CTO Circle: 350 engineering leaders share playbook for AI-native organizations

Snowflake convened its inaugural CTO Circle event during Snowflake Summit 2026 in San Francisco, bringing together more than 350 CTOs from financial services, telecommunications, retail and technology to exchange practical lessons on building AI-native engineering organizations. The discussion moved well beyond the early enthusiasm for coding assistants and focused instead on how engineering organizations are being redesigned, what is working in production, and where leaders are investing to create long-term competitive advantage. Three themes emerged: using AI in production, balancing velocity and risk, and designing engineering teams for AI.

Vivek Raghunathan, SVP of Engineering at Snowflake, challenged leaders to think much more broadly about AI adoption than simply speeding up coding. He argued that building an AI-native engineering organization starts with a shift in management philosophy. Rather than viewing developer productivity as an engineering or culture problem, Snowflake began treating it as a product. "What if you treated your developers like customers?" became the foundation for Snowflake's engineering transformation. Instead of assuming leadership knew what engineers needed, the team applied the same product management principles they use to build customer-facing products. They interviewed developers to understand where work slowed down, mapped friction points across the software development lifecycle, established baseline metrics, and ran experiments to gauge the impact of every change. The approach combined clear executive sponsorship with bottom-up adoption. The results were measurable: 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. More importantly, that improvement translated into an engineering organization capable of delivering software more efficiently and adapting more quickly as AI capabilities continued to evolve. Vivek explained that adoption alone is not enough; the high leverage comes from the depth of usage and true mastery of tools at scale, a journey that evolves through three stages: adoption (when developers learn to use AI tools in daily work), mastery (as engineers discover repeatable workflows that consistently produce better outcomes), and optimization (when those workflows become organizational knowledge that every engineer can benefit from). One example is the collection of engineering design patterns developed internally at Snowflake—early adopters experimented with prompting techniques, planning methods and debugging approaches, and over time the organization documented the patterns that consistently delivered better results and made them available across engineering. This emphasis on workflows also changes how organizations think about software development itself: as AI reduces the effort required to transform ideas into working software, everyone becomes a builder, and code increasingly becomes the fastest way to test assumptions.

Jon McNeill, author of The Algorithm, expanded on this theme by encouraging leaders to reverse how they approach technology. Too often, companies begin with the technology and search for places to apply it. Successful organizations instead begin by identifying the few business constraints that matter most and redesign their engineering systems around solving those problems. As organizations move AI into production, success will be measured less by who generates the most code or consumes the most tokens and more by who builds the simplest, fastest and most effective engineering system.

Jeremy Burton, General Manager of the Observability Business Unit at Snowflake, argued that AI has entered a new phase where early experimentation is giving way to production deployment, and organizations are increasingly expected to demonstrate measurable business value rather than isolated technical successes. AI introduces entirely new operational challenges: AI agents generate more telemetry, interact with more systems and make decisions using information distributed across increasingly complex environments. Jeremy challenged the assumption that better models simply need better data. In reality, AI is only as effective as the context it can access. That context extends far beyond raw telemetry to include the semantics that describe what data means, relationships captured through ontologies and knowledge graphs, and business context that connects systems. AI agents need standardized interfaces such as APIs, CLIs and Model Context Protocol (MCP) to reliably retrieve and act on information. Organizations that store logs, metrics, traces, operational data and business context across disconnected systems make it difficult for AI to reason accurately about production environments. Aditya Gaur, Engineering Manager at Netflix, demonstrated this principle 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 than on AI itself. Years before introducing AI agents, Netflix invested in connecting fragmented telemetry across systems, modeling operational relationships through an ontology and knowledge graph, and creating a shared context layer. By the time AI entered the picture, the foundation already existed.

Caitlin Colgrove, CTO at Hex, urged leaders to rethink what it means to move fast in the AI era. Organizations cannot afford to be partially AI native; they must commit fully by restructuring how teams build products, make decisions and incorporate AI into everyday work—what she described as "burning the boats." Hex initially created a dedicated AI product team, which delivered useful features but also created an organizational bottleneck. Hex ultimately disbanded the centralized AI organization and distributed responsibility across every product team. Today, Hex delivers product rapidly and embeds AI capabilities throughout the product because ownership lives with engineers closest to the customer problem. Chris Kozlowski, Managing Director at Barclays, brought an enterprise perspective and highlighted that speed only creates value when matched with governance and trust. Corey Burke, SVP Engineering at Dialpad, and Arun Rajamanickam, VP of Engineering at project44, described how AI is changing the pace of software development: as AI agents become capable of implementing significant portions of a feature independently, engineers spend less time writing code and more time defining intent, orchestrating multiple agents and validating outcomes. They emphasized that maximizing velocity requires building engineering platforms that allow teams to experiment quickly without compromising reliability.