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11 minutes, 43 seconds
No one has actual data on the future. That includes self-described futurologists and the growing ranks of self-appointed experts who continually produce endless streams of precise-sounding statistics and facts about what will happen to jobs, skills, careers, and humanity itself. Even so, there are obvious reasons to be concerned about what lies ahead.
If we look at the amount of change experienced in the past five years alone—and no prizes for guessing that AI is the source—organizations and leaders are still struggling to adapt to or adjust to today. Obsessing about the future is often a useful way to avoid dealing with the present. But it is safe to think that even more change is coming, whether incremental or exponential. To dismiss this would be reckless; to adopt a business-as-usual strategy might be madness.
Think of organizations and leaders as ships in a storm, staring at an even darker horizon and knowing there is still a long way to sail. Or, if you prefer railroads: the light at the end of the tunnel might be a train coming straight toward them.
If uncertainty is the only certainty—and, to be honest, I am not even certain of that—then human skills such as creativity, curiosity, adaptability, and ingenuity become essential. They become a competitive advantage or a differentiator. Every organization and leader has access to the same information, and AI has democratized and largely commoditized the ability to turn that information into insights. But certain things remain beyond AI’s reach, including how AI itself will unfold, how best to prepare for it, and any deeper or broader insights into the future.
There is also a great deal of room for variation in making bets about the future, and especially in acting on our own specific assumptions to enact the behaviors that bring those bets to life. That may mean pursuing your own unique path through the storm or finding a way to the top—or at least out of the tunnel. Strategy, innovation, transformation, and every major business imperative only make sense when the result is unknown. If we knew the cause-and-effect link between current actions and future outcomes, none of those concepts would have any meaning.
Even though the future is uncertain, the unspoken assumptions we make about it—more change, more complexity, difference from the past, greater volatility, less stability—are enough to allocate resources strategically rather than arbitrarily. This is especially true because our models of the future are always based on the past. Even things that look superficially “unprecedented” can often be explained by past experiences, occurrences, and data points.
Needless to say, there is no other way to strategize or prepare for anything. If we assume the future is made up of unknown unknowns, or is categorically different from everything in the past and therefore not captured by any past or present model, the result is inaction or random action. The same applies to hyperbolic visions of the future—whether utopian (exuberant prosperity for all) or dystopian (humanoid robots in charge). Those extremes make planning pointless.
One of the most widely shared tacit—and sometimes explicit—assumptions about the future is that leadership talent, or the ability to coordinate collective human activity from small teams to large organizations and societies, will become more important, not less. Why does this feel so obvious? Because when the stakes are higher, challenges grow in complexity, and collective adaptability is hard to master, the need for sophisticated orchestration, direction, and mobilization increases. That requires proficient leadership talent and, above all, leadership potential.
Talent is the ability to deliver extraordinary levels of performance. Potential is the probability that a person will develop higher levels of talent in the future. We can observe and quantify talent by examining past and present performance. Potential, on the other hand, is a bet we place on a person’s likelihood to display new forms of talent in the future.
In a simple world where performance is easy to master, leadership matters less. Even people with inadequate or rudimentary leadership skills may deliver acceptable results, and teams and organizations may perform well independently of the leader’s actions. But in a complex world where performance is difficult to master, leadership becomes the indispensable source of team effectiveness. The variability between teams’ performance becomes larger and strongly dependent on the leader’s talents.
Furthermore, when the world keeps changing and the nature of our challenges keeps growing and evolving—meaning that your ability to solve today’s problems may be unrelated to your ability to solve tomorrow’s—the crucial challenge is to evolve leadership itself, or to bet on individuals who have the potential to develop and evolve in line with the challenges they face.
If we treat AI not as a tool or a related set of tools, but rather as the defining leadership challenge of our times—and the likely challenge humanity will need to master in the next few years or decades, whether that means dealing with disruptions, productivity gains, unfulfilled promises, re-organization to achieve value realization, strategic business advantage or differentiation, and perhaps even economic bubbles—one thing is certain. Exactly as in every previous chapter of our human evolution, the ability to organize collective human activity, which today also includes the ability to coordinate the human-AI interface and to understand how humans can be augmented by AI, will require competent leadership.
In practical terms, groups, teams, organizations, and nations that are led well can expect to outperform those that are led poorly. Given the scale of the challenge—and the size of both the risks and the opportunities—having the right leaders in place and being able to future-proof them should be treated as a strategic priority for organizations and nations alike. As the chart above illustrates, when it comes to leadership, AI does not change everything, but it still has a significant impact on some of the critical features that will determine whether leaders can be effective in the future and help their teams navigate the human-AI age.
