Artificial intelligence is now entering the stage of industrialization. Beyond technical challenges, this scaling phase raises a strategic question for the European continent: the conditions required for the emergence of high-performing, autonomous, and trustworthy AI.
As part of OUTSCALE EXPERIENCES 2026, Igor Caron (Co-founder, Lighton), Emmanuel Leroux (CEO, Bull), and Mathieu Feuillet (CISO, Dassault Systèmes) shared their perspectives on the pillars of this European success.
Infrastructure at the heart of technological sovereignty
Digital independence cannot be real without full control of the value chain. Emmanuel Leroux emphasizes that sovereignty necessarily requires control over “compute,” encompassing physical components, firmware, and software platforms alike.
Today, Europe faces strong dependence on foreign GPUs and the uncertainties of global supply chains. To address this, it is crucial to strengthen industrial capabilities and consolidate infrastructure within the continent.
Ultimately, without solid hardware and software autonomy, Europe’s capacity for innovation will remain fragile and dependent on external decisions.
Ethics as a lever for competitiveness
Far from being a mere constraint, trust is becoming the key driver of technology adoption. It cannot be imposed; it must be built patiently through tangible proof.
At Dassault Systèmes, this approach takes the form of a rigorous framework built on fundamental pillars: the primacy of human oversight, system security, strict respect for intellectual property, as well as fairness, transparency, and environmental responsibility.
For businesses, the question has shifted from the technical feasibility of AI to its reliability. Addressing this challenge requires providing concrete assurances regarding data governance, effective control of decision-making processes, and full traceability of outcomes.
Leveraging domain expertise through AI
The effectiveness of an AI solution is not measured solely by the sophistication of its algorithms, but above all by its ability to mobilize a company’s informational assets.
As Igor Caron points out, needs originate in the field: business units want AI tools integrated into the enterprise, capable of securely leveraging internal knowledge and sensitive data.
To address this challenge concretely, LightOn focuses on a technical architecture designed for real-world business use. The idea is not to systematically rely on heavy and costly infrastructure, but to favor more efficient processing, particularly using CPU-based approaches when they meet business needs.
This approach also relies on the RAG methodology, which consists of connecting the AI model to the organization’s internal documents and knowledge. Instead of depending solely on the raw power of a large model, the AI retrieves relevant information from company sources, then reformulates it in a usable, traceable, and contextualized way.
The benefits are twofold: on one hand, infrastructure and computation costs are significantly reduced; on the other, the relevance of responses improves because the system relies on up-to-date internal data. This makes it possible to build AI that is more affordable, easier to deploy at scale, and more profitable for organizations.
Beyond sovereignty: the imperative of excellence
While sovereignty is an essential foundation, it cannot be an end in itself.
To compete with American and Chinese powers, Europe must develop technologies whose competitiveness extends internationally. This ambition requires moving beyond a purely defensive posture to embrace a true culture of overall performance.
The fundamental objective is no longer just to protect digital borders, but to bring superior solutions to market.
Prioritizing use cases to catalyze deployment
The lack of concrete use cases is a major, though often overlooked, barrier to AI development in Europe.
Beyond infrastructure gaps, the continent suffers from limited real-world adoption of technological tools. Since usage drives demand, it is the only true guarantee of return on investment.
Rather than focusing exclusively on increasing computing power, efforts should concentrate on designing specialized business applications rooted in the needs of research, healthcare, and industry.
Architecture serving strategic autonomy
Achieving independence does not rely on using a monolithic tool, but on deploying intelligent structures.
The example of Dassault Systèmes highlights this approach: by orchestrating a multitude of specialized models, each fulfilling a defined role, it becomes possible to guarantee high-level results. This approach drastically reduces dependence on a single provider.
This organizational model thus makes it possible to reconcile technical performance requirements with sovereignty imperatives.
Trust as the cornerstone of European AI
The emergence of trustworthy European AI does not depend on a single factor, but on a synergy of requirements: controlled infrastructure, a robust ethical framework, the use of company-specific data, strong technological capabilities, and the development of concrete use cases.
The continent already has the expertise and partners needed to win this race. The main challenge now is to unite these strengths behind a common goal: to build AI that protects sovereignty, performs on a global scale, and is widely integrated into our economies.
Key takeaways
1. Sovereignty starts with control of infrastructure
Europe cannot claim autonomous AI without controlling the entire technological stack, from hardware (especially GPUs) to software. Today, dependence on foreign players weakens its capacity for innovation. The priority is therefore to strengthen industrial capabilities and build sovereign infrastructure, an essential condition for independent decision-making and sustainable innovation.
2. Trust and ethics become a competitive advantage
European AI must differentiate itself through reliability. This requires strong guarantees: human oversight, security, transparency, respect for data and intellectual property. The challenge is no longer just to make AI work, but to prove that it is trustworthy. This requirement can become a lever for competitiveness against other global powers.
3. Impact depends on real-world use cases and performance
Beyond sovereignty, Europe must create real value. This involves business use cases (industry, healthcare, etc.), leveraging company-specific data, and efficient architectures (such as specialized models rather than a single general model). The dual objective is to accelerate adoption and deliver globally competitive solutions.
These three pillars infrastructure, trust, and use cases together form the foundation of a European AI that is sovereign, credible, and high-performing.
