What I Learned in the U.S. About AI, Leadership, and the Future of Executive Search
Over the past few months, I’ve traveled to the U.S. several times, spoken at conferences, and discussed artificial intelligence with entrepreneurs and executives. One observation has been confirmed time and again: The conversation about AI there is already quite different.
Of course, the focus in the U.S. is also on models, agents, automation, and productivity. But behind all of this lies an increasingly important question: What does AI mean for the way we run businesses?
For me, that is precisely where the key difference lies. In Germany, we’re still having very intense discussions about what AI is capable of. In the U.S., the focus is more on what companies need to change right now. It’s about speed, organization, decision-making, leadership, and talent. AI is thus becoming a CEO’s responsibility—not at some point in the future, but right now.
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“The key question regarding AI is not what technology we use, but whether our organization can adapt quickly enough to what is already technologically possible.”
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And this is precisely where a contradiction arises that I consider to be one of the biggest strategic mistakes of the coming years: Companies are investing billions in the technologies of the future. They are developing new business models, automating processes, and building AI agents. But they continue to fill their most important leadership positions based on the criteria of the past.
We’re building companies for 2030—and looking for leaders based on the success models of 2015
That can’t work. Because if AI changes the nature of work, leadership must change as well. And if leadership changes, the people we entrust with leadership must inevitably change as well.
For decades, the path to leadership was relatively clear: Those who knew a great deal became experts. Those who remained experts long enough became leaders. Experience generated authority, knowledge generated status, and expertise generated power. This system worked. But now AI is challenging precisely this logic.
Knowledge that used to take people years to acquire is now available in a matter of seconds. AI analyzes data, develops scenarios, scrutinizes arguments, structures decisions, and provides virtually unlimited access to knowledge. This is shifting the value of human leadership. For the first time, expert knowledge is losing its power as the most important leadership resource. That doesn’t mean that knowledge is becoming unimportant. But knowledge alone is becoming less and less of a differentiator.
The more important questions going forward are:
- Can a manager ask the right questions?
- Can she evaluate AI results?
- Can she make decisions when she’s uncertain?
- Can she question her own beliefs?
- Can she learn faster than her market is changing?
- Can it bring people and AI together in a way that leads to better decisions?
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“In the future, the best leader will no longer necessarily be the person with the most answers. It will be the person who asks the better questions.”
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AI does not replace leadership. On the contrary: AI raises the bar for leadership. The more knowledge and operational tasks machines take over, the more important skills become that cannot simply be automated: judgment, responsibility, direction, courage, trust, and the ability to learn.
For me, that is precisely the essence of AI-augmented leadership. AI does not take over leadership. It enhances the capabilities of good leadership. However, this development has a consequence that we have not discussed nearly enough so far: We need to redefine the criteria by which we select leaders.
Many executive search processes still follow familiar patterns: They look for 20 years of industry experience, 10 years of leadership experience, the right company size, the right career milestones, and the right titles. We look for people whose backgrounds match our own as closely as possible.
But that’s exactly where the problem lies: We are not clinging to the past. At this turning point, we are seizing the future.
Of course, experience and expertise remain important. But experience doesn’t automatically mean future-readiness. Twenty years of experience can mean twenty years of learning. Or one year of experience, repeated twenty times.
That is why we need to ask different questions:
- How quickly does this person learn?
- Just how curious is he?
- How does he respond to developments for which there is no prior experience?
- How does he work with AI?
- Can he question his own knowledge?
- Can he make decisions even though the information available is not complete?
- Can he lead an organization whose future business model may not even be fully defined yet?
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“The perfect résumé can become the greatest risk in a hiring decision if it lulls us into a false sense of security about a future that has less and less to do with the past.”
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AI readiness thus becomes leadership readiness. As a result, the new selection criteria are no longer limited to experience, knowledge, and industry expertise. They now also include the ability to learn, sound judgment, curiosity, adaptability, decision-making speed, and the ability to collaborate between humans and AI. This fundamentally changes the executive search process.
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“We can no longer select leaders based solely on what they have learned over the past 20 years. What will be decisive is how quickly they can learn over the next five years.”
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Almost every large company is currently developing an AI strategy. What tools should we use? What models? Which processes can we automate? Which use cases should we prioritize? How much should we invest?
I would ask CEOs one more question: Who is actually running the company that results from this?
After all, an AI strategy without a leadership strategy remains incomplete. Technology can be purchased, models can be implemented, and processes can be automated. Leadership, however, cannot. That is why the AI transformation will ultimately also be a talent transformation.
This means that CEOs and members of supervisory boards today must ask questions that may be uncomfortable:
- Looking ahead to 2030, which of our key positions would we still fill exactly the same way today?
- What skills will our future leaders need that we aren’t even systematically assessing today?
- Which of our most successful executives are truly willing to question their existing models for success?
- Who would we hire if our future—rather than our past—were to define the job requirements?
The winners of the AI era won’t simply be the companies with the best AI models. They will be the ones that transform technology, organization, and leadership all at once.
That is why the key question is not how much a company invests in AI.
The key question must be: Do we have the right people in leadership positions to turn this investment into a bright future?
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“We don’t need better leaders for the old world.”
“We need the right leaders for the new era.”
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And perhaps that is precisely why a company’s most important AI investment is ultimately not a technology decision, but a personnel decision.
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by Frank Rechsteiner