CTO Circle brings 350 engineering leaders together to share lessons on building AI-native organizations

The inaugural CTO Circle, held during Snowflake Summit 2026 in San Francisco, brought together more than 350 CTOs from financial services, telecommunications, retail and technology to address a critical gap in engineering leadership. Few opportunities exist for CTOs to openly compare what they are learning as they transform their organizations for AI; these conversations typically remain inside individual companies and are shaped by competitive pressures and rapidly evolving technology. Snowflake, as a platform where thousands of organizations build data and AI strategies, positioned itself to convene this conversation and give engineering leaders a trusted forum to challenge assumptions and learn from peers navigating the same transformation.

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 should invest for long-term competitive advantage. Three themes emerged as central to building an AI-native engineering organization. The first, AI in production, addressed what breaks and what scales. Vivek Raghunathan, SVP of Engineering at Snowflake, challenged leaders to think broadly about AI adoption by shifting management philosophy. Rather than viewing developer productivity as an engineering or culture problem, Snowflake treated it as a product. "What if you treated your developers like customers?" Raghunathan explained that the team applied the same product management principles used for customer-facing products—interviewing developers to understand friction points, mapping points of slowdown across the software development lifecycle, and establishing baseline metrics before running experiments. 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. Raghunathan emphasized that adoption alone is insufficient; high leverage comes from depth of usage and true mastery at scale. The journey progresses through three stages: adoption (when developers learn to use AI tools daily), mastery (as engineers discover repeatable workflows that consistently produce better outcomes), and optimization (when those workflows become organizational knowledge). Snowflake documented engineering design patterns developed by early AI adopters—including prompting techniques, planning methods, and debugging approaches—and made them available across engineering, so every developer accessing the same tools could benefit from proven workflows. As AI reduces effort to transform ideas into working software, everyone becomes a builder: product managers can prototype, designers can validate concepts in code, and domain experts can add guardrails.

The second theme addressed balancing velocity and risk. Jeremy Burton, General Manager of the Observability Business Unit at Snowflake, argued that AI has entered a production phase where organizations must demonstrate measurable business value. 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 need better data; in reality, AI is only as effective as the context it can access—including semantics, ontologies, knowledge graphs, and business context. Aditya Gaur, Engineering Manager at Netflix, demonstrated this through the company's 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, modeling operational relationships through an ontology and knowledge graph, and creating a shared context layer. By the time AI entered, the foundation existed. Caitlin Colgrove, CTO at Hex, urged leaders to "burn the boats" by committing fully to AI—restructuring how teams build, decide, and work. Hex initially created a centralized AI product team, but that created a bottleneck; the company disbanded it and distributed AI responsibility across product teams. Chris Kozlowski, Managing Director at Barclays, highlighted that speed only creates value when matched with governance and trust, especially in regulated environments. Corey Burke, SVP Engineering at Dialpad, and Arun Rajamanickam, VP of Engineering at project44, described how AI changes the pace of software development: engineers spend less time writing code and more time defining intent, orchestrating agents, and validating outcomes. Maximizing velocity requires platforms that allow teams to experiment quickly without compromising reliability.

The third theme, designing engineering teams for AI, addressed organizational structure. Qi Jin, EVP at Cerebras Systems, suggested that organizations optimized for previous generations of software development are often the biggest barrier to AI adoption, as established processes and boundaries were designed to scale proven ways of working, not to continuously adapt.