CTOs Learn How to Build AI-Native Engineering Organizations

Snowflake hosted the inaugural CTO Circle during Snowflake Summit 2026 in San Francisco, convening more than 350 CTOs from financial services, telecommunications, retail and technology. The event was designed as a trusted forum for engineering leaders to compare notes on a shared challenge: how to build AI-native engineering organizations when there is no established playbook and technology is evolving rapidly.

Vivek Raghunathan, SVP of Engineering at Snowflake, centered the discussion on a fundamental shift in management philosophy. Most organizations introducing AI into engineering workflows focus on narrow productivity gains—coding assistants help developers write code faster, documentation becomes easier to produce. But Raghunathan argued that building an AI-native organization requires treating developer productivity as a product. Snowflake applied the same product management discipline it uses for customer-facing products: the team interviewed developers to identify friction points across the software development lifecycle, established baseline metrics, and ran experiments to measure the impact of each change. The results were concrete. 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. That improvement translated into an engineering organization capable of delivering software more efficiently and adapting more quickly as AI capabilities evolved.

Raghunathan also outlined a maturity model for AI adoption within engineering organizations. The journey progresses through three stages: adoption (when developers begin using AI tools in daily work), mastery (when engineers discover repeatable workflows that consistently produce better outcomes), and optimization (when those workflows become organizational knowledge available to every engineer). At Snowflake, early AI adopters experimented with prompting techniques, planning methods and debugging approaches; the organization then documented the patterns that consistently delivered better results and made them available across engineering. Every developer had access to the same AI tools, but those using proven workflows consistently outperformed engineers stuck at earlier adoption levels. The competitive advantage came from institutionalizing successful ways of working, not from deploying another AI assistant.

Jon McNeill, author of The Algorithm, encouraged leaders to reverse the typical approach to technology adoption. Too often, companies begin with the technology and search for places to apply it. McNeill argued that successful organizations identify the few business constraints that matter most and redesign engineering systems around solving those problems. As AI moves into production, success will be measured not by who generates the most code or consumes the most tokens, but by who builds the simplest, fastest and most effective engineering system.

On the operational side, Jeremy Burton, General Manager of the Observability Business Unit at Snowflake, emphasized that AI has entered a new phase. Early experimentation is giving way to production deployment, and organizations are increasingly expected to demonstrate measurable business value. But AI introduces entirely new operational challenges: AI agents generate more telemetry, interact with more systems and make decisions using information distributed across complex environments. Burton challenged a common assumption that better models simply need better data. In reality, AI is only as effective as the context it can access—and that context 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. AI agents need standardized interfaces such as APIs, CLIs and Model Context Protocol to reliably retrieve and act on information.

Aditya Gaur, Engineering Manager at Netflix, demonstrated this principle through Netflix'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 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. Rather than searching across disconnected logs and dashboards, AI agents could reason over structured operational knowledge and generate far more meaningful hypotheses during incident investigations.

Caitlin Colgrove, CTO at Hex, urged leaders to commit fully to AI-native transformation, describing the process as "burning the boats." Hex initially created a dedicated AI product team, which delivered useful features but 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 engineers closest to the customer problem. Chris Kozlowski, Managing Director at Barclays, highlighted that in highly regulated environments, 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 shortens the path from identifying a customer problem to validating a solution, but only if teams have the infrastructure, shared context and operational discipline to iterate confidently.