CTO Circle: Lessons on Building AI-Native Engineering Teams

CTO Circle: Lessons on Building AI-Native Engineering Teams

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Blog / At Snowflake / Inside the CTO Circle: Lessons from Engineering Leaders Building AI-Native Organizations

AUG 05, 2026 / 12 min read At Snowflake Copy post link Open in Claude Open in ChatGPT

Inside the CTO Circle: Lessons from Engineering Leaders Building AI-Native Organizations

Shruti Bhat +1

Every engineering leader is trying to answer the same questions as AI is changing the rules: How should engineering teams be organized? Where should organizations invest as foundation models continue to improve? How do you increase engineering velocity without introducing unacceptable operational risk? What is the difference between AI-augmented and AI-native organizations?

There is no established playbook for building an AI-native engineering organization and few opportunities for engineering leaders to openly compare what they're learning. These conversations often remain inside individual companies and are shaped by competitive pressures and rapidly evolving technology.

As the platform where thousands of organizations build their data and AI strategies, Snowflake is uniquely positioned to convene these conversations. CTO Circle was created to give engineering leaders a trusted forum to learn from peers navigating the same transformation and to challenge assumptions. Held during Snowflake Summit 2026 in San Francisco, the inaugural event brought together more than 350 CTOs from various industries including financial services, telecommunications, retail and technology to exchange practical lessons.

The discussion moved well beyond coding assistants and model selection. Instead, it focused on how engineering organizations are being redesigned, what is working in production, and where leaders are investing to create long-term competitive advantage. The discussion converged around the following three themes that define what it means to build an AI-native engineering organization: using AI in production, balancing velocity and risk, and designing engineering teams for AI.

AI in production: What breaks, what scales

Many organizations begin their AI journey by introducing coding assistants into existing engineering workflows. Developers write code a little faster and documentation becomes easier to produce. These improvements are meaningful, but they leave the underlying engineering system largely unchanged.

Vivek Raghunathan , SVP of Engineering at Snowflake, challenged leaders to think much more broadly about AI adoption. Vivek argued that building an AI-native engineering organization starts with a shift in management philosophy. Rather than viewing developer productivity as an engineering or culture problem, Snowflake began treating it as a product.

"What if you treated your developers like customers?" That question became the foundation for how Snowflake approached its own engineering transformation. Instead of assuming that leadership knew what engineers needed, the team applied the same product management principles they use to build customer-facing products.

They interviewed developers to understand where work slowed down and mapped points of friction across the software development lifecycle. The team then established baseline metrics and ran experiments to gauge the impact of every change. The approach combined clear executive sponsorship with bottom-up adoption. This ensured that improvements reflected how engineers actually worked rather than how leaders expected them to work.

Figure 1: An AI-native approach.

The results were measurable. In 18 months, Snowflake increased its internal developer Net Promoter Score by more than 30 points. This resulted in a 4:1 ratio of satisfied to dissatisfied developers within that time frame. More importantly, that improvement translated into an engineering organization capable of delivering software more efficiently and adapting more quickly as AI capabilities continued to evolve.

Figure 2: Customer satisfaction with overall developer velocity at Snowflake.

Vivek also explained that adoption alone is not enough to drive meaningful productivity gains. The high leverage comes from the depth of usage and true mastery of the tools at scale. At Snowflake, that journey has evolved through three stages:

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.

Figure 3: AI-augmented versus AI-native organizations.

One example is the collection of engineering design patterns developed internally at Snowflake. Early AI adopters experimented with prompting techniques, planning methods and debugging approaches. 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 the engineers using proven workflows consistently outperformed those stuck at the early levels of tooling adoption. The competitive advantage came from institutionalizing successful ways of working rather than simply deploying another AI assistant.

This emphasis on workflows also changes how organizations think about software development itself. As AI reduces the effort required to transform ideas into working software, everyone becomes a builder. Product managers can prototype new experiences, designers can validate concepts directly in code, and domain experts can add guardrails for other personas. Instead of debating ideas through presentations or documents, teams can validate them by building working software. Code increasingly becomes the fastest way to test assumptions.

Jon McNeill , author of The Algorithm , expanded on this theme by encouraging leaders to reconsider assumptions that have shaped engineering organizations for decades. Too often, companies begin with the technology and search for places to apply it. Jon argued that successful organizations reverse that equation. They begin by identifying the few business constraints that matter most and redesign their engineering systems around solving those problems. 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...