Artificial intelligence is here – and everyone is talking about new skills.
However, before training and courses start, it should be clear where a company wants to develop. As long as it remains unclear how the business model, roles and processes will change, it is difficult to say which skills are actually needed.
Learning needs direction – and structures that enable change. Without both, there is a risk that further training will remain well-intentioned but change little.
Competence models fall short if they do not simultaneously question the organization, its logic and its business model.
Starting point
Artificial intelligence (AI) is changing everything – but not everywhere at the same time.
While technologies are advancing rapidly, many organizations are still stuck in the old way of thinking: in structures that limit learning, in training courses that explain tools but do not change anything.
And at the same time, many feel it: Things can’t go on like this.
This article invites you to rethink – not with ready-made answers, but with open questions.
For everyone who feels that AI is not only changing tools, but also mindsets, structures and expectations. And we are right in the middle of it.
Five theses
Thesis 1
Competence thinking individualizes a systemic problem.
The widespread calls for employees to develop AI skills distract from the fact that the actual blockages are structural in nature.
- There is a lack of rooms for use.
- No access to suitable tools
- Silo structures that prevent learning.
- Managers who do not allow personal responsibility.
AI expertise is not created in the head, but in the system.
If you only address the individual, you leave the organization untouched – and reproduce the problem.
Thesis 2
The fixation on training obscures the view for real transformation.
In many places, “learning AI” is equated with “conducting training”, with the focus often being on tool knowledge or prompt basics.
But:
What is the point of training if what has been learned can neither be used nor implemented?
As long as organizations see AI as an additional task and not as part of their operational logic, any training will fall flat – no matter how well it is carried out.
Thesis 3
L&D becomes a repair store – instead of being a driver of transformation itself.
The focus on increasing efficiency through AI in L&D (e.g. content automation, translations) is convenient, but reactive.
What is missing is a systemic answer: how is AI changing our entire understanding of learning, our formats, our interactions?
L&D should be rethinking the learning ecosystem – instead it is optimizing the status quo.
This is not transformation, but self-soothing.
Thesis 4
Organizations do not need a competency model – they need a new operating system.
The central question is not:
What should people learn?
But rather:
How must an organization be built so that people can work cooperatively, creatively and autonomously with AI?
This concerns:
- Time models
- Role models
- Decision logics
- Technology access
- Product development cycles
In short: the structure of the organization itself.
Thesis 5
Peer learning is romanticized – because people don’t want to ask the real system question.
Peer learning formats (e.g. Working Out Loud, Promptathons) are valuable – but often merely symptoms of system failure.
If employees only have to help each other because there is no structured space for learning, this is not proof of a culture of innovation – but of its absence.
Peer learning must be embedded in an organization that allows for unlearning, trial and error and collective exploration.
Many companies are not talking about this.
A practical example from the legal profession
When the market demands AI – not the organization.
“AI has gone from being a nice-to-have to a must-have in the legal profession.”
– Jeannette zu Fürstenberg, quoted by Holger Schmidt on LinkedIn
A look at other sectors shows: The dynamics that are still being discussed in the education sector are already a reality elsewhere.
Business journalist Holger Schmidt refers to an interview in the F.A.Z. with Jeannette zu Fürstenberg, European head of venture capitalist General Catalyst.
In it, she describes how large law firms are coming under increasing pressure from clients to not only be aware of AI, but to use it systematically.
The arguments are clear:
- Efficiency through automation of standard tasks
- Cost transparency through package solutions instead of hourly rates
- Competitive advantage through speed, quality and use of technology
What does that have to do with education?
Educational organizations will also soon have to justify themselves:
- “Do you use AI?”
- “What does the machine do for you?”
- “What am I still paying full price for?
The legal sector was long regarded as the last bastion of analogue work – and is now being forced by customer pressure to change its organizational logic, business models and service structure.
The same will happen in the education sector – not because the sector wants it, but because demand requires it.
An example from practice: A webinar provider reported that a corporate customer could not understand why he should pay a didactics expert’s fee – on the grounds that AI could now take care of this.
Such statements show: It is no longer a question of whether, but how – and the question of which educational achievements will still be considered valuable in the future.
Summary
AI is not only changing what we learn – but also how organizations deal with learning. Many debates revolve around skills and the “empowerment” of employees. Important – but not enough.
Because in times of change, the micro level is not enough. You also need to look at the system: at structures, roles, decisions – and at what really makes organizations capable of acting.
Further training is more than just knowledge transfer. It is part of a company’s survival strategy in the age of AI.
Perhaps now is the time to rethink the operating system of learning – not only didactically, but also organizationally.
Für Organisationen, die nicht nur auf Wandel reagieren wollen – sondern ihn selbst gestalten.
What do you think?
Where does your organization stand – on the micro or already on the macro level of learning?
We look forward to hearing your perspectives.
Transparency note: This article was created with AI support.
The idea, the direction and the attitude came from FROLLEINFLOW.
The machine helped with the writing, without its own consciousness,
but with impulses that move forward when the goal is clear.