For decades, in what is largely known as the human capital age, leadership selection and development focused overwhelmingly on hard skills, expertise, and intellectual capital—what you know, often signalled through formal credentials and specialized knowledge. But those aspects of leadership talent have been severely disrupted by AI, which has already won the IQ battle against humans. Even rudimentary direct-to-consumer LLMs and generative AI platforms know much more about most things than most, if not all, humans do. This changes the very meaning of expertise: instead of knowing the answers to questions, leaders must now know how to ask the right questions, and they must know enough to vet or edit the answers AI provides. That includes the ability to ignore what is irrelevant and to use AI better than a novice would.
For decades, organizations also assumed that the best predictor of future leadership performance was past performance. If you wanted a safe pair of hands, or someone who would deliver results in the future, you looked for a strong track record and deep experience. The assumption was that there was no better recipe for the future than to copy-paste from the past. But the less the future resembles the past, the more irrelevant past performance becomes as an indicator of future performance. AI has already impacted most jobs and roles to the point that even when we remain in the same functional or formal role as before, the way we actually add value in that role has changed dramatically. We now outsource some tasks to AI, AI agents, and automation technologies in order to free up time and skills for higher-value human activities.
While we don’t know how fast AI will continue to develop, how far it will go, or in what direction it will unfold, it is logical to assume that the distance from the past will continue to increase. Hiring leaders for what they have done will therefore become less useful than hiring them for what they could do. And what they will need to do is likely to look less and less similar to what they have done in the past.
Transformational leadership was once regarded as a niche or specialized subtype of leadership, evocative of unconventional misfits who were hired to drive change and infect the organization with the innovation virus, or similar clichés. That view has now changed. Transformational leadership has become a foundational leadership capability. Nobody should be seen as a leader unless they have some transformational foundations in their DNA, or are at least capable of developing a transformational mindset. Driving and leading change is now the fundamental task of leaders. Likewise, transformational leadership is no longer a specific role or formal title; it is a central leadership activity for anyone responsible for a team, a business, or a unit.
Importantly, while we may not be able to predict either the future or the specific hard skills leaders will need to develop, it is safe to assume that leadership potential—defined as the ability to develop the leadership skills and talents needed to be effective in the future—will mostly centre around the competencies and qualities that AI is unlikely to replace or automate. These are exactly the qualities followers will need most from their leaders, not least because they won’t be able to source them from AI or outsource them to it.
As the chart above shows, these competencies include:
The astute reader may have noticed that these soft skills, while presented as central to future leadership, are not exactly new. Even before AI went mainstream with the recent breakthroughs of LLMs and generative AI platforms, a salient trend was already underway: the growing importance of soft skills co-existed with the rapid devaluation of hard skills. Leadership potential came to be understood less as a fixed set of answers and more as a collection of foundational attributes that enable leadership to evolve, adapt to new challenges, and become smarter—or at least more adaptable—over time, much like AI itself does.
The apparent paradox is that the more advanced our technologies become, the more valuable our oldest human capabilities seem to become.
There is nothing particularly futuristic about curiosity, judgment, resilience, or the ability to inspire others. Aristotle would have recognized them. Confucius would have recognized them. Artificial intelligence has not suddenly made these qualities important. Rather, it has exposed how much we had been underestimating them while overvaluing knowledge, credentials, and experience as proxies for future performance.
The AI revolution is forcing us to rediscover what leadership was always supposed to be: not demonstrating that you have the answers, but creating the conditions in which people can collectively discover better ones.
There is a broader lesson here. Every technological revolution eventually changes what it means to be exceptional. Once machines outperform humans at a particular activity, excellence simply migrates elsewhere. For example:
Likewise, the widespread availability of AI means that expertise itself becomes less scarce than the judgment required to deploy it wisely. The frontier of human advantage has always moved, and there is little reason to believe this time will be different.
Perhaps, then, the future of leadership is less about preparing people for a world dominated by artificial intelligence than about ensuring they do not become artificially intelligent themselves: endlessly optimizing, predicting, calculating, and producing, while gradually surrendering the curiosity, imagination, and moral judgment that made those optimizations worth pursuing in the first place.
Technology has always rewarded those who learned to master it without becoming like it. There is little reason to think AI will prove the exception.
In the end, the defining question for leaders will not be whether they can keep pace with increasingly intelligent machines, but whether they can continue cultivating the distinctly human qualities that machines, however capable they become, still struggle to explain, let alone inspire.
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