The AI champions program: training your internal experts

Finding your champions: look for behavior, not job titles

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Your AI champions aren't necessarily your most senior people or your most technical people. They're the people who are already using AI and helping others without being asked.

Look for the person who's constantly sharing discoveries in your company communication channels. The person who sees a colleague struggling and walks over to help troubleshoot. The person who's always experimenting with new approaches and talking about what worked and what didn't. These people are already doing champion work. You're just going to formalize it and support them.

Don't pick champions based on who volunteers or who their manager recommends. Pick them based on observed behavior. Watch your pilot groups, your weekly sessions, your internal channels. The real champions reveal themselves through their actions. They're the ones who can't help but share when they figure something out. They're the ones who get energized by helping others learn. They're the ones other people naturally go to for help.

You want champions in every department. Not just one per department, but enough that no one is more than one degree away from someone who can help them. In a 50-person marketing team, you might need three or four champions. In a 200-person sales organization, you might need 15. The right number depends on how spread out your teams are and how much they already collaborate.

What to ask of them: be specific about the commitment

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Champions need to know exactly what you're asking before they agree. Vague expectations kill programs like this because people either do too much and burn out or do too little and feel guilty.

Here's what you should ask. Dedicate four to six hours per week to champion activities. This isn't on top of their regular job. This is part of their job now. Get their manager's explicit agreement that this time is protected. If you can't get that agreement, don't make that person a champion.

Run weekly office hours where anyone can drop in with AI questions. One hour per week, same time, same place. They don't need to prepare. They just need to show up and help whoever comes. Some weeks nobody will show up. That's fine. The consistency matters more than the attendance.

Monitor your organization's internal channels and respond to questions within 24 hours. They don't need to solve every problem themselves. Sometimes the answer is "I don't know but let me ask around" or "that's outside my expertise, let me connect you with someone who can help." The goal is to make sure no one feels ignored. If you're using a platform like kursi.ai, champions should also monitor the shared prompt library and contribute proven templates that work for their department.

Run one hands-on workshop per month for their department. Not a presentation, a working session where people bring their actual tasks and the champion helps them figure out how to use AI for those specific tasks. These sessions should be small, 10 to 15 people maximum, so there's time for individual help.

Share discoveries with the broader champion network. When they figure something out that might help other departments, they document it and share it in the champions channel. This keeps the collective knowledge growing.

That's it. Four to six hours per week. If you ask for more than that, you're going to burn people out. If you ask for less, you won't get the coverage you need.

Training your champions: make them experts through practice

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Your champions don't need formal training to start. They need structured practice and peer learning.

Bring all your champions together for a kickoff session. Half day, in person if possible. The goal isn't to teach them AI. They already know AI better than most people in your organization. The goal is to teach them how to teach others. How to diagnose why someone is stuck. How to show someone a technique without doing it for them. How to scale their impact beyond just answering individual questions.

Run monthly champion meetings. Two hours, every month, same time. First hour is show and tell. Each champion shares one thing they figured out this month and one problem they couldn't solve. This creates a knowledge-sharing loop where everyone learns from everyone else's experiments. Second hour is skill building. Bring in someone to teach facilitation skills, or troubleshooting techniques, or how to create good documentation. Rotate topics based on what the champions say they need.

Create a dedicated space for champions to collaborate. If you're using a platform like kursi.ai, this becomes your central hub where champions share prompt templates, document use cases, and coordinate on cross-departmental issues. The reporting features let you track which prompts are being used most across the organization, helping champions identify what's actually working. This visibility becomes incredibly valuable because champions learn as much from seeing usage patterns as they do from direct conversation.

Pair experienced champions with new ones. When you add new champions, don't just throw them in alone. Have them shadow an experienced champion for a month. Sit in on their office hours. Co-run a workshop. Learn by watching someone who's already figured out what works.

The training happens through doing, not through courses. Your champions get better by helping people, reflecting on what worked, sharing with other champions, and trying new approaches. This is how expertise actually develops.

Supporting your champions: give them tools and authority

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Champions fail when they have responsibility but no resources or authority. If you want them to succeed, you need to set them up properly.

Give them a budget for tools and experiments. Not a huge budget, but enough that they can try new AI tools when someone asks "could we use AI for this?" and they want to test it before rolling it out. The ability to say "let me get access to that tool and test it this week" instead of "let me submit a request and maybe we'll get approval in two months" makes a massive difference.

Give them access to leadership. Champions should have a direct line to your AI transformation leader and ideally to relevant department heads. When they identify a systemic issue that's blocking AI adoption, they need to be able to escalate it to someone who can fix it. If they're just collecting problems with no way to solve them, they'll get frustrated and quit.

Give them recognition that matters. Public recognition is good. "Employee of the month" style callouts in company meetings. But what really matters is recognition from their manager. Make sure their champion work shows up in their performance reviews as a positive contribution. Make sure it's factored into promotion decisions. If champion work is invisible to the people who control their career progression, you're asking them to volunteer their time for no career benefit. That's not sustainable.

