I drink a lot of tea. English breakfast, usually, in a real cup, every day. So, when my team came to me with an idea for a new series where they wanted to provide a platform for AI leaders to share how they are learning the name felt relatable. What’s the tea? captures something I find genuinely energizing right now: the pace at which leaders are transforming their organizations with AI, and how generously and honestly they’re sharing what they’re learning with one another.
That generosity is the whole reason my podcast, What’s the Tea?, exists. As I told Rachel Thornton, Enterprise CMO at Adobe, in our very first conversation, what excites me most about this moment isn’t only the speed of change, it’s “the eagerness for leaders to learn from one another.” This is the most significant technology shift of our careers, and it’s playing out not in years, but in days. I thought there was no better way to make sense of it than to get leaders together over a cup of tea and ask them, plainly, what they’re seeing inside their businesses. We bring in people who are driving real transformation, pour the tea, and drink in what they spill. That’s the tea.
Across five conversations—with CMOs at Adobe, Lumen, and Sitecore, a behavioral scientist who studies how teams perform, and one of the most provocative voices in modern marketing—a clear pattern emerged. Transformation is a question of mindset that supersedes any technology question. This first post is about the shift in how leaders and their people relate to AI, and why that shift is the precondition for everything else.
A year ago, the dominant emotion around AI in most organizations was apprehension. Rachel named it precisely. She described “the shift from maybe some trepidation around AI and what would it mean,” to people realizing, “Hey, this is really a game changer.” I’ve watched that exact arc play out, not just in marketing but across functions: the move from quietly wondering whether these tools would replace people, to discovering they can compress work that used to take weeks into minutes.
Ryan Asdourian, EVP and Chief Marketing Officer at Lumen Technologies, took the point to its logical conclusion, and his framing has stuck with me. Reflecting on the instinct to apologize for using AI, he said he’d flipped it entirely: “You should almost apologize if you don’t use AI and you don’t use tools.” For a leader running a function inside a large enterprise, that’s a profound reset of the default. Adoption is no longer the exception to be justified; it’s the expectation. The leadership task is to normalize it—to make it, as Ryan put it, “just common practice to say that, of course I use AI.”
The trepidation faded fastest wherever leaders stopped framing AI as a personal-productivity gadget and started treating it as a way to raise the intelligence of the whole organization. Jon Levy, the behavioral scientist and author of Team Intelligence, made the sharpest case. The earliest phase of AI adoption, he observed, fixated on cost savings and reductions—which is what happens at the start of every technological revolution. But the real prize is bigger. “The ultimate goal of any organization,” he told me, “is to be as smart as possible. How quickly can you and your team solve the problem?”
That reframing matters enormously for transformation, because it moves the unit of value from the individual to the team and ultimately, the enterprise. Ryan gave me the line I now repeat most often: an individual versus an individual with AI, a team versus a team with AI—”the team with AI always wins.” Rachel described the healthy version of the same idea, that AI “amplifies what I can do, supports what I can do. Not necessarily, oh, this is totally replacing me.” Enterprise impact doesn’t come from a thousand people each saving a few minutes. It comes from teams solving harder problems faster, together—across customers, operations, and growth.
If the fear has flipped and AI genuinely raises collective intelligence, the decisive variable becomes leadership. Specifically, the ability to move an organization from scattered experimentation to scaled adoption. Every one of my guests came back to this.
Ryan made the case for leading visibly and vulnerably. “Vulnerability as a leader is so important,” he said, “to show how we are using these tools to enhance what we can do with our team.” He practices it: in a company town hall with roughly five thousand people on the call, he used Copilot live, in the moment, to verify an answer before giving it. That’s a leader modeling the behavior he wants to scale.
Rachel described the structural side of the same work—building what she calls “a culture of experimentation,” where training, reskilling, and upskilling become “critically important” and happen continuously, in bite-size pieces, every week rather than once a quarter. That cadence is how experimentation becomes an operating model rather than a pilot.
Jon offered the deeper psychology of why scale so often stalls. People, he reminded me, don’t relate to the present—they relate to the future they believe they’re heading toward. “It is the responsibility of leadership,” he said, “to make sure that their people feel the future will be brighter.” I learned that the hard way. I once unveiled an impressive AI-native platform to my team without first painting the picture of where it would take them, and I watched the energy leave the room. All they could see was a tool that might replace what they did every day. I hadn’t given them a future to walk toward—and without that, adoption never scales.
That’s the through-line of this series. The technology is extraordinary, but transformation is dependent on leaders bringing people with them.
In the episodes ahead, I’ll explore how AI is reshaping the way work gets done and the outcomes it produces, and what the agentic enterprise looks like. For now, the tea is simple: the fear is fading, the value compounds at the level of teams and the enterprise, and the leaders who move deliberately from experimentation to scale are the ones whose organizations will transform.
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