To eliminate data silos, Snowflake consolidated all departmental data analysts into one central intelligence team under the Chief Data Officer. This team serves the entire go-to-market organization, while departmental RevOps teams act as business stakeholders, defining problems for the central team to solve.
Snowflake established a cross-functional AI council with volunteers who dedicate 10-20% of their time to experimentation. This avoids chaotic, duplicated efforts from a company-wide mandate. The council then shares learnings and rolls out proven use cases to the broader team quarterly, ensuring structured adoption.
Snowflake mandated technical certification for every member of its solutions engineering team, from ICs to senior leaders. This created a baseline of deep technical expertise, which was critical for quickly adopting AI tools to create complex, customized demos and maintain credibility with customers in the AI landscape.
Snowflake's hiring philosophy for the AI era prioritizes adaptability over specific, perishable skills. Recognizing that today's tools will be obsolete tomorrow, they screen for lifelong learners by asking questions like, 'How do you advance your craft?' rather than focusing on current tool proficiency.
Snowflake moved beyond basic AI tools by building proprietary agentic models. One agent analyzes campaign data in real-time to optimize ad spend and ROI. A second 'competing agent' provides on-demand talking points for sales and marketing to use against specific competitors, solving a massive enablement challenge.
While empowering employees to experiment with AI is crucial, Snowflake found it's ineffective without an executive mandate. If the CEO doesn't frame AI as a top strategic initiative, employees will treat it as optional, hindering real adoption. Success requires combining top-down leadership with bottom-up innovation.
Snowflake's former CRO offers a pragmatic view of AI, calling it a 'task automator.' He stresses that for enterprise adoption, AI tools can't just be 'cool.' They must deliver a clear return on investment by either generating revenue or creating significant cost savings, like the 418 hours per week saved by their support team.