When I think about my first professional experience, I realise how fragile a career beginning can be.
During my studies, I got a job in a small insurance company. I did not get it because I had a strong CV, rare expertise or a clear professional identity. I got it because a friend knew the owner, the director agreed to meet me, and the company had very little money to invest. In a way, the match was simple: they could not afford an expert, and I could not afford to wait for the perfect job.
The work itself was modest, almost basic. The company worked with external insurance salespeople and brokers who were constantly facing objections from potential clients. “I am not interested.” “I already have car insurance.” “I already have a broker.” “I do not need anything.” “Call me later.”
My task was to listen to these salespeople, collect the objections they heard in the field, classify them, and build a practical booklet with possible answers. The idea was to give brokers a simple tool they could use when prospecting by phone or meeting clients.
Today, artificial intelligence could probably produce a first version of that booklet in two minutes.
And that is precisely what makes me think
If AI had existed at that level at the time, would that small company have hired me? Probably not. The director might have asked an AI tool to generate a sales objection guide, adapt it to insurance, translate it, structure it and improve it. The result would have looked cleaner, faster and cheaper than anything I could have produced as a young person with no experience.
But I would have lost something essential.
That first job did not only teach me how to write answers to sales objections. It taught me what a company is. It taught me how salespeople speak after a difficult client meeting. It taught me what rejection feels like when it is repeated every day. It taught me how to listen to people who are under pressure, how to understand a product I did not know, and how to turn messy field information into something useful.
It also stayed with me much longer than I expected. Later, as a recruiter, when I had to speak about salaries, benefits, compensation, insurance, social charges and employment conditions, that early exposure helped me understand faster. Even in my personal life, it helped me understand insurance better. Years later, when I explored links between insurance, recruitment, career coaching and employability, that first experience was still somewhere in the background.
This is the hidden value of entry-level work
It is rarely only about the task itself.
It is about the professional formation that happens through the task.
A first job also gives something that no AI tool can generate: professional socialisation.
The first manager, the first colleagues, the first client meetings, the first mistakes, the first corrections, the first moments of trust or tension — all of these shape the way a young person understands work. They teach behaviours that often stay for life: being on time, preparing properly, listening before speaking, respecting commitments, accepting feedback, observing hierarchy without losing initiative, understanding what can be said in a meeting and what should be discussed privately.
These lessons may look small from the outside, but they become an inner professional compass. Years later, when we move into another role, manage a team, deal with a difficult client or face pressure, the voices of those first professional figures often remain somewhere in the background. A former manager. A colleague who showed patience. A demanding boss who insisted on standards. A mentor who explained not only what to do, but how to behave.
This is why the disappearance of entry-level work would not only be an economic issue. It would also be a cultural issue. If young people lose access to these first environments, they lose access to the informal education that turns academic knowledge into professional maturity.
And this is where the arrival of AI creates a real challenge for young people entering the labour market today
The traditional paradox has always been there: to get a job, you need experience; to get experience, you need a job. But AI makes this paradox sharper. Many of the tasks traditionally given to junior employees, interns or graduates are exactly the tasks that generative AI can now perform quickly: drafting, summarising, researching, preparing first versions, classifying information, writing client scripts, analysing basic data, preparing presentations, producing job descriptions, screening CVs, creating sales material.
From an employer’s perspective, the temptation is obvious. Why give a slow, imperfect task to a beginner when a tool can deliver something acceptable immediately?
But from a talent development perspective, the risk is serious. If we remove the small tasks, we may also remove the learning path. We may save time today while weakening the professional judgment of tomorrow.
This is especially relevant in Switzerland. The Swiss labour market remains relatively resilient, but it is also under pressure from demographic change, skills shortages, digitalisation and productivity expectations. The problem is not simply a lack of jobs. The deeper issue is access to first experience, especially meaningful experience.
For young people leaving university, business school, vocational training, apprenticeship or a first career transition, the question is no longer only “What job title should I target?” The better question is: “What problem can I learn to solve?”
This distinction matters
A job title can disappear, change or be redefined. A problem remains. Companies will continue to need people who can understand clients, improve processes, reduce friction, explain complex topics, support teams, build trust, detect risks, interpret data, sell, negotiate, coordinate and learn. AI will change how these problems are addressed, but it will not remove the need for human judgment around them.
For young professionals, this means that the objective should not be to compete with AI on speed. AI will usually win. The objective should be to build the abilities that make AI useful, safe and relevant: asking better questions, checking outputs, understanding context, detecting errors, speaking with clients, observing reality, and connecting information with human behaviour.
In other words, the new entry-level worker must learn to become a problem interpreter, not only a task executor.
This also changes the responsibility of employers
A company that automates all junior tasks without redesigning learning pathways may solve a short-term productivity issue, but it also creates a long-term talent problem. Senior professionals do not appear by magic. They are built through years of exposure to imperfect situations: difficult clients, unclear instructions, internal politics, failed attempts, corrections, feedback and repetition.
