What if in a few months this tool will have completely changed or even been superseded?
Until recently it was simple: you learned a software, it became a standard and the time spent navigating menus and shortcuts paid off for years.
Today, as soon as you touch AI, that deal is off.
Interfaces change, results change, prices change, rules change. And above all the “champion” changes: the tool that seems indispensable today is tomorrow superseded or absorbed by another. Exhausting? Very, but that’s the new context.
So yes, the uncomfortable question is legitimate: does it still make sense to run courses to learn a specific tool?
The answer I think is “it depends”, but with one firm point: if the training is only “learn tool X”, you’re buying milk with an aggressive expiry date. If instead you shift the focus onto transferable skills, the story changes.
Because tools change but the principles you use to govern them remain (surprisingly) stable.
The point isn’t knowing where to click. The point is understanding what you’re actually using.
If you’re clear on what generative AI is, where it’s strong and where it’s dangerous, why it occasionally “invents” things, how it manages context and how you steer it towards a verifiable result, then you take home a universal key you can apply to almost all AI tools (and those that now have AI integrated).
A bit like learning chords instead of memorising just one song: when the stage changes, you don’t panic.
From this comes a competence that today is worth more than any certification: knowing how to communicate with the system. Not just knowing how to write a prompt but understanding how to turn a confused need into a clear brief. You need to ask for alternatives, impose constraints, define quality criteria. If you think this way you don’t panic when the tool changes, because you recognise the same logic in a different interface.
You don’t learn AI only in the classroom. You learn it every day, through real use cases.
The key word is experimentation, but not the nerd kind that collects tools. The useful kind: you try, you measure, you understand what works and why. You run small tests on real tasks. You build a library of good prompts, you note down typical errors, you recognise patterns.
If the learning doesn’t lead to a concrete result, it becomes entertainment, doesn’t it? And in a company, entertainment costs.
The factor that really makes the difference: sharing
AI improves when it becomes a conversation between people, not just between a person and a machine.
Sharing what works, telling stories of successful and failed experiments, saying “I didn’t get there” without theatrics, creates collective acceleration. It’s method. In an environment that changes this fast, knowledge kept in a drawer gets stale. Shared knowledge updates itself.
In FAIRFLAI, for example, we have a simple ritual: a weekly one-hour “Sharing Slot” where each person brings a case, a tool, a learned practice. It’s not a philosophical meeting but a way to maintain the team’s collective brain. And yes, it’s replicable: you just need it to be regular and to end with reusable material.
It’s not enough to “have” the tools. They need to be set up to work well.
You need to configure them and clarify policies for using AI with criteria and not as a toy. If you introduce it badly it adds noise, but if you introduce it well it creates value!
And here comes the point we feel most strongly about at FAIRFLAI: in a company it’s not the one who “uses ChatGPT best” who wins. The winner is the one who makes use collective.
AI becomes an advantage when it is consistent; otherwise it remains a sum of individual experiments: useful, but not scalable.
Starting tomorrow, then, do three things:
- Stop chasing the tool of the month and invest in a method you can carry with you everywhere.
- Choose two real activities of your team and turn them into a laboratory: try, measure, correct, document.
- Create a fixed internal sharing moment, brief but regular, and make sure it produces reusable material.
And if you don’t know where to start, write to us!
