Every organization has that one person the whole team assumes will be the last to jump on the tech bandwagon. In one Fortune 500 client team, that person was Mark, a senior leader who still swears by his paper planner. The kind with color-coded sticky tabs and a fountain pen clipped to the side. He's a people-first leader, the type who remembers everyone's kids' names and prefers a real conversation over a chat message any day.
He's also the guy who still hands out business cards and actually files the ones he gets back. When his company rolled out AI tools and began training everyone on Microsoft Copilot, he did what good leaders do: he showed up. He sat through the courses. He listened to the trainers. But it all felt like noise in the middle of a very busy day. He wasn't opposed to AI.
He just didn't see how any of it connected to his actual work. He was thinking: "I don't have time to figure this out." Mark sat through the training. He understood the concepts. But when he got back to his desk with actual work to do, none of it connected. Learning from scenarios wasn't the same as working on Thursday's presentation. Understanding what AI could do wasn't the same as knowing what to do with it.
A few months later, his leadership was calling him "the AI guy" on the team. What changed?
The Shift From Learning to Doing
When we met Mark, he was overwhelmed but curious. He didn't want another class or a slide deck on AI concepts. He wanted help getting things done. His organization brought us in to create an AI Discovery Lab for his department, a team that could embed these patterns into their daily work. The lab met weekly for an hour. People brought what was actually on their plate that week.
The presentation due Thursday. The proposal sitting on someone's desk. The 40 pages of notes that needed to become something useful. In an early session, someone tackled summarizing reports for their team. The group watched, asked questions, figured out what worked. They started naming the patterns. "Oh, that's a synthesis task." "That's more like a translation."
They built Copilot agents they could all reuse. A few weeks in, Mark brought his own challenge: "Can it help me find examples that'll make this story land?" But he wasn't starting from zero. He was building on what the group had learned the week before. Each small win built confidence. Each task showed AI's potential in concrete ways. They developed a shared language.
Shared templates. Shared instincts about when AI helped and when it didn't. And soon, Mark stopped thinking about AI as a tool he had to learn and started seeing it as a partner that helped him focus on what mattered most: leading, deciding, and connecting. The lab ran for 12 weeks. Weekly sessions. Real work only. Consistent practice with actual problems. Mark wasn't racing to become an expert.
He was building fluency the same way he'd built every other skill in his career. Slowly. Consistently. With people who understood the work.
A Different Kind of Fluency
He hasn't changed who he is. He still takes notes on paper. He still schedules coffee meetings instead of firing off Slack messages. But now when he walks into those meetings, he's sharper. Better prepared. With more time to think about the human side of the conversation. AI didn't make him more technical. It made him more effective. It amplified the wisdom and intuition that come from decades of experience.
That's what's often missing in AI adoption stories. We talk about tools and productivity, but not about identity. For many seasoned leaders, the real challenge isn't "how to prompt." It's reconciling new ways of working with who they already are. When you meet people where they are and respect their existing strengths, adoption becomes empowering instead of forced.
And when his teammates run into an AI question, they work together. Because they’ve been in the lab. Now Mark speaks the language they're all building together.
Building organizational capability
When one person in the lab cracks a better way to structure a brief, everyone benefits. When someone figures out a Copilot agent that works for client presentations, the whole team uses it. The lab becomes a place where knowledge compounds instead of scattering. Seasoned leaders don't need to become someone new. They need to see how AI amplifies who they already are.
And they need to build that understanding with people they trust, at a pace that respects the work they're already doing. The lesson here goes beyond one leader. AI adoption requires culture change. The Discovery Lab gives teams a space to develop shared fluency. To learn from each other. To build patterns they can apply long after the lab ends. The lab helps the relationship builders, the veterans, the steady hands of the organization translate AI into their own language of leadership.
Mark became "the AI guy" because he found a way to make it his own. He discovered how AI could help him do what he already did best: think clearly, lead thoughtfully, and show up prepared.
The takeaway:
AI fluency requires moving together. Organizational capability comes from consistency, shared experience, and the patience to let people integrate new tools into workflows they've spent years developing. Leaders need to see how AI can take the grind out of their day, surface insights faster, and give them more time for what truly matters: people, decisions, and purpose.
So yes, Mark still carries business cards. He still uses that paper calendar. But when someone calls him the "AI guy" now, he smiles. He knows it's about being willing to learn, adapt, and lead. One real task at a time.