-level enterprise intelligence, organizations can safely scale agentic workflows, govern autonomous decisions, and turn AI experiments into measurable, long-term business growth.
Key Learning Points
- The Dual Impact of Agentic AI: AI is simultaneously reshaping customer-facing touchpoints (making interactions more conversational and contextual) and internal enterprise operations (enabling agents to interpret signals, automate tasks, and coordinate workflows).
- The Trusted Context Layer: Bridging real-time engagement and enterprise intelligence requires a shared context foundation—combining live user signals, identity, and consent with deep historical, operational, and financial data.
- Complementary Roles of Tealium & Snowflake:
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- Tealium: Captures, governs, enriches, and activates real-time customer and agent interaction signals.
- Snowflake: Establishes semantic views, deep analytics, and governed control planes where AI agents can safely reason and execute business decisions.
- Pragmatic Action Over “Real-Time Everything”: Not all customer data requires sub-second processing; success comes from intentionally identifying which specific signals truly need real-time speed vs. deeper warehouse context.
- Governance, Policy, & Control: Transitioning agents from simple Q&A tools to decision-making entities requires strict permission boundaries, consent orchestration, and continuous measurement connected back to business outcomes.
- Actionable Implementation Playbook: Focus on specific workflows or experience friction points, govern reusable data assets, align AI directly into existing customer data foundations, and continuously measure lifecycle impact.
