How to train internal AI champions

You can commission Syntalith to develop a team training program for employees who will help others use AI. We propose combining practice on company tasks with learning to explain choices and give useful feedback. The scope covers choosing work worth teaching, preparing context and data, reviewing AI responses, and developing reusable examples. Before agreeing on the program, we discuss participants' work and the tools their company allows. The proposal specifies the sessions, materials, and any follow-up support.

A champion needs more than tool skills

A good candidate understands their department's work and notices where colleagues get stuck. Enthusiasm for new AI features can help, but this role also takes patience with questions that now seem obvious to an experienced user. The manager should agree on time for supporting colleagues so that helping the team does not become an extra responsibility squeezed between the champion's own tasks.

Start with work that colleagues actually repeat and know how to assess. Preparing commentary on a report calls for a different explanation than comparing documents. Champions learn to show why they use AI at a particular point, what the tool needs from them, and how they judge its response. That helps a colleague recognize when an example applies to their own work.

Participants' roles shape the choice of tasks. Someone supporting sales will use different materials from a champion in operations. AI experience is a separate consideration: some participants need practice checking sources, while others use the tools confidently but skip steps a beginner needs explained. Showing a new feature will not address both needs.

Fixing a prompt does not finish the teaching

Suppose an employee asks a champion for help summarizing a document. The champion adds context to the prompt, edits the response, and sends back the finished text. When the next document arrives, the colleague asks for the same help again. They do not understand why the extra information mattered or how the summary was checked.

In training, this example is a way to examine how the champion helps. The participant explains what was missing from the request and shows how they compared the response with the document. Feedback also addresses whether they gave their colleague a chance to handle the next step independently. Producing an accurate summary and explaining the approach clearly to someone else each deserve attention.

What champions need when helping colleagues

Work on context includes explaining the purpose of a task and who will use the finished work. Participants practice explaining their choice of tool and identifying the source material it needs. They work with examples that follow company data rules, so they can later explain what a colleague may use in that environment.

When reviewing an AI response, champions need to recognize a missing source, an omitted fact, or a conclusion the document does not support. They also need to describe the problem in a way that helps the person revise their work. Saying "try again" leaves the colleague with the same difficulty. Useful feedback identifies what needs to change and where to check the answer.

A reusable example should show when it is useful, what data it needs, and how to check the response. Participants can develop one together during training and add questions their colleagues already ask. The person responsible for the materials updates the explanation when the tool or workflow changes. After a period of practice, a participant can independently develop a new example for a colleague and review the explanation and checking approach with the trainer.

Where peer support ends

Champions need to know whom to approach when training cannot resolve a problem. Missing tool access calls for IT support; unclear rules for using a document need an answer from the person responsible for the data or process. The company identifies those contacts before asking champions to support others. Champions can then refer the issue without setting new rules themselves.

In its Copilot adoption resources, Microsoft suggests networks of advanced users, hands-on support and training, and collecting feedback. A company adopting this approach needs to make time for colleagues to work together. Naming champions alone does not tell them when or how they should help.

If you do not yet know why employees stop using a tool, first consider the reasons for low AI use after buying licenses. A champions program makes sense when the company wants selected employees to regularly help colleagues learn through their actual work.

Start with the help that keeps coming back

Tell us about a task colleagues repeatedly bring to your most experienced AI user. We can discuss what future champions need from training and how to review the explanations they prepare. Ask about team training. You can also visit Syntalith's pricing page.