Send the team on an afternoon course and everyone can work with AI afterwards. That is how an AI workshop is often sold, and how many business owners hope it works.

It doesn't work that way. One workshop won't make your team AI-proficient, just like one driving lesson won't get you a license. The real return is in what employees do differently a week later. And that return depends on the format of the workshop just as much as on the content.

That's no reason to skip training. It's a reason to choose more carefully. Because a good AI workshop does pay off, just in a different way than most companies expect.

Why a one-off presentation changes so little

Knowledge you don't use right away evaporates. Everyone knows the feeling from the average course day: interesting in the moment, two weeks later you vaguely remember what it was about. An AI workshop is no exception. If all people take home is a handout and some inspiration, that inspiration is gone by the next busy week.

With AI training there's an extra catch. Research by strategic consultancy firm McKinsey shows that 7 out of 10 employees ignore formal training videos. They prefer to learn by doing and from colleagues. That's not unwillingness; a video about someone else's examples simply never touches their own working day. A workshop built around slides and demos comes in exactly the format your team already skips on its own.

Statistics Netherlands (CBS) sees the same pattern at company level. The productivity gains from AI depend heavily on what a company does around the technology, and above all on training for employees outside IT. Without that investment, the effect stays close to zero. Buying AI tools without bringing your people along is mostly an expensive way to pay for licenses.

Learning by doing measurably works better

Harvard Business Impact compared employees who became AI-proficient through their own experimentation with their colleagues. The outcome is hard to argue with: that first group is 81% more productive. People who experiment themselves discover where AI fits in their own work and where it doesn't. You don't learn that from a presentation; you learn it by trying it on your own tasks.

That is why the format of an AI workshop determines the return just as much as the content does. The same material, explained on slides or practiced on real work, produces two completely different results. An afternoon of listening feels like learning, but the skill that sticks only appears once someone runs their own work through an AI tool.

How do you recognize a good AI workshop?

The core is simple: participants apply what they learn to their own work, during and right after the workshop. In practice, that means:

• Everyone brings their own tasks. One person the weekly report, another the quote follow-up or writing up meeting notes.

• Laptops open, hands on. Watching a demo is not practicing.

• Everyone goes home with at least one task that will be faster tomorrow.

• The content is current: tools as they work now, not as they worked last year.

• There is a follow-up. Participants know where to go with questions and keep room to keep trying things.

A rule of thumb worth remembering: if nobody does a single task differently a week after the workshop, it was a presentation, not training.

That asks something of you as an employer too. Train your people and then bury them in deadlines, and everything they learned evaporates anyway. A few hours a month to experiment is enough to make it stick.

So what does a good AI workshop actually deliver?

The first return is immediately visible: time saved on recurring tasks. Reports, standard emails, minutes and summaries go from hours to minutes. That gain shows up in the very first week, precisely because the workshop practiced on those exact tasks.

The second is less visible but just as valuable: a shared foundation in the team. Everyone speaks the same language and knows the same rules about what does and doesn't belong in an AI tool. That prevents both uncontrolled use and cold feet.

The third matters most: employees who keep experimenting afterwards. They find the applications nobody could have thought of in advance, because they know their own work best.

For SMEs this is no luxury. According to Statistics Netherlands, 29.8% of Dutch SMEs used AI in 2025, versus 66.2% of large companies. That gap says nothing about what's possible, but plenty about who is already practicing. And a team that works with AI every week closes that gap faster than any tool purchase.

Want to give it a try?

Curious what this hands-on approach looks like for your team? Have a look at our AI training. Your team practices on its own tasks and goes home with results that save time the very next working day.

Sources

1. McKinsey – Redefine AI upskilling as a change imperative (2025). https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-organization-blog/redefine-ai-upskilling-as-a-change-imperative 

2. CBS – Kenmerken van bedrijven die AI-technologie gebruiken (2025), chapter Productiviteit. https://www.cbs.nl/nl-nl/longread/rapportages/2025/kenmerken-van-bedrijven-die-ai-technologie-gebruiken/5-productiviteit 

3. Harvard Business Impact – Why Hands-On Learning Is Key to Building an AI-Fluent Workforce (2025). https://www.harvardbusiness.org/insight/learning-through-experimentation-why-hands-on-learning-is-key-to-building-an-ai-fluent-workforce/ 

4. CBS – Gebruik van AI-technologie door Nederlandse microbedrijven (2026). https://www.cbs.nl/nl-nl/longread/rapportages/2026/gebruik-van-ai-technologie-door-nederlandse-microbedrijven 

5. SkillsTown – Waarom is een AI-cursus essentieel voor toekomstbestendig werken? https://skillstown.com/kennis/waarom-is-een-ai-cursus-essentieel-voor-toekomstbestendig-werken/