The AI Champion Model

An AI champion is a person inside a team who helps their colleagues adopt a workflow: answering questions, demonstrating the result, and feeding back what is not working. The model works well in small businesses, and it fails in a predictable way when it is built around one enthusiastic person rather than a designed role.

This guide covers how to choose champions, what to give them, how to support them, and how to avoid turning the program into one person’s unpaid second job. It is part of the Skills, Change & Measurement pillar.

A champion is not the AI expert. They are the person your colleagues already trust.

Why the model works

Three advantages, and they are the reason it is worth designing rather than improvising.

• Peer credibility. A colleague demonstrating a genuine time saving is more persuasive than a trainer, particularly with sceptical teams.
• Proximity. Champions answer questions at the moment they arise, which is when people are most likely to ask.
• Feedback. Champions surface what is not working, which is the cheapest source of workflow improvement a program has.

The third advantage is the one most often overlooked. A champion is not only a conduit for the workflow; they are the program’s most reliable channel for bad news.

Choosing champions

Five criteria, and the first is the one that matters.

• Credibility with their team. A skilled, well-liked practitioner. Enthusiasm alone produces an advocate nobody listens to.
• A practitioner, not a manager. The champion should be doing the work the workflow changes.
• Willingness. It should be an offer, not an assignment. A reluctant champion is worse than none.
• Adequate capacity. The role needs hours, not goodwill.
• One per team, rather than one per business, so the proximity advantage holds.

The most common error is choosing the most enthusiastic person in the business, who is frequently not the most credible and quickly becomes overloaded.

What to give them

Four things, and the absence of any one produces the burnout pattern.

• A mandate. An explicit remit: help colleagues run the workflow, answer data-rule questions, feed back what is not working.
• Time. Named hours, acknowledged in their workload rather than added to it.
• Access. To the program owner, and to whoever owns the tooling, so a blocked question is not a dead end.
• A network. Regular contact with the other champions, so they are not solving the same problem alone.

The last of these is the cheapest and the most effective. A monthly conversation between champions eliminates most duplicated effort.

Keeping the accountability in the right place

A champion helps; they do not own the outcome.

• The use-case owner remains accountable for the workflow and the output.
• The program owner remains accountable for the roadmap and the measures.
• The champion is a helper with a remit, not a scapegoat when something goes wrong.

Where this line blurs, two failures follow. The champion carries accountability without authority, and the accountable owner stops paying attention to the workflow they are supposed to own.

When the champion leaves

The model’s structural weakness is that it depends on people, and people move.

• Document the workflow and the playbook, so the process does not depend on the champion.
• Have a second champion in each team where the workflow is critical.
• Keep the accountability with the owner, so the champion’s departure is a capacity loss rather than a program failure.
• Treat the role as transferable, with a short handover note covering what the team asks most often.

A champion model built on documented workflows survives a champion leaving. One built on the champion’s personal knowledge does not, which is why the documentation is part of the role rather than a separate activity.

A worked arrangement

A fourteen-person firm runs two workflows and appoints two champions.

• The finance champion is the management accountant, who has run the reporting workflow since the pilot and answers questions from the finance team without escalating.
• The operations champion is a team leader who ran the meeting-notes workflow and now helps two colleagues adopt it.
• Time: each has four hours a month named in their workload, which is roughly what the role consumes in practice.
• Network: they meet monthly with the program owner for twenty minutes, which is where most of the improvements originate.
• Accountability: the finance lead and operations manager remain accountable for the workflows and the outputs.

The arrangement produces two effects that are worth naming. Questions get answered in minutes rather than waiting for a training session. And the monthly conversation surfaces small problems — a prompt that misfires on a particular document type, a data-rule question that has come up twice — long before they become a reason to abandon the workflow.

What the champion does not do

The boundary is worth stating as a list, because blurring it is how the model fails.

• They do not own the workflow or its output. That is the use-case owner.
• They do not own the policy, though they should know it well enough to answer routine questions.
• They do not own the tooling, though they need access to whoever does.
• They do not become the only person in the team who can run the workflow, which is why documentation is part of the role.
• They do not absorb the training burden for new joiners without the time to do it.

A champion who does all five has become a de facto program owner without the title, the time or the authority — and the model will be blamed when they leave.

Common mistakes

• One champion for the whole business. Overload, and a single point of failure.
• Enthusiasm as the main criterion. Credibility matters more.
• No time allocated. The role becomes unpaid work, and it stops.
• No network. Champions duplicate effort and lose momentum.
• Accountability blurred. The champion carries outcomes without authority.
• No documentation. The workflow leaves when the champion does, along with the answers to the questions the team asks most often.

Frequently asked questions

What is an AI champion?

A person inside a team who helps colleagues adopt a workflow — answering questions, demonstrating the result, and feeding back what is not working. They are a helper with a remit, not the accountable owner.

How many AI champions should we have?

One per team where a workflow is running, rather than one per business. Proximity is what makes the model work, and a single champion becomes overloaded.

Should the champion be the most enthusiastic person?

Not necessarily. Choose for credibility with the team and for being a practitioner of the work. Enthusiasm helps; it is not the main criterion.

Should champions be paid extra?

They should have allocated time, acknowledged in their workload. Whether that is additional pay is a company decision; what matters is that the role is not an unpaid addition.

What happens when a champion leaves?

If the workflow and playbook are documented and accountability sits with the use-case owner, the departure is a capacity loss rather than a program failure. Have a second champion where the workflow is critical.

How do champions stay effective?

Give them time, access, a network with the other champions, and a mandate — and keep the accountability for outcomes with the use-case owner.

Should the champion be a manager?

Usually not. The role works best when the champion does the work the workflow changes, because that is what makes them credible to colleagues and fast at answering questions. A manager can sponsor without championing.

Do we need a champion for every workflow?

Where a workflow runs in one team, usually yes; where it is used by one person, no. The role exists to answer questions in proximity, so it is worth having only where there are questions to answer.

Next step

Choose one champion per team on credibility rather than enthusiasm, give them time and a remit, and start a monthly champions’ conversation. See Change Management for AI and AI Skills by Role, or book a team training session and we will set up the model with you.

Sources

• Champion and community-of-practice models reflect standard change-enablement practice applied to AI adoption: peer credibility, allocated time, a network, and documented processes.

No statistic in this article is invented; where figures appear in the linked guides, they are cited there with their source and date.