The previous unit established that AI adoption is fundamentally a people challenge—and that diagnosing cultural barriers, leading with empathy, and demanding a clear end game are the first moves any people leader should make. But once the organization is leaning in, you face a different problem: where do you actually start building? The conversation turned to this exact question, and the answer was refreshingly disciplined—not a sprawling AI roadmap, but a focused method for establishing a common language, surfacing the right problems, and letting early wins generate their own momentum.

Before diving into tools, the focus was on education and alignment. The team began by building a common vocabulary—reading the same books and learning together—to ensure everyone was speaking the same language. Only then did the approach shift to bottom-up problem discovery. The team asked two deceptively simple questions: "What bothers you the most about your job today?" and "What could technology do to fix that, help that, change that?"

The result was a surge of a thousand use cases. To avoid being "whipsawed into try this, do that," the team used a value-versus-effort rubric to sort submissions:

• High value but hard to build: Worth pursuing deliberately.
• Easy to do and super important: "Just get on with it."
• Not fully baked or governance risks: Deferred until a stronger case could be made.

This rubric narrowed a thousand ideas down to 28 actionable pilots, providing the focus needed to move from experimentation to execution.

Narrowing to 28 wasn't the finish line; it was the start of a "flywheel of confidence." This cycle relies on three disciplined actions:

1. Monitor ROI to Go "A Mile Deep": Every use case was monitored to see what actually drove value. When a pilot in People Operations allowed the team to scale their impact from serving 450 employees to 1,000, they didn't cut staff. Instead, they went "a mile deep," moving those people into higher-value work like training the AI or solving more complex cases.
2. Document Like an Anthropologist: The journey wasn't just managed; it was recorded. By documenting how HR practices, mindsets, and work design evolved with each iteration, the team created a shareable playbook that allowed the rest of the organization to learn from their footprints.
3. Celebrate to Fuel the Flywheel: Successes were shared with visible enthusiasm. This "showing that enthusiasm, celebrating that work" is the engine of the flywheel—each win lowers the barrier of fear for the next experiment, drawing more people into the process and accelerating adoption.