AI Leadership Principles / People

How do we build AI skills across the organisation?

Build learning paths by role: everyone, managers, builders and risk owners. Make the practice hands-on, on people’s own work, with coaching. Protect time for it, redesign roles alongside the training, and keep a record of who has learned what.

A guide by Ville Ylläsjärvi, Herman Foundry

Why it matters now

Skills, not models, are now the most common thing holding companies back. A one-off course does not change how people work on Monday morning. Regular practice on real tasks does, with someone to ask and a manager who makes room for it. In the EU there is also a legal reason: organisations that use AI are expected to make sure their people understand it.

Common mistakes

  • One-off, generic e-learning that nobody applies to their own work.
  • Training people on tools without redesigning their roles.
  • Expecting people to learn in their spare time.
  • Assuming managers and executives already know enough.

What to do instead

  1. Set paths by role. Everyone needs the basics. Managers need to lead the change, builders need depth, and risk owners need to know what can go wrong.
  2. Practise on real work. Use people’s own tasks and documents, with coaching from someone who has done it.
  3. Protect the time. Put learning time in the calendar and treat it as work, not a favour.
  4. Redesign roles alongside. As people learn, change what their role expects of them, or the new skills fade.
  5. Keep records. Note who was trained on what and when. It helps you plan, and in the EU it supports your AI-literacy duty. Check the details with counsel.

Your first step this week

Ask each executive to name one task in their own week where they will practise with AI, and to share what they learned at your next meeting. Skills start at the top.

All AI leadership principles and guides · 100 AI use cases by function

Sources

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