In sport, everyone understands this immediately. A football club that wants to compete at the highest level cannot only buy experienced players forever. It needs an academy. It needs young players. It needs training fields, friendly games, lower-pressure matches, patient coaches and moments where a talented young player is allowed to enter the game before being fully ready.
Lionel Messi did not become Messi because Barcelona waited until he was already a finished product. He became Messi because a club invested in him early, gave him a framework, exposed him progressively to higher levels of play, and accepted that development requires time.
The same is true in hockey, basketball, tennis or athletics. Countries, clubs and organisations that want future champions invest in youth structures. They create places where young talent can practise, fail, learn, repeat and grow. Without that system, the pipeline dries up. At first, the problem is invisible because the senior players are still there. But ten years later, there is no reserve, no next generation, no depth on the bench.
Companies face a similar risk with AI. If they remove the small entry-level tasks because AI can perform them faster, they may also remove the training ground where young professionals learn judgment, client sense, business reality and responsibility. The risk is not only that young people struggle to find a first job. The risk is that organisations wake up later with too few people able to take over complex roles, lead teams, understand clients and make decisions under uncertainty.
There is also a social responsibility dimension
Every generation benefits from doors that were opened by someone else. Someone accepted to listen to us when we had no experience. Someone gave us a first chance. Someone tolerated our mistakes, corrected us, exposed us to reality and helped us understand how work really functions. The best way to honour that is not only to be grateful. It is to do the same for the next generation.
In Switzerland, this responsibility has traditionally been strong. The culture of apprenticeship, professional training and progressive entry into work has long been one of the strengths of the labour market. It has created a bridge between education and employment, between theory and practice, between young people and companies.
But with the acceleration of the employment industry, cost pressure, digitalisation and now AI, this responsibility can slowly erode. Not necessarily because employers do not care, but because the immediate efficiency gain becomes easier to see than the long-term talent loss.
That is why the question is not simply whether AI can do the junior task. Very often, it can. The real question is: what did this junior task teach, and how do we preserve that learning in a new form?
Employers do not need to protect obsolete tasks. But they do need to protect the learning function behind those tasks. They need to put young people back in the game, even if the game has changed. They need to create the equivalent of professional academies inside organisations: internships with real exposure, junior roles redesigned around AI supervision, mentoring, project-based learning, client observation, feedback loops, and progressive responsibility.
Because if we do not let young people play the small matches, we cannot expect them to perform in the Champions League later.
One of the most powerful ways to redesign entry-level work in the AI era may be to give young professionals a new mission: become internal AI scouts
Instead of asking them only to perform the old junior tasks, companies could ask them to observe how work is really done across departments and identify where AI could help. A junior employee could spend time in HR, finance, sales, customer service, marketing or operations, not as a passive trainee, but as someone looking for friction: repetitive tasks, manual reporting, duplicated data entry, unnecessary emails, slow approval chains, poor documentation, recurring questions, lost information, or tasks that people continue to do manually simply because no one has had the time to rethink them.
Their role would not be to impose technology on experienced colleagues. It would be to listen, understand, map the work, suggest improvements, build small prototypes, test AI tools, create demos and show what could be saved in time, quality or energy. A junior in HR, for example, could observe how job descriptions are written, how candidates are screened, how interview notes are summarised, how onboarding documents are prepared, how internal FAQs are answered, and then propose practical AI-supported workflows.
This could become one of the best forms of intergenerational cooperation. Senior employees bring business judgment, institutional memory, client understanding, professional standards and risk awareness. Younger employees bring digital reflexes, curiosity, speed of experimentation and a different relationship to tools. Middle managers can translate both sides into operational reality. Together, they can redesign work without opposing generations against each other.
This is also a better use of young talent than asking them to compete with AI on execution speed. Let them use AI to question how work is done. Let them build simple demonstrations. Let them calculate the hours that could be saved. Let them show how a process could become more user-friendly, less repetitive and more intelligent. In return, they will learn the business from the inside: how departments function, where resistance comes from, what quality means, what compliance requires, and why not every apparently simple automation is actually simple.
In that model, the entry-level role becomes valuable again
The junior is no longer only the person who does the small task. The junior becomes the person who helps the organisation see the task differently.
Concretely, companies could give entry-level professionals AI-era missions such as mapping repetitive tasks in one department, documenting where employees lose time during the week, testing AI tools on low-risk internal processes, creating first drafts of prompts, templates and checklists, building small demos for managers, comparing human output and AI-assisted output, checking the quality and reliability of AI-generated content, collecting feedback from employees on what works and what does not, helping create internal AI usage guidelines, and supporting reverse mentoring between younger and more experienced colleagues.
These are not artificial junior tasks. They are real business tasks. They create value for the company while giving young people exposure to processes, people, culture, technology, constraints and decision-making.
This also changes the salary conversation
How much should an entry-level person be paid? What is fair for the young person, and what is sustainable for the company?
This question is sensitive because both sides have legitimate concerns. Young people do not want to be exploited. They want independence, dignity and recognition. They are right to expect respect, proper working conditions and a salary that acknowledges their contribution.
