Effective AI adoption requires a three-part structure. 'Leadership' sets the vision and incentives. The 'Crowd' (all employees) experiments with AI tools in their own workflows. The 'Lab' (a dedicated internal team, not just IT) refines and scales the best ideas that emerge from the crowd.

The key to driving AI adoption isn't always a dedicated technical team. It's about identifying internal champions in any department—even Legal—who have successfully automated their own processes. Embedding these individuals in other teams can effectively spread practical knowledge and inspire wider adoption.

To overcome inertia and build confidence, leaders should give every person on their team a specific task to complete using an AI tool. This hands-on, mandated experimentation is more effective than broad directives, as it accelerates learning, builds momentum, and demystifies the technology across the organization.

To encourage widespread use of new AI tools, Qualcomm identifies key people to become 'super users'. As these evangelists demonstrate the tool's value and efficiency, they create a Fear Of Missing Out (FOMO) effect, generating organic demand and pulling the rest of the organization toward adoption rather than pushing it on them.

Contrary to traditional efficiency models, leaders should allow teams to build similar AI tools or agents. In this early stage, widespread hands-on experimentation and learning are more valuable than preventing redundant work. The goal is to get everyone testing, not to achieve premature standardization.

Instead of immediately seeking outside consultants, leaders should identify and empower employees who are already using AI effectively. This validates their initiative, leverages existing knowledge, and provides them with a clear path for professional development and company-wide impact.

For large, traditional companies, the most critical first step in AI adoption isn't building tools, but fostering deep understanding. Provide teams sandboxed access to AI models and company data, allowing them to build intuition about capabilities before crafting strategy.

To avoid issues like Amazon's AI-related outages, companies should highlight and incentivize early, enthusiastic adopters within the organization. Holding up their successful use cases fosters organic adoption and establishes best practices, which is more effective than forced, top-down mandates.

While many leaders feel behind on AI, there's a strategic benefit to having waited. Cautious brands can now learn from the costly mistakes and "battle scars" of early movers, allowing them to implement AI more effectively and with less business risk.