CTO Circle: How to Build AI-Native Engineering Organizations

Snowflake Summit 2026 in San Francisco hosted the inaugural CTO Circle, an invitation-only forum designed to give engineering leaders a trusted space to share lessons from building AI-native organizations. More than 350 CTOs from financial services, telecommunications, retail and technology attended, reflecting the breadth of industries grappling with the same core question: how to redesign engineering organizations for an AI-driven era.

Vivek Raghunathan, SVP of Engineering at Snowflake, opened the discussion by challenging a widespread assumption: that AI adoption in engineering is primarily a developer productivity or culture problem. Instead, he argued, it should be treated as a product. This shift in mindset led Snowflake to apply the same customer-research principles used for external products to internal engineering. Teams 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 approach combined clear executive sponsorship with bottom-up adoption, ensuring that improvements reflected how engineers actually worked rather than how leaders expected them to work. The results were tangible: 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 translated into an engineering organization capable of delivering software more efficiently and adapting quickly as AI capabilities evolved.

Raghunathan also outlined a three-stage progression for moving beyond surface-level AI adoption: adoption (when developers learn to use AI tools in daily work), mastery (when engineers discover repeatable workflows that consistently produce better outcomes), and optimization (when those workflows become organizational knowledge that every engineer can benefit from). Snowflake documented engineering design patterns developed by early AI adopters—experimenting with prompting techniques, planning methods and debugging approaches—and made them available across the organization. This institutionalization of successful workflows, rather than simply deploying another AI assistant, created the competitive advantage. The implication: every developer had access to the same AI tools, but engineers using proven workflows consistently outperformed those stuck at earlier adoption levels.

Jon McNeill, author of The Algorithm, urged leaders to reverse a common approach: instead of starting with technology and searching for applications, successful organizations identify the few business constraints that matter most and redesign engineering systems around solving those problems. He argued that as AI reduces the effort to turn ideas into working software, 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.

Jeremy Burton, General Manager of the Observability Business Unit at Snowflake, addressed the second major challenge: maximizing velocity while containing operational risk. AI has entered a new phase where early experimentation is giving way to production deployment, and organizations must demonstrate measurable business value. However, AI introduces new operational challenges: AI agents generate more telemetry, interact with more systems, and make decisions using information distributed across complex environments. Burton challenged the assumption that better models simply need better data. In reality, AI is only as effective as the context it can access—which includes not just raw telemetry but also semantics describing what data means, relationships captured through ontologies and knowledge graphs, and business context connecting systems. Standardized interfaces such as APIs, CLIs and Model Context Protocol (MCP) enable AI agents to reliably retrieve and act on information.

Aditya Gaur, Engineering Manager at Netflix, demonstrated this principle through Netflix's automated root cause analysis project. Although typically 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 arrived, 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 an organizational perspective, urging leaders to commit fully to becoming AI-native 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, but this approach created an organizational bottleneck. The company disbanded the centralized AI organization and distributed responsibility across every product team. Today, Hex delivers products 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, emphasizing that speed only creates value when matched with governance and trust, particularly in highly regulated environments. 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 features independently, engineers spend less time writing code and more time defining intent, orchestrating multiple agents and validating outcomes. They stressed that maximizing velocity requires engineering platforms allowing teams to experiment quickly without compromising reliability.