Which is better, internal AI champions or an outside AI trainer?
Internal champions fit when you already have a few people who use AI well, know your work deeply and have time set aside to help colleagues. An outside trainer fits when nobody inside has that depth yet, when you need the whole team moving at once, or when leadership needs a structured program with rules and measurement. Most firms do best combining them: an outside trainer to set the foundation and train the champions, then champions to keep it going day to day.
The deciding question is whether your best internal AI users have both the skill and the time to teach.
Who this is for
Owners, managing partners, COOs and team leads who want their people using AI on real work and are deciding who should do the teaching.
The short version
- Champions are close to the work and cheap to start, but they usually teach what they personally use, and the role often gets squeezed by their day job.
- An outside trainer brings structure, breadth and a fresh view, but costs more up front and can’t sit next to your team every day.
- Either option fails if there are no clear rules for which tools and data are allowed, and nobody checks whether people actually changed how they work.
What each option means
Internal AI champions are employees who already use AI well and are asked to help others: running short demos, answering questions, sharing prompts and showing how they handle real tasks. It is usually a part-time role layered on top of their normal job.
An outside AI trainer is a person or firm brought in to design and run training: live sessions, cohort programs, exercises on your own documents and workflows, and often help with tool setup and usage rules.
How the two usually compare
These are typical patterns, not rules. Results depend on the people involved and how the program is set up.
| Factor | Internal champions | Outside trainer |
|---|---|---|
| Upfront cost | Low, mostly staff time | Higher, a fee for the program |
| Knowledge of your work | Deep | Has to learn it first |
| Breadth of AI knowledge | Limited to what they use | Usually wider, across tools and use cases |
| Structure and curriculum | Often informal | Planned, with sequence and exercises |
| Speed to reach everyone | Slow, depends on their spare time | Faster, sessions can cover whole teams |
| Credibility with peers | High if respected | High if training uses your real work |
| Staying power | Good if the role is protected | Ends unless follow-up is planned |
| Rules, safety and policy | Often left out | Usually part of the program |
| Best when | Skill and time already exist inside | You need a foundation or a fast start |
When internal champions are the better choice
- You already have two or three people using AI well on the firm’s actual work, not just experimenting.
- Leadership is willing to protect part of their week for helping others, in writing.
- Your team is small enough that informal, desk-side help can reach everyone.
- The basics are already in place: approved tools, clear rules on client data and some shared prompts or examples.
- You want help that is always nearby, not a scheduled session.
Champions are especially good at the “how do I actually do this on my file” questions that come up after training. See get partners to use AI for how peer examples change senior people’s minds.
When an outside trainer is the better choice
- Nobody inside the firm has used AI deeply enough to teach it with confidence.
- You need a whole team or office trained on a timeline, not over many months.
- Leadership wants a clear program with goals and a way to measure results. Our guide to measuring AI training results covers what to track.
- You don’t yet have rules on which tools and data are allowed. See how to write an AI acceptable use policy.
- Some staff are anxious or skeptical, and a neutral outside voice will land better than a colleague. Our page on employees afraid AI will replace them covers that conversation.
The honest trade-offs
Champions burn out or get pulled back. When client work gets busy, the champion role is usually the first thing to slip. If you choose champions, give the role real hours and recognition, or expect it to fade.
Champions teach their own habits. Someone great at using AI for drafting emails may not know how to use it for analysis, research or document review. Breadth is the gap most informal programs leave.
Outside training can feel generic. A trainer who uses canned examples instead of your documents and workflows will get polite attention and little change. Ask any trainer how they will use your real work. Our guide on how to choose an AI training company lists what to check.
Outside training ends. A strong program still fades if nothing happens the week after. This is where champions matter most. Our comparison of a one-time workshop vs ongoing coaching covers this in detail.
Tools change quickly. AI products change features, names and pricing often. Whoever teaches your team needs a way to keep the material current, whether that’s an outside trainer’s refresh or champions sharing what’s new.
A combined approach that often works
Many firms get the best result by using both in sequence. An outside trainer sets the foundation: rules, approved tools, and hands-on sessions built around the team’s own work. During that program, the trainer identifies and coaches a small group of champions more deeply, so they can answer questions and spread good examples afterward. The firm then protects some of the champions’ time and checks in on usage after a few months. For a longer look at this path, see building AI capability inside your firm.
Questions to ask before choosing
- Who inside the firm uses AI well on real work today, and how many of them are there?
- Will leadership protect their time to help others?
- Do we have clear rules on which tools and data people can use?
- How quickly do we need the whole team using AI?
- How will we know, in three months, whether people actually changed how they work?
If you are still deciding whether to bring in outside help at all, see do AI adoption yourself vs hire an AI consultant.
How Ruby helps
Ruby Advisory’s recommended path for most firms is to combine the two: we run the foundation training on your team’s own work, then help you pick and coach internal champions so the habits last after we step back. We help with:
- AI training: live sessions and cohort programs built around your real documents and workflows, plus deeper coaching for champions.
- AI strategy: choosing tools, writing the rules and deciding where to start.
- AI engineering: agents and automations for the work your team repeats most.
To talk through which approach fits your team, book a call.