Leadership by Algorithm

What happens when your boss isn’t a person but an algorithm? In Leadership by Algorithm: Who Leads and Who Follows in the AI Era, David De Cremer, a global thinker in business and AI ethics, explores this provocative question. His central argument is simple but profound: while algorithms might soon manage us, only humans can truly lead. As artificial intelligence becomes embedded in our economies and organizations, our challenge isn’t to compete with machines but to redefine what leadership means in a world where data—not intuition—often makes decisions.

De Cremer argues that algorithms are brilliant at consistency, prediction, and control—the trademarks of management. But leadership, he contends, stems from human traits machines cannot replicate: empathy, curiosity, ethical reasoning, imagination, and moral judgment. As we automate more of what humans do, we risk automating the very soul out of leadership unless we consciously design technology to serve—not replace—our humanity.

From Management to Leadership

Drawing on decades of research and vivid examples—from Google’s AlphaGo triumph to Amazon’s automated HR systems—De Cremer distinguishes management (control and stability) from leadership (vision and transformation). Algorithms can handle management with precision: they analyze massive datasets, optimize efficiency, and minimize errors. But leadership, he insists, is about meaning-making. It is about connecting what we do to why we do it. Machines may predict human behavior, but they can’t inspire human purpose.

The Threat and the Opportunity

The book opens with a dinner story where a young executive confidently proclaims that technology will solve all problems—and De Cremer wonders: could such blind faith make humans obsolete? Today’s AI systems learn, reason, and decide faster than any human. Algorithms decide what you watch, how doctors diagnose patients, and even when employees are promoted or fired. The risk, De Cremer warns, is not just job displacement—it’s the erosion of human agency. Will humans remain the decision makers, or will we simply follow machine logic disguised as optimization?

Despite this fear, De Cremer is no Luddite. He believes AI can and should augment humanity if guided by authentic, ethical leadership. The goal, he writes, is not for humans to race against algorithms but to cooperate with them—to build organizations where humans lead with values while algorithms manage data. This approach, he calls “co-creation,” envisions technology and humanity working symphonically, each amplifying the other's strengths.

A Map of the Book

De Cremer’s exploration unfolds in ten chapters that move from the rise of algorithmic decision-making to an entirely new vision of leadership. The early chapters reveal how automation has reshaped management—from hiring algorithms at Unilever to autonomous compliance systems at JPMorgan Chase. He shows how this data obsession fuels what he calls the “us versus them” mentality—humans fearing replacement by machines. He then dissects why such fear exists: because algorithms replicate what we do, but not who we are. Later chapters draw a powerful contrast between a machine’s rational efficiency and a leader’s irrational but essential human wisdom.

By mid-book, De Cremer turns teacher, outlining how leadership must evolve. He introduces the concept of “authentic intelligence” to balance the rise of artificial intelligence. Leadership must shift from “being smart” to “being wise.” Wise leaders, he says, recognize that data does not decide—it informs. They use technology not as replacement but as a reflective mirror to deepen human judgment.

In the final chapters, he reimagines corporate life around purpose, inclusion, and co-creation. Drawing on examples from Hyundai’s robotics program and Huawei’s AI symphony project, he illustrates what human–machine collaboration can look like when trust, transparency, and empathy guide design. The book closes with a warning that technology without humanity leads to dehumanization, not progress. But if humans lead and algorithms manage—as he proposes—we can build a more ethical, adaptive, and truly intelligent world.

“Humans lead, algorithms manage.” De Cremer’s statement might become the defining mantra of the 21st-century organization—one that balances technological brilliance with moral clarity.

In short, Leadership by Algorithm is both a diagnosis and a prescription. It shows how we got addicted to technological control and offers a human-centric alternative for leading in the age of AI. Whether you’re a manager, data scientist, or policy maker, De Cremer invites you to ask a crucial question: not what can AI do, but what should humans still do? And it’s in that question where true leadership begins.

The idea of algorithms as leaders feels futuristic, but De Cremer shows it’s already happening. From Amazon’s AI automatically firing warehouse employees to venture-capital firm Deep Knowledge appointing an algorithm to its board, organizations are experimenting with automated authority. But a fundamental question looms: can something that doesn’t understand context, emotions, or morality truly lead?

