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.

For leaders overwhelmed by AI, a practical first step is to apply a lean startup methodology. Mobilize a bright, cross-functional team, encourage rapid, messy iteration without fear, and systematically document failures to enhance what works. This approach prioritizes learning and adaptability over a perfect initial plan.

For AI tools that fundamentally alter workflows, a simple software deployment is insufficient. Success requires a dedicated team of 'forward deployed' experts (e.g., ex-lawyers for legal tech) to manage the enormous change management undertaking, ensuring adoption and proficiency across the client organization.

Unlike traditional software, AI adoption is not about RFPs and licenses but a fundamental mindset shift. It requires leaders to champion curiosity and experimentation. Treating AI like a standard IT project ignores the necessary changes in workflow and thinking, guaranteeing failure.

Many engineering teams stall at "AI adoption"—simply providing tool licenses. The key to unlocking compounding value is "AI management"—designing a controlled, observable system where agents operate within the SDLC. Teams that get chaotic results often blame the model when the real failure is the process architecture around it.

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.

A successful AI rollout requires a holistic strategy. Start with "People" (training, identifying champions), define new "Processes" (how data is logged), select the right "Platform" (testing tools methodically), and measure success with "Proof" (attaching KPIs to every initiative).

To get teams to embrace AI, leaders should ditch generic mandates like "use more AI." Instead, focus on specific business transformations and highlight the customer value they create. Using company-wide forums for "show and tell" sessions where teams demonstrate unarguable successes makes adoption organic and outcome-driven, not a top-down chore.

Companies fail with AI when executives force it on employees without fostering grassroots adoption. Success requires creating an internal "tiger team" of excited employees who discover practical workflows, build best practices, and evangelize the technology from the bottom up.