At the same time, many companies, especially SMEs, hesitate to hire entry-level profiles if the cost, regulation or administrative burden becomes too high. If hiring a junior person feels almost as expensive and risky as hiring an experienced professional, the economic logic disappears. The result is predictable: large corporations will continue to offer structured graduate programmes, while smaller companies may simply stop opening the door.
That would be a mistake.
The entry-level labour market needs a balance. It must remain flexible enough for employers to take a chance on someone who is not yet fully productive. But it must also remain ethical enough to prevent disguised cheap labour.
The right question is not: “How little can we pay a beginner?”
The right question is: “What is a fair learning contract between the company and the young professional?”
A fair entry-level opportunity should include four elements
- First, a decent salary or allowance that respects the person’s time and living reality. It does not need to be equivalent to an experienced salary, but it must not be exploitative.
- Second, real learning. If a company pays little and teaches nothing, it is exploitation. If a company pays modestly but provides exposure, supervision, feedback, responsibility and employability, the equation is different.
- Third, transparency. The young person should understand what they are accepting: the salary, the duration, the learning objectives, the supervision, the potential next steps and the loss of trust among the young generation. The answer is not ideological. It is practical: fair pay, real learning, clear supervision and progression.limits of the role.
- Fourth, progression. An entry-level salary should not become a permanent status. If the person learns, contributes and becomes more autonomous, the compensation should evolve.
For young professionals, there is also an uncomfortable but important message: at the very beginning of a career, experience may sometimes have more value than the immediate optimization of salary. That does not mean accepting abuse. It does not mean working for free without structure. It does not mean saying yes to everything.
But it does mean understanding that independence is not built solely through the money earned during the first months of a career. Long-term independence is built through skills, judgment, networks, confidence, and employability. A modestly paid first job that offers genuine exposure can sometimes be far more valuable than a better-paid role that teaches almost nothing.
This is difficult to hear in a world where the cost of living is high and where young people want to achieve financial autonomy quickly. But a career is built over time. A first professional opportunity is not merely a transaction. It is an investment. The company invests time, supervision, and trust. The young professional invests effort, humility, and the willingness to learn.
The ethical balance is essential. We should not romanticize low salaries. But we should also not exclude young people from opportunities because the associated costs are too high.
In the Swiss context, this balance is particularly important. Switzerland does not have a national minimum wage, although certain cantons and collective labor agreements establish specific rules. This decentralized approach leaves room for negotiation, but it also requires responsibility. Flexibility only works when it is supported by trust, fairness, and professional ethics.
If we regulate too rigidly, we risk unintentionally reducing access to first opportunities, especially within SMEs. If we regulate too little, we risk creating frustration, exploitation, and a loss of trust among younger generations. The answer is not ideological. It is practical: fair compensation, real learning opportunities, clear supervision, and progression.
AI makes this even more urgent
If a junior person is only hired to perform low-value repetitive tasks, AI will replace that role quickly. But if the junior person is hired to learn how to work with AI, understand clients, challenge outputs, structure information, observe business reality and develop judgment, then the role becomes valuable again.
This question has become more personal for me as I watch the next generation prepare to enter the labour market. When you see a young person close to you looking for that first real opportunity, before university or at the beginning of adult life, the subject is no longer abstract. You realise that the first job is not just about earning a salary. It is about entering the game, meeting adults outside the family and school system, understanding expectations, building confidence, and discovering what kind of professional you may become.
This is where employers, schools, universities, recruiters, HR leaders and career coaches have a shared responsibility. We need to redesign entry-level work instead of simply removing it.
If I were starting my career today, I would not try to look “AI-proof.” I would try to become AI-relevant
I would build a portfolio of problems solved, not only a CV of diplomas. I would show how I use AI, but also how I challenge it. I would document projects, even small ones: a sales script improved after client feedback, a process simplified, a customer journey analysed, a report rewritten for clarity, a recruitment workflow tested, a market map built and corrected.
I would show evidence of learning.
I would also network differently. My first job came through a human connection. That part has not disappeared. In fact, it may become even more important. When applications are filtered by algorithms and candidates use AI to produce similar CVs and cover letters, trust becomes more valuable. A recommendation, a conversation, a project, an internship, a short mission, a visible contribution: these are ways to escape the anonymous application pile.
For Swiss entry-level candidates, apprentices, graduates and young professionals, the message is not to panic. But it is also not to be naïve. AI is changing the first steps of a career. The old ladder is being redesigned while people are still trying to climb it.
The answer is not to reject AI. The answer is to make sure it does not remove the experiences through which people become competent.
My first insurance job was not prestigious. It was not well paid. It was not strategic. But it gave me access to the real world of work. It allowed me to observe, listen, fail, structure, understand and grow.
Many young people still need that kind of first door.
The challenge for employers, educators, recruiters and career coaches is to keep that door open, even if the work behind it now looks different.
Because a career does not start with expertise.
It starts with exposure.
