350 CTOs Learn to Build AI-Native Engineering Organizations

Snowflake convened the inaugural CTO Circle during Snowflake Summit 2026 in San Francisco, gathering more than 350 CTOs from financial services, telecommunications, retail, and technology to discuss how engineering organizations must transform as AI reshapes software development. The event was framed as an answer to a critical gap: while thousands of organizations are attempting to build AI-native engineering organizations, few CTOs have opportunities to openly compare what they are learning, since these conversations typically remain inside individual companies and are shaped by competitive pressures and rapidly evolving technology.

Vivek Raghunathan, SVP of Engineering at Snowflake, presented the company's own transformation as the foundation for the discussion. Rather than focusing narrowly on coding assistants and model selection, Snowflake approached AI adoption by treating developer productivity as a product. This required a shift in management philosophy: instead of assuming leadership knew what engineers needed, Snowflake applied product management principles, interviewing developers to understand where work slowed down and mapping friction points across the software development lifecycle. The team then established baseline metrics and ran experiments to measure the impact of every change. The results were substantial: in 18 months, Snowflake increased its internal developer Net Promoter Score by more than 30 points, achieving a 4:1 ratio of satisfied to dissatisfied developers. More importantly, this improvement translated into an engineering organization capable of delivering software more efficiently and adapting more quickly as AI capabilities evolved.

Snowflake identified three stages of AI adoption within organizations. Adoption begins when developers learn to use AI tools in their daily work. Mastery develops as engineers discover repeatable workflows that consistently produce better outcomes. Optimization occurs when those workflows become organizational knowledge that every engineer can benefit from. A key example is the collection of engineering design patterns Snowflake developed internally: early AI 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. Every developer had access to the same AI tools, but engineers using proven workflows consistently outperformed those at earlier adoption levels—demonstrating that competitive advantage comes from institutionalizing successful ways of working rather than simply deploying another AI assistant.

Jon McNeill, author of The Algorithm, encouraged leaders to reverse the typical approach to AI adoption. Too often, companies begin with technology and search for places to apply it; instead, successful organizations identify the few business constraints that matter most and redesign their engineering systems around solving those problems. As organizations move AI into production, McNeill argued, 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 the operational dimension of AI at scale. As AI moves from early experimentation to production deployment, organizations must 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. Burton challenged the common 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 describing what data means, relationships captured through ontologies and knowledge graphs, and the business context connecting systems. AI agents need standardized interfaces—APIs, CLIs, and Model Context Protocol (MCP)—to reliably retrieve and act on information, while engineers need intuitive ways to drill into data and validate AI-generated insights.

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, allowing AI agents to reason over structured operational knowledge rather than searching across disconnected logs and dashboards.

Caitlin Colgrove, CTO at Hex, approached the question of velocity from an organizational perspective. She argued that organizations cannot afford to 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 described as "burning the boats." Hex initially created a dedicated AI product team, but while the approach delivered useful features, it also created an organizational bottleneck. The company 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 the engineers closest to the customer problem.

Chris Kozlowski, Managing Director at Barclays, brought an enterprise perspective by highlighting that speed only creates value when matched with governance and trust. In highly regulated environments, engineering teams cannot afford to 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 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. Both emphasized that maximizing velocity requires building engineering platforms that allow teams to experiment quickly without compromising reliability.

The article indicates that discussion also touched on the implications for engineering team structure and leadership models, noting that organizations optimized for previous generations of software development can become barriers to AI adoption, though the full extent of those insights was not detailed in the article.