Could machines become more popular leaders than humans? – Thoughts from Episode 2

Do people actually want an AI boss?

Right now, the answer is mostly no – human leaders are still preferred. But the hesitation is already weaker among younger generations, who have grown up with technology as a natural part of everyday life. As I noted in the episode: leadership won’t disappear. It will evolve. The best leaders will be humans who know how to lead with AI, not against it.

When I think about this change, I don’t see humans and machines in opposition. I see the next phase of a longer continuum. The moment we are living through now has a lot in common with the early days of industrialization. Back then, machines started doing the things humans had previously controlled and supervised. Production no longer needed to be guided “by hand” in the same way; the machine did what was expected, and the human role shifted. Later, digitalization did something similar for information and manual processes built on data.

Now AI does this to mental and creative work. It doesn’t erase leadership, but it changes what leadership is aimed at. If a system can guide processes, schedules and information flows, then the real leadership challenge moves to a different level: how we create meaning, direction and trust around that work.

One reason AI leadership sounds tempting is that we often see it as more neutral than humans. A machine doesn’t get tired, it doesn’t have favourites and it doesn’t act on a bad day. That’s why many people feel it could be fairer in situations like evaluation, promotion or resource allocation, where human bias is a real fear. At the same time, this “fairness” can feel distant. AI’s decisions are harder to influence, and it doesn’t explain itself in the intuitive way a human can. It creates a new kind of transparency problem: how do you accept a leader you can’t really talk round?

And the neutrality is only half the story. AI makes decisions based on data, and data reflects the past. If history is skewed, the model can quietly continue the skew – just in a smoother, more consistent format. A human, in contrast, can see when fairness requires breaking the pattern: showing empathy, using judgment or giving someone a chance that the statistics don’t predict.

There is also a whole layer of leadership that AI simply cannot read. It cannot really evaluate what we might call metaskills: creativity, self-reflection, the ability to learn from mistakes, or the potential to grow into something more than your current role. Those show up in interaction, not in spreadsheets. AI can measure performance, but only humans are any good at sensing potential. That is why, even if we sometimes prefer an “impartial” system, we still look to people when we need decisions that feel genuinely fair and human.

As technology takes over more routine work, the role of the leader changes with it. When AI handles follow-up, reporting and part of the day-to-day coordination, a lot of the old visible management work simply disappears. But leadership itself doesn’t shrink – its focus shifts. The less we need to “lead the work,” the more we need to lead people and learning. Leaders are increasingly there to help others develop their metaskills: critical thinking, judgment, the ability to question AI’s output, and the confidence to take responsibility in a landscape where systems and humans share the work.

This also affects careers. If entry-level tasks on critical levels become fewer or simpler, organizations have to think much more carefully about how people get into their careers in the first place. How do young employees gain experience when AI does part of the “learning work” for them? How do they get mentoring, how do they build judgment, how do they grow into roles that require real responsibility? Traditional paths like “junior → specialist → supervisor” won’t necessarily work the same way if the middle steps are eaten away by automation. Companies will need to create new kinds of steps and development paths – otherwise the risk is that the “entry gates” to expertise and leadership become too narrow, and the long-term talent pool shrinks.

So could a machine one day be the more popular leader? In some situations, maybe. When all that matters is consistency and neutrality, many people may find an AI-based decision easier to accept than a human one. But popularity is not the whole story in leadership. The deepest part of leadership is not just about who decides, but about whose decisions people are willing to live with – and that still grows out of human interaction.

Episode 2 in Yle Areena

If this topic sparks your curiosity, watch the full discussion!

👉 Tune in now and join the conversation about what leadership becomes when machines step onto the stage:

https://areena.yle.fi/1-76509103