What Kind of Leaders Will We Need in the Age of AI?
16 hours ago
13 min read
AI is changing more than just the way we work. It is also reshaping what it means to be a leader. There comes a point when technological change is no longer simply about introducing a new tool, but begins to redefine how an organization operates — creating the need for an entirely new approach to leadership. It seems we have reached that point.
AI is increasingly capable of performing tasks that once consumed a significant amount of leaders’ and professionals’ time: gathering information, analyzing data, preparing reports, identifying patterns, making recommendations, summarizing documents, and even independently carrying out increasingly complex processes. This creates an interesting paradox. The more AI can do, the less a leader’s value will depend on being the person who knows the most.
And that is precisely why expectations of leaders are changing.
Less Information Processing, More Meaningful Decision-Making
In traditional organizational structures, information was one of a leader’s key sources of influence. Reports flowed to the leader, who received updates from different functions, had an overview of the numbers, and often had to gather the data needed to make informed decisions. AI is gradually reducing this information asymmetry.
Today, a leader can access analyses, summaries, and potential scenarios within minutes — tasks that previously took hours or even days to complete. Reviewing and interpreting these materials also used to require considerable time. AI can quickly extract and highlight the key points from lengthy documents, and it does more than provide information: increasingly, it suggests possible solutions and recommends next steps.
As a result, the leadership role is not simply becoming more “AI-competent.” The very focus of leadership work is shifting.
McKinsey’s 2026 CEO research makes this point clear: as information becomes more readily accessible, employees will increasingly look to leaders not for ready-made answers, but for decisions, context, and direction.
This fundamentally changes what it means to be a leader.
Good Questions Lead to Better Decisions
In the age of AI, the most effective leaders will not necessarily be those who have all the answers, but those who know how to ask the right questions, critically evaluate the responses they receive, and interpret the opportunities offered by technology in a business context.
What are we really trying to achieve?
Which decisions should we delegate to AI, and which should we retain human responsibility for?
What data and assumptions underpin this recommendation?
What might we be overlooking?
What happens if we get it wrong?
Where is human judgment and involvement essential to the decision-making process?
What impact will this decision have on people?
And above all, what business value will this decision create?
A 2024 study examining the relationship between AI and leadership found that AI can significantly support data-driven decision-making, predictive analytics, talent management, and leadership development. However, the research also emphasizes that interpreting AI-generated outputs and placing them in the right context remain human responsibilities. Sound decisions still require experience, intuition, empathy, and ethical judgment.
AI does not eliminate the need for leadership decisions. It raises the bar for them.
Five Leadership Qualities That Will Become Increasingly Valuable in the Age of AI
AI Fluency — Without Needing to Be a Technology Expert
Future leaders do not need to be programmers, but they do need a solid understanding of AI and how it works.
They must understand what AI can do, where its limitations lie, how to use it effectively, what risks it presents, and how to align the technology with business objectives.
According to McKinsey’s State of Organizations 2026 research, 86% of leaders feel their organizations are not adequately prepared to integrate AI into day-to-day operations. Meanwhile, only 14% of organizations report that their leaders consistently support AI adoption through a clear strategy and concrete actions. This represents a significant leadership challenge.
You cannot credibly lead an AI transformation without using AI yourself.
McKinsey’s 2026 CEO research suggests that leaders do not necessarily need to become AI experts. What matters more is their willingness to engage actively in the learning process: asking questions, experimenting, using new tools, and remaining open to learning from colleagues with greater AI expertise.
A leader’s credibility does not come from having all the answers, but from setting an example through continuous learning and adaptability.
Decision-Making Over Information Processing
When access to information is no longer a bottleneck, simply possessing information becomes a less significant competitive advantage. The real value lies in what we do with it.
AI can outline multiple strategic options, model the financial implications of a decision, and compare different scenarios. However, determining which option is best for a particular organization cannot be based on analysis alone.
It requires a deep understanding of the business, the organization’s specific context, people’s motivations, customer needs, and corporate values. Only with this broader perspective can leaders turn the possibilities presented by AI into sound business decisions.
One of the defining leadership competencies in the age of AI will be the ability to distinguish between data and interpretation, information and genuine knowledge, and a recommendation and an accountable decision.
