At Takeda, AI Adoption Scales Through Executive Champions and Grassroots Advocates

Generative AI isn’t scaling evenly. Across organizations, the difference between power users and everyone else often comes down to a single instinct: pausing to consider how AI could reshape the task before tackling it the usual way. That habit compounds quickly, turning occasional use into workflow transformation. Building it at scale isn’t a training problem, it’s an organizational design challenge that requires shaping how work begins, not just how tools are used.

David Porter, Head of Data, Digital and Technology Learning and Development at Takeda, who previously led similar initiatives at Moderna, sees this reality firsthand. Porter specializes in discovery-based development and hyper-personalized training programs, advising C-suite leaders on talent and learning strategy. His recent work focuses on using generative AI to support employee skill development and retention, treating its adoption as a practical question of how work actually gets done.

“You cannot underscore how important it is to have not just a passively approving leadership, but an active set of champions as your leaders. That makes all the difference,” says Porter. To drive that kind of organizational change, Porter says a layered strategy is required, combining top-down modeling with bottom-up advocacy, encouraging adoption from champions on both levels.

• The divide: Porter says that what separates advanced users from beginners isn’t technical knowledge, but the simple habit of daily use. However, what keeps employees from adopting AI in their work is diverse, from outdated assumptions to the initial learning curve. Porter notes reasons given during previous focus groups, from believing that AI can’t do something because they tried it a year or two ago, to the assumption that AI is forbidden in certain industries, which leads some to avoid it entirely.

• Reimagining workflows: On an individual level, Porter says that it’s a simple inclination that can jumpstart the habit of adopting AI in daily workflows. “Is your initial impulse to simply start doing the task, which is the world that we all grew up in, or is your first impulse to say, ‘I wonder if I could reimagine this assignment or this task with AI? Let me start there instead.’ And that seems to make all the difference.”

• Bottom-up advocacy: Porter advises companies to build a community of champions by identifying passionate users who already exist and giving them a microphone. These unofficial leaders serve as the bridge between corporate goals and practical, local applications. “It’s about having people who are champions. Lower-level ‘unofficial’ leaders, who are respected without having leader as a title, who know their stuff, and people listen when they talk.” Once identified, internal marketing teams can amplify their stories so they are clearly tied to the company’s KPIs. Porter suggests giving these champions the floor during executive meetings to present a “live breathing human person” who can show “why it was built and the value that they’re experiencing.”

Grassroots enthusiasm accelerates when paired with visible executive modeling. Porter advocates for a tipping point strategy, targeting a core group of leaders who use AI in their daily workflows. Even getting about one in four leaders to actively talk about and demonstrate the tools can completely alter how the technology is perceived. In one recent pilot in partnership with a major university, that kind of top-down leadership taking ownership of AI strategy became a hands-on reality, shrinking the timeline from exploration to execution. By engaging with technologists and asking concrete questions about feasibility and resources, these leaders replace uncertainty with specific examples of what is possible.

• Follow the leader: Visible executive commitment signals that it is safe to experiment and helps develop human champions across the organization. Seeing higher-ups use the tools shows employees that the behavior is sanctioned, which can encourage hesitant workers to do so as well.

• Exploring what is possible: More than just encouraging the use of AI, leaders can give real examples that demystify with practical applications of AI in their own work. Porter suggests starting a dialogue: “Here’s how I’m using AI. Let’s have five minutes of AI talk in our next team meeting or in a series of team meetings.” He says the next step is “connecting your team with the decision-makers or technologists, to explore the art of what is possible. That is absolutely critical from an organizational perspective.”

Porter maps a pragmatic journey for hesitant employees that begins well before total work transformation. To keep the message inclusive, he extends flexibility to beginners who treat AI as a basic search tool while they get comfortable. Acknowledging that moving past the “Google on steroids” phase takes time, Porter describes his own perspective. “If you’re using AI as a Google on steroids, it’s not the best way, but it’s not a bad way either,” he says. “If that’s your entry into AI, then great. Do that.” Over time, he wants people to move toward viewing AI as a series of teammates that actively collaborate on tasks.

Building on that foundation, Porter encourages employees and leaders to keep asking “how else” they can use the technology, starting with a basic insight engine, moving into data analysis, and then into task execution and process redesign. For professionals looking to cross the behavioral gap, Porter offers a practical thought experiment to rethink the nature of their daily output. “If you consider the work that you do and you ask yourself the question, ‘how would it look and operate differently if AI existed then, like it does now?'” Porter says. “That’s a great thought experiment, and that is the doorway to true transformation.”