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Train your teams for practical AI use

Useful training starts with the tasks participants need to perform. It teaches them to prepare a request, review an output and recognise when a colleague or an established method is still needed.

Your starting point

For leaders, managers and business teams building a shared, supervised way of working.

Skills, tools and expectations often differ within the same team. A general presentation is not enough to establish new habits. Exercises need to remain close to the working context.

How to move forward

1

Tailor the learning path

Review current use, approved tools and difficulties. Define observable learning objectives and select exercises compatible with the organisation's rules.

2

Practise and review

Work through prepared situations: structure a request, improve an answer, spot invented information and verify facts. Compare approaches with the participants.

3

Build lasting habits

Choose uses to practise after the session, define review points and identify an internal contact. Plan how difficulties will be collected and practices adjusted.

Agree before starting

  • Exercises linked to real job tasks.
  • Habits for review and confidentiality.
  • Objectives suited to the starting level.

Your questions

Can beginners and regular users learn together?

Yes, when the learning path supports it. An initial review helps establish common ground and differentiated exercises where skill gaps are significant.

Should we bring internal documents?

It is not essential. Prepared or anonymised material can support exercises. Any use of internal documents should be agreed in advance.

How can training be evaluated?

By observing completed tasks and the ability to explain the checks applied. Evaluation and any follow-up are agreed according to the learning objectives.

One process. One next step.

Start with your daily work to decide what is worth checking together.