For The Power List 2025, we invited entrants to tackle three big questions facing the field, including: How do we help today’s analytical scientists become tomorrow’s science leaders? Here, three honorees whose essays ranked in the top ten by our judging panel come together to explore what leadership in analytical science really means.
In Part One, Isabelle Kohler, Charlotta Turner, and Lourdes Ramos examined how leadership styles, culture, and collaboration are evolving within analytical science. In this second installment, the conversation turns to the future – how artificial intelligence might reshape scientific leadership, how success should be measured in the next era of research, and how to foster trust, resilience, and individuality in the leaders of tomorrow.
What role might AI play in the future of science leadership?
Charlotta Turner: I believe that the question of generative AI is a big issue. What worries me is that, as it stands, most of the development is happening within large tech companies. If we don’t pay attention to this rapidly changing landscape, we might end up somewhere we don’t want to be.
I worry that generative AI could, in a way, make students less thoughtful. Some early studies suggest that depending on how people use it, it can encourage laziness and limit critical thinking. If we stop thinking, that’s a real problem. We need scientific studies on the impact of using generative AI – and we need to learn how to use it in ways that enhance, rather than limit, our capacity. That’s a key part of leadership, too.
Isabelle Kohler: If we want to teach students how to use generative AI responsibly, we as lecturers also need to understand it ourselves – which means gaining expertise in something that isn’t our primary field. It’s another layer of learning, and it’s moving incredibly fast. Our goal now is to let students use it, but also to redesign assignments so that they’re still learning and thinking critically.
Lourdes Ramos: It’s all moving very fast – maybe too fast for us to control. For me, it feels quite distant – perhaps because I belong to an older generation. We need real experts in AI to support us, and they should be involved in the analytical process too, because AI can definitely improve many aspects of our work.
But we also need to talk about ethics. We tend to assume that scientists behave ethically and that data are reliable, but that’s not always the case. We’re already seeing more corrections and retractions in journals than ever before. So we have to think carefully about how AI is used – and about what leadership really means in that context.
Could generative AI reshape how we define and measure success in science – and what that means for leadership?
Ramos: Impact and leadership are closely related. I don’t like the idea that there’s a clear “road” to becoming a leader. Leadership isn’t about speed or following a checklist. For me, it’s more about the long-term impact of your work. That’s what matters.
Kohler: The way we measure impact today – by numbers of papers or grants – feels outdated. AI is disrupting that whole system. Maybe we need to redefine what “impact” means, and move away from metrics that encourage people to publish as much as possible, regardless of quality or ethics.
Turner: Yes, this is such an interesting discussion. Maybe we can finally get rid of the H-index! Impact on society might be a better measure – for instance, where our graduates go and what they do after they finish. That seems more meaningful than just counting papers.
Kohler: Most PhD students – around 95 percent – don’t stay in academia. I hear from many who say that when they apply for industry jobs, having four papers in high-impact journals doesn’t help them. Hiring managers don’t care about impact factors; they care about skills. At the same time, the current publishing system – with so many commercial and even predatory journals – is exhausting. Reviewers are burned out, and many refuse to review because there’s no recognition or reward.
Turner: And now we’re at the point where you could use AI to write a paper and AI to review it. What’s the point? We have to remember why we write and read papers in the first place – to think. That’s what we must protect, even as AI becomes part of the process.
Kohler: Instead of spending all those PhD years focused solely on writing papers, maybe we should spend more time developing other skills – communication, collaboration, and leadership. That would prepare students better for the real world.
Ramos: But that also depends on the supervisor’s priorities. If the supervisor only cares about publishing, students don’t have time to learn those other abilities. They become obsessed with writing papers, going to conferences, and presenting the same posters again and again.
As supervisors, we have to moderate our own interests. We can’t forget that we’re teachers and mentors. Our goal should be to help students develop as scientists and as people – not just to add more publications to our CVs.
Kohler: I completely agree. And we need to put less emphasis on output – on treating students like “output machines,” as you said – and more on the learning process itself. We have to remind PhD students that they’re students. As you said, the goal of a PhD isn’t to produce papers; that’s a byproduct. The real goal is to develop as a scientist – to learn how to think, analyze, and create independently.
They need time for that. If it takes six months to write an introduction, that’s fine – that’s part of learning. But many feel pressured to publish quickly, to keep producing. I think they lose sight of the fact that the PhD is about becoming an independent researcher, not a paper factory.
Turner: Here in Sweden, there’s already concern that smartphones have affected an entire generation – attention spans, focus, patience. Add generative AI on top of that, and we could face an even bigger challenge. We really do need to develop strategies quickly to help the next generation of scientists stay thoughtful, creative, and capable of real, deep work.
