I’ve watched organisations try to scale AI adoption through central training programs alone. It rarely works. The training team delivers sessions, people learn something, then return to work where no one around them uses AI and old habits reassert themselves.

The missing ingredient is distributed expertise—people embedded throughout the organisation who model AI use, answer questions, and provide ongoing support.

Enter the AI champions network.

What an AI Champions Network Does

An AI champions network consists of volunteers (or appointed individuals) across the organisation who:

• Model effective AI use in their own work
• Help colleagues troubleshoot AI challenges
• Share tips and successful approaches
• Identify use cases specific to their area
• Connect local needs to central resources
• Provide feedback to improve organisation-wide efforts

Champions aren’t AI experts. They’re enthusiastic early adopters who are a step or two ahead of their colleagues and willing to help others along.

Why Networks Beat Central Training

Central training has inherent limitations:

Timing mismatch. Formal training happens on a schedule. Questions arise continuously. By the time the next session happens, people have moved on.

Context gap. Central teams can’t know every function’s specific challenges. Training tends toward generic content that doesn’t address particular use cases.

Scale constraints. There are only so many L&D professionals. They can’t provide ongoing support to everyone.

Credibility dynamics. Peer advice often lands differently than advice from central functions. “Sarah in our team does it this way” is more compelling than “the training said to do it this way.”

Champions address all of these:

• They’re available when questions arise
• They understand their area’s specific context
• They multiply support capacity across the organisation
• They’re trusted peers, not central authorities

Selecting Champions

Not everyone is suited to be a champion. Look for:

Enthusiasm for AI. Champions should genuinely find AI interesting and valuable, not just comply with being assigned the role.

Learning orientation. AI evolves rapidly. Champions need to stay current, which requires continuous learning.

Communication skills. Being good at AI isn’t enough. Champions need to explain things clearly to less experienced colleagues.

Patience. Helping others with technology requires patience with varying learning speeds and repeated questions.

Credibility. Champions should be respected in their areas. Endorsement from someone people trust matters.

Availability. Champion work takes time. People who are already overloaded won’t have capacity.

A mix of functions, levels, and locations provides broader coverage. Don’t recruit only from one area.

Structuring the Network

Networks can be structured various ways depending on organisational context:

Hub and Spoke

Central coordination (usually L&D or IT) supports distributed champions:

• Central team provides training, resources, and coordination
• Champions operate semi-independently in their areas
• Regular connection points maintain alignment

Peer Network

Champions connect primarily with each other:

• Regular forums for sharing experiences
• Peer learning and problem-solving
• Less central coordination, more organic development

Embedded Team Model

Champions formally assigned partial time to the role:

• Explicit time allocation for champion activities
• Clear expectations and accountability
• More structured than volunteer approaches

The right structure depends on organisational culture, size, and existing communication patterns.

Developing Champions

Champions need development to be effective. A typical program includes:

Initial Foundation (1-2 days)

• Deeper AI skill development than general workforce
• Understanding of champion role and expectations
• Facilitation and coaching basics
• Resources and support available

Ongoing Development

• Regular updates on new AI capabilities and use cases
• Advanced skill development sessions
• Troubleshooting forums for challenging situations
• Peer learning opportunities

Just-in-Time Support

• Access to expertise for questions champions can’t answer
• Resources for common situations
• Escalation paths for complex issues

Community Building

• Regular champion gatherings (virtual or in-person)
• Shared channels for ongoing communication
• Recognition for champion contributions
• Opportunities to influence organisation-wide approaches

AI consultants Sydney across Australia can support champion networks by providing structured skill development pathways and communities of practice infrastructure.

Supporting Champion Activities

Champions need support to succeed:

Time Allocation

Champion work takes time. If it’s purely additive to existing workloads, it won’t happen. Consider:

• Explicit time allocation in workload planning
• Recognition that champion activities are legitimate work
• Manager support for time spent on champion duties

Resources

Equip champions with:

• Quick reference guides they can share
• Approved templates and examples
• Access to extended resources for complex questions
• Direct line to central expertise

Recognition

Acknowledge champion contributions:

• Visibility for champion achievements
• Inclusion in champion branding
• Career development credit
• Leadership access and influence opportunities

Authority

Clarify what champions can and can’t do:

• What guidance can they provide directly?
• When should they escalate?
• How do they connect to formal support channels?
• What decisions can they make?

Measuring Champion Impact

Champion networks should demonstrate value:

Activity Metrics

• Number of colleagues supported
• Questions addressed
• Sessions or demonstrations conducted
• Resources shared

Outcome Metrics

• AI adoption in champion areas vs. non-champion areas
• Capability development in champion-supported teams
• Time to proficiency for new AI users
• Satisfaction with AI support

Qualitative Feedback

• Colleague perceptions of champion helpfulness
• Champion perceptions of their own effectiveness
• Specific examples of impact
• Improvement suggestions

Don’t over-engineer measurement—champions shouldn’t spend more time tracking than helping. But some visibility into impact supports the investment case.

Common Pitfalls

Selecting the Wrong People

Not every eager volunteer makes a good champion. Being good at AI doesn’t mean being good at helping others with AI. Screen for the full set of required attributes.

Insufficient Support

Champions left without development, resources, or time allocation become frustrated and ineffective. The network requires ongoing investment.

Disconnection from Strategy

Champion networks that drift away from organisational AI strategy become random and inconsistent. Maintain connection between champions and central direction.

Role Overload

Successful champions get overwhelmed with requests. Build in mechanisms to prevent burnout—whether through capacity limits, escalation paths, or additional champions.

Stagnation

Champion networks can become stale over time. Regular refreshment—new champions, new challenges, renewed development—maintains energy.

Evolution Over Time

Champion networks evolve as AI adoption matures:

Early Stage: Champions are pioneers, exploring what’s possible and sharing discoveries with cautious colleagues.

Growth Stage: Champions focus on scaling adoption, supporting the majority as they catch up with early adopters.

Maturity Stage: Champions address advanced use cases and edge situations as basic AI use becomes routine.

Integration Stage: Champion skills become expected of all employees. The dedicated network may become less necessary.

Plan for these transitions rather than assuming the network’s role remains static.

Building the Case

To secure investment in a champion network:

Connect to adoption goals. What AI adoption outcomes is the organisation seeking? How do champions accelerate those outcomes?

Show the gap in current approach. Where is central training insufficient? What support needs aren’t being met?

Quantify potential impact. How much faster could capability develop with champions? What’s the value of that acceleration?

Propose pilot approach. Start with a contained pilot in one area, demonstrate results, then expand.

Address concerns. How will consistency be maintained? What happens if champions provide wrong guidance? How does this integrate with existing support?

The Human Element

At its best, a champion network isn’t just a training delivery mechanism. It’s a community of people who believe in AI’s potential and want to help others realise it.

That human element—people helping people—is often what makes the difference in adoption. Technology adoption is a human process. Networks of committed individuals accelerate it in ways that programs alone cannot.

Build the network. Develop the champions. Support their work.

The investment pays off many times over.