The Limits of Machine Wisdom

Artificial intelligence works by learning patterns from data. These patterns help make predictions—and predictions guide decisions. But, as De Cremer emphasizes, decisions are not leadership. Algorithms can identify the most efficient route from point A to point B, yet leadership often requires navigating ambiguity, prioritizing values, and making ethical trade-offs. AlphaGo may have mastered Go, but it didn’t understand why winning mattered to humans like champion Lee Sedol, who retired after losing—feeling life itself had changed. This disconnect between excellence and meaning reveals why algorithmic leadership fails at the deepest level.

The Black-Box Problem

One of the greatest challenges in trusting algorithmic authority is transparency. Even engineers struggle to explain why deep-learning systems make certain decisions. De Cremer notes that over 40% of consumers distrust AI in finance, and only 4% trust algorithms in hiring. This “black-box problem” turns machine leadership into an act of faith rather than understanding. When people cannot see or question decision logic, leadership loses legitimacy. True leaders don’t just decide—they explain why decisions make sense. Algorithms, by design, cannot explain intent or fairness.

Trust and the Human Touch

De Cremer cites research showing that humans instinctively prefer human judgment, even when it errs. Psychologists call this algorithm aversion. We are willing to forgive a person’s mistake but not a machine’s. That’s because we see moral agency in humans: they can learn, empathize, and take responsibility. Machines can’t. As a result, people resist algorithmic control in subtle ways—like ignoring automated advice or sabotaging processes—unless leaders build bridges of transparency and trust. De Cremer concludes that without human interpretation and oversight, algorithms can only manage, not lead.

Leadership without empathy may be efficient—but it isn’t human. And leadership without humanity is merely administration.

In short, algorithms can execute strategy flawlessly, but they cannot give people meaning, dignity, or vision. Strategic decisions must remain grounded in human awareness—because meaning, not math, is what makes people follow.

De Cremer makes a sharp distinction between management and leadership. Management exists to maintain order—leadership exists to drive change. Algorithms are tailor-made for management. They thrive on control, measurement, and consistency. As organizations become data factories, algorithms naturally take over the administrative core.

The Rise of Automated Administration

Across industries, De Cremer documents how algorithms already oversee major managerial functions. IBM uses AI to evaluate employee performance; HR software automates onboarding and compliance; banks employ machine learning to manage risk portfolios. Even blockchain, described as “the trust machine” by The Economist, acts as a digital overseer—providing verifiable records and preventing manipulation.

These developments solve old managerial headaches: bias, inefficiency, and inconsistency. Yet they also threaten the human side of management. When managers become mere data custodians, the “art” of management—listening, motivating, mentoring—gets lost. De Cremer’s message to today’s MBAs is almost paradoxical: the more you see your job as an administrator, the easier you are to replace.

When Data Becomes Bureaucracy

The book shows how too many firms drown in data-driven bureaucracy. De Cremer paints vivid images of organizations obsessed with ticking boxes and hitting KPIs, while innovation suffocates. Algorithms magnify this culture by automating control itself. The risk: we become prisoners of performance dashboards. Real leadership requires challenging the status quo, not optimizing it.

To avoid this trap, De Cremer insists that organizations must pair algorithmic management with human leadership. Algorithms can process information, but they can’t frame it in moral or strategic terms. Human leaders must decide why certain data matters and how to interpret it within a vision larger than efficiency.

“Data is the new oil,” De Cremer notes—but leadership is the compass that tells us where to drill.

Algorithms may manage work far more efficiently than humans. But only when guided by purposeful, ethical, and visionary leadership can this data-driven machinery serve human progress rather than dominate it.

If AI represents artificial intelligence, De Cremer argues for a second AI—authentic intelligence. Algorithms may mimic reasoning, but only humans can create meaning. Human leaders, therefore, must cultivate abilities machines cannot emulate: critical thinking, curiosity, imagination, emotional intelligence, empathy, and ethical judgment.

