TRAINING PLANNING / A GUIDE FOR HR & LEARNING TEAMS
How to choose
an AI trainer.
Start with what your people need to do differently. Then look for a trainer who can turn that goal into clear explanations, relevant practice and a way to evaluate the result.
1. Define the outcome before comparing trainers
“We want to learn AI” is a starting point. A more useful brief names an audience, a task and an observable result. For example: “Our administration team should be able to turn approved meeting notes into a checked action list and follow-up email.”
That brief helps you distinguish a general awareness session from a practical skills workshop. It also gives the trainer enough information to propose suitable exercises and a sensible starting level.
Write down two or three tasks, the participants’ current experience and the tools your organisation permits. If the group includes beginners and regular users, say so. A shared job title does not necessarily mean a shared starting point.
2. Look at teaching experience as well as tool knowledge
An AI trainer needs to explain a process clearly and help people practise it. Ask how the trainer introduces unfamiliar ideas, responds when participants get different outputs and supports people who need more time.
Look for evidence of adult teaching and relevant practical work. A trainer’s applications, creative projects or workflow examples can help you understand their approach. Read experience claims carefully: years in a broader profession and years teaching AI are different pieces of information.
You can review my training background and credentials, application projects and creative portfolio to see how those parts of my practice connect.
3. Ask what participants will actually do
A useful workshop gives participants something to attempt, inspect and improve. Ask for an example activity and the learning it is meant to support. The activity should fit the audience’s tasks and the time available.
For a writing exercise, participants might compare a draft with a supplied brief. For content planning, they might develop an outline and explain why they chose one direction. For a recurring workflow, they might document the inputs, prompt and review steps.
Ask what tools, accounts and source materials are needed. Resolve access and permissions before the session so practice time can be used for learning. The AI workshop preparation checklist covers these arrangements.
4. Check how output quality will be taught
A confident AI response can still contain an error, omit an important detail or misunderstand the task. A practical programme should make review part of the exercise.
Ask how participants will check accuracy, identify assumptions, compare the response with source material and edit for the audience. They should also know when a task needs specialist knowledge or a different process.
It helps to define a simple review standard in advance: the required facts are present, unsupported claims are removed, the format fits the task and a person takes responsibility for the final result.
5. Plan what happens after the workshop
Choose a small follow-up task participants can repeat in their own work. Keep a sample input, reusable instructions and a short review checklist. Ask people to explain what they changed and why, rather than only sharing a finished output.
Evaluate the result against the original learning goal. Did the participant use the right source material? Could they improve a weak response? Can they describe the checks they made? This gives you more useful evidence than confidence alone.
When comparing proposals, look at the fit between the audience, exercises and outcomes. A longer list of tools does not automatically make a programme more useful.
Turn the guide into a brief
Include your audience, current experience, relevant tasks, approved tools and preferred arrangements. Then ask how the proposed training will help participants practise and evaluate those tasks.