What remaining value do leaders have in the era of artificial intelligence?

When artificial intelligence can complete information collection, solution comparison, report writing, and even propose decision suggestions in seconds, what remaining value do leaders have? In the past, leaders often gained authority by mastering more information, possessing more experience, and being able to provide answers. But in front of AI, knowledge is rapidly becoming widespread, and answers are easily accessible. A new question arises: if AI can take on more and more analytical, judgment-supporting, and management tasks, will leaders also be replaced?

What truly needs to be rethought may not be how many AI skills leaders need to learn, but what leadership itself means. AI can organize information, yet cannot decide which problems are worth solving; it can generate solutions, yet cannot judge which path is worth the organization's investment; it can predict outcomes, yet will not take responsibility for the final choice. The value of future leaders will no longer primarily lie in "knowing the answers," but in asking the right questions, identifying truly important directions, uniting diverse people, and making choices when the path ahead is unclear.

In an era where answers are increasingly abundant, leadership has become even more important. This article will start from the basic functions of leadership, explore what AI will replace, what it cannot replace, and how future leaders should guide organizations, people, and technology toward a future worth reaching.

AI can replace tasks, but it cannot replace leadership

When artificial intelligence can quickly collect information, analyze data, compare solutions, write reports, and even propose decision suggestions, many people will naturally ask: Will leaders also be replaced by AI? Before answering this question, we must first distinguish two concepts: the specific tasks performed by leaders are not the same as the basic functions undertaken by leadership.

1. Leadership is a function, not just a position

When people mention leaders, they easily think of management positions on the organizational chart: department manager, director, general manager, or chairman.

But having a position does not equal true leadership; having no formal title does not mean one cannot exercise leadership. Some managers have decision-making power, yet cannot point the direction for the team, nor can they unite people's hearts in difficult times. On the contrary, some people, even without formal positions, can identify real problems, influence others' judgments, and lead the team out of difficulties.

Therefore, instead of first asking "who is the leader," it is better to ask first: Why does an organization need leadership? What functions does leadership ultimately need to fulfill?

2. The five basic functions of leadership

From the perspective of organizational operation, leadership usually undertakes five basic functions.

First, clarify the direction. Leaders need to judge where the organization should go, which problems are truly worth solving, and which things are not worth investing resources in even if they can be done.

Second, attract and cultivate talents. Leaders not only assign tasks, but also discover talents, stimulate human potential, and help employees develop the ability to think independently and take responsibility.

Third, stabilize key relationships. When disagreements arise among customers, employees, partners, or other stakeholders, leaders need to stabilize the situation, build trust, and enable the organization to move forward.

Fourth, break boundaries and promote collaboration. Many important problems cannot be solved independently by a single department. Leaders need to cross job, department, and organizational boundaries to connect different people, resources, and capabilities.

Fifth, create the future. When the original methods can no longer solve new problems, leaders need to challenge conventions, take risks, and gradually turn uncertain possibilities into reality. These functions will not disappear because of the emergence of AI. What really changes is the specific way to fulfill these functions.

3. AI replaces part of the tasks in leadership work

In the past, leaders needed to spend a lot of time processing information, such as collecting materials, organizing data, comparing solutions, writing emails, preparing reports, generating meeting minutes, formulating preliminary plans, and summarizing the team's work progress.

These tasks are important, but essentially they still belong to tasks that can be disassembled, repeated, and standardized. What artificial intelligence is best at is precisely this type of work.

For example, AI can read a large amount of materials in minutes, identify common points and differences:

It can generate action lists from meeting records;

It can analyze operational data and detect abnormal trends;

It can also quickly generate multiple solutions around a problem.

Therefore, in the future, leaders will most likely no longer need to personally complete a large number of information organization and document production tasks. Many management advantages built by "mastering more information" and "generating reports faster than others" will gradually weaken. But reducing these tasks does not mean that the leadership function itself will be replaced.

AI can generate ten solutions, yet cannot truly decide which solution is worth the organization's investment; it can predict the possible outcomes of different choices, yet will not bear the consequences of wrong choices; it can analyze employee performance, yet cannot judge whether a person is worth long-term cultivation solely based on data. AI can provide answers, but it cannot decide which questions are worth answering.

4. Asking the right questions remains the responsibility of leaders

Artificial intelligence is very good at finding answers within the scope of clearly defined problems. As long as the problem is clear enough, it can quickly organize knowledge, compare cases, form solutions, and give seemingly very reasonable suggestions. But real-world leadership problems are often not so clear-cut.

Are the requirements put forward by customers truly their most important needs? Is the product delivery delay ultimately a project management problem, or the result of the organization long avoiding resource conflicts? Is poor employee performance due to insufficient personal ability, or a problem with the process design itself?

