Founding essay

When Management Meets the Absurd

Camus, work and meaning in the age of AI

Most discussions about artificial intelligence and work begin with prediction. Which jobs will disappear? Which tasks will be automated? Which skills will remain valuable? Which organizations will become more efficient? Which workers will be left behind?

These questions matter. But they do not yet reach the deepest layer of the problem.

Work is not only a set of tasks. Management is not only coordination. Organizations are not only systems for producing outputs. For many people, work has become one of the main places where identity, dignity, usefulness, recognition and belonging are negotiated. To lose work, to be displaced in work, or to feel that one’s judgment is no longer needed is therefore not only an economic event. It can become an existential event.

This is why the age of AI requires more than technological literacy. It requires existential literacy. It requires us to ask what happens to the person who works when work changes its meaning.

The manager as an existential figure

Management often presents itself as a discipline of control. It promises planning, coordination, prediction, measurement and improvement. In this picture, the manager appears as someone who makes uncertainty manageable.

Yet the lived experience of management is often very different. Managers repeatedly encounter situations that cannot be fully controlled: crises, interpersonal conflicts, moral dilemmas, organizational failures, competing expectations, emotional exhaustion, uncertainty and the fragile dependence of human projects on other people.

This way of thinking has an older management genealogy. George S. Odiorne’s idea of the existential manager already described the manager as someone who must act within uncertainty, ambiguity and situational limits rather than from the safety of a complete theory. A related existential-systems approach to managing organizations later connected existential concerns with systems thinking and organizational life.

This is where existential self-development becomes important. It asks not only how managers improve performance, but how they understand the crises, anxieties and responsibilities that shape who they become at work.

In this sense, the manager is not only a decision-maker. The manager is an existential figure: someone who must act without complete certainty, take responsibility without full control and remain human within systems that often reward only performance.

Camus and the recurrence of crisis

Albert Camus helps us understand why managerial life often feels cyclical.

In the myth of Sisyphus, the human being pushes the stone uphill only to see it roll down again. The point is not simply that life is meaningless. That is the shallow reading. Camus is interested in the confrontation between the human desire for order and the world’s refusal to provide final guarantees.

My earlier work on Camus and management explored this link between absurdity, recurring crisis, revolt and the ethical value of human life in management.

Management knows this confrontation well. A team is stabilized, and then a new conflict appears. A process is improved, and then the context changes. A crisis is solved, and another arrives. A plan is completed, and the future refuses to behave as planned. The organization asks for certainty, but the world answers with ambiguity.

This is not an accidental feature of management. It is one of its existential conditions.

The temptation, in such a condition, is to seek a final system: a method, ideology, technology or managerial doctrine that will remove contradiction once and for all. Camus warns against this temptation. Revolt is not the fantasy of total control. Revolt is the refusal to surrender human value to abstraction. It is the decision to act without pretending that action can eliminate the absurd completely.

This matters in the age of AI. AI systems promise new forms of prediction, optimization and automation. They may help us see patterns, improve decisions and reduce burdens. But they can also strengthen the illusion that the human condition itself can be solved by better systems.

The existential question is not whether AI can support management. It can. The question is whether organizations will use AI to deepen human responsibility or to avoid it.

Work after work

Meaning After Work begins from a double meaning.

It does not mean only leisure after the workday. It asks what remains meaningful when work can no longer carry the identity, dignity and purpose we have asked it to carry.

Modern societies have placed an enormous burden on work. Work has been asked to provide income, status, community, self-realization, discipline and moral worth. We often ask people not simply what they do, but who they are through what they do.

AI intensifies the question because it enters not only manual labour, but also domains associated with knowledge, creativity, judgment and expertise. It does not merely threaten repetitive tasks. It unsettles the symbolic structure through which educated workers have understood their value.

If a machine can write, summarize, design, advise, calculate, code, translate and decide, then the question is not only: what tasks remain for humans?

The deeper question is: what forms of human presence still matter?

Labour, work and post-work emptiness

This question also connects with Joe Alan Jones’s expanded understanding of work in response to AI . Jones argues that debates on automation often reduce work to paid employment or economic necessity. But work is not only what we do to survive. It can also be a mode of meaningful human activity.

His distinction between labour and work is useful here. Labour names activities bound to necessity, survival and welfare. Work, in his account, names meaningful activity through which a person can articulate selfhood beyond mere necessity.

This matters because automation may remove burdens, but it may also remove the very practices through which people learn, struggle, create, relate and find meaning. A future beyond employment is therefore not automatically a future rich in meaning. It may become empty if the human practices that once carried meaning are also automated away.

Meaning After Work begins precisely at this threshold. It does not ask for a simple escape from work. It asks how human beings can preserve meaning when employment, usefulness and productivity can no longer serve as the main foundations of identity.

The crisis of usefulness

Many people fear AI because they fear becoming useless.

