The Problem with the Solo AI Champion Model
In our work with nonprofits, foundations, and associations, we see the same pattern repeat itself. A staff member or leader attends a training, gets excited, starts experimenting, and identifies compelling use cases. Then momentum stalls — not because the ideas are wrong, but because the organization around them isn’t equipped to move with them.
This is the solo champion model, and it’s more common than most organizations realize. It looks like progress, and in some ways it is. But it has real structural limitations:
- Progress is bottlenecked by one person’s capacity and continued enthusiasm
- Cross-functional opportunities — the ones that often carry the most potential — require coordination that a single champion can’t drive alone
- The knowledge gap between the champion and their colleagues widens over time, making organizational buy-in harder to achieve, not easier
For some organizations, the solo champion approach is the right one — at least for now. Capacity is limited, change is real, and going slowly is sometimes the most responsible path. But it’s worth being clear-eyed about where that model hits its ceiling.
What a Shared AI Foundation Actually Looks Like
The alternative isn’t about turning your entire leadership team into AI experts. That’s neither realistic nor necessary. What it is about is building a common baseline, a shared language and understanding of what AI does well, where it struggles, and what’s genuinely relevant to your organization’s work.
We’ve seen what becomes possible when that foundation exists. A foundation exploring how AI could serve both grant seekers and grant makers simultaneously requiring alignment across program, learning, and technology teams. An association reimagining member onboarding in a way that integrates marketing, operations, and technology.
These aren’t one-person projects. They require a leadership team that can have an informed conversation about trade-offs, risks, and priorities.
That’s the shift a shared foundation enables: from one person driving AI adoption to a team that can evaluate, decide, and move together.
A Framework for Thinking Through AI Opportunities
Shared understanding is the starting point, but organizations also need a structured way to evaluate where AI can actually help. In our work, we’ve developed an AI roadmapping approach built around four core questions:
- Where are the real use cases? — Grounded in your organization’s actual work, not theoretical possibilities
- What data is available and usable? — AI is only as useful as the information it can access
- What are the risks? — Every potential use case carries considerations around privacy, equity, accuracy, and trust
- What will adoption actually require? — Tools don’t implement themselves; people, process, and culture all matter
None of these questions is new. What’s different in the AI context is how interconnected the answers are and how much harder it becomes to work through them if only one person in the room has enough context to engage strategically.
Two Paths Forward — and How to Choose
When we work with organizations on AI readiness, we typically see two approaches emerge:
Path 1: The Iterative Champion Approach
One person or a small team leads experimentation and advocates internally. The organization learns by seeing concrete examples. Only a few people gain real AI literacy. Others engage as things become more tangible. This works in organizations with limited capacity or significant competing priorities. But progress is slower and more susceptible to disruption.
Path 2: The Shared Foundation Approach
The organization invests earlier in building common understanding across a broader group of leaders. This requires asking already-busy people to step back from day-to-day work to develop a shared baseline together at a real cost. But when the opportunity is genuinely cross-functional, this investment is often what unlocks the next phase of AI readiness.
The honest answer is that most organizations will eventually need to move toward Path 2, regardless of where they start. The question is when and how to recognize that inflection point before it becomes a bottleneck.
The Real Question Isn’t Whether to Use AI
We hear a lot of anxiety in this space. There is an unfortunate belief that organizations are behind, that the gap is widening and that action is immediately required. I’d push back on that framing, at least for most organizations. The forward paths are not yet clear, and the organizations we see rushing into AI deployment without a strategic foundation often create more problems than they solve.
The more useful question is: At what point does relying on a single champion stop being enough?
Because that point will come. And when it does, how far your organization can go will depend far less on any individual’s expertise and far more on whether your leadership team has built an AI foundation – a shared understanding of what AI can, and cannot, actually do.
Walk, don’t run. But walk together.
Interested in how your organization can build a structured AI roadmap? Read our strategic framework for nonprofit AI investment, or get in touch to talk through where your organization stands.
