What is an AI Champions Program?
An AI Champions program is an internal train-the-trainer initiative that creates a network of AI advocates within your organisation. Rather than relying solely on external trainers or a central IT team, AI Champions become the go-to resource for their departments — answering questions, sharing best practices, and driving adoption from within.
This model is proven across industries and geographies. It scales AI adoption far more effectively than top-down mandates or one-time training events.
Why AI Champions Work Better Than Central IT
| Approach | Advantage | Limitation |
|---|
| Central IT team | Technical expertise | Limited understanding of business context |
| External trainers | Professional delivery | Leave after the workshop, no ongoing support |
| AI Champions | Know the department, speak the language, always available | Need initial investment in training |
AI Champions bridge the gap between technical AI knowledge and business context. They understand their department's workflows, challenges, and culture — which means they can translate AI capabilities into practical applications that their colleagues will actually adopt.
How to Build an AI Champions Program
Step 1: Define the Program Structure
Before selecting champions, define the program:
- Program name: Choose something that sounds aspirational but not gimmicky (e.g. "AI Champions", "AI Ambassadors", "AI Leads")
- Time commitment: Typically 10-15% of their working time (4-6 hours per week)
- Duration: Initial intensive training (2-3 days), then ongoing role for 6-12 months
- Reporting line: Champions report to their existing manager but have a dotted line to the AI governance committee
- Recognition: How will champions be recognised? (Certification, performance review credit, title, etc.)
Step 2: Select Your Champions
Selection Criteria
Not every enthusiastic employee makes a good AI Champion. Look for:
- Influence: Respected by peers, good communicator, natural leader
- Curiosity: Genuinely interested in AI, willing to experiment and learn
- Business acumen: Understands their department's workflows and pain points
- Reliability: Follows through on commitments, meets deadlines
- Diversity: Represent different departments, levels, and backgrounds
How Many Champions?
| Company Size | Recommended Champions |
|---|
| 10-50 employees | 2-3 |
| 50-200 employees | 5-8 |
| 200-500 employees | 10-15 |
| 500+ employees | 1 per 30-50 employees |
Selection Process
- Nominations — Invite self-nominations and manager nominations
- Applications — Short application form: why they want to be a champion, their department's AI opportunities, and their availability
- Interviews — Brief conversation with the AI governance lead to assess fit
- Confirmation — Selected champions receive a formal appointment letter and their manager is notified of the time commitment
Step 3: Train the Champions
Champion Training Curriculum (2-3 days intensive)
Day 1: Advanced AI Skills
- Deep prompt engineering (chain-of-thought, few-shot, role-based)
- Advanced use of approved AI tools
- Evaluating AI output quality and accuracy
- Troubleshooting common AI tool issues
Day 2: Governance and Risk
- Company AI policy deep dive
- Data privacy and PDPA compliance
- AI risk assessment process
- Incident identification and reporting
- Handling employee questions about AI ethics
Day 3: Teaching and Advocacy
- How to deliver informal training sessions
- Creating department-specific use cases and prompts
- Change management techniques for AI adoption
- Handling resistance and fear
- Measuring and reporting adoption progress
Ongoing Champion Development
After the initial training, champions need continued support:
- Monthly champion meetings — Share experiences, discuss challenges, learn from each other
- Quarterly skill updates — Training on new tools, techniques, and policy changes
- Access to resources — Prompt libraries, templates, and external learning materials
- Direct line to IT/governance — Fast escalation for technical issues and policy questions
Step 4: Deploy Champions
Champion Responsibilities
Each AI Champion is responsible for:
- Department AI training — Conducting informal training sessions (lunch-and-learns, desk-side coaching, team meetings)
- Use case development — Identifying and prototyping AI applications for their department
- First-line support — Answering questions about AI tools, prompts, and policies
- Adoption tracking — Monitoring and reporting AI usage in their department
- Feedback collection — Gathering user feedback and reporting to the governance committee
- Policy compliance — Ensuring their department follows the AI acceptable use policy
Champion Activity Calendar (Monthly)
| Activity | Frequency | Time |
|---|
| Department training/demo | 1-2 per month | 1 hour each |
| Champion team meeting | Monthly | 1 hour |
| Use case development | Ongoing | 2-3 hours/week |
| Ad-hoc support | As needed | 1-2 hours/week |
| Progress reporting | Monthly | 30 minutes |
Step 5: Measure and Iterate
Champion Program KPIs
| Metric | Target | Measurement |
|---|
| Department AI adoption rate | 60%+ using AI weekly | Tool usage analytics + survey |
| Training sessions delivered | 2+ per champion per month | Activity log |
| Use cases developed | 3+ per champion per quarter | Use case register |
| Employee satisfaction with AI support | 4.0/5.0+ | Quarterly survey |
| AI incidents in champion departments | Below company average | Incident log |
| Champion retention | 80%+ stay in programme | Programme records |
Funding the Program
Direct Costs
- Champion training (2-3 days): Claimable under HRDF (Malaysia) or SSG (Singapore)
- AI tool licences for champions: Often included in enterprise licence
- Champion recognition/rewards: Modest budget for certificates, events, etc.
Opportunity Cost
- 10-15% of each champion's time redirected to AI activities
- This is the primary "cost" and should be agreed with department managers upfront
ROI
- Companies with AI champion programmes typically see 2-3x higher AI adoption rates
- The productivity gains from widespread AI adoption far exceed the champion time investment
- Champions often develop into future technology leaders, benefiting their career development
Common Mistakes
- Selecting champions based on seniority rather than suitability — Choose the right people, not the most senior
- Under-investing in champion training — Champions need deeper training than regular employees
- Not protecting champion time — If managers do not honour the time commitment, the programme fails
- Ignoring champion burnout — Monitor workload and provide support; rotate champions if needed
- Not recognising champion contributions — People need to feel valued for their extra effort
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