Building an AI champions network inside your organisation
By TechlyUpUpdated 2 min readTransformation and L&D leaders
Quick answer
An AI champions network is a group of trained employees across departments who help colleagues adopt AI safely and effectively. Select curious, respected people from each team, give them deeper training and time, connect them in a regular forum, and have them collect and share working methods and problems.
Select the right people
Champions should be respected by peers, curious, and good communicators — not necessarily the most technical.
Train and equip them
Champions need more than the basic course.
- Deeper training on tools, policy, and evaluation.
- A shared library of prompts and case notes.
- A direct line to IT/security for questions.
- Agreed time allocation for champion work.
Run a regular forum
Monthly sessions where champions share what worked, what failed, and questions from their teams keep the network active.
Recognise the work
Include champion contributions in goals and reviews so it isn't unpaid extra work.
Champion network mistakes
These cause networks to fade.
- No time allocated, so champion work competes with everything else.
- No central coordination or shared resources.
- Champions chosen only for technical skill.
- No recognition in performance reviews.
A launch plan
Month one: select and train champions, set up a shared library and channel. Month two: each champion runs a short session for their team. Month three: champions share results in a forum and collect the next set of use cases.
Review the network quarterly and rotate or add champions as needed to keep energy high.
Try it yourself
Identify one potential champion in each department and draft what you'd ask of them.
Frequently asked questions
How many champions do we need?
Roughly one per team or department is a common starting point.
Should champions be volunteers?
Volunteers bring energy, but agree time and recognition formally.
What do champions do day to day?
Answer questions, share methods, collect feedback, and flag risks.
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Sources and further reading
Examples are authored practice material, not measured learner outcomes. Tool behavior can change. Found an error? Contact TechlyUp with the page URL and correction.