Sense-Making as Core Leadership

Leaders of the future, De Cremer says, are not calculators but interpreters. Their unique task is sense-making—helping people understand why change matters and how it aligns with human purpose. In complex, fast-changing systems, data can overwhelm decision makers. Algorithms can describe “what” is happening, but leaders must define “why.” This interpretive layer—the fusion of fact, feeling, and value—is something no machine can compute.

The Skillset Machines Can’t Master

De Cremer identifies eight uniquely human abilities that power authentic intelligence: critical thinking, curiosity, agility, imagination, creativity, emotional intelligence, empathy, and ethical judgment. Each one expands a leader’s ability to make sense of technology’s insights. For instance, curiosity fuels learning, imagination builds innovation, and ethical judgment ensures impact aligns with values. These are not soft skills—they are survival skills for the age of automation.

Wise, Not Smart

Peter Drucker once called the computer “the dumbest tool we’ve ever had” because it forces us to think clearly before we act. De Cremer resurrects this lesson to propose a new paradigm: be wise, not just smart. Smartness chases efficiency; wisdom seeks meaning. Machines can handle the former, humans must master the latter. Wisdom integrates emotion and logic, recognizing context and consequence in each decision—a complexity algorithms cannot grasp.

In a world obsessed with artificial intelligence, authentic intelligence is your last competitive edge.

The coming decades, De Cremer predicts, will not belong to those who know data best, but to those who can translate data into human meaning. That’s the art of leadership in the algorithm age.

Empowerment lies at the heart of meaningful leadership. In algorithm-driven workplaces, however, empowerment changes shape. De Cremer explains that tomorrow’s leaders must empower both people and machines—each in very different ways.

Empowering Humans

When companies introduce AI systems, employees often experience fear, uncertainty, and a loss of control. People fear being replaced or unfairly evaluated by algorithms they don’t understand. Leaders must therefore manage aversive emotions, build trust, and create transparency. The antidote to algorithm aversion, De Cremer argues, is clear communication. Explain how and why the algorithm is used, what data it analyzes, and how human oversight prevents abuse. When people understand the purpose, they’re less anxious and more engaged.

He provides a practical checklist: make leaders tech-savvy, promote continuous education, ensure employees have a voice, and humanize technology through transparency. Empowerment is not about blind trust—it’s about informed confidence.

Empowering Algorithms

Leaders must also delegate effectively to algorithms. That means giving machines autonomy over repetitive decisions while retaining responsibility for oversight. De Cremer calls this “purpose-driven delegation.” It requires leaders to identify which data truly matters, and to use company purpose as a lens for framing the right questions. “Garbage in, garbage out” becomes a moral statement here: if data collection isn’t guided by purpose, technological accuracy becomes meaningless.

The Human Shield

Ultimately, the human leader remains the ethical buffer between machine logic and moral reality. Algorithms may automate tasks, but humans must preserve values. Delegating without accountability is not empowerment but abandonment. Empowerment, rightly practiced, turns algorithms into collaborators that free humans to innovate, empathize, and lead creatively.

Empowering machines without empowering people leads to obedience, not innovation.

Effective leadership, De Cremer concludes, means empowering humans with understanding, and algorithms with purpose. Both must serve the same vision of human flourishing.

De Cremer envisions a new kind of organization where humans and algorithms collaborate like orchestra members playing under a shared vision. Co-creation is his model for the future—a partnership based on trust, communication, and mutual respect between humans and machines.

The New Diversity

In traditional diversity, we think of gender, race, or culture. In the automation age, diversity expands to include intelligence type—human and artificial. Managing this new diversity requires leaders to reinterpret inclusion: not just connecting people, but connecting systems of thought. The best leaders of tomorrow will be part manager, part conductor, balancing the precision of algorithms with the creativity of humans.

Real-World Examples

Hyundai’s exoskeleton robots, which prevent factory injuries, and Stitch Fix’s AI stylists, which merge human curation with machine learning, show how co-creation boosts both performance and purpose. De Cremer’s favorite metaphor comes from Huawei’s experiment in finishing Schubert’s Unfinished Symphony using AI. The algorithm generated melodies, but human composer Lucas Cantor shaped them into music that touched hearts. This, De Cremer argues, is exactly what business co-creation should feel like—machines supply data melodies; humans compose meaning.