The leader’s role will therefore become less about gathering and communicating information, and more about interpreting it, recognizing the relevant connections, and setting the direction for decisions.
3. Building Trust in Times of Uncertainty
Implementing AI is not just a technological challenge; it is also an organizational and human one. When technology can perform certain tasks faster or more efficiently, employees naturally begin to ask: How will my role change, and what does this mean for my future?
Questions of accountability also come to the forefront. Who oversees AI-generated analyses and recommendations? Who takes responsibility when a system makes a mistake or an automated decision leads to unintended consequences?
A leader’s role is not to dismiss these concerns with empty promises, but to create space for questions, communicate honestly about the potential impact of change, and establish clear boundaries for how AI will be introduced and used.
McKinsey’s research highlights that the barriers to organizational AI adoption extend beyond technological challenges. Resistance to change, organizational readiness, and trust are also critical factors. Successful AI implementation therefore requires more than ensuring that the technology works: people need to understand why the change is happening, how it will affect their work, and what role they will play in the new operating model.
This calls for a different kind of leadership presence. It is not enough to emphasize the need for change. Leaders must provide a clear sense of direction, make decisions transparent, clarify the boundaries of human accountability, and build trust in an environment where solutions and expectations are constantly evolving.
4. From Control to Facilitative Leadership
AI is challenging one of the traditional assumptions of leadership: that effective management is achieved through continuous control. If a leader has traditionally ensured that the organization runs smoothly by acting as the central point for all critical information, approving every decision, and closely monitoring execution, this model can easily become an obstacle to speed and adaptability in the age of AI.
AI accelerates the flow of information, supports analysis, and increasingly enables tasks to be completed without direct managerial intervention. If every step still requires a leader’s approval, some of the time savings offered by the technology are lost. The leader can end up becoming a bottleneck in the decision-making process.
McKinsey’s CEO research highlights another risk: the greater transparency enabled by AI can easily lead to increased micromanagement. Whereas leaders may previously have relied on periodic reports to assess performance, AI can now provide much more detailed, continuous visibility into processes. However, this does not mean that every detail needs to be monitored. More information does not necessarily justify tighter control.
The most effective leaders therefore focus not on extending oversight, but on reassessing where their direct involvement is genuinely necessary and where employees and AI-powered systems can be given greater autonomy. This requires clearly defined decision-making authority, accountability boundaries, levels of oversight, and situations in which human review is mandatory.
Control is not replaced by the complete absence of control, but by deliberately cultivated trust and accountability. The leader’s role is to establish clear boundaries, define expected outcomes, and ensure that accountability remains clear even as AI plays an increasingly significant role in execution.
This also reflects a broader transformation of the leadership role. The emphasis gradually shifts from continuously monitoring execution to creating the conditions for success. Leaders provide direction, coordinate work, facilitate collaboration, support independent problem-solving, and intervene when genuinely necessary.
A facilitative leader does not relinquish control. On the contrary, they establish the framework within which employees and AI systems can operate with greater autonomy while remaining aligned with shared objectives. Leadership value is therefore measured less by how many decisions pass through the leader’s hands and increasingly by how effectively the organization can operate without constant managerial involvement — while ensuring that appropriate human judgment and accountability remain in place for the decisions that matter most.
5. The Growing Importance of Delegation
In the age of AI, leadership effectiveness will depend less and less on how many decisions pass through a leader’s hands. It will depend far more on their ability to create the conditions in which the right decisions can be made at the right level and at the right time.
This requires more than simply delegating tasks. Leaders must clarify which matters employees can decide independently, when they can rely on AI-generated recommendations, and which situations require managerial intervention or human review. Delegation is therefore not about shifting responsibility elsewhere, but about deliberately defining decision-making authority and accountability.
Accordingly, the leader’s role will increasingly focus on the following:
Defining the framework: Establishing clear objectives, operating rules, and accountability boundaries.
Setting priorities: Deciding where the organization should focus its resources, attention, and energy.
Clarifying decision-making authority: Determining which decisions employees can make independently, where AI can provide support, and when managerial approval is required.
Coaching and development: Strengthening employees’ independent thinking, judgment, and problem-solving skills rather than having the leader provide the answer to every question.
