The goal is capability, not adoption.
AI Adoption is a useful leading indicator for your AI transformation, but usage alone doesn't tell you how well your staff are using AI, or if that usage is aligned to business needs. As many of us are seeing, it's easy to mis-use AI, introducing slop that slows down collaboration rather than improving it, or burn through tokens on projects that have no business impact.
An AI-enabled workforce is one that is capable of using AI safely, effectively, and aligned to business goals. Those skills are the ones that allow your staff to know both when and how to use AI. They're the skills that allow you to go from "Are you using AI?" to "How has the work changed with AI?"
In this resource you'll see what's required to build capability by focusing on activating two critical roles in each team: the AI Champion and the People Manager.
What capability requires
Individual skill is the natural place to start, and is genuinely necessary. A champion who knows how to use Copilot for a variety of tasks, spots meaningful opportunities to improve how they work, and experiments thoughtfully is the raw material needed to change how one works.
But look at what has to happen for any of that to change how a team works:
- Someone decides which opportunity within the team is pursued.
- Someone decides that an experiment's result becomes the team's new way of working, and sets the guardrails around it.
- Someone leads the team through the change: the switchover, the communication, the resistance.
None of those are a champion's call. They belong to the people manager. Which means team-level capability is not one trained role, but two coordinated sets of responsibilities.
The champion is the doer: hands on experimentation, understanding what is possible. The manager is the decision-maker: deciding what changes and leading the change. The champion brings what is possible, the guardrails, and the limitations. The manager makes the call.
The AI Partnership Map
The easy assumption is that this is a division of labor: the champion takes some of these responsibilities, the manager takes the rest. It doesn't work that way. The champion and manager amplify one another. There are six domains that make up team-level capability and both roles are active in every one.
Take three teams and apply this map over each one. Somewhere you'll find a manager holding both columns, somewhere a champion with nobody to decide, somewhere neither side named at all. All three can be true in the same organization on the same day. Scaling capability means every team ends up with both columns covered, in whatever combination that team can sustain.
A partnership requires training both sides
Develop only the champion and six months in you have a stack of well-documented experiments that haven't been rolled out to the team. The champion keeps producing. The team keeps working the old way. Everyone involved is doing their job.
Likewise, a manager ready to make calls, with no experimenting or understanding how to actually apply AI to the team's workflows, and when to redesign them, is running change management on an empty pipeline.
Either way the partnership runs at half capacity, and the missing half is the half that changes the work.
What each side needs
The two roles need different development, not the same training at different depths. The champion's track is technical and enabling: prompting and context building, experiment design, documentation, training design and facilitation. The manager's track is evaluative: enough AI fluency to ask informed questions, plus the frameworks for prioritizing opportunities, judging experiment results, and leading a team through a workflow change.
And the tracks cannot run independently. In every domain on the map, the champion's output is the manager's input. What each side learns, and when, has to line up, or the champion arrives with something the manager is not yet equipped to receive.
The AI Champion
Technical and enabling: prompting and context building, experiment design, documentation, training design and facilitation.
The People Manager
Evaluative: enough AI fluency to ask informed questions, plus the frameworks for prioritizing opportunities, judging experiment results, and leading a team through a workflow change.
Talk it through
I design and run programs for both champions and people managers. If you're interested in what this could look like in your organization, schedule a discovery session.
The AI Partnership Map