Licences are not adoption
Many businesses buy ChatGPT, Copilot or Gemini seats and then see a handful of enthusiasts use them while everyone else carries on as before. The tool is rarely the problem. People need to see what AI can do for their own work, what it should not be trusted with and where the company draws the line on data. Without that, usage stays shallow and the risk stays unmanaged.
Teach by role, not by feature
A finance team, a sales team and an operations team need different examples. Finance might reconcile statements and draft variance notes. Sales might research accounts and prepare proposals. Operations might summarise reports and write procedures. Short sessions built around real tasks from each team are more useful than a general tour of every feature.
Cover judgement as well as prompts
Good prompting matters, but so does knowing when an answer needs checking, how to give a model the right context and which information should never be pasted into a public tool. Include confidentiality, accuracy checks and citation habits in every session. A short responsible-use policy gives people a clear reference after the training ends.
Move from using AI to building with it
Once teams are confident, the next step is often a simple custom assistant or agent built around a repeated task. Training is a good place to collect those ideas. The people doing the work usually know exactly where a well-scoped assistant would save time.
Measure what changes
Agree a few signals before the programme starts: active use, time saved on selected tasks and the number of workflow ideas raised. Review them after a month. Dubai Chambers’ agentic AI training drive for its member companies shows how quickly expectations are moving; the businesses that benefit most will be the ones whose people know how to use the tools well.
Further reading
Prepared with AI assistance; technical references checked against the documentation linked above. These are planning considerations, not a guarantee of project results.
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