AI Materia

For a first Global Dialogue on AI Governance, success would be about building a strong foundation rather than achieving full agreement. This includes establishing shared principles like safety, transparency, and accountability, while promoting interoperability to avoid fragmented regulations. Concrete next steps, such as roadmaps, working groups, and timelines, would demonstrate momentum beyond discussion. Meaningful engagement of both governments and industry, along with practical governance tools like evaluation standards and auditing frameworks, is essential. Trust-building among major powers, focus on real-world impact, and broad global participation, especially from emerging economies, would further signal success. Ultimately, a successful dialogue creates alignment, fosters collaboration, and lays a clear path toward coordinated global AI governance.

• Safe, secure and trustworthy AI
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Interoperability of governance approaches
• Transparency, accountability, and human oversight

I would like to share my perspectives in the field of science and manufacturing, where AI is already reshaping how we discover, design, develop, and produce. From my perspective, the primary purpose of this Dialogue is to connect global governance discussions with real-world systems. In laboratories and on factory floors, AI is no longer theoretical. Yet, governance frameworks often lag behind these realities. The gap we must address is the disconnect between high-level principles and operational implementation, especially in industrial contexts where safety, reliability, and scalability are critical. To help ensure the effectiveness of this Dialogue, I would respectfully suggest focusing on a few priority areas: First, trustworthy AI in industrial systems, ensuring that AI deployed in manufacturing is safe, explainable, and robust under real-world conditions. Second, data governance and infrastructure, as industrial AI depends heavily on high-quality, interoperable, and often sensitive data. Third, sustainable and inclusive innovation, leveraging AI to reduce waste, improve energy efficiency, and enable broader participation in advanced manufacturing ecosystems. And fourth, standards and interoperability, which are essential for scaling AI solutions across borders and industries. Equally important is how we structure our engagement. We should move toward practice-driven dialogue, bringing case studies from industry, pilot projects, and cross-sector collaborations into the conversation. The Scientific Panel's report can serve as a strong foundation, but its value will depend on how well we translate it into technical guidelines, standards, and actionable policies that practitioners can adopt. The Global Dialogue should aim to deliver practical and measurable outcomes. This could include: - A set of implementation-oriented guidelines for AI in industrial and scientific applications; - The establishment of international pilot initiatives in areas like smart manufacturing or sustainable production; - And a platform for ongoing collaboration, where best practices, data frameworks, and standards can be shared and refined.