According to Adobe's CMO, the number one question from customers about new AI tools is not about features, but about how to get their teams to adopt them. The solution lies in identifying internal champions who are excited about the change and can act as catalysts to bring others along.

Ramp's VP of Growth warns that new technology like AI follows a "J-curve" of productivity. Teams may initially become less efficient as they spend time learning and reorganizing workflows away from old tasks. This dip is a necessary investment before productivity explodes, a crucial expectation for leaders to manage.

A private equity firm's AI champion succeeded not due to his technical skills, but his deep understanding of people dynamics and team bandwidth. He recognized that implementing AI is fundamentally a change management problem focused on user capacity and psychology.

A clear framework for managing AI-driven change is essential. It involves four key steps: 1) Secure absolute buy-in from leadership. 2) Involve frontline workers in the conversation. 3) Have leadership consistently and transparently communicate positive intent. 4) Create a safe environment for experimentation and learning.

Bill Glenn suggests a phased AI rollout for teams. Phase 1 focuses on efficiency and automating repeatable tasks to gain productivity. Phase 2 moves to strategic work, using AI for insights and decision-making assistance. This provides a clear, manageable roadmap for adoption.

Employees progress through three stages of AI adoption: 1) Fearing AI will take their job, 2) Fearing a person using AI will take their job, and 3) Realizing they cannot perform their job without AI. Leaders must actively guide their organization to this third level of indispensability.

The path to enterprise AI adoption follows a typical change curve. To bypass initial fear and rejection, organizations should first apply AI to transform familiar, high-friction workflows. This strategy builds momentum and demonstrates value before tackling entirely new, innovative business models.

Leaders often misjudge their teams' enthusiasm for AI. The reality is that skepticism and resistance are more common than excitement. This requires framing AI adoption as a human-centric change management challenge, focusing on winning over doubters rather than simply deploying new technology.

To transform a product organization, first provide universal access to AI tools. Second, support teams with training and 'builder days' led by internal champions. Finally, embed AI proficiency into career ladders to create lasting incentives and institutionalize the change.