Perhaps we need to strengthen the seminar culture. It’s not just about the end product – the paper or report – but about the process of getting there. That’s what we should emphasize more in education: the process of inquiry, discussion, and critical thinking.
What does “success” really mean in the context of scientific leadership?
Turner: I don’t think trust – and by extension, leadership – comes from success. For me, trust is fundamental to building leadership, and it comes from transparency and from being willing to make mistakes. Lowering the barriers, daring to say what you think, and being open about challenges rather than staying stiff and formal – that’s how you build trust.
Kohler: I think it’s also important to remember that everyone defines success differently. I often hear people say, “This is how I’ll become successful,” but success looks different for each of us. Being honest with yourself about what success means to you, and being transparent with others about that, can actually encourage leadership. It helps people realize they can lead in their own way, based on their own values.
Turner: Yes, and I think we have too many of those perfect PowerPoint presentations that signal “success.” Maybe we should spend more time talking about the things that don’t work – the challenges in the lab, the failures. That would be much more productive than all the time we spend presenting polished, flawless results.
Ramos: You know how difficult it is to publish a paper on negative results? I have one – just one – in my whole career. It took a long time to publish, but I think it’s one of the most interesting pieces of work I’ve done. It showed that something that worked for others didn’t work for me under certain conditions. It was valuable, but very hard to get accepted.
Turner: Same here – I have just one like that.
Ramos: Publishing such work is difficult, but it’s important. And that ties back to what we were saying about success and leadership. We all have different perceptions of what those words mean. You might have one definition, I might have another.
And that raises a deeper question: what’s the difference between a great scientist and a leader? I think it depends on how you see it. We can all recognize great science – it’s easier to agree on that. But identifying a leader is more subjective. Leadership is a matter of personal perception, and that’s part of the challenge.
How can someone begin to grow as a leader?
Turner: I always think that, for anyone who wants to be a good leader – not just young people – the first step is to start listening to others. That’s usually where things break down. Leadership often fails because people stop listening.
Kohler: Go talk to people – or rather, listen to them. Really hear what they have to say, understand their perspectives, and build your opinions from there. That’s how you learn what people want or need, and how you can lead effectively.
Ramos: Yes, always listen. Look at their work, look for solutions to real problems, and don’t be afraid of new ideas. Sometimes the key is in the unusual or the unexpected. If the problem is different, maybe the approach has to be different too. Don’t be afraid to try something unconventional or to take a risk. Even if you’re in a hurry, you can always explore something in parallel. And maybe that’s where the breakthrough comes from.
Please tell us about an inspirational leader from your own career
Turner: When I was at that stage in my career between postdoc and starting my own research group – which is not an easy transition – there was someone who was already a senior professor and department head. She believed in me, listened to me, and took the time to give support and advice. She became a mentor. That belief – that trust she showed in a young scientist – meant so much to me, and it’s stayed with me throughout my career.
Kohler: I think a good leader is, first and foremost, a good mentor. I can’t name just one, because I’ve had several who were truly inspiring. And what they all had in common was that they cared deeply about others – they helped without expecting anything in return. They listened, they mentored, they guided. For me, that’s the definition of a leader.
Ramos: For me, it’s also difficult to choose a single person. But, like Charlotta, I can think of someone who was the first to really believe in me and let me do what I wanted in the lab – Professor Udo A. Th. Brinkman. I’ll always be grateful for that.
And I’d also like to mention another kind of leader – a humble leader. For me, that was James Lovelock. He produced an enormous amount of work and generated new ideas with a profound, long-term impact. He lived quietly near London toward the end of his life, but he never stopped doing science. Those are the two kinds of leaders I would highlight.
If you could offer one message to the next generation of science leaders, what would it be?
Turner: I’d say – be stubborn. For young scientists especially, if you believe in something, go for it. When I was a PhD student I was told by someone (also an important mentor for me) that: if you really want something, go and get it. Be stubborn – it usually works. Stubbornness is underrated.
Kohler: And be yourself. Build your own career in your own way, and make it work for you. That’s the best way to feel fulfilled – and to lead.
Ramos: I would say: believe. Believe in yourself, believe in your work, believe in your ideas and your project. That belief will take you far.
Isabelle Kohler is Assistant Professor, Division of BioAnalytical Chemistry, Department of Chemistry and Pharmaceutical Sciences, Vrije Universiteit Amsterdam, The Netherlands; and CEO and Founder, NextMinds; Charlotta Turner is Professor and Vice Dean of Education, Lund University, Faculty of Science, Sweden; and Lourdes Ramos is Senior Research Scientist, Department of Instrumental Analysis and Environmental Chemistry, Institute of Organic Chemistry (CSIC), Madrid, Spain
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