How to Build an AI Champions Program at Your Organization

Your best source of AI knowledge isn’t external consultants. It’s your own team.

In every organization, some people naturally gravitate toward new tools. They experiment. They figure things out. They become the person others ask when they have questions. That person is an AI champion, whether you formalized it or not.

Most organizations ignore this dynamic. They leave champions in their regular roles, competing with their primary job. Champions then get overwhelmed and stop advocating for AI adoption. Everyone loses.

A structured champions program changes this. You identify the right people, give them time to develop expertise, create forums for them to share what they learn, and position them as your internal experts. This accelerates adoption, creates resilience (knowledge isn’t bottlenecked in one person), and builds a stronger AI culture.

This guide walks you through building a champions program from scratch. By the end, you’ll know how to identify champions, what to ask them to do, how to support them, and how to measure whether the program is working.

Why Champions Programs Work

Before diving into structure, understand why this approach works.

Champions are credible to peers in a way external consultants aren’t. When a consultant says “you should automate this,” there’s distance. When a colleague you know and trust says “I automated this and it saved me three hours a week,” that lands differently.

Champions are in the trenches. They understand your workflows, your constraints, your tool ecosystem, and your culture. They can recommend solutions that actually fit your situation rather than generic best practices.

Champions solve adoption problems. The biggest barrier to AI adoption isn’t capability. It’s that people don’t know how to use it. Champions become the go-to experts for questions. They reduce friction.

Champions sustain adoption. Once a tool is deployed, using it correctly requires ongoing learning. Champions keep that alive by experimenting, sharing discoveries, and pushing the organization forward.

Finally, champions are low-cost. You’re paying people you’re already employing to develop expertise. This is far cheaper than hiring external consultants for ongoing training and support.

Who Makes a Good AI Champion

Not everyone should be a champion. You want to identify the right people.

Good champions are curious. They like learning new things. They don’t need you to tell them to explore a new tool. They want to.

Good champions are respected peers. People listen to them. They have credibility with their colleagues. If the shy developer nobody talks to is your best AI enthusiast, they’re not your champion. If the respected project manager who everyone values is interested in AI, they are.

Good champions are communicators. They enjoy explaining how things work. They’re patient with people who don’t understand immediately. They translate technical concepts into practical language.

Good champions are not necessarily your most technical people. The best developers at your organization might not be good champions because they speak a different language than the rest of the team. Identify people who are technically competent but also good at cross-functional communication.

Good champions have bandwidth. They can’t be drowning in their primary role. If they have 5% discretionary time, that’s enough to be a champion. If they have zero, they can’t do it.

Good champions are committed. They genuinely believe AI will help your organization. Skeptics won’t drive adoption.

You probably have 2-5 natural candidates. If you have one, that’s fine. That’s your starting point.

Structure: What Champions Actually Do

Be clear about what you’re asking champions to do. Vague commitment leads to burnout. Clear structure leads to sustainable programs.

Here’s a typical champion commitment:

Monthly learning time (4 hours). One hour per week to explore new tools, learn about AI capabilities, deepen skills. This isn’t optional. It’s built into their schedule. They use this time to experiment with new approaches, take online courses, explore beta tools, whatever keeps them current.

Peer training (2 hours per month). Run a 30-minute session twice per month where they share something they learned with the broader team. This might be “Here’s how to use ChatGPT for client research” or “Here’s a template that saves time on reporting.” Structured, not random.

Support and Q and A (2 hours per month). Make themselves available for questions from colleagues. You might create a Slack channel. You might block their calendar for office hours one afternoon a week. The point is people can get help.

Problem-solving (as needed). When the organization wants to try a new tool or automate a workflow, champions help figure out how. This is above and beyond the regular commitment, but it’s part of their role.

Total: 8-10 hours per month, or about 2-2.5 hours per week.

This is meaningful time but not overwhelming. It’s real commitment without derailing their primary work.

How to Launch the Program

Start by identifying your 2-4 champion candidates. Have a conversation with each individually.

Explain what you’re trying to do. “We want to accelerate our AI adoption. I’ve noticed you’re naturally curious about these tools and respected by your peers. I’m starting a champions program and I want to know if you’d be interested in being part of it.”

Be clear about the commitment. “This would be about 8-10 hours per month: learning time, sharing sessions, and being a resource for colleagues.” Many people will be excited about this. Some won’t have capacity. That’s okay.

Explain what you’ll do to support them. This is important. Champions need to know you’re not just adding work to their plate. You’re making space for it.

Ask about interest. If they say yes, move forward. If they say “I’m interested but don’t have time,” ask when they might have capacity. Or ask if they’d be interested in a smaller role.

Create a formal role description. This makes it real. It gives them something to point to when explaining their time to their direct manager. It protects them from being treated as available for other work just because they technically have “slack time.”

Supporting Champions

Once you have champions, support them actively. This isn’t “you’re on your own” work.

Give them access to tools and resources. Budget for courses, tool subscriptions, AI tool access. If you want them to explore new tools, give them budget to try them.

Protect their time. When someone asks if they can pull a champion for a project, the answer should be “no, because they’re committed to the champions program.” Make it clear that this time is protected.

Connect them with each other. If you have multiple champions, create a forum for them to discuss what they’re learning. They’ll accelerate each other’s growth.

Give them interesting problems to solve. “We want to automate client reporting” is more interesting than “be available for questions.” Mix structured learning with interesting real-world challenges.

Recognize their contribution. Public acknowledgment matters. In all-hands or team meetings, acknowledge what your champions are doing. Thank them. Make it clear that their contribution is valued.

