Snowflake VP of AI on Why Enterprises Hide Behind Governance to Avoid Real AI Transformation | Baris Gultekin | E296

In this episode of The Product Podcast, host Carlos interviews Baris Gultekin, Vice President of AI at Snowflake, about the critical intersection of data strategy and AI transformation in enterprises. Gultekin emphasizes that true AI adoption requires breaking down data silos and building a governed, unified data environment before deploying AI—what Snowflake calls the 'AI data cloud.' He explains how Snowflake enables businesses to run AI next to data, rather than sending data to models, which enhances security, accuracy, and governance. A key enabler is the semantic layer—defining business meaning on top of raw data so AI can answer questions correctly. Gultekin shares Snowflake’s internal transformation story: a top-down mandate from the CEO combined with bottom-up access to their own CortexCode agent, which empowered employees across functions to build AI-driven workflows and products. This dual approach led to measurable productivity gains and revenue growth, proving that AI can be both an operational and strategic driver. The episode also addresses common enterprise excuses—poor data quality, access barriers, and governance fears—and offers a practical path: start small with a high-ROI use case, build a semantic model, layer governance, and scale with support from Snowflake’s forward-deployed engineering teams. Key takeaways include: 1) No AI strategy exists without a data strategy—clean, governed, and unified data is foundational; 2) AI democratizes data access by enabling business users to query data in natural language, reducing dependency on analysts; 3) The semantic layer is essential for accurate AI responses, translating business meaning into machine-readable context; 4) Governance should not be a barrier but a framework—start small, scope tightly, and build guardrails incrementally; 5) Internal AI adoption works best when combined with top-down leadership and bottom-up empowerment; 6) AI can directly drive revenue by increasing data utilization and enabling new products; 7) Snowflake’s model of running AI next to data, within a secure boundary, makes governance easier and more effective; 8) Success requires not just tools, but people, processes, and change management—especially for scaling AI across functions.