Snowflake is the AI Data Cloud behind some of the world's largest enterprises — $4.68 billion in annual revenue, 29% year-over-year growth, and over 760 Forbes Global 2000 companies as customers. Baris Gultekin, VP of AI at Snowflake, leads the product efforts that sit at the center of how those enterprises actually operationalize AI. Before Snowflake, he co-founded Google Assistant and scaled it from 10 million to 500 million monthly users.
What you'll learn:
Why our data isn't clean enough is a delay tactic — and the scoped approach to move past it
What the semantic layer is and how it lets AI answer business questions accurately, not just fluently
Why running AI next to data (instead of sending data to models) makes governance dramatically easier
How Snowflake deployed AI internally: a CEO-level non-optional mandate combined with bottom-up access to their own Cortex coding agent
Why context — not just data — is what agents need to operate reliably at enterprise scale
Key takeaways:
Start with one scoped use case, build the semantic model around it, layer governance — don't wait for perfect data
Context is a shared reality for agents: unified data + business semantics + codified workflows
AI adoption compounds when leadership sets a hard mandate and simultaneously gives everyone a tool to experiment with
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