If the problem is defined incorrectly from the beginning, AI will only answer the wrong question more efficiently. Therefore, the important value of future leaders is no longer just "knowing the answers better than others," but being able to change the perspective and boundaries of problems, see contradictions that others have not noticed, and raise problems that are truly worth the organization's investment.

In an era where answers are increasingly easy to obtain, good questions will instead become scarcer. The judgment of what is "good" cannot be entirely handed over to AI. AI can compare the cost, speed, risk, and benefit of solutions, but it is difficult for it to decide for the organization: what is the result truly worth pursuing.

Some choices bring high short-term benefits, yet may damage customer trust; some solutions can greatly improve efficiency, yet may weaken employees' capabilities; some decisions seem reasonable financially, yet may conflict with the values that the enterprise has long adhered to.

These problems cannot rely solely on data calculations. Leaders must make trade-offs between efficiency, quality, responsibility, long-term value, and human feelings, and clearly tell the organization:

- Which outcomes we are willing to pursue,

- Which costs we cannot accept.

This judgment is not only the result of knowledge, but also comes from long-accumulated experience, values, and understanding of the real situation. AI can assist in judgment, but it cannot replace leaders in completing the final value selection.

Uniting people's hearts is not a task that can be completed automatically. The truly difficult moments for an organization are usually not the lack of solutions, but the lack of willingness to move forward together. When an enterprise is facing transformation, different departments may have completely different interests; when new technologies are introduced, employees may worry that their positions will be replaced; when a project fails, the team may shift blame to each other.

AI can analyze the causes of conflicts and generate communication plans, yet it cannot build trust solely through a piece of text.

Leaders need to understand people's concerns, face real contradictions, promote people with different positions to reach a consensus, and make the team believe that even if there is uncertainty ahead, it is still worth everyone's joint investment.

Just like a sports team, gathering several outstanding players does not automatically make an excellent team. Only when trust, collaboration, and common goals are formed among individuals can the overall performance exceed the simple sum of individual capabilities. This ability to unite different people into a team is precisely the part of leadership that is difficult to replace.

5. AI can make suggestions, yet will not take responsibility

The most fundamental difference between leaders and AI lies in responsibility. AI can make suggestions to close factories, adjust personnel, or stop a certain project, but it will not face the employees who lose their jobs as a result; it can suggest accepting a certain quality risk, yet will not face customer complaints and safety accidents; it can predict the success probability of an investment, yet will not bear the organizational losses after failure.

In the end, it can only be humans who decide "we will move forward along this path." Leaders must take responsibility for the direction, trade-offs, and consequences. When the results are not ideal, one cannot simply say "this is the answer given by AI."

Therefore, the AI era has not weakened leadership; instead, it has made the most core part of leadership clearer: asking questions, making judgments, uniting people's hearts, promoting actions, and being responsible for the final results. AI will replace a large number of tasks in leaders' work, but it cannot replace the leadership function itself. On the contrary, when information, solutions, and answers become increasingly abundant, organizations need someone even more to judge what is most important, which direction to choose, and what kind of future is worth moving toward together.

With more and more answers, leaders must decide where to go

Artificial intelligence has made obtaining answers easier than ever before. In the past, a team might spend several days checking materials, doing analysis, and listing solutions; now, AI can complete most of the preliminary work in minutes. It can quickly generate multiple choices and simulate the possible outcomes of different paths.

But the more answers there are, the more complex the problems the organization faces may become. Because the real difficulty has never been "whether there is a solution," but:

Which problem is most worth solving?

Which choices should be abandoned?

What kind of future does the organization ultimately want to move toward?

This is precisely the most important responsibility of leaders in the AI era.

1. Leaders must first decide: What problem are we truly trying to solve

AI is good at answering questions, but the premise is that the questions have been correctly raised. If the initial problem is wrong, the more efficient AI is, the further the organization may deviate.

For example, if an enterprise finds that product delivery is getting slower and slower, it may ask AI to analyze how to improve planning accuracy. But the real problem may not lie in planning, but in excessive product varieties, frequent R&D changes, or sales commitments that have exceeded the organization's capabilities.

If we only optimize the planning, we may just make an already unreasonable system run faster. Therefore, leaders should not directly take the answers given by AI as decisions, but keep asking:

· Is this a superficial problem, or a fundamental problem?

· Do the requirements put forward by customers represent their real needs?

· Does the process we are optimizing still need to exist in itself?

· Is this problem worth investing the organization's most valuable resources?

AI can help broaden horizons, but redefining problems still requires leaders to have a sufficiently in-depth understanding of the business, customers, and reality. In an era where answers are increasingly easy to obtain, the real gap between leaders will increasingly be reflected in what questions they ask.

2. Leaders must decide: Which things not to do

AI will continuously create new possibilities. More product solutions, more market opportunities, more automation scenarios, and more improvement suggestions may emerge in a short period of time. Organizations can easily fall into a new busyness: every department has projects, everyone is experimenting with AI, but no one can clearly explain what is most important.

