Artificial intelligence has placed managers at the center of a powerful ecosystem of data and algorithms. This transformation is redefining traditional leadership and highlighting deeply human qualities — emotional intelligence and a strong sense of ethics. A good manager knows how to engage in dialogue with the machine without losing control. At the same time, they must be able to explain and justify their decisions to maintain trust and prevent doubt among their teams.
Data Dependency: Managers and the Question of Legitimacy
AI is redefining the foundations of trust and legitimacy. “Assisted” management now relies on a bedrock of data — data that must be understood, questioned, and, above all, explained.
Making Sense of Autonomous Information
With the rise of artificial intelligence, a manager’s role is no longer to collect information but to interpret it. They must bridge the gap between raw data and real-world experience.
Analytical and forecasting tools generate insights that are accessible to everyone. As a result, managers no longer hold exclusive control over information. Team members can challenge decisions and expect well-reasoned justifications.
In this new context, a manager’s discernment and interpersonal intelligence become central. They must give meaning to automated results and connect figures to field realities.
Understanding Data Logic and Maintaining Critical Distance
This new landscape requires a minimum level of technical literacy:
- knowing how data is produced,
- understanding on what basis an algorithm establishes a correlation,
- and recognizing why an indicator changes over time.
Such knowledge is now essential to avoid being ruled by the machine.
Adapting the Managerial Posture in Industry
The rise of data-driven measurement is transforming management itself. Since decisions are now traceable and comparable, they must be justified not by authority but by coherence.
In industrial environments, this shift is reshaping leadership. Technical expertise must now be paired with the ability to translate numerical results into understandable and credible actions for teams.
This shift in the center of managerial gravity forms the foundation of the AI era — one that demands a new kind of responsibility, at once more analytical and profoundly human.
Collaborating with AI: Managers Facing a New Form of Intelligence
Artificial intelligence is often described as just another tool available to professionals. In management, however, it should be viewed as a complementary form of intelligence.
AI Learns, Evaluates, Recommends, and Arbitrates
In industry, it predicts maintenance needs or production fluctuations. In sales and marketing departments, it forecasts market trends and guides pricing decisions.
This constant presence introduces a new player into the decision-making chain. Its distinct feature? It lacks intuition but possesses superior analytical capacity.
Avoid Delegating Decision-Making Under Pressure
Some managers see AI as a way to reduce administrative workload, while others feel the growing pressure of accelerated decision-making.
AI certainly frees up time — but it also demands continuous responsiveness. It can foster cognitive dependency: the more efficient the tools become, the more they shape our reasoning.
The main risk is not making mistakes, but unconsciously delegating one’s judgment to the system.
The Four Levels of AI Integration
In “How Companies Reinvent Themselves” (from Les Cahiers Français, Fall 2025), Cécile Dejoux — Professor at CNAM and Director of the Learning Lab Human Change — identifies four stages of AI integration:
- Automation
- Augmentation
- Coevolution
- Preservation
Most organizations remain at the first two stages (automation and augmentation), but the shift is moving toward coevolution — a model where managers and AI learn from each other.
At the final stage, preservation, certain levers must remain within the human decision-making domain. According to Cécile Dejoux, this includes emotional intelligence and ethical judgment.
AI thus becomes a cognitive partner, compelling managers to rethink delegation and responsibility.
The Question of Trust
Teams may fear that AI will be used as a tool for control or evaluation.
Here, the manager’s attitude determines acceptance — through:
- transparency about objectives,
- clarity around decision criteria,
- and communication about the system’s limitations.
AI is not neutral; it mirrors the design biases and priorities of the organization.
In this setting, performance depends not only on model quality but also on the manager’s relational maturity when interacting with technology.
Working with AI requires above all lucidity: knowing when to follow its insights, when to question them, and when to decide differently.
Governing Through Trust: The New Ethics of Management
The rise of artificial intelligence is transforming management into a true function of governance.
AI Oversight: The Manager’s Obligations
The European regulatory framework — notably the Artificial Intelligence Act (Regulation EU 2024/1689) — requires users of high-risk AI systems to ensure human oversight and proper training. These measures guarantee that machine-assisted decisions remain both controllable and reversible. This responsibility places the manager as the guardian of human discernment in AI-supported decision-making — a stance also endorsed by the CNIL’s recommendations.
However, large-scale data use can undermine trust if it fuels feelings of surveillance. The manager’s role is to ensure that collected data genuinely serves the intended purpose. They must also protect employees’ privacy and make sure that every performance indicator is used within a clearly defined ethical framework.
Hybrid Teams: Sustaining Cohesion
The growing use of intelligent tools has created work environments where humans and digital assistants collaborate. In these hybrid teams, technology can weaken cohesion if not properly guided.
It is the manager’s responsibility to clarify the role of automated systems, support employees as they adapt, and foster a culture of methodical doubt.
The more technology advances, the greater the need for an empathetic, explanatory style of management.
New Training Priorities for the “Augmented” Manager
Management in the AI era requires a dual mindset — analytical and human.
Managers don’t need to become data scientists, but they must understand how data works while strengthening key emotional competencies.
Les Cahiers Français emphasize this hybridization, describing a new professional horizon where value lies not in technical mastery, but in the ability to bridge different forms of knowledge.
Key areas to develop include:
- Digital literacy: understanding how algorithms rank and filter information;
- Critical thinking: questioning outputs and identifying bias;
- Emotional intelligence: maintaining trust in an automated environment;
- Practical ethics: connecting decisions to their human consequences.
The goal of this transformation is not speed or productivity, but a redefinition of management’s very purpose. Tomorrow’s leadership will be measured by the ability to create meaning in an age of information abundance.
In this new era of managerial work, leading no longer means deciding alone — it means orchestrating collective intelligence across human, digital, and organizational dimensions.
