Snowflake VP of AI on Why Enterprises Hide Behind Governance to Avoid AI | Baris Gultekin | E296
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
00:00 🎙️ Introduction & What Is Snowflake?
01:53 🚧 Why Dirty Data Is an Excuse to Delay AI
06:11 🗄️ Structured vs. Unstructured Data at Enterprise Scale
08:07 🔓 Democratizing Data Access With AI
10:00 🧠 The Context Layer: Beyond Raw Data
14:10 🛡️ What Governance Actually Means
16:29 🏁 How to Start Small and Find Quick Wins
19:49 🏔️ How Snowflake Scaled AI Internally
23:01 ⚔️ Snowflake's Position in the New AI Competitive Landscape
24:00 💰 Using AI as a Revenue Driver, Not Just a Productivity Tool
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