Leadership as Orchestration

Acting as conductor, leaders must orchestrate the teamwork between human and machine. They allocate tasks, integrate outputs, and ensure the whole produces more than the sum of its parts. Leaders must also encourage psychological safety so employees can experiment, fail, and learn with machines rather than fear them. Co-creation, then, is not a technological shift but a cultural transformation—one built on humility, empathy, and transparent collaboration.

The orchestra of the future has both brass and binary. The leader’s baton belongs only to humans.

De Cremer’s vision of co-creation turns AI from competitor into collaborator. Success in this new diversity, he says, depends on the human capacity to listen, connect, and create purpose out of data noise.

In one of the book’s most practical sections, De Cremer defines the twin pillars of future leadership: purpose-driven and inclusive leadership. Both are essential to keeping humanity at the helm of technological change.

Purpose-Driven Leadership

Purpose aligns people and data toward a shared “why.” De Cremer warns that many digital transformations fail because they collect data without meaning. He argues that ethical and visionary leaders must clarify purpose before launching AI initiatives. Organizations need direction: what kind of value are we trying to create for humans, not just shareholders? Purpose-centered leaders use data to serve values, not replace them. They frame the right questions, integrate new business demands with company values, and explain clearly why data is collected. In doing so, they restore trust in technological decisions.

Inclusive Leadership

Inclusion, for De Cremer, is the bridge between humans and algorithms. He describes four traits of inclusive leaders: connection, trustworthiness, diversity-mindedness, and humility. Connection means listening before speaking; trustworthiness requires transparency; being diversity-minded calls for integrating differences rather than fearing them; humility means inviting challenges rather than suppressing them. Leaders who combine these traits, he argues, will make organizations more adaptable and ethical.

Practical takeaways abound: promote cross-department data cooperation, integrate data scientists into everyday work, ensure transparency in algorithmic use, and build cultures where feedback and moral awareness are celebrated. In short, purpose gives direction; inclusion gives cohesion.

Purpose asks, “Why do we exist?” Inclusion answers, “With whom do we build?” Both questions must be led by humans.

When leaders lead with both purpose and inclusion, technology becomes an ally in building not just smarter but wiser organizations. Algorithms process vast realities; humans give them moral direction.

De Cremer concludes on a philosophical note: the future belongs to those who remember that humanity—not perfection—is our evolutionary advantage. As algorithms race toward precision, leaders must preserve tolerance for imperfection. Compassion, forgiveness, and moral reflection are the ultimate technologies for human survival.

The Danger of a Perfect System

When societies aim for flawless efficiency, they risk dehumanization. Facial recognition and total surveillance may create safety, but also erase freedom. De Cremer warns that a “perfect” machine society will treat deviation as error—and dissent as defect. Humanity, by contrast, thrives on imperfection. Progress, learning, even love depend on failure and unpredictability. Leaders must thus champion moral communities that tolerate mistakes and protect human dignity.

Humans Lead, Algorithms Manage

The book’s closing mantra—humans lead, algorithms manage—summarizes De Cremer’s vision. Let algorithms optimize what can be measured; let humans interpret what cannot. Human leadership will remain essential for defining direction, ethics, and meaning. Machines can be efficient servants of purpose, but disastrous masters of value.

Education and Ethical Awareness

To make this future possible, De Cremer calls for continuous education that blends technology literacy with the humanities—philosophy, psychology, and ethics. Leaders should understand code but also conscience. He echoes Norbert Wiener’s early cybernetic warning: if we can’t control the machines we build, we had better ensure they serve purposes we truly desire. Humanity itself must become a guiding algorithm—one that values compassion over computation.

Technology without ethics is power without direction. Humanity gives AI its compass.

In the end, Leadership by Algorithm isn’t about machines replacing humans—it’s about what kind of humans we must become to lead machines wisely. The algorithm may manage tomorrow’s world, but only humanity can decide where that world should go.