Top-down AI mandates only go so far. Real adoption happens peer-to-peer—when employees see colleagues using AI successfully and learn from them.
An AI change champion program creates a network of internal advocates who accelerate adoption, provide peer support, and surface issues before they become crises. Well-designed, it's one of the highest-leverage change management investments you can make.
This guide covers how to identify, recruit, train, and enable AI champions who drive sustainable adoption across your organisation.
Executive Summary
- Champions bridge the gap between leadership mandates and daily work—they make AI real for peers
- Selection criteria matter: Look for influence, capability, and willingness—not just enthusiasm
- Champions need training beyond regular employee training: facilitation, troubleshooting, feedback collection
- Structure the network: Clear roles, manageable scope, regular connection, and visible support
- Sustain engagement: Champions burn out without recognition, resources, and continuing development
- Measure champion impact: Track adoption in champion-supported areas vs. others
- Champions evolve: Early champions differ from scaling champions; plan for both phases
Why This Matters Now
AI adoption faces a credibility gap. Employees hear about AI benefits from vendors, executives, and training materials—but they trust peers more.
Champions solve this:
They translate AI to real work. Where formal training gives generic examples, champions show how AI applies to the actual job, with local context.
They provide safe support. Employees reluctant to ask "stupid questions" in formal channels will ask a trusted peer.
They surface real issues. Champions hear concerns that never reach project teams. This feedback loop enables rapid response.
They scale expertise. You can't provide dedicated support to every employee. Champions extend your support capacity.
They demonstrate possibility. "Someone like me is using AI successfully" is more persuasive than any executive presentation.
What Is an AI Change Champion?
An AI change champion is an employee who:
- Uses AI effectively in their own work and can demonstrate practical applications
- Advocates for AI adoption among peers, providing encouragement and sharing benefits
- Supports colleagues learning to use AI—answering questions, troubleshooting, showing techniques
- Gathers feedback about AI tools, training, and challenges—serving as a listening post
- Connects with the AI team to share insights and receive updates
Champions are not:
- IT support staff (though IT can have champions)
- Full-time AI roles (though champions may spend some hours on this)
- The most technically advanced users (relationship skills often matter more)
- People assigned against their will
The AI Champion Blueprint
Champion Program Structure
Coverage Model
| Organisation Size | Recommended Champion Ratio |
|---|---|
| <100 employees | 1 champion per 10-15 employees |
| 100-500 employees | 1 champion per 15-25 employees |
| 500-2000 employees | 1 champion per 25-40 employees |
| >2000 employees | 1 champion per 40-50 employees + regional leads |
Ratios should be higher (more champions) for:
- Early adoption phases
- Organisations with high AI resistance
- Complex or high-impact AI implementations
- Distributed or remote workforces
Selecting AI Champions
Selection Criteria
Essential:
| Criterion | Why It Matters | How to Assess |
|---|---|---|
| Influence | Champions must be listened to | Peer reputation, informal networks |
| Credibility | Their endorsement must be trusted | Work quality, respect from colleagues |
| AI Capability | Must use AI effectively themselves | Current usage, training performance |
| Willingness | Must genuinely want the role | Voluntary interest, time availability |
| Communication | Must explain clearly and listen well | Observation, manager input |
Desirable:
| Criterion | Why It Matters |
|---|---|
| Patience | Supporting frustrated learners requires it |
| Optimism | Enthusiasm is contagious |
| Problem-solving | Champions will troubleshoot issues |
| Feedback orientation | Collecting insights requires this skill |
| Resilience | Navigating resistance requires persistence |
Where to Find Champions
- Training sessions: Who asked good questions, helped others?
- Early adopters: Who started using AI before it was required?
- Manager nominations: Who influences their peers?
- Self-nomination: Open applications for interested employees
- Network analysis: Who do people go to for help?
Cautions
Don't select:
- The most technically advanced user (they may lack patience or relationship skills)
- Only enthusiasts (skeptics-turned-advocates are often more credible)
- People with no time (champions need hours to devote)
- People who can't say no (they'll burn out)
- Managers only (peer champions are essential)
Training AI Champions
Champions need training beyond what regular employees receive.
