A champion network is a group of staff, usually one or two per team, who take on an extra role helping colleagues adopt a new way of working. For AI agents, champions are trained early and more deeply, answer colleagues' questions, and feed problems back to the agent owner. They are a bridge, not a help desk.
Why build one?
Because most questions about working with an agent arise at the desk, in the moment. Why did it hold this? Am I allowed to approve that? Is this refusal right? A central team cannot answer every such question quickly across every shift and site. A colleague two desks away can, and people ask colleagues more readily than they raise a ticket.
Champions also see what the central team cannot. They notice that the agent keeps holding a case type that should be routine, that one shift has started skipping the reason field, or that a new starter is approving without reading the facts. Reported early, those become fixes rather than incidents.
What does a champion do?
- Answers questions on the agent, the verdicts and the review process.
- Models good practice, such as reading provenance, writing reasons and escalating properly.
- Gathers feedback and passes patterns to the team lead and agent owner.
- Supports new starters through their first weeks on the queue.
- Attends a regular champions' meeting to share what they are seeing across teams.
Champions should not become the people who decide everyone's hard cases. That concentrates judgement and stops others building it. Their role is to help colleagues decide, not to decide for them.
An example
Take a hypothetical building society with branches and a central operations hub. When an agent goes live for account servicing, each branch and each hub team names a champion. Champions complete their training two weeks ahead of colleagues and spend time on the agent in shadow. In the first month, a branch champion notices that colleagues are confused by refusals for joint account changes, which the agent correctly refuses when only one holder has asked. She explains the rule locally and reports the confusion; the central team adds a clearer reason to the refusal text. The setup steps are in how to set up an internal AI agent champion network.
How is it different from upskilling?
Upskilling is the process of teaching people new skills for their existing role. A champion network is a structure that supports it, by putting trained people close to those learning. Upskilling can happen without champions, through courses alone, but it usually sticks better with them. Reskilling is different again: training people for a different role, such as a processor becoming a reviewer.
A champion network also differs from a centre of excellence. The centre is a small central team that sets standards; champions are spread through the business and help apply them locally.
How do you keep champions effective?
Give them real time for the role, not just a title; recognise the work; train them first and more deeply; and refresh them as the agent changes. Write down what is expected in a champion network charter. The common pitfalls, such as choosing enthusiasts without credibility, are covered in how do you build an internal champion network for AI agents. The guide to training teams for AI agents shows where champions fit.
Where the Academy fits
Champions benefit from being ahead of their colleagues. Pryme Intelligence Academy's Foundations track takes about four hours; champions who will support reviewers should also complete the Operator track and its practicum, so their help rests on a certified standard rather than enthusiasm.
Questions people ask
How many champions does a team need?
One per team or shift is a common starting point, with a second where teams are large or work across sites.
Should champions be the most senior people?
Not necessarily. Credibility with colleagues matters more than rank. A respected reviewer often makes a better champion than a manager.