Swiss AI Summit

A successful Global Dialogue on AI Governance should move beyond high-level principles and deliver practical, actionable outcomes that can be implemented across jurisdictions and industries. First, it should establish a shared understanding of what "responsible AI in practice" means, grounded in real-world use cases rather than abstract frameworks. This includes identifying common standards for testing, validating, and scaling AI systems in controlled environments. The industry is in the core execution phase, and pilots need to take off. Guidane, in this regard, is crucial. Second, alignment between public sector, industry, and technology leaders, ensuring that governance approaches are both effective and implementable. Multi-stakeholder collaboration is essential to bridge the gap between regulation and innovation. Third, success would include the creation of concrete pilot initiatives or sandboxes where AI governance principles can be tested in real-world scenarios. Data has become a critical enabler, with its responsible use and governance underpinning all trustworthy AI development and deployment. Finally, the dialogue should result in a roadmap for ongoing collaboration, ensuring continuity beyond the initial meeting. This includes mechanisms for knowledge sharing, cross-border cooperation, and iterative policy development as AI technologies evolve.

Interoperability of governance approaches;Social, economic, ethical, cultural, linguistic and technical implications of AI;AI capacity-building;Transparency, accountability, and human oversight;

Safe, secure AI, as well as transparency & accountability, will always be a priority when using AI. However, what requires urgent attention is the interoperability of governance approaches as well as the social & economical approaches is critical given the global nature of AI. Without alignment across jurisdictions, organizations face fragmentation, which slows innovation and creates regulatory uncertainty. A coordinated approach enables scalability and cross-border collaboration. This goes in line with the 4 topics listed below as emerging issues.

1. real-world AI use case testing and validation environments. While many frameworks focus on principles, there is a lack of structured mechanisms to test how these principles perform in practice. Industry does not know what that actually means. Establishing AI sandboxes, guidelines, and controlled testing environments should be a global priority. 2. Another emerging issue is the gap between policy and implementation. 3. Geopolitical aspects that many of the enterprises face today. 4. Scaling and data usage

Governance gaps have a direct and tangible impact on how companies adopt, scale, and operationalize AI. While policy frameworks and guiding principles are essential, there is often a disconnect between theory and practice. In many cases, regulatory approaches are perceived as difficult to implement in real-world environments, which can unintentionally slow down innovation and hinder scalable adoption. As a result, companies may delay AI deployment, seek more flexible jurisdictions, or, in some cases, proceed despite regulatory uncertainty. This creates fragmentation and reduces the overall effectiveness of governance efforts. Bridging the gap between policy and practice is therefore critical. When governance frameworks are practical, implementable, and aligned with real-world conditions, organizations are more likely to adopt AI responsibly and at scale. Finally, enforcement remains a significant challenge. Effective governance requires not only well-designed rules but also the capacity to monitor, assess, and enforce them consistently. Without credible enforcement mechanisms, even the most comprehensive frameworks risk losing their impact.

The AI Dialogue can play a pivotal role as a neutral, multi-stakeholder platform that bridges the gap between national approaches and enables meaningful international coordination on AI governance.

The AI Dialogue should build upon and connect with existing international initiatives such as the OECD AI Principles, the Global Partnership on AI (GPAI), the UNESCO Recommendation on AI Ethics, and regional regulatory efforts, including the EU's AI framework. These initiatives have established important foundations in defining principles, ethical guidelines, and governance approaches. However, a key gap remains in practical implementation and interoperability. The AI Dialogue can add value by acting as a convening layer that connects these frameworks and translates them into actionable practices. This includes aligning interpretations, identifying overlaps, and reducing fragmentation across jurisdictions. In addition, the Dialogue can provide a platform for real-world validation, supporting pilot projects, sandboxes, and use case testing that demonstrate how governance principles function in practice.

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The issue is that voices, communities, etc., are siloed. It should be a holistic approach

Use- case testing roundtable discussions