Champions Network
The Champions Network operates as a learning system that turns hands-on AI usage into shared enterprise capability. It connects discovery, enablement, feedback, and validation to sustain adoption beyond the pilot.
At its core, the Champions Network is a cross-functional group that translates real AI experimentation into repeatable, enterprise-ready practices. Champions bridge strategy and execution by surfacing use cases, enabling workflows, sharing best practices, and validating readiness for broader rollout. This structure prevents AI adoption from fragmenting into isolated efforts and ensures lessons learned are captured and reused. Over time, the network becomes a critical feedback loop that informs platform decisions, governance refinements, and roadmap priorities.
In practice, the network meets on a regular monthly cadence to exchange insights and identify emerging patterns. Between sessions, Champions contribute prompts, examples, and workflows into shared spaces accessible through the AI portal. This creates visible momentum, reinforces a culture of knowledge sharing, and establishes a scalable foundation that supports enterprise expansion of AI initiatives.
Ideal Candidates
The Champions Network is most effective when participants are actively applying AI in their day-to-day work and motivated to help others learn. Ideal candidates are not defined by title, but by curiosity, credibility within their teams, and a willingness to translate experimentation into shared understanding.
Ideal candidates typically:
Regularly use AI tools as part of real workflows, not just exploration
Are curious, pragmatic, and eager to improve how work gets done
Have enough context in their role to identify meaningful use cases
Are comfortable sharing examples, lessons learned, and failures
Act as informal advisors or points of contact within their teams
Suggested Launch Themes
Launching the Champions Network with clear themes helps focus discussion, accelerate learning, and create early momentum. Themes should emphasize practical application and progress naturally from individual enablement to enterprise readiness.
Suggested early themes include:
Foundations: Prompting, GPTs, agents, and effective setup practices
Workflow Enablement: Breaking down work and embedding AI responsibly
Productivity & Quality: Improving speed, consistency, and outputs
Research & Synthesis: Using AI for sense-making and insight generation
Decision Support: Human-in-the-loop AI for judgment and trade-offs
Readiness & Scale: What is working, what needs guardrails, and what to standardize next
These themes can rotate monthly and evolve as the organization’s AI maturity grows.
Return to the table of contents or use the navigation below to continue.
Looking for more support?
Leadership Lab: Learn alongside other leaders as you apply AI in real workflows, share lessons learned, and build the leadership skills needed to guide teams through change.