Conflict management: Reconciling differing perspectives, managing tensions, and facilitating shared decisions.
Managing exceptions and critical situations: Focusing attention on cases that genuinely require careful judgment, human intervention, or leadership accountability.
Unlocking team performance: Creating an environment in which employees can work with greater autonomy, speed, and efficiency.
As technology takes over an increasing number of routine information-processing, analytical, and coordination tasks, the leader’s role will be about more than simply overseeing the work that remains. It will increasingly involve aligning human and technological capabilities, ensuring the conditions for responsible decision-making, and helping the organization channel the capacity freed up by AI into the creation of genuine business value.
Less Management, More Genuine Leadership and Direction
This shift is particularly significant for middle managers, who have traditionally been responsible for much of the day-to-day operational management. According to McKinsey, in human–AI operating models, managers may spend less time gathering and communicating information, and more time exercising judgment, coaching their teams, and handling exceptional situations. Some decision-making cycles could shrink from weeks to hours, while decisions that do not require human intervention may disappear altogether.
This means that the role of middle management will also undergo a fundamental transformation.
Those whose work primarily involves:
Preparing reports
Communicating information
Tracking status updates
Overseeing administrative tasks
Managing approvals
will find that an increasing proportion of their responsibilities can be automated.
By contrast, those who:
Develop their people
Navigate difficult decision-making situations
Bring together different perspectives
Resolve conflicts
Motivate their teams
Interpret the business context
Lead teams that work alongside AI
will see the value of their role increase.
AI, therefore, does not simply reduce the volume of leadership work. It changes its nature.
Human-Centred Leadership as a Key to Organizational Performance
According to McKinsey’s State of Organizations 2026 research, self-awareness, mindfulness, and the quality of human relationships are becoming increasingly important in leadership. Empathy, flexibility, authenticity, psychological safety, and genuine attentiveness are more than simply desirable leadership qualities. In a rapidly changing environment, they are essential to effective collaboration and adaptability.
The traditional command-and-control leadership model, built primarily around giving instructions and monitoring performance, is becoming increasingly insufficient on its own. As AI adoption grows, workflows, roles, and areas of responsibility are also evolving, creating greater uncertainty. In this environment, leaders must do more than provide direction: they must build trust, make changes understandable, and create an environment in which employees feel comfortable asking questions, raising concerns, and experimenting with new solutions.
This is far more than a “soft” HR issue. Human-centred leadership also has a tangible impact on organizational performance.
McKinsey’s research associates human-centred leadership with the following organizational outcomes:
56% – higher employee satisfaction and retention
56% – stronger trust
42% – better decision-making
40% – greater organizational adaptability and resilience
These findings highlight that trust, psychological safety, and empathy are not alternatives to high performance. On the contrary, they can help employees collaborate more effectively, adapt more quickly, and play a more active role in implementing change.
In the age of AI, technology alone cannot guarantee organizational success. Performance also depends on whether leaders can create the human and organizational conditions in which technology can deliver genuine value.
Self-Reflection Is Becoming a Business Competency
Perhaps this is one of the most interesting findings of the research. McKinsey identified differences between more reflective and less reflective leaders. More reflective leaders have greater confidence in their organizations’ ability to adapt, a clearer understanding of their most important strategic priorities, and a more active role in leading AI adoption.
This matters because, in the age of AI, a leader’s own approach to leadership is constantly being put to the test.
How do I respond when I am no longer the fastest?
How do I react when a younger colleague has significantly greater expertise in a particular technology?
Am I willing to admit that I do not know something?
Can I learn from someone who holds a more junior position than I do?
Am I willing to change an approach that has worked successfully in the past?
In the age of AI, these are not merely personal development or self-awareness issues. They are leadership competencies.
One of the Leader’s Most Important Responsibilities Is to Foster a Culture of Continuous Learning
A leader cannot promise their team that everything will work the same way five years from now.
What they can do is create the conditions that enable the team to adapt to whatever the future brings.
According to McKinsey’s research, a significant proportion of jobs will require new combinations of skills. Alongside technological proficiency, social, emotional, and higher-order cognitive skills will become increasingly important. Demand for AI fluency in US job postings has increased sevenfold in just two years, and the research suggests that around 75% of current roles will need to be redesigned in some way.