Give them each other. The champion network should feel like a special club. They're the people who are ahead of the curve, who are shaping how the company uses AI, who are building expertise that will matter for years. Foster that identity. Make them feel like they're part of something important, because they are.

Measuring champion impact: track what matters

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You need to know if your champion program is working. Not just activity metrics like "how many office hours did they run" but actual impact metrics.

Track adoption rates in teams with champions versus teams without. If teams with active champions have 80% AI adoption and teams without champions have 40% adoption, your program is working. If there's no difference, something's wrong. If you're using a platform like ours, the reporting dashboard shows you exactly this data. You can see which departments are actively using AI, which prompts are spreading across teams, and where skills are actually developing versus where people are stuck.

Survey employees about where they get AI help. Ask "when you have a question about using AI, what do you do?" If people say "I ask my champion" or "I go to office hours," that's success. If they say "I google it" or "I just give up," your champions aren't visible enough or accessible enough.

Track champion satisfaction. Every quarter, survey your champions. Are they enjoying this role? Do they feel supported? Are they learning and growing? Do they have what they need? Are they burning out? If champion satisfaction is dropping, you're asking too much or supporting too little.

Measure time to productivity for new employees. How long does it take a new hire to start using AI effectively? Teams with active champions should get new people productive faster. If they don't, your champions might not be proactively reaching out to new hires.

Count cross-pollination moments. How often do insights from one department spread to other departments through the champion network? If the sales team discovers something and within two weeks the marketing team is using a variation of it, your champion network is functioning as intended. Having a shared prompt library makes this visible. You can actually see when a sales prompt gets adapted and used by marketing.

These metrics tell you if the program is actually driving adoption or if it's just theater.

Preventing burnout: the program killer you have to watch for'

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Champion burnout kills more programs than any other single factor. People start excited, take on too much, get overwhelmed, quietly stop doing champion work, and eventually leave the program or worse, leave the company.

Watch for these warning signs. Champions who used to respond quickly start taking days to answer questions. Champions who used to share discoveries stop posting in the channel. Champions who used to run workshops start canceling them. Office hours that used to happen weekly start getting skipped. These are burnout signals.

When you see them, intervene immediately. Have a one-on-one conversation. "I've noticed you've been less active lately. What's going on?" Often the answer is "my regular work picked up and I don't have time anymore" or "I feel like I'm helping the same people with the same problems over and over" or "I don't feel like leadership cares about this work."

The first problem requires renegotiating with their manager to protect their champion time. The second problem requires helping them create better documentation or training so they're not answering the same questions repeatedly. This is where having a good knowledge base matters. If champions can point people to documented answers and proven prompts instead of explaining the same thing ten times, they save massive amounts of energy. The third problem requires more visible recognition and connection to leadership.

Sometimes the answer is "I'm just tired of this and want to focus on other things." That's okay. Let them step down gracefully. Thank them publicly for their contribution. Don't guilt them into staying. Burned out champions who stay out of obligation create a toxic dynamic and hurt the program more than helping it.

Rotate champions every 12 to 18 months unless they actively want to continue. Give people a natural exit point. "You've been a champion for a year. That's the standard term. Do you want to continue for another year or would you like to step down and let someone else take a turn?" This prevents the slow drift into burnout and keeps the program fresh with new perspectives.

Create a champions emeritus program. People who step down don't disappear. They're still knowledgeable, they're still connected. Create an emeritus channel where former champions can stay connected, share insights occasionally, and mentor new champions without the time commitment. This keeps their expertise in the network and honors their contribution.

Growing your champion program: start small, expand deliberately

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Don't try to launch with 50 champions across every department on day one. That's too many people to support properly and you'll create a chaotic mess.

Start with five to eight champions in your highest-priority departments. The ones where AI adoption matters most for your business. Sales, customer service, engineering, whatever makes sense for your company. Run with that small group for three months. Figure out what works. Refine your approach. Learn what support champions actually need versus what you thought they would need.

After three months, if it's working, add another five to eight champions in the next tier of departments. Bring them in as a cohort. Have your experienced champions help onboard them. Build the program iteratively based on what you've learned.

Within a year, you should have champions covering most of your organization. Not everyone everywhere, but enough coverage that most employees are one connection away from someone who can help them. That's when the program becomes self-sustaining. New employees get onboarded by champions. Common problems get documented and shared. The champion network becomes part of how your company operates, not a special program that requires constant attention.

At that point, your AI transformation leader's job shifts. Instead of driving adoption directly, they're supporting the champion network. Running monthly champion meetings. Removing obstacles. Feeding insights from champions into company strategy. Celebrating champion wins. The champions are doing the day-to-day work of spreading AI adoption, and your transformation leader is enabling them to succeed.

That's when you know the program is working. When it's no longer dependent on any single person. When it's woven into how your organization learns and grows. When champions aren't special volunteers doing extra work, but recognized contributors doing important work that's valued and supported.

That's what a successful champion program looks like. Not perfect. Not formal. But effective, sustainable, and genuinely helping people get better at using AI in their daily work.

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