No seniors without juniors

AI is transforming large parts of working life with tremendous force. Stories about major efficiency gains are everywhere. Several business leaders have argued that they will need fewer recent graduates in the future. Whether this is solely due to AI, I am not sure. But I am certain that AI has the potential to change the way we work. How we adapt to this shift will be critically important, because there is a significant potential pitfall here.

As a consulting firm, we are already seeing how effective AI tools have become. Research, analysis, modelling, and the preparation of presentations and reports are all examples of tasks that can now be completed far more efficiently than before. And the pace of development is rapid.

In consulting, junior employees have traditionally spent much of their time gathering and structuring data, producing first drafts of analyses, and creating presentations. In banking and financial services, young analysts have worked on credit assessments, documentation, and reporting. In technology environments, junior developers have written test code, documented solutions, and fixed straightforward bugs. In the media industry, younger employees have prepared draft articles, analyses, and summaries. These are all tasks that can increasingly be automated or significantly streamlined using AI.

The challenge is that these very tasks have traditionally given young employees the opportunity to truly learn their profession. It is through this type of work that people gradually develop understanding, judgement, and professional confidence, eventually becoming highly capable professionals.

When I was in secondary school, my Norwegian teacher used to say that learning should hurt a little at first – and feel rewarding afterwards. Later, when I studied business administration at BI Norwegian Business School, I had a professor of econometrics who insisted that we enter data manually rather than simply downloading it into Excel. He believed this gave us a deeper understanding of the data and a stronger intuition for the problem at hand. At the time, I did not fully appreciate the significance of his message. Today, I see the value of having done so much work from first principles and of developing an intuitive understanding through practice.

This is where AI can become a challenge. Because one thing we have learned so far is this: AI needs people.

AI can write good text, but skilled writers are still required to ensure quality, accuracy, and tone. AI can generate code, but experienced developers must validate it and understand the architecture, risks, and consequences. AI can compile data, create analyses, and build models, but reviewing quality and discussing implications is still best done by experienced professionals. And perhaps most importantly, implementing change will continue to require substantial human effort.

Professionals with five, ten, or twenty years of experience are relatively secure. Their foundations are already in place. The question is how the next generation will build theirs.

My greatest concern is that we create a lost generation of young workers who never develop professional intuition. If that happens, we will have failed as a society. We all share a responsibility to ensure that young employees do not merely become proficient users of AI, but also develop genuine expertise.

Many stakeholders have a role to play. Employers must continue to hire recent graduates and give them opportunities to learn through trial and error, guidance, and experience. That includes organising work so that not only the end result matters, but also the process, the judgement involved, and the learning gained along the way.

Managers and more experienced colleagues must help navigate which tools genuinely create value and which may deliver short-term efficiency at the expense of long-term learning. Employees themselves must also recognise that some skills are best developed through repetition, and that building a solid foundation of understanding takes time.

Of course, AI also creates opportunities that did not previously exist. It can free people from routine tasks and allow them to spend more time on creative work and problem-solving, which many find more motivating and developmental. But this does not remove the fundamental need to build real competence.

We are still in the early stages of the AI era, and much about the future of work remains uncertain. But we should be careful not to end up in a situation where those of us who are already established pull the ladder up behind us. Not intentionally. But because AI now performs so much of the work through which we ourselves learned our craft.

Read the article in Finansavisen in Norwegian.