Training Curriculum
Module 1: Champion Role and Expectations (2 hours)
- Champion program purpose and structure
- Role description and boundaries
- Time commitment and manager support
- Success measures
- Support available to champions
Module 2: Advanced AI Skills (4 hours)
- Deep dive on AI tools beyond basic training
- Troubleshooting common issues
- Advanced techniques to share with others
- Staying current with AI updates
Module 3: Supporting Others (3 hours)
- Adult learning principles
- Showing vs. telling techniques
- Handling resistance and frustration
- Knowing when to escalate
- Creating psychological safety
Module 4: Feedback Collection (2 hours)
- What feedback to gather
- How to ask effective questions
- Documenting and reporting feedback
- Closing the loop with reporters
Module 5: Champion Community (1 hour)
- How the champion network operates
- Communication channels
- Meetings and check-ins
- Resources and support
Total: ~12 hours + ongoing development
Enabling AI Champions
Training isn't enough. Champions need ongoing support and resources.
Time Allocation
Champions need time to fulfill the role. Work with managers to allocate:
| Champion Activity | Suggested Time |
|---|---|
| Peer support (informal) | 2-3 hours/week |
| Champion community meetings | 1 hour/every 2 weeks |
| Feedback collection and reporting | 1 hour/week |
| Self-development (staying current) | 1 hour/week |
| Total | 5-6 hours/week |
Ensure manager agreement before someone becomes a champion.
Resources to Provide
| Resource | Purpose |
|---|---|
| Quick reference guides | To share with colleagues |
| Troubleshooting FAQs | To resolve common issues |
| Demo scripts | For showing AI applications |
| Feedback templates | For consistent information gathering |
| Communication with AI team | Direct access for questions |
| Peer champion network | Learning from other champions |
| Recognition mechanisms | Acknowledgment of contribution |
Champion Community
Build community among champions:
- Regular meetings: Every 2 weeks to share experiences, learn, troubleshoot
- Communication channel: Slack/Teams channel for ongoing discussion
- Resource library: Shared folder of materials
- Recognition: Celebrate champion contributions
- Escalation path: Clear way to raise issues
RACI for Champion Program
| Activity | Responsible | Accountable | Consulted | Informed |
|---|---|---|---|---|
| Program design | Change Lead | Project Sponsor | HR, IT | Champions |
| Champion selection | Managers | Change Lead | HR | Candidates |
| Champion training | L&D/Change Lead | Change Lead | AI Team | Managers |
| Ongoing support | Change Lead | Change Lead | AI Team | HR |
| Resource creation | AI Team | Change Lead | Champions | All |
| Community facilitation | Change Lead | Change Lead | Champions | HR |
| Feedback collection | Champions | Change Lead | AI Team | Leadership |
| Recognition | HR/Managers | Change Lead | Champions | All |
| Program evaluation | Change Lead | Project Sponsor | Champions | Leadership |
Common Failure Modes
1. Selecting for Technical Skill Over Influence
Your best AI user isn't necessarily your best champion. Champions need relationship skills and credibility, not just technical prowess.
2. No Time Allocation
Champions asked to "fit it in" alongside full workload will abandon the role. Ensure real time is allocated and protected.
3. Launch Without Sustainment
Initial enthusiasm fades. Without ongoing support, recognition, and community, champions disengage.
4. Isolation
Champions working alone burn out and lose perspective. Build community and peer support.
5. No Clear Role Boundaries
Champions who become de facto IT support or therapists for frustrated employees exceed their scope. Set clear boundaries.
6. Ignoring Champion Feedback
Champions collect valuable information. If it disappears into a void, they stop collecting—and lose faith in the program.
7. Manager Non-Support
If champions' managers don't support their role, champion activities get deprioritized. Secure manager buy-in.
Implementation Checklist
Design Phase
- Define champion role and expectations
- Determine coverage model (how many, where)
- Design selection process
- Develop training curriculum
- Create support resources
- Plan recognition approach
- Secure manager commitment to time allocation
- Design feedback mechanisms
Selection Phase
- Communicate champion opportunity
- Collect nominations and applications
- Assess candidates against criteria
- Confirm manager support for each champion
- Select champions
- Notify selected champions and set expectations
Enablement Phase
- Deliver champion training
- Provide resources and tools
- Establish communication channels
- Launch champion community
- Brief managers on champion support needs
- Publicize champions to the organisation
Sustainment Phase
- Hold regular champion meetings
- Collect and act on champion feedback
- Recognize champion contributions
- Provide ongoing development
- Add/replace champions as needed
- Evaluate program effectiveness
- Evolve program based on learnings
Metrics to Track
Champion Activity Metrics
| Metric | Measurement | Target |
|---|---|---|
| Champion participation | Meeting attendance, channel activity | >80% active |
| Peer interactions | Self-reported or logged | >5/week average |
| Feedback submissions | Reports to AI team | Regular flow |
| Training completion | Champion training modules | 100% |
Champion Impact Metrics
| Metric | Measurement | Target |
|---|---|---|
| Adoption in champion areas | AI usage in champion-supported teams | Higher than average |
| Support requests handled | Issues resolved without escalation | Increasing |
| Peer satisfaction | Survey of employees supported | >4/5 |
| Time to adoption | Days from training to regular use | Lower in champion areas |
Program Health Metrics
| Metric | Measurement | Target |
|---|---|---|
| Champion retention | % remaining active after 6 months | >80% |
| Champion satisfaction | Champion survey | >4/5 |
| Manager support | Manager feedback | Positive |
| Pipeline | Candidates for future champions | Sufficient |
Tooling Suggestions
Communication
- Team chat platform (Slack, Teams) for champion channel
- Email for formal communications
- Meeting platform for champion community calls
Resources
- Shared document repository (SharePoint, Google Drive)
- Knowledge base or wiki for champion materials
- Training platform for champion learning
Tracking
- Simple tracking spreadsheet or app for interactions
- Feedback collection form
- Adoption analytics linked to champion-supported teams
Frequently Asked Questions
How much time should champions spend on the role?