This fear is not irrational. Usefulness has become one of the dominant moral languages of modern work. To be useful is to justify one’s place. To be productive is to prove one’s value. To be employable is to remain socially legible.

But a human being is not reducible to usefulness.

This is easy to say and difficult to live. Organizations are built around usefulness. Careers are built around usefulness. Even self-development often becomes another form of usefulness: improve yourself, optimize yourself, become more resilient, become more productive, become more adaptable.

Existential management asks whether this language is sufficient.

The problem is not that usefulness is bad. The problem is that usefulness becomes dangerous when it becomes the only language through which we understand human worth. A person who is not currently useful is still a person. A person who fails is still a person. A person who cannot adapt quickly enough is still a person. A person whose work is transformed by AI is still not a remainder of automation.

Co-being after optimization

One of the deepest losses in organizational life is the loss of real relationship.

Managers and workers can become functions to one another. The leader becomes a role. The employee becomes a resource. The customer becomes a data point. The colleague becomes an obstacle. The self becomes a performance project.

My research on managerial crisis and co-existence examined how critical situations in managerial life are often linked to disrupted interpersonal relationships and to the need to rediscover more relational ways of being at work.

This is why co-being matters. Co-being is the practice of remaining present with oneself and another person without reducing them to performance, usefulness, diagnosis or failure. It is not sentimental. It is not a rejection of organization. It is a condition of any organization that still wants to remain human.

In AI-mediated work, co-being becomes more important, not less.

As more communication is filtered through platforms, metrics, dashboards and intelligent systems, organizations risk mistaking information about people for contact with people. A system may know that someone is underperforming. It does not know what it means to sit with them in their exhaustion. A model may identify risk. It does not take responsibility for how a human being is addressed.

The future of human-centred AI cannot therefore be only interface design, fairness metrics or compliance frameworks. These are necessary, but insufficient. Human-centred AI must also ask how human beings remain capable of attention, dialogue, responsibility and care around intelligent systems.

Existential contradictions

The AI age will not remove contradiction. It will multiply it.

We will be pulled between efficiency and dependency. Between creativity and skill erosion. Between personalization and surveillance. Between assistance and displacement. Between adaptation and self-betrayal. Between responsibility and automation. Between the desire to remain relevant and the desire to remain whole.

These contradictions cannot always be solved by choosing one side.

Sometimes responsible action means learning to inhabit contradiction without collapsing into resignation or escaping into shortcuts. Resignation says: nothing can be done. The shortcut says: only survival matters. Revolt says: I cannot control everything, but I will still act in a way that preserves human value.

This is not heroic in the dramatic sense. Often it is quiet. It may look like refusing to use AI to deceive. It may look like protecting time for real conversation. It may look like admitting uncertainty instead of performing mastery. It may look like redesigning a workflow so that human judgment is not merely decorative.

Existential contradiction is not a failure of management. It is one of the places where management becomes human.

Social enterprise and the value of human life

Social entrepreneurship offers an important lesson here.

Social enterprises live with contradiction from the beginning. They must combine economic survival with social mission. They must remain financially viable without betraying the people or communities they exist to serve. They must work with scarcity, uncertainty and competing expectations.

This is why research on social entrepreneurship and philosophical management is relevant for the AI age. It shows that management can be understood not only as technical coordination, but also as a value-driven practice shaped by interpersonal relationships, uncertainty and paradoxical situations.

The lesson from social enterprise is not that mission language solves everything. It does not. Mission can drift. Values can become branding. Ethical language can hide exploitation. But social enterprise shows that management can begin from a different question: not only what produces value, but what kind of value is worth producing.

In the AI age, this question becomes urgent. A technically successful system can still produce human loss. An efficient organization can still become existentially empty. A productive worker can still feel that their life is being drained of meaning.

Beyond responsible AI

Many initiatives in AI ethics ask how we can make AI responsible. This is important. But it is not enough.

We must also ask how humans and organizations can remain responsible around AI. Responsibility cannot be delegated entirely to systems, policies or tools. It must be carried by people who are willing to remain answerable for what they build, adopt, automate and normalize.

This is the starting point of existential human-centred AI.

The question is not only whether an AI system is aligned with human values. The question is whether the human world around the system still cultivates people capable of valuing.

Are workers still able to question? Are managers still able to listen? Are organizations still able to protect spaces where human judgment matters? Are people still recognized beyond performance? Are relationships still possible in systems optimized for speed?

AI may become more intelligent. That does not guarantee that organizations will become wiser.

Meaning after work is not the end of work

Meaning After Work is not an anti-work project.

Work will continue. People will still build, care, teach, organize, repair, design, decide, trade, create and serve. The question is not whether work disappears completely. The question is whether work can be reimagined without making human worth depend entirely on productivity.

The future may require a different relationship between work and life.

