How can a critical industrial project be designed and managed when uncertainty, technological innovation, and regulatory constraints coexist over the long term? Accelerated cycles, increasingly stringent compliance requirements, and the progressive integration of artificial intelligence are profoundly transforming the conditions of project governance. The temptation to rely on a single methodology is strong, even though experience shows that reality resists both overly rigid frameworks and excessively fluid approaches. Hybridization therefore emerges as a new governance approach, capable of articulating these tensions without denying them and, above all, of turning them into productive drivers.
Moving beyond methodological oppositions
V-model or Agile? Predictive or adaptive? Project mode or product mode? These oppositions continue to structure professional discourse and, in some cases, organizational design itself. While they offer convenient solutions, they struggle to reflect the complexity of large-scale industrial projects. Reducing this reality to a methodological choice often oversimplifies dynamics that instead require a more refined understanding of the multiple timeframes at play.
Hybridization is neither about juxtaposing frameworks nor about arbitrating between opposing camps. It calls for moving beyond such alternatives in order to articulate complementary approaches within a logic of considered adaptation. The search for an ideal method gives way to the need for continuous discernment, aligned with the complexity of reality.
Deciding without the illusion of certainty
In complex industrial projects, uncertainty is not an anomaly but a structural condition. Decision-making is therefore no longer about waiting for perfect information, but about acting on informed assumptions that will inevitably need to be revisited.
Project governance thus becomes an exercise in clarity and responsibility. This tension requires governance models capable of combining expertise, debate, and accountability. Seeking unanimous agreement can sometimes paralyze action; yet a decision can still be made without consensus, provided that no reasonable objection remains. The challenge then lies in translating collective intelligence into concrete action, distinguishing what must be stabilized from what should remain open to exploration.
Solid and liquid: an operational reading of reality
Not all components of an industrial project can be managed in the same way. Some require stabilization and control, such as safety, compliance, and irreversible decisions, while others demand openness and learning. How, in this context, can a project be governed without attempting to reduce this plurality?
The distinction between solid and liquid offers a practical way to make this reality more intelligible. The solid refers to what must be secured, while the liquid represents what remains evolving.
Artificial intelligence illustrates this dynamic. While relatively effective in stable environments, how does it perform in the face of uncertainty and the unknown? It should be used as a tool for analysis and insight, not as a substitute for human decision-making. Hybridization thus becomes a way of representing reality, making its different temporal dimensions visible rather than attempting to flatten them into a single model.
Adopting hybridization as an approach, not a method
Hybridization cannot be reduced to the optimal combination of frameworks, tools, and techniques. It is a deliberate managerial approach grounded in the acceptance of complexity and the inherent incompleteness of models. Neither excessive rigidity nor unstructured flexibility, sustainable performance arises from the ability to articulate heterogeneous logics without opposing them.
All models, including those based on artificial intelligence, eventually reveal their limitations. Perhaps the central question is not which method to choose, but whether we are willing to accept the resistance of reality and learn how to work with it.
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