350 CTOs Share Lessons from Building AI-Native Engineering Organizations

More than 350 CTOs gathered at Snowflake Summit 2026 in San Francisco for the inaugural CTO Circle, a forum created to let engineering leaders openly compare lessons from building AI-native organizations. The event moved beyond the typical focus on coding assistants and model selection to examine how engineering organizations are being redesigned, what is working in production, and where leaders are investing for long-term competitive advantage.

Vivek Raghunathan, SVP of Engineering at Snowflake, challenged attendees to think far more broadly about AI adoption. He argued that building an AI-native engineering organization begins with a shift in management philosophy: rather than viewing developer productivity as an engineering or culture problem, Snowflake began treating it as a product. The guiding question became "What if you treated your developers like customers?" Instead of assuming leadership knew what engineers needed, Snowflake applied the same product management principles used for customer-facing products. The team interviewed developers to understand where work slowed down, mapped friction points across the software development lifecycle, and established baseline metrics to measure the impact of every change. In 18 months, Snowflake increased its internal developer Net Promoter Score by more than 30 points, resulting in a 4:1 ratio of satisfied to dissatisfied developers. Vivek emphasized that adoption alone is insufficient; the competitive advantage comes from mastery and optimization. He described three stages: adoption (developers learn to use AI tools daily), mastery (engineers discover repeatable workflows that consistently produce better outcomes), and optimization (those workflows become organizational knowledge every engineer can benefit from). One example is Snowflake's collection of internal engineering design patterns, developed as early AI adopters experimented with prompting techniques, planning methods, and debugging approaches; the organization documented patterns that consistently delivered better results and made them available across engineering.

Jeremy Burton, General Manager of the Observability Business Unit at Snowflake, addressed the challenge of maximizing velocity while containing risk. He argued that early AI experimentation is giving way to production deployment, and organizations are expected to demonstrate measurable business value rather than isolated technical successes. AI introduces new operational challenges: AI agents generate more telemetry, interact with more systems, and make decisions using information distributed across increasingly complex environments. Jeremy challenged a common assumption that better models simply need better data. In reality, AI is only as effective as the context it can access—which extends far beyond raw telemetry to include the semantics describing what data means, relationships captured through ontologies and knowledge graphs, and business context connecting systems. Aditya Gaur, Engineering Manager at Netflix, demonstrated this in practice through the company's work on automated root cause analysis. Although often described as an AI initiative, its success depended far more on data architecture. Years before introducing AI agents, Netflix invested in connecting fragmented telemetry, modeling operational relationships through ontologies and knowledge graphs, and creating a shared context layer. When AI entered the picture, the foundation already existed.

Caitlin Colgrove, CTO at Hex, argued that organizations cannot be partially AI-native and must eventually commit fully by restructuring how teams build products, make decisions, and incorporate AI into everyday work—what she called "burning the boats." Hex initially created a dedicated AI product team, which delivered useful features but created an organizational bottleneck. The company ultimately disbanded the centralized AI organization and distributed responsibility across every product team, enabling rapid iteration and embedding AI throughout the product. Chris Kozlowski, Managing Director at Barclays, brought an enterprise perspective, emphasizing that speed only creates value when matched with governance and trust. In highly regulated environments, engineering teams cannot choose between innovation and control.