Brex formed a small, centralized AI team by asking, "What would a company founded today to disrupt Brex look like?" This team operates with the speed and focus of a startup, separate from the main engineering org to avoid corporate inertia.
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The CEO's strategy to combat the AI threat was directly inspired by Clayton Christensen's "Innovator's Dilemma." He created an autonomous team with different incentives, shielded from the core business, to foster radical innovation—a practical application of the well-known business theory.
To accelerate AI adoption, Block intentionally dismantled its siloed General Manager (GM) structure, which had given autonomy to units like Cash App. They centralized into a functional organization to drive engineering excellence, unify policies, and create a strong foundation for a company-wide AI transformation.
The true challenge of AI for many businesses isn't mastering the technology. It's shifting the entire organization from a predictable "delivery" mindset to an "innovation" one that is capable of managing rapid experimentation and uncertainty—a muscle many established companies haven't yet built.
Competing in the AI era requires a fundamental cultural shift towards experimentation and scientific rigor. According to Intercom's CEO, older companies can't just decide to build an AI feature; they need a complete operational reset to match the speed and learning cycles of AI-native disruptors.
While traditionally creating cultural friction, separate innovation teams are now more viable thanks to AI. The ability to go from idea to prototype extremely fast and leanly allows a small team to explore the "next frontier" without derailing the core product org, provided clear handoff rules exist.
Snowflake established a cross-functional AI council with volunteers who dedicate 10-20% of their time to experimentation. This avoids chaotic, duplicated efforts from a company-wide mandate. The council then shares learnings and rolls out proven use cases to the broader team quarterly, ensuring structured adoption.
To innovate quickly without being bogged down by technical debt, portfolio companies should ring-fence new AI development. By outsourcing it and treating it as a separate "skunk works" project, the core tech team can focus on existing systems while the new initiative succeeds or fails on its own merits.
Small firms can outmaneuver large corporations in the AI era by embracing rapid, low-cost experimentation. While enterprises spend millions on specialized PhDs for single use cases, agile companies constantly test new models, learn from failures, and deploy what works to dominate their market.
Brex structures its AI teams into small pods, combining young, AI-native talent who think differently with experienced staff engineers who understand the existing codebase, product, and customer needs. This blends novel approaches with practical execution.
Brex organizes its AI efforts into three pillars: buying tools for internal efficiency (Corporate), building/buying to reduce operational costs (Operational), and creating AI products that become part of their customers' own AI strategies (Product).