Snowflake CEO: AI success hinges on data management

Snowflake‘s pragmatic approach to enterprise AI shifts focus from flashy demonstrations to methodical data-first implementation. CEO Sridhar Ramaswamy advocates for incremental AI projects that deliver consistent value rather than massive, speculative investments. His perspective challenges the industry’s fixation on cutting-edge models by emphasizing that successful AI deployment depends fundamentally on properly organized, accessible data—a reality that many enterprises overlook in their rush to adopt artificial intelligence.

The big picture: Snowflake CEO Sridhar Ramaswamy recommends a pragmatic, value-driven approach to AI implementation rather than pursuing ambitious but potentially unfocused “big bang” projects.

• “AI should not be a Big Bang. It should be a series of little projects that show value every step of the way,” Ramaswamy explained during an interview.
• His roadmap for enterprise AI success prioritizes starting with data organization rather than investing in flashy demos or expensive models.

The agentic AI reality: Behind the varying definitions of “agentic AI” lies practical technology that must move beyond concepts into tangible business applications.

• Ramaswamy identifies a growing market demand for AI that can act autonomously—from conducting pre-meeting research to updating internal systems.
• He outlines a three-step progression: making information accessible, allowing models to determine what data to retrieve, and finally orchestrating these components into automated workflows.

Key challenges: Many enterprises are making a fundamental mistake with their AI investments by prioritizing models over mission.

• “A lot of folks went out and bought GPU capacity or model licenses without thinking about where that’s going to create value,” Ramaswamy noted.
• External AI tools like ChatGPT face significant limitations when they cannot access internal systems and unified enterprise data.

Industry implications: The traditional SaaS model is evolving from supporting human efficiency to actively handling work through AI capabilities.

• Future software will increasingly incorporate natural language interfaces, democratizing data querying beyond specialized analysts.
• As Snowflake positions itself as an end-to-end data and AI platform, Ramaswamy asserts: “In a world where AI is thriving, Snowflake will thrive, because we are the layer underneath that powers this data access.”

The talent perspective: Ramaswamy prioritizes adaptability over technical expertise when identifying valuable skills in the AI landscape.

• The ability to experiment, maintain curiosity, and critically evaluate AI outputs outweighs purely technical capabilities.
• This perspective aligns with the broader shift toward data-centric AI implementation rather than model-centric approaches.