At this time, the value of leaders is not to continuously add tasks, but to make trade-offs. Leaders need to clarify:

· Which opportunities align with the long-term direction of the organization;

· Which projects, although seemingly advanced, cannot create real value;

· Which work can be paused;

· Which old processes should be eliminated;

· Which risks are not worth taking even if they can be withstood.

A real strategy is never just deciding what to do; more importantly, it is deciding what not to do. If leaders cannot make trade-offs, the infinite possibilities provided by AI will instead disperse resources, making the organization look busier without actually improving performance.

3. From connecting people to connecting people, AI, data, and processes

In the past, collaborative leadership mainly focused on connecting different people and departments. For example, getting R&D, procurement, quality, and production to jointly solve a project problem, or coordinating the relationship between the enterprise and customers and suppliers. After AI is introduced, the objects of collaboration have become more complex. Leaders not only need to connect people, but also connect:

· Data from different sources;

· Different AI tools and models;

· The enterprise's original business processes;

· External partners;

· The experience and judgment of professionals.

For example, AI can predict equipment failures, but whether the prediction results can automatically enter the maintenance plan. When the system issues a risk warning, who will confirm it? Is the production department willing to shut down the equipment, does the maintenance department have the resources, and who will review the false warning?

If these links are not connected, no matter how advanced AI is, it can only stay on reports or screens. Therefore, future collaborative leadership is not just about having meetings across different departments, but about organizing people, technology, data, and processes into a system that can truly take action.

Leaders need to clarify the responsibility boundaries among them: what AI is responsible for, what humans are responsible for, who makes the final decision, and who handles it when an error occurs.

4. Let AI enhance human capabilities, not make people lose their capabilities

AI can undertake a large number of complex intermediate tasks, including analysis, organization, comparison, and drafting. This will significantly improve efficiency, but it also brings an easily overlooked risk: if employees hand over the entire thinking process to AI for a long time, they may become more and more adept at accepting answers, but gradually lose the ability to ask questions and make independent judgments.

For example, a young engineer can ask AI to quickly generate a problem analysis report, but if he does not personally observe the equipment, ask the operators, and compare the defective samples, it will be difficult for him to form a real sense of the site.

AI can provide many possible causes, yet it cannot replace the experience that humans build in the real environment. Therefore, when cultivating talents, leaders should not only teach employees how to use AI, but also consciously retain some work that must be done personally by people:

· Observe real problems on site;

· Communicate directly with customers and employees;

· Verify the conclusions of AI;

· Make judgments when information is incomplete;

· Review failures;

· Personally take charge of an important project.

Talent cultivation is not just about enabling employees to complete tasks faster; more importantly, it is about letting them gradually form their own understanding, judgment, and standpoint. Leaders must ensure that AI becomes an amplifier of human capabilities, not a substitute for human thinking.

5. Cross boundaries to gain real experiences that AI cannot provide

AI can help people quickly understand unfamiliar fields, yet it cannot replace the impact a person experiences when they truly enter an unfamiliar environment. When a person leaves a familiar department, industry, region, or culture, their original experience may suddenly no longer apply. They will find that the way of expression they are used to is not understood by others, and the methods that worked in the past may be completely ineffective in the new environment.

This experience will force people to re-recognize their own limitations. Artificial intelligence can simulate different perspectives, yet it cannot let a person truly experience the process of being rejected, misunderstood, questioned, or changed. Only by interacting with real people, organizations, and environments can human judgment be continuously corrected.

Therefore, in the AI era, cross-departmental, cross-industry, cross-cultural, and cross-organizational experiences will not lose their value; instead, they will become more important. Leaders need to encourage employees to step out of their familiar environments and let them participate in cross-functional projects, customer site visits, supplier improvement initiatives, overseas business, or new business exploration.

These experiences can help people form independent understandings different from AI's generic answers, and provide a deeper foundation for future judgments.

6. Turning possibilities into reality is an inescapable task for leaders

Artificial intelligence has significantly reduced the cost of generating ideas and designing solutions. Prototypes that used to require a professional team to complete can now be quickly made by a few people with the help of AI. Therefore, the scarcest thing in the future may not be ideas, but the ability to turn ideas into reality.

Turning a solution from concept to results still requires solving many practical problems:

· Is there anyone willing to invest?

· Where will the resources come from?

· Do the original departments support it?

· Do customers accept it?

· Who will bear the risks?

· How to move from small-scale trials to formal operation?

AI has no hands and no organizational identity. It cannot procure equipment, persuade employees, coordinate departments, nor bear pressure when a project encounters obstacles.

Leaders need to turn immature possibilities into experiments, allowing the team to start verification on a small scale. Stop failed solutions in a timely manner, continuously invest in effective practices, and eventually turn them into formal processes, products