CTO Circle: Engineering Leaders Share AI-Native Organization Playbook

Snowflake held the inaugural CTO Circle during Snowflake Summit 2026 in San Francisco, bringing together more than 350 CTOs from financial services, telecommunications, retail and technology industries to share practical lessons on building AI-native engineering organizations. As the platform where thousands of organizations build their data and AI strategies, Snowflake created the event as a trusted forum for engineering leaders to learn from peers navigating the same transformation and to challenge assumptions shaped by competitive pressures and rapidly evolving technology.

The discussion moved well beyond coding assistants and model selection to focus on how engineering organizations are being redesigned, what is working in production, and where leaders are investing to create long-term competitive advantage. Vivek Raghunathan, SVP of Engineering at Snowflake, challenged leaders to think broadly about AI adoption beyond simple productivity gains. He argued that building an AI-native engineering organization requires a shift in management philosophy: instead of viewing developer productivity as an engineering or culture problem, Snowflake treated it as a product. The foundational question became "What if you treated your developers like customers?" Rather than 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, established baseline metrics, and ran experiments to gauge the impact of every change. 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, which translated into an engineering organization capable of delivering software more efficiently and adapting more quickly.

Vivek explained that adoption alone is insufficient; the real leverage comes from depth of usage and true mastery at scale. The journey 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 patterns that consistently delivered better results and made them available across engineering. Every developer had access to the same AI tools, but engineers using proven workflows consistently outperformed those at early levels of tooling adoption. The competitive advantage came from institutionalizing successful ways of working rather than simply deploying another AI assistant.

Jon McNeill, author of The Algorithm, encouraged leaders to reconsider assumptions that have shaped engineering organizations for decades. Successful organizations, he argued, begin by identifying the few business constraints that matter most and redesign their engineering systems around solving those problems, rather than beginning with technology and searching for places to apply it. 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, addressed a second challenge: as AI becomes embedded throughout the software development lifecycle, every improvement in development velocity increases the need for reliable systems and operational discipline. Jeremy 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. He 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 that describe what data means, relationships captured through ontologies and knowledge graphs, and business context that connects systems. Aditya Gaur, Engineering Manager at Netflix, demonstrated this in practice through automated root cause analysis. 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, allowing AI agents to reason over structured operational knowledge and generate far more meaningful hypotheses during incident investigations.

Caitlin Colgrove, CTO at Hex, approached velocity from a different perspective, urging leaders to rethink what moving fast means in the AI era. Organizations cannot afford to be partially AI native and 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, but this created an organizational bottleneck. The company ultimately disbanded the centralized AI organization and distributed responsibility across every product team, allowing Hex to deliver product rapidly and embed AI capabilities throughout because ownership lives with engineers closest to the customer problem. Chris Kozlowski, Managing Director at Barclays, highlighted that speed only creates value when matched with governance and trust, particularly in highly regulated environments where engineering teams cannot choose between innovation and control. Corey Burke, SVP Engineering at Dialpad, and Arun Rajamanickam, VP of Engineering at project44, described how AI is changing the pace of development: as AI agents become capable of implementing significant portions of features 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.