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AI at work: 72% use it, 79% have never been trained

A majority of working people in French-speaking Switzerland already use AI in their daily work, but almost none have received structured training on these tools, changing how skills are now assessed.

AI at work: 72% use it, 79% have never been trained

Nearly three-quarters of working people in French-speaking Switzerland use generative AI in their work, but a large majority of them have never received training on these tools. In other words, the skill has been built independently, through trial and error, while organizations looked the other way.

This gap has a direct impact on employability: knowing how to use an AI tool no longer guarantees anything in itself, since almost everyone already does so, often poorly or inconsistently. What now distinguishes a candidate or employee is their ability to demonstrate controlled, documented, and measurable use—not simply a line on a CV.

What this changes in practice

  • Recruiters are beginning to test actual AI use during interviews rather than relying on a generic mention on a CV.
  • Companies that invest in structured training create a measurable competitive advantage over those that leave their teams to figure things out on their own.
  • For candidates, unstructured self-training becomes a hidden risk: habits developed without guidance may include methodological errors that are difficult to correct later.

This week: choose an AI tool you already use regularly and note three specific use cases, supported by a quantified or verifiable result (time saved, an error avoided, or improved quality). These concrete examples—not a vague statement about using AI—will make the difference in an interview or annual review.

LinkedIn post idea to adapt

“I have been using AI at work for several months. Here are 3 concrete cases where it helped me save a measurable amount of time, and 1 mistake it caused me to make before I learned to check its output systematically.” This factual and nuanced format demonstrates genuine proficiency rather than simply following a trend.

Educational approach for trainers

Ask each participant to bring a recent, real example of using AI in their work, along with the result achieved. Build the session around these cases rather than a generic presentation of the tool: this immediately reveals differences in proficiency within the same group.