How can artificial intelligence (AI) be turned into something more than just a powerful tool? At the Center for Scientific Management (CGS) at Mines Paris – PSL, AI is not approached as a mere technological feat. It is studied as an object of management, design, and transformation of organizations, in an international context that promotes AI in the service of people, the planet, and progress, as exemplified by the India AI Impact Summit 2026 to be held in New Delhi from February 16 to 20.
Led by Cédric Dalmasso, the CGS is developing research ranging from predictive maintenance of wind turbines to creative work augmented by generative AI. This work, presented at the AI Workshop held at Mines Paris – PSL on December 10, 2025, addresses a central question: how can AI be leveraged to drive performance, innovation, and sustainability without losing sight of human, organizational, and social dynamics?
AI at CGS
Research rooted in the history of organizations
The work currently being carried out on AI at CGS is part of a long-standing research tradition. Historically, the center has contributed significantly to the development of formalized models in operational research, design theory, and decision support, which are capable of producing theoretically high-performance solutions. However, a realization has emerged through industrial collaborations: models that look promising on paper sometimes have little concrete effect in organizations.
This discrepancy has led the CGS to develop an original approach that systematically combines two inseparable dimensions:
- Formalized and rigorous models, now enhanced by artificial intelligence and machine learning.
A detailed analysis of organizational dynamics, work practices, norms, representations, and responsibilities associated with these technologies.
AI is thus part of a research program that seeks not only to make algorithms work, but also to understand how they transform human and collective activity.
Generative creativity… with well-defined boundaries
The spectacular rise of generative AI, capable of producing text, images, music, and concepts, is a major area of study for the CGS. Behind the apparent creativity of these tools lies a central scientific question: what does “generate” really mean?
The work of Antoine Bordas, Pascal Le Masson, and Benoît Weil shows that the generativity of current AI is often weak and limited. In other words, these systems excel at producing plausible variations on what already exists, but struggle to challenge frameworks of thought, categories, or the very dimensions of problems.
In contrast, human design reasoning is capable of:
- Redefining objectives
- Transforming the parameters of a problem
- Introducing new and unexpected dimensions
The CGS’s research thus aims to finely characterize the different regimes of AI generativity and to identify how organizations can mobilize them in a relevant way. Inspired by a renewed reading of Taylorism, this program explores models of “good management” of generative AI: organizational mechanisms, methods, and dedicated roles to frame exploration, avoid routinization, and support collective learning.
From prediction to decision
AI in the face of industrial realities
Another key area of CGS research concerns the integration of AI into critical operational decisions. The work of Marie Bouilloud and Michel Nakhla on predictive maintenance of wind farms offers a concrete illustration of this.
Thanks to machine learning and deep learning models, it is now possible to detect weak signals that indicate potential failures, which are invisible to conventional statistical methods. The stakes are high: anticipating failures makes it possible to avoid costly unplanned downtime and increase the reliability of renewable energy production infrastructure.
But prediction alone is not enough. Researchers show that the real difficulty begins after the prediction:
- How can this information be integrated into maintenance schedules?
- How can a balance be struck between immediate costs and future risks?
- How can models be adapted to the real constraints of organizations?
The CGS’s work therefore proposes an integrated approach, combining AI, operational research, and decision analysis, to transform an algorithmic advance into a measurable industrial impact.
When the machine becomes a colleague
Beyond industrial sectors, generative AI is profoundly transforming design, creation, and consulting activities. Research by Nicolas Ricci, Sophie Hooge, and Tristan Guyon highlights the emergence of a new way of working: “Alone Teamwork.”
In this new regime, professionals work alone, but in constant interaction with generative AI, which becomes a true cognitive partner. The study of creative processes shows that AI is involved at every stage:
- Inspiration: rapid exploration of avenues
- Framing: reformulation of problems
- Prototyping: production of intermediate artifacts
- Validation: testing, comparisons, adjustments
This transformation does not eliminate human creativity, it reconfigures it. New skills become central: curation (selection and filtering), iterative dialogue with the machine, and above all, final control of the consistency, authenticity, and ethics of productions.
Read the full article on the Mines Paris PSL website