Starting a career in the AI ​​age: New tasks for HR

AI takes over classic entry-level tasks and creates a dangerous gap in the talent pipeline. When junior roles disappear, the most important learning phase is lost for the next generation – with consequences for companies. Johann Wachs, Managing Director at eightplaces, analyzes why starting a career in the age of AI requires a new learning and career architecture – and how HR can build the bridge between algorithms and expertise.

The first job must be reinvented – thanks to AI

Job advertisements for entry-level jobs are currently hard to find. One of the reasons: AI is taking on more and more tasks that career starters traditionally start with. Companies now need to rethink entry structures – or they will lose a generation.
Anyone who is looking for a job today as a young business graduate with a master’s degree can sing a song about it: “We are looking for someone with more experience” is the response, unless the rejection comes automatically.

The corresponding vacancies – such as junior consultant, marketing trainee or junior analyst – were classic entry-level positions just three years ago. Not anymore today. Many of these tasks are now carried out by algorithms.

An evaluation by Stepstone confirms the experiences that many young people are currently having. The proportion of entry-level jobs advertised in the first quarter of 2025 was actually 45 percent below the five-year average. Administrative and data processing activities are particularly affected: entry-level positions in sales fell by 56 percent, in human resources by 50 percent, and in administration by 34 percent.

The proportion of entry-level jobs advertised in the first quarter of 2025 was 45 percent below the five-year average. The classic junior roles are disappearing, particularly in human resources and sales.

The reasons for this slump are complex. The economic situation is depressing the willingness to hire, companies are saving on junior positions first. But at the same time, artificial intelligence is changing task profiles: research, data preparation, drafts, summaries – activities that career starters traditionally start with and gain routine and self-confidence are now carried out by AI tools.

What remains are complex, experience-intensive activities that require expertise and judgment. For example, a senior analyst can immediately recognize when an AI-generated evaluation is completely unrealistic. A young professional who has never learned to structure raw data himself only sees the finished result – and has no reference with which to question it. The learning loop breaks off – and this is exactly where the problem arises for the young people. They become prompt operators without understanding what is happening behind them.

Three fields of action for HR and leadership

As a result, companies that fail to respond now risk creating a double divide: between generations and between learners and performers. But change can be used constructively. The first job doesn’t have to disappear – it “just” has to be rebuilt. Three approaches have proven successful in practice:

1. From tools to “decision cases”: onboarding in the AI ​​age

Classic onboarding starts with software training: “This is how our CRM works, this is how you use Excel, this is how the workflow works.” That’s no longer enough. Instead, context is needed: Why do we use these tools? What decisions do we make with this? Where does AI help – and where doesn’t it?

An example: A publisher wants to integrate volunteers. In the first week, a young professional works with experienced colleagues to analyze how AI tools can be used for research and texts – without violating the press code. The learning effect: The junior understands how to deal with the AI ​​systems critically: for example, to check sources and not to accept fake news, rumors or discrimination.

Onboarding in the AI ​​age means moving from routine to reflection. We have to teach young people not to find the right answer, but to ask the right questions.

Such “decision cases”, in which real decision-making situations are played out, bring onboarding into the AI ​​age. The task of the new, young employees is not to find the right answer, but to ask the right questions. A senior gives feedback, not on the result, but on the thought process.

2. Making empirical knowledge trainable: tandem models between junior, senior and AI

Any company that sees AI as a replacement for juniors is making a fatal mistake. Instead, AI should act as a third player in the team: juniors operate the AI, senior employees check the results – and both learn from each other. In a strategy consultancy, this type of human-AI co-creation could take place as follows: The junior uses AI to identify patterns in market research data and formulate initial hypotheses. The senior checks whether the patterns are valid or whether the AI ​​is confusing correlations with causation. Then they both reflect together: What did the AI ​​miss? What questions could she have answered better? What have we learned?

Anyone who sees AI today as a mere replacement for juniors is making a fatal mistake: tomorrow there will be a lack of experts who can question AI results with real judgment.

This makes experiential knowledge trainable, not just observable. The junior doesn’t learn through shadowing, but through active doing – with feedback in real time. The senior, in turn, learns to articulate more precisely what is important. Practical pair sessions, i.e. short reflections after each AI-supported project, are helpful. An internal prompt playground in which juniors practice tasks and experienced colleagues comment reinforces this effect.

3. Learn with the AI, not from it: GPT Virtual Coaches

In so-called AI mentoring buddy systems, virtual GPT coaches accompany new employees in tasks that require critical thinking: They give feedback on texts, support structuring analyzes or help to clearly formulate hypotheses. The aim is not to replace human mentoring, but to expand it – with a digital sparring partner who is available at all times and continuously stimulates thought.

This principle can be specifically anchored in everyday work using AI augmented learning journeys or micro-learning modules, for example through focused, interactive “Critical Thinking with AI” units. Questions like “What would happen if this AI output was wrong?” or “What assumption is behind this result?” sharpen judgment.

The first job must become an AI-optimized learning system

Because the task profile in the first job is shifting from simple routine tasks to the targeted use of AI, companies have to rebuild the classic career ladder. New structures must combine learning ability and experiential knowledge. HR and management should therefore see AI as an opportunity to replace previously poor learning structures.

Many onboardings have so far been inefficient: juniors completed monotonous tasks that had little to do with strategic thinking. AI is now forcing you to question these routines. Today it’s about designing entry-level positions so that people learn from the start what AI can’t do – namely creating context, dealing with contradictions and making decisions under uncertainty.

Companies that now establish AI-compatible onboarding, tandem models and new mentoring formats are securing the ability to survive in a world where algorithms are fast – but people set the direction. You not only gain young talent, but also innovative strength.

Also read the following posts: