How to Go from AI Power User to AI Leader

An AI champion is a “peer plus one”: someone who does their team’s work while helping colleagues adopt AI through practical examples, guidance, and connections to central teams. The role can range from demonstrating tools and training peers to building solutions for a business team, sometimes through production. A well-structured champion program can turn individual discoveries into shared capabilities; the speaker describes it as the highest-ROI AI transformation activity observed in enterprises, while noting that results have varied across companies.

Before launching a network, organizations should define its purpose, what champions may build, what is expected of other users, how frontline needs reach central teams, and when internal teams or vendors should be involved. Strong candidates already use AI without prompting, are people their peers seek out for guidance, and have managers willing to allocate time for the role. They also need business knowledge, AI skills, initiative, and influence without formal authority. Choosing someone as a reward or simply because they are a new hire can be ineffective; senior leaders are better placed to shape strategy through an AI committee than to nominate themselves as champions.

The role needs real capacity, ideally one or two days or otherwise enough time to help others and advance use cases. Programs benefit from both a community-focused sponsor and an AI-savvy professional sponsor who can align work with company priorities and help secure tools, access, and approvals. Training should use champions’ own work and test whether they can build or promote a real team use case. Certification alone is not enough: without continued ownership, champions can return to old habits and produce little business value. Regular meetings, manager support, recognition, and renewed nominations can sustain the network.

Champions should take responsibility for a use case from problem definition through deployment and measurement. They need to establish who has the problem, how often it occurs, what it costs, whether a solution already exists, and which company performance indicator should improve. Before building, they should compare reusing an existing solution, buying, building, or leaving the need in a backlog, then choose the simplest option that works. A reusable prompt or file may be sufficient; automation or an agent is warranted only when the need justifies it. Requirements should name a real audience rather than “everyone,” and estimates of cost and benefit should be conservative.

A viable solution also requires a plan for where it will live, who will approve and support it, how people will adopt it, and whether its quality, limitations, and risks are acceptable. Before building, teams should specify intended uses and prohibited behavior, who checks outputs, and where human judgment is required. They should define test cases early to expose edge cases and evaluation problems before committing to development. The speaker argues that experimenting to build with AI is relatively easy to learn, while evaluating what has been built is a more distinctive skill; champion training should emphasize evaluation alongside business, technology, and people practices.

Organizations should enable less experienced employees to build with AI by providing infrastructure and training subject-matter experts on appropriate use cases, rather than shutting them down. Agentic coding tools can let nontechnical staff tackle work that was previously out of reach, but they also create security and guardrail challenges and may escape containment or spread across systems. Moving from power user to champion therefore means taking responsibility for the full path from idea to production, including clear role definitions, allocated time, ongoing ownership, and appropriate checks as tools change. A champions bootcamp is being established to train champions and their sponsors’ teams; the role is expected to become more mission-critical as AI changes employees’ work and creates new organizational challenges.