Not work as the sole source of identity. Not leisure as mere recovery from exhaustion. Not AI as a replacement mythology. Not management as control over uncertainty. Not ethics as compliance after the fact.

Rather, a more difficult possibility:

• work as one place where meaning may appear, but not the only one,
• technology as assistance, but not destiny,
• management as responsibility, not only efficiency,
• organizations as relational worlds, not only systems,
• human beings as more than their usefulness.

This is not a solution. It is a direction.

Camus did not offer escape from the absurd. He offered revolt within it. Existential management does not offer escape from crisis, contradiction or uncertainty. It asks how we might remain human inside them.

That may be the central task of the AI age: not only to ask what work remains for human beings, but to ask what kind of human beings we become when work no longer explains us.

Essay 02

Staying Human Is a Skill

Why empathy, judgment and co-being become existential capacities in the AI age

We often speak about artificial intelligence as if the central question were technical: which tasks will be automated, which jobs will change, which tools people must learn to use. These questions matter. But they do not go deep enough.

The more intelligent systems enter work, education, management and daily life, the more visible another question becomes: what human capacities are needed to remain responsible, relational and meaningful when work is accelerated by machines?

In this sense, the language of “soft skills” has become misleading. In the AI age, empathy, judgment, communication, responsibility, adaptability and co-being are not soft additions to technical competence. They are existential capacities. They are the abilities through which people remain human under pressure.

1. The problem with calling them “soft”

The phrase “soft skills” suggests something secondary: useful, pleasant, perhaps desirable, but not essential. Technical skills appear hard, measurable and economically serious. Human skills appear vague, personal and difficult to assess.

But this distinction becomes less convincing in a world shaped by artificial intelligence. When intelligent systems can generate text, summarize meetings, produce images, classify data, support decisions and imitate expertise, the distinctiveness of human work shifts. What becomes crucial is not only what humans can produce, but how they judge, relate, interpret, refuse, care and take responsibility.

Recent research on soft skills in AI-driven Society 5.0 points in this direction. It identifies interpersonal, communication, thinking-related, leadership and coping-with-uncertainty skills as central for a society in which AI is deeply integrated into work and life. The finding is not merely that people need more skills. It is that the most needed skills are often the ones societies, organizations and educational systems do not sufficiently cultivate.

This is the first shift: soft skills are not soft anymore. They are the human infrastructure of the AI age.

2. AI does not remove the need for people. It changes what people are needed for.

A common fear is that AI will replace human work. A more subtle danger is that AI may narrow our imagination of what human work is for.

If work is understood only as the execution of tasks, then automation appears as a simple substitution problem. Machines do tasks; humans move elsewhere. But work has never been only task execution. Work also carries identity, recognition, judgment, contribution, dignity and belonging. It is one of the ways people experience themselves as useful, responsible and connected to others.

The growing importance of social skills in the labour market already showed that work depends on coordination, cooperation and the ability to understand others. Artificial intelligence does not make this less important. It may make it more visible. The more machines can perform isolated cognitive tasks, the more valuable human capacities for interpretation, collaboration and moral orientation become.

This does not mean that everyone must become more “emotionally intelligent” in a shallow managerial sense. It means that the future of work depends on capacities that cannot be reduced to speed, output or optimization.

3. Skills lists are necessary, but not sufficient

Global debates on future skills rightly emphasize analytical thinking, resilience, flexibility, leadership, creativity, technological literacy and lifelong learning. Similarly, policy discussions of a resilient digital transition underline the importance of skills for adaptation, inclusion and social resilience.

These frameworks are useful. They show that AI transformation is not only a technological transition but also a skills transition. Yet there is a risk: skills can become another language of adaptation. Workers are told to reskill, upskill, adapt and remain employable. Organizations are told to transform. Education systems are told to prepare people for the future.

But prepare them for what kind of future? And for what kind of human life within that future?

If skills are treated only as instruments of employability, we miss the deeper issue. The AI age does not only require people who can use new tools. It requires people who can live responsibly with powerful systems, resist harmful simplifications, preserve attention to others and remain capable of meaning when work itself changes.

This is why staying human is not an attitude. It is a skill. More precisely, it is a set of cultivated capacities.

4. AI literacy must become human literacy

It is important to teach people how AI works. People should understand data, models, prompts, limitations, bias, hallucination, automation and accountability. Without this, they become passive users of systems they do not understand.

But AI literacy cannot stop at tool use. UNESCO’s AI competency framework is important precisely because it includes a human-centred mindset and ethics, not only technical understanding. The question is not simply whether people can operate AI systems. The question is whether they can judge when to trust them, when to question them, when to slow down and when to keep responsibility human.

The same applies to human-centered AI. Human-centred design is not anti-technology. It does not reject automation. Rather, it asks how automation can support human self-efficacy, mastery, creativity and responsibility. This distinction matters. The goal is not less AI. The goal is AI that does not quietly reduce the human being to a monitor, consumer, data point or compliance object.

