Human resources management: Does the development of personal skills require the comparatively simple and often repetitive activities that career starters have previously had to do?
Erik Strauss:
This is one of the central questions of our time. Historically, junior roles have long been the primary learning venue for graduates. In these roles they gained experience that qualified them for more complex tasks in the future. If AI now takes over these very entry points, we risk losing the entire “learning ladder” that previously led to senior positions.

What does this mean for the individual learning process?
We need to redefine learning, and we need to find ways to develop the depth and expertise needed without the traditional apprenticeship years. Training can no longer be based on learning the tools of the trade through hard work. Instead, we need to enable young talent from day one to work with AI support and demonstrate their added value through complementary tasks.

Info

Where do you fear gaps?
The looming experience gap arises at the interface between universities and the job market. We see that the transformation of the world of work is simply overtaking the education system. Universities cannot adapt their curricula and approaches at the speed at which AI is changing practice. The result is that graduates enter the market with a skillset designed for a world without AI, while employers expect comprehensive “AI proficiency” on day one.

Are there other skills that are particularly important for beginners?
In the AI ​​age, the focus is shifting away from technical routines and towards “human factors”. Skills such as critical thinking towards AI outputs, the so-called “problem framing” – i.e. the ability to formulate a problem in such a way that an AI can help – as well as ethical judgment and a high level of learning agility become particularly important.

How can these skills be learned?
By creating safe spaces in which AI can be experimented with. We also need to design AI-supported onboarding processes and establish mentoring programs that focus on judgment, coordination and creativity. We have to train the next generation to become “AI-augmented analysts” or “AI-supported specialists”.

Are companies drying out their own talent pool in the medium to long term by reducing entry-level jobs?
The risk is absolutely real. When companies eliminate entry-level positions purely for efficiency reasons, they destroy their own foundation for the future. Career paths designed for a pre-AI world no longer fit today’s reality. This development is unlikely to be worthwhile in the medium term either, because the costs saved today will probably be significantly higher in the future if expensive experts have to be “bought in” from outside.

Do you have any recommendations for HR on how to handle entry-level jobs?
HR has to pull three key levers. First: changing measurability. We need to move away from pure output metrics and towards new metrics that assess the quality of human-AI collaboration and the AI ​​adoption rate. Second: We need to design entry-level roles in such a way that junior employees can take on responsibility more quickly by relieving the burden on them through AI. AI should be positioned as a learning tool, not a replacement. Third: joint responsibility. Companies and universities must pull together. We need a common language and concepts for AI competence. My appeal to HR is: Open your doors to joint pilot projects with universities in order to test new learning formats such as “Embedded Roles” directly in practice. We can only build the necessary AI proficiency through joint experiments.

This interview comes from our issue 05/06-2026.


Christina Petrick-Löhr is responsible for the Talent & Learning magazine section as well as reporting on training and further education. She is also responsible for the editorial planning of various special human resources publications as well as the German Human Resources Prize.

Share.
Leave A Reply

Exit mobile version