Artificial Intelligence (AI) stands at the center of economic, political, and societal concerns. At the Elevate 2025 event, experts highlighted the challenges and opportunities surrounding generative AI, emphasizing the critical choices ahead. While AI promises revolutionary advancements, it also raises major issues in regulation, technological sovereignty, and economic impact.
1. Generative AI: A Game-Changer with Conditions
The rise of generative AI has led to breakthroughs in language processing, image recognition, and automation. However, as Rim Teraoui, president of the event, pointed out, « there is no automatic reason why technological innovations bring shared prosperity and general well-being. » AI is not a magic solution; its benefits depend on strategic economic, social, and political decisions.
A key concern is European sovereignty. The continent faces two major competitors:
- The U.S., home to AI giants like OpenAI, Google, and Meta.
- China, which has been investing heavily in open-source AI models such as DeepSec, a system claiming to rival top-tier AI models while consuming 50 times less energy.
The challenge for Europe is to define its own AI strategy, rather than becoming dependent on foreign technologies.
2. Regulation and Sovereignty: Striking the Right Balance
Europe has long promoted ethical, transparent, and accessible AI. However, while the EU talks about open-source and frugal AI, China has already taken action. As Teraoui put it, « European grand speeches were never backed by real industrial strategies or sufficient funding. »
To address this, the AI Competitiveness Compass was launched to strengthen Europe’s digital infrastructure and AI capabilities. Yet, regulation remains a double-edged sword. The AI Act, while ambitious, creates significant compliance burdens. As highlighted at the event, 40% of every €100 invested in AI startups goes toward compliance. Meanwhile, more than 60% of European companies do not fully understand their obligations under the AI Act.
If Europe wants to compete globally, it must streamline its regulatory framework while fostering innovation.
3. Infrastructure: The Make-or-Break Factor
Generative AI relies on vast computing power. Yet, Europe lags behind in AI infrastructure, compared to the U.S. and China. Bertrand Laforge, founder of Converso, underscored this gap:
- Nvidia controls 90% of the GPU market.
- Microsoft owns 1.8 million GPUs, while some European research institutions operate with barely 1,000 GPUs.
Recognizing this, the EU has introduced AI Factories and an AI-on-Demand platform to provide access to AI computing resources. But this won’t be enough unless it is matched by long-term industrial investment.
4. AI Adoption: The Need for Education and Awareness
While major corporations are adopting AI at scale, small and medium enterprises (SMEs) are struggling to keep up. In France, fewer than 15% of SMEs use generative AI, compared to over 60% in the U.S..
Several obstacles explain this gap:
- Lack of AI literacy among business leaders, who view AI as complex and expensive.
- Data security concerns and sovereignty issues.
- Difficulty in identifying high-value AI use cases.
As Philippe Rambach, Chief AI Officer at Schneider Electric, pointed out, « the problem is not a lack of information but an overload of scattered information. »
Schneider Electric has taken action by making AI training mandatory for all 140,000 employees. Their goal? To ensure that AI adoption is driven by business needs rather than tech hype.
5. Measuring AI ROI: The Toughest Challenge
One of the biggest challenges for companies is measuring AI’s return on investment (ROI). Unlike traditional IT projects, AI doesn’t always deliver immediate or easily quantifiable financial returns.
As Rambach noted, « Measuring AI ROI is like measuring the ROI of ERP systems or laptops—it’s possible, but not always a simple P&L line item. »
Instead, AI should be assessed through:
- Productivity: Time savings and operational efficiency.
- Quality improvements: Enhanced decision-making and process optimization.
- Business impact: Faster time-to-market and competitive advantages.
Companies must move beyond traditional ROI models and adopt KPIs tailored to AI’s long-term impact.
6. Ethical AI: A Diversity and Inclusion Challenge
Beyond economic concerns, AI presents fundamental ethical challenges. Giada Pistilli, AI ethicist at Hugging Face, warned about AI’s Anglo-centric bias, driven by training datasets predominantly in English.
This highlights the need for AI models that reflect linguistic and cultural diversity. As Pistilli emphasized, « language encodes values, worldviews, and different ethical systems. »
Conclusion: AI as a Strategic Lever for Europe
AI is at a crossroads. Europe must accelerate adoption by strengthening technological sovereignty, simplifying regulation, and investing in scalable infrastructure. But AI should not be pursued for the sake of AI itself—it must serve clear economic, societal, and environmental objectives.
As Laurence Devillers, professor of AI at Sorbonne University, put it: « We must move beyond AI fascination and focus on its real-world impact. »
By fostering pragmatic, inclusive, and responsible AI, Europe can transform AI into a true driver of innovation and competitiveness.
Key Takeaways:
Europe must develop a stronger AI infrastructure to compete globally. Regulation should be clear and business-friendly, not a compliance burden. AI education is crucial for SME adoption and workforce transformation. The ROI of AI must be assessed beyond short-term financial impact. Ethical AI requires cultural and linguistic diversity in model training.
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Sources: Elevate 2025 Event Transcripts AI Competitiveness Compass European AI Act Hugging Face Research on AI Bias Nvidia, Microsoft, and EU AI Infrastructure Reports