That is why one of a leader’s most important responsibilities will not be to organize a single AI training session. It will be to create a culture in which continuous learning, experimentation, and relearning become second nature.
McKinsey describes this as “fearless learning”: leaders learn openly, ask questions, make mistakes, and reflect on their experiences — thereby giving their teams permission to experiment, too.
Leaders Must Now Manage Not Only AI, but Also the Relationship Between AI and People
One of the biggest leadership challenges in the age of AI is not technological at all. It is deciding what to entrust to machines and what should remain the responsibility of people.
AI is playing an increasingly significant role in preparing, analyzing, and executing decisions. As a result, defining clear boundaries for decision-making is becoming ever more important.
When can AI make decisions autonomously?
When is human approval required?
Who is accountable for decisions made by AI?
Who verifies the quality of the output?
What happens when an algorithm produces an incorrect or biased result?
The leadership study also highlights the challenges of overreliance on AI, transparency, accountability, data privacy, and preserving human intuition. The authors argue that AI should be designed and used to complement — rather than displace — human knowledge and judgment.
This creates a new leadership responsibility: leaders must not only lead people, but also establish the rules governing human–AI collaboration.
What Happens to the CEO’s Role?
Perhaps this is where the most significant transformation is taking place. According to McKinsey’s CEO research, published in September 2026, AI is no longer simply a technology or IT issue. It affects strategy, organizational structure, talent, capital allocation, technology, culture, and the customer value proposition simultaneously. No single function can therefore take ownership of this challenge alone.
There are three things a CEO cannot delegate:
1. Redefining Strategic Direction
What can the company achieve with the help of AI?
Where can genuine growth be unlocked?
Which areas require a radical rethink of how the business operates?
2. Redesigning the Organization
How should people and AI work together?
Which tasks can be automated?
What capabilities will the organization need in two to three years?
Where should the capacity freed up by AI be redeployed?
3. Redefining the Culture
What does learning mean in our organization?
How do we respond to mistakes?
How much room is there for experimentation?
How do we build trust?
And what do we expect from our leaders?
The Biggest Leadership Risk: Assuming That AI Is an IT Project
Technology leaders naturally play a critical role. But AI transformation is not an IT project.
In their 2026 CEO research, McKinsey’s authors distinguish between three types of leadership approaches. Some leaders view AI primarily as a technology initiative; others see it as a tool for improving productivity and reducing costs. Then there are the so-called AI visionaries, who use AI to redefine their company’s competitive advantage.
The difference is not simply who uses AI. It is who understands that AI requires a fundamental rethink of the organization itself.
What Does This Mean for the Future of Leadership Selection?
Leadership recruitment will also need to evolve. Traditional questions — such as the size of the teams a candidate has led, the results they have achieved, and their industry experience — will remain important.
However, these factors alone may no longer be sufficient. Increasingly, the following capabilities are likely to become critical:
Learning agility — How quickly can they master new skills and concepts?
AI fluency — Do they use the technology, and do they understand its business potential?
Judgment and decision-making — Can they make sound decisions even when there is no ready-made answer?
Adaptability and resilience — Are they willing to rethink the approaches that have made them successful in the past?
Self-awareness — Do they recognize their own reactions, blind spots, and limitations?
Empathy and connection — Can they create a sense of psychological safety during periods of uncertainty?
An experimental mindset — Are they willing to move forward even when there is no perfect playbook?
Ethical judgment and accountability — Can they distinguish between what is possible and what is actually the right thing to do?
Some of these capabilities have always been important. But in the age of AI, leading effectively will become much more difficult without them.
The Importance of Leadership Is Not Diminishing — It Is Moving to a Higher Level
Conversations about the age of AI often focus on how many jobs will disappear.
Perhaps we should also ask a different question: What work will we leave to people — and what kind of leaders will we need to lead them?
AI can take over information gathering. It can handle parts of the analytical process, produce reports, automate workflows, prepare decisions, and, increasingly, execute certain tasks itself.
But none of this diminishes the importance of someone being able to say:
Where are we going?
Why?
What must we not do?
What do we believe in?
When do we need to intervene?
And how do we bring people along with us?
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