Share what they’re learning with the broader organization. When a champion discovers something useful, communicate it not just in their peer training session but in team emails, company updates, etc. Amplify their learning.

The Peer Training Sessions

The most visible part of the program is the peer training. Run these well and adoption accelerates. Run them poorly and people don’t come.

Keep sessions short. 30 minutes maximum. 20 minutes is better. People are busy. Respect their time.

Make attendance optional but encouraged. Make it easy to attend (lunch and learn, or 3:30pm office hours). Remove friction.

Have a concrete topic. “AI for marketers” is vague. “How to use ChatGPT for client research” is clear. People come for clarity.

Include practical examples. If you’re teaching prompt engineering, show the exact prompt you use. If you’re teaching a tool, walk through a real workflow. Don’t be theoretical.

Encourage questions. Create psychological safety for people to ask obvious questions. The person asking might be asking what 10 others wondered.

Share recordings and notes. Some people can’t make the session live. Record it and share it. This makes the learning available asynchronously.

Repeat popular topics. If “How to write good prompts” was popular, run it again the next quarter. New people joined. Others want to deepen their learning.

Building Momentum

A champions program can fizzle if you don’t feed it. Keep it moving.

Celebrate wins. When someone uses an AI tool successfully, share it. “Sarah automated her client reporting with ChatGPT and saved 3 hours per week.” This shows impact and encourages others to try.

Identify emerging champions. Your first champions won’t be your only ones. As people experiment, others will naturally gravitate toward being champions. Invite them in.

Expand into teams. Once you have a foundational program, consider expanding. Design team-specific training. “How AI helps designers” is different from “how AI helps strategists.”

Set challenges. “This month, everyone experiment with AI for one task in your primary role.” Make it safe to fail. Celebrate experiments, not just successes.

Use champions to solve real problems. When you want to automate a workflow or deploy a new tool, ask champions to lead the pilot. Give them interesting, meaningful work.

Create a knowledge base. As champions learn things, document them. Create an internal wiki or document repository. Make it easy for new people to access what’s been learned.

Measuring Program Success

How do you know if your champions program is working?

Track adoption metrics. Are people using AI tools? Are adoption numbers growing? Your program is probably contributing if adoption is accelerating.

Track training attendance. Are people coming to peer training sessions? Do you have consistent attendance? Declining attendance suggests the program isn’t resonating.

Track impact. What workflows have been automated? What time has been saved? What quality improvements have happened? Champions’ work should translate to measurable organizational impact.

Track skill development. Are people moving from “never used this” to “I use this regularly”? Are people becoming new champions? The program should create skill growth.

Ask for feedback. What are people learning from peers that they wouldn’t learn elsewhere? What’s missing? Use feedback to improve the program.

Ask champions about their experience. Are they feeling supported? Is the time allocation realistic? Are they enjoying it? If champions are burning out, you’ll burn through them.

Common Mistakes

Starting with too many champions. Five people is too many for a starting program. Start with 2-3. Expand once the program is stable.

Not protecting their time. If champions’ learning time gets stolen for other work, they’ll become bitter. Protect it fiercely.

Expecting them to do this without support. Champions aren’t volunteer evangelists. They’re employees getting paid to develop expertise. Support them with resources, time, and recognition.

Running boring training sessions. If peer sessions feel like obligation, people won’t come. Make them valuable and interesting.

Not using what they learn. If champions learn something but the organization doesn’t apply it, they feel ignored. Apply their learning visibly.

Focusing on tools rather than skills. Champions should teach how to think about automation and AI, not just “here’s tool X.” Tool-specific learning gets outdated. Skill learning compounds.

FAQ

Q: Can we have champions in a small organization of 5-10 people?

A: Yes. You might have one champion instead of three. The principle is the same. It’s even more valuable in small organizations because one person can influence the whole culture.

Q: What if people are resistant to having a champions program?

A: Resistance usually comes from one of a few sources. Some think it’s extra work or favoritism. Show that it improves everyone’s productivity. Some are skeptical of AI. Have honest conversations about specific concerns. Some think it’s unnecessary. Start small and let results speak.

Q: How much should we pay champions?

A: Some organizations add a small stipend ($100-200/month). Some give champions career development opportunities or time off. Some give internal prestige. Pay doesn’t have to be money, but recognition of their contribution matters.

Q: What if our champion leaves?

A: This is why you want 2-3, not one. If you have only one and they leave, you lose momentum. Build redundancy. Once you have a champion, invest in developing a second one. Knowledge transfer is important.

Q: Should managers have a role in the champions program?

A: Yes. Managers need to protect champions’ time and support their learning. They’re not running the program, but they’re enabling it. Make sure your management team understands the program’s value.

The Takeaway: Expertise Compounds

The compounding effect of a champions program is powerful. Month one, you have two people experimenting. Month three, they’ve shared what they’ve learned and five more people are using tools. Month six, you have a genuine culture shift where people think about AI naturally.

Your organization doesn’t need to hire new people to develop AI expertise. You need to invest in the people you have. Champions programs do that efficiently.

Start small. Pick 2-3 natural champions. Clarify their role. Support them with time and resources. Run peer training sessions. Measure impact. Iterate.

If you want to systematize this learning and connect it to your broader AI transformation strategy, including assessing skill gaps across your team and creating a roadmap for what comes next, an Agentic Readiness Audit includes AI literacy and training as one of its eight dimensions.

For now though, identify your first champions. Make the commitment clear. Support them properly. Watch what happens. You’ll likely be pleasantly surprised by how quickly this accelerates adoption.