How To Create AI Champions On Your Team: A Manager's Guide
You've started using AI yourself. Maybe ChatGPT helps you draft emails or prepare for meetings. But here's the challenge every manager faces: how do you move from personal experimentation to genuine team adoption?
The solution is developing AI champions: team members who naturally adopt AI, discover what works, and help others get unstuck. This approach is how businesses develop internal ai champions for workforce training without expensive consultants or formal programs that nobody uses.
What Is An AI Champion In A Team?
An AI champion isn't a job title or formal role. Think of them as early adopters who experiment with AI, find practical applications, and share what they learn with teammates.
They're the person who mentions in a meeting, "I've been using ChatGPT to draft client emails. Want to see my approach?" They're the colleague who helps someone get unstuck when a prompt isn't working. They're the team member who spots a repetitive task and thinks, "AI could probably handle this."
Most importantly, AI champions make adoption feel normal rather than intimidating. When your team sees peers using AI to solve real problems, they're far more likely to try it themselves than after watching a training video.
Why Internal AI Champions Support AI Fluency Training
Here's what happens with most AI rollouts: companies announce an AI mandate, direct employees to online AI literacy courses, and hope everyone figures out how to use AI.
But the reality is that not everyone learns at the same pace or same style.
AI champions solve a different problem. They provide just-in-time help when someone's actually trying to use AI for a real task. They normalize experimentation. They demonstrate what's possible in your specific context, not in a generic demo.
When you have effective AI champions, people don't just build AI literacy through abstract concepts. They develop practical AI fluency by seeing colleagues use AI tools to streamline status reports, prepare for difficult conversations, or summarize long documents.
That peer influence creates sustained adoption in ways formal training rarely achieves.
The Three Types of AI Champions (And Why You Need All Three)
Not all AI champions serve the same function. Understanding the difference helps you identify and develop the right people at the right time.
The Explorer discovers new use cases through constant experimentation. They're first to try something and quick to share both wins and failures. Explorers expand what your team thinks is possible with AI tools.
The Translator takes what works and makes it accessible to others. They create simple templates, explain AI skills without jargon, and help people adapt solutions to their specific needs. Translators move you from early adopters to broader team adoption.
The Strategist connects AI capabilities to business outcomes. They help the team see where AI creates real value versus where it's just novelty. Strategists keep your AI adoption focused on impact, not activity.
You need all three types, but at different stages. Early on, you need Explorers. As patterns emerge, you need Translators. When you're scaling, you need Strategists to maintain focus.
How Do I Pick The Right AI Champions?
This is the question most managers get wrong. You don't pick AI champions. You identify people already showing champion behaviors and support them to do more of what they're naturally doing.
Look for team members who:
Experiment without being asked
Share discoveries naturally with teammates
Help others without making them feel incompetent
Focus on solving problems, not using tools for their own sake
Already have credibility and trust within the team
Notice what's missing: technical expertise, job title, seniority. The best AI champions often aren't your most tech-savvy people. They're your best problem-solvers who happen to be curious about new approaches.
For most teams, you want 2-3 active champions per 10-12 people. This gives you enough coverage that people have someone to ask, but not so many that it dilutes impact.
What Should An AI Champion Actually Do?
AI champions should do three specific things:
Experiment with AI for real work tasks. They try using AI for actual problems like preparing for performance conversations, summarizing project documents, or drafting team updates. This isn't theoretical exploration.
Make their learning visible. They share prompts that worked, explain their process, and show teammates how they're using AI through Slack messages, quick demos, or a shared resource library.
Provide peer support. When a teammate asks for help getting better results or figuring out where to start, champions offer practical guidance based on what they've actually done.
What champions should NOT do: become the team's AI help desk, attend endless strategy meetings, create formal training materials, or be responsible for everyone else's adoption. Those responsibilities kill champion energy fast.
Through this process, champions develop deep AI fluency: not just knowing what AI tools can do, but understanding when and how to apply them to specific work challenges.
How Do I Support AI Champions Without Adding Extra Workload?
This is the critical question. If being an AI champion means extra meetings and added responsibilities on top of existing work, people will burn out or quietly decline.
Here's how to avoid that trap:
Integrate champion work into existing responsibilities. Champions should use AI for their actual job, not do AI work on top of their job. If someone manages client relationships, they experiment with AI for client communication.
Remove low-value tasks to create capacity. When you ask someone to take on the champion role, explicitly take something else off their plate. This could be a recurring meeting, an administrative task, or a reporting requirement.
Keep sharing mechanisms lightweight. A 5-minute demo in your weekly team meeting works better than asking champions to create polished presentations. A quick Slack message sharing a useful prompt beats formal documentation.
Make it visible to leadership. When champions create value, make sure your manager sees it. Their contribution should show up in performance conversations and be recognized as real work.
Set clear boundaries. Be explicit that champions aren't responsible for other people's adoption. They make resources available and offer help, but each team member owns their own learning.
The key principle: champion activities should enhance someone's ability to do their existing job well, not add a second job on top of it.
Should AI Champions Be Formal Or Informal Roles?
Most successful AI champion programs start informal and stay that way. Making it a formal role with an official title and defined responsibilities often kills the organic energy that makes champions effective.
Keep it informal by referring to people as champions in conversation without making it an official designation. Acknowledge their contributions publicly without creating a formal role structure. Let champion status emerge based on behavior rather than assignment.
Consider making it more formal only if you need to allocate dedicated time officially, you're in a highly structured organization where informal roles don't get recognized, or champions are asking for more formal recognition.
Even then, think "working group" rather than creating permanent new positions. The moment it feels like bureaucracy, you've lost what makes champions work.
How Do I Find Early Adopters For AI?
You don't need to search hard. Early adopters reveal themselves quickly once you create permission to experiment.
Try this in your next team meeting: "For the next month, I want everyone to spend 30 minutes per week testing how AI might help with something you actually need to do. Not a hypothetical exercise. A real task. We'll share what we learned in our weekly check-ins. Some experiments will fail. That's expected."
Watch what happens over the next two weeks. Some people will jump in immediately and come back with multiple experiments. Those are your early adopters. They don't need permission or encouragement. They need structure and visibility for what they're already inclined to do.
Ready to help your team use AI?
We’re launching our AI Adoption Toolkit for Managers in 2026 featuring ready-to-use templates, champion selection frameworks, and implementation plans. Curious? Contact us to learn more.