5-6 hours per week is typical. Less than this, and impact is limited; more may be unsustainable. Adjust based on adoption phase and need.
Should champions be volunteers or assigned?
Strong preference for volunteers—forced champions rarely succeed. But recruiting from likely candidates is fine; pure self-selection may miss strong candidates who didn't think of it.
Do champions need to be experts?
No. Champions need to be competent and confident, but deep expertise isn't required. Being relatable to peers often matters more than being the best user.
Should managers be champions?
Managers can be champions, but peer champions are essential. Employees often relate better to non-manager peers. Include both.
How do we prevent champion burnout?
Clear boundaries on scope, adequate time allocation, peer support, recognition, and periodic breaks. Watch for signs and intervene early.
What if a champion isn't working out?
Have an honest conversation about what's not working. If issues persist, transition them out gracefully—thank them for their service and find a replacement.
Should champions get extra compensation?
Recognition is essential; financial compensation is optional. If compensation is provided, ensure it's meaningful and doesn't create resentment. Some organisations use this as a development opportunity rather than paid role.
When should we start the champion program?
Before or at launch—not after adoption problems emerge. Champions are most valuable during initial rollout when support needs are highest.
How long does a champion program last?
Ideally, it evolves into sustained practice. Early intensive champion support may scale back as general competency increases, but ongoing peer support adds value indefinitely.
Can one person be a champion for multiple AI tools?
Yes, if scope is manageable. But don't overload champions. Better to have focused champions than stretched ones.
Taking Action
A strong champion network can transform AI adoption from a struggle to a success. Champions provide the peer credibility, local support, and feedback loop that formal programs can't replicate.
But champion programs don't run themselves. They require thoughtful design, proper resourcing, and sustained attention. Invest in your champions, and they'll multiply your AI adoption impact.
Ready to build your AI champion network?
Pertama Partners helps organisations design and launch effective AI change champion programs. Our AI Readiness Audit includes change management capacity assessment and champion program design.
References
- Prosci. (2024). The Role of Champions in Change Management.
- Change Management Institute. (2024). Champion Networks Best Practices.
- McKinsey & Company. (2023). Scaling AI: The Role of Internal Advocates.
- Harvard Business Review. (2024). Peer Influence in Technology Adoption.
- ATD Research. (2024). Building Internal Change Capability.
Frequently Asked Questions
A network of employees across the organization who receive advanced AI training and support colleagues through AI adoption. They bridge the gap between central AI teams and frontline users.
Look for informal influencers, those open to technology, respected by peers, with strong communication skills, and time available for champion activities. Avoid only selecting enthusiasts.
Provide thorough training, give them advance information, allocate time for champion work, recognize their contributions, and create channels for them to share feedback upward.
References
- Prosci. (2024). *The Role of Champions in Change Management*.. Prosci *The Role of Champions in Change Management* (2024)
- Change Management Institute. (2024). *Champion Networks Best Practices*.. Change Management Institute *Champion Networks Best Practices* (2024)
- McKinsey & Company. (2023). *Scaling AI: The Role of Internal Advocates*.. McKinsey & Company *Scaling AI The Role of Internal Advocates* (2023)
- Harvard Business Review. (2024). *Peer Influence in Technology Adoption*.. Harvard Business Review *Peer Influence in Technology Adoption* (2024)
- ATD Research. (2024). *Building Internal Change Capability*.. ATD Research *Building Internal Change Capability* (2024)