Human-centred AI becomes existentially serious when we ask not only whether systems are usable, safe or trustworthy, but also whether they help people remain agents in their own lives.

5. Ethics needs capacities, not only principles

Much work in AI ethics has focused on principles. This is necessary. Discussions of AI ethics principles such as beneficence, non-maleficence, autonomy, justice and explicability provide an important foundation for responsible AI.

But principles do not enact themselves.

Autonomy requires people who are capable of agency. Justice requires people who can notice exclusion and resist convenient unfairness. Explicability requires people who are willing to ask who is responsible. Beneficence requires people who can still ask what is genuinely good for human beings, not merely what is efficient or scalable.

This is where ethics becomes existential. The question is not only which principles should guide artificial intelligence. The question is what kinds of human capacities are needed to live those principles under pressure.

In ordinary organizational life, pressure is rarely dramatic. It appears as deadlines, dashboards, automation targets, managerial expectations, client demands, fatigue, competition and the quiet temptation to let the system decide. Under such conditions, ethics becomes less a declaration and more a practiced ability to pause, interpret and respond.

6. Co-being: the forgotten human skill

Many skills frameworks mention communication, collaboration, empathy or leadership. Meaning After Work adds another word: co-being.

Co-being is not simply teamwork. It is the recognition that human beings do not remain human in isolation. We become ourselves through relations: through care, conflict, responsibility, listening, dependence, vulnerability and shared meaning.

In the AI age, co-being becomes fragile. Digital systems can connect people while reducing presence. They can improve coordination while weakening attention. They can summarize human expression while bypassing the slow work of listening. They can make decisions appear objective while hiding the human consequences of those decisions.

This is why empathy is not enough if it remains only an individual feeling. Judgment is not enough if it remains only a cognitive skill. Responsibility is not enough if it remains only a formal role. These capacities need a relational horizon. They need co-being.

Co-being asks: who is affected, who is heard, who is made invisible, who carries the cost, and what kind of human relationship is being created by this system?

7. Staying human under pressure

The AI age will reward speed. It will reward adaptation. It will reward those who can learn new tools quickly. But speed is not the same as wisdom, and adaptation is not the same as meaning.

A person can be highly adaptive and still lose agency. A team can be productive and still lose trust. An organization can be efficient and still become morally thin. A society can become technologically advanced and still forget what human dignity requires.

Staying human, then, is not automatic. It is not guaranteed by having values on a website, ethics principles in a policy, or soft skills in a curriculum. It must be practiced in concrete situations: when a decision is delegated too easily; when a dashboard replaces a conversation; when a worker feels reduced to usefulness; when care appears inefficient; when uncertainty makes people reach for shortcuts.

This is why reflective practice matters. We need spaces where people can notice how pressure changes them. Where they can ask what they are optimizing for. Where they can distinguish between responsible use of AI and quiet surrender to automation. Where they can explore not only what AI can do, but what humans should still carry.

8. From soft skills to existential capacities

The phrase “soft skills” may remain useful in education, policy and workplace learning. But for the AI age, we need a deeper interpretation.

Empathy is not only interpersonal warmth. It is the capacity to perceive another human being when systems abstract them into data.

Judgment is not only decision-making. It is the capacity to remain answerable when decisions are mediated by machines.

Adaptability is not only flexibility. It is the capacity to change without losing one’s ethical centre.

Communication is not only information exchange. It is the capacity to sustain meaning between people when communication is accelerated, summarized and automated.

Leadership is not only influence. It is the capacity to protect human dignity, responsibility and relational life inside systems that reward speed and scale.

Co-being is not only collaboration. It is the capacity to remain human with others.

These are not soft. They are the hard work of remaining human.

9. Meaning after work

Meaning After Work begins from a simple but uncomfortable question: what happens to the meaning of life when the meaning of work begins to collapse?

The question does not imply that work will disappear. It asks what happens when work can no longer carry identity, dignity, usefulness and purpose in the way modern societies have asked it to carry them.

Artificial intelligence intensifies this question. It changes what counts as expertise. It changes the value of cognitive labour. It changes how people experience usefulness. It changes the relationship between judgment and output. It changes what organizations expect from human beings.

In this context, staying human becomes a central task of education, leadership and organizational life. The future does not only require more technical adaptation. It requires existential formation: the cultivation of people who can preserve meaning, agency, responsibility and co-being when intelligent systems reshape work.

The AI age will ask many people to become faster, more flexible and more technologically fluent. But the deeper question is whether they will also become more responsible, more relational and more capable of meaning.

That is why staying human is a skill.

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Essay 03

The Useful Tool and the Bad Conscience

Why AI adoption is a moral negotiation, not just a technical choice.