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A successful outcome of the Global Dialogue on AI Governance would be the establishment of a framework that moves beyond high-level principles and provides clear guidance on operational implementation. In particular, success would include the recognition that AI governance must address not only ethics, safety, and accountability at a conceptual level, but also how AI-related incidents are managed in real-world environments. This includes defining mechanisms for incident detection, classification, escalation, and coordinated response across organizations and jurisdictions. Without such an operational layer, governance frameworks risk remaining theoretical and difficult to apply in practice. Another key outcome would be the development of shared structures for multi-stakeholder coordination, especially in cross-border contexts where responsibilities and responses must be clearly aligned. Additionally, integrating feedback loops from operational incidents into governance processes would allow continuous improvement and adaptation of policies based on real-world experience. Ultimately, success would be achieved if AI governance frameworks become not only normative, but also actionable, enabling institutions and operators to respond effectively to incidents, ensure resilience, and maintain trust in AI systems.

Safe, secure and trustworthy AI;Interoperability of governance approaches;AI capacity-building;Social, economic, ethical, cultural, linguistic and technical implications of AI;

The selected priorities reflect the need to ensure that AI governance frameworks are not only principle-based but also operationally effective. "Safe, secure and trustworthy AI" is fundamental, but achieving this requires more than high-level guidelines. It depends on the ability of organizations to detect, manage, and resolve AI-related incidents in real-world environments. "Interoperability of governance approaches" is critical in multi-actor and cross-border contexts, where coordination between stakeholders is essential. Without aligned frameworks, responses to AI incidents risk being fragmented and ineffective. "Transparency, accountability, and human oversight" are key to maintaining trust, but they must be supported by clear operational processes that define responsibilities, escalation paths, and decision-making structures. "AI capacity-building" is necessary to ensure that institutions and operators have the skills and structures required to implement governance frameworks in practice. Together, these priorities highlight the importance of integrating an operational incident management perspective into AI governance, ensuring that frameworks can function effectively in complex, real-world systems.

A key cross-cutting issue not fully captured in the listed themes is the need for a structured operational incident management layer for AI systems. While current discussions address safety, accountability, and governance frameworks, there is limited focus on how AI-related incidents are handled in real-world operational contexts. This includes defining standardized approaches for incident detection, classification, escalation, coordination, and resolution across multiple stakeholders. As AI systems become increasingly interconnected and embedded in critical infrastructures, incidents are likely to involve multiple actors, jurisdictions, and dependencies. Without a common operational framework, responses may become fragmented, delayed, or inconsistent. Another emerging issue is the need to treat AI governance as part of broader systemic resilience, where technical failures, cyber threats, and operational disruptions intersect. This requires integrating AI governance with existing incident management and resilience practices across sectors. Addressing these gaps would help ensure that AI governance frameworks are not only principle-based but also capable of functioning effectively in complex, real-world environments.

In the European context, including Luxembourg, AI governance is advancing rapidly through regulatory initiatives such as the EU AI Act and related digital resilience frameworks. This creates a strong foundation for trust, accountability, and compliance. However, a key governance gap remains in the translation of these frameworks into operational reality. While policies define requirements and responsibilities, there is limited guidance on how organizations should manage AI-related incidents in practice, particularly in complex, cross-border environments. One of the main challenges is the fragmentation of approaches between sectors and jurisdictions, which can lead to inconsistent responses when incidents occur. This is especially relevant in interconnected systems such as finance, telecommunications, and space-based services, where dependencies are high and disruptions can propagate rapidly across domains. Another challenge is the lack of standardized incident classification and escalation models for AI-related events, making coordination between stakeholders more difficult. At the same time, this situation presents a significant opportunity. Europe is well positioned to lead not only in regulatory frameworks but also in defining operational governance models that bridge policy and implementation. By integrating structured incident management and resilience practices into AI governance, it would be possible to enhance coordination, improve response effectiveness, and strengthen overall system trust. This would allow governance frameworks to evolve into actionable systems capable of addressing real-world complexity.

The AI Dialogue can play a critical role in advancing international cooperation by aligning not only principles, but also operational approaches to AI governance. While many existing initiatives focus on ethics, standards, and regulatory frameworks, effective cooperation requires shared mechanisms for how AI-related incidents are managed across borders and sectors. The Dialogue provides an opportunity to develop common reference models for incident detection, classification, escalation, and coordinated response, enabling stakeholders to act consistently in complex, multi-actor environments. It can also facilitate the exchange of operational best practices, allowing lessons learned from real-world incidents to inform governance frameworks and improve resilience over time. In addition, the Dialogue can help bridge gaps between policy, technical, and operational communities by promoting interoperability of governance approaches and clarifying roles and responsibilities across jurisdictions. By combining high-level governance principles with practical operational coordination mechanisms, the AI Dialogue can strengthen trust, enhance resilience, and enable more effective international collaboration in managing AI risks.

The AI Dialogue should build on and connect with existing international initiatives such as those led by the UNESCO, the International Telecommunication Union, and regional frameworks such as the EU AI Act. These initiatives provide strong foundations in ethics, standards, and regulatory approaches. However, a key added value of the AI Dialogue would be to bridge these efforts by introducing a stronger focus on operational implementation and coordination. Currently, many initiatives define what should be achieved, but fewer address how stakeholders should respond collectively when AI-related incidents occur in real-world environments. The Dialogue could serve as a platform to connect governance frameworks with operational practices by promoting shared incident management models, coordination protocols, and feedback mechanisms across initiatives. This would enable greater interoperability between existing frameworks and reduce fragmentation across regions and sectors. By complementing existing initiatives with an operational perspective, the AI Dialogue can help transform governance from a set of parallel efforts into a more cohesive and actionable global system.

Different stakeholders can contribute to the AI Dialogue by bringing complementary perspectives across policy, technical development, and operational implementation. Governments and regulators can define strategic priorities and frameworks, while industry actors provide practical insights from deployment environments. Academia and research institutions contribute analytical depth, and operational practitioners offer experience in managing real-world incidents and system complexity. To ensure effective participation, the Dialogue should be structured in a way that balances these perspectives. A recommended approach would include thematic working groups combining policy, technical, and operational stakeholders, supported by regular cross-group exchanges. In addition, the Dialogue should incorporate scenario-based discussions, where stakeholders collaboratively explore realistic AI-related incidents. This would allow participants to move beyond abstract principles and engage with concrete coordination challenges. A continuous feedback mechanism should also be established, ensuring that insights from operational experiences are integrated into governance discussions. By structuring participation around collaboration, practical scenarios, and iterative feedback, the AI Dialogue can enable meaningful contributions from diverse stakeholders and produce more actionable outcomes.

Global discussions on AI governance often underrepresent operational practitioners, smaller organizations, and stakeholders from regions with limited institutional resources. While policy-makers and large technology actors are well represented, those responsible for implementing and managing systems on a day-to-day basis are less visible in these discussions. This creates a gap between governance design and real-world execution. In addition, perspectives from developing regions are often underrepresented, despite the fact that AI systems increasingly operate across global infrastructures and affect diverse socio-economic environments. To address this, the AI Dialogue should actively include operational experts, such as incident managers, system operators, and resilience specialists, who can provide insights into how governance frameworks function in practice. Mechanisms to support broader participation could include targeted outreach, capacity-building initiatives, and the use of hybrid formats to enable remote engagement. Ensuring linguistic accessibility and reducing barriers to entry would also be important to diversify participation. By including these underrepresented voices, the Dialogue can better reflect the realities of global AI deployment and strengthen the effectiveness of governance frameworks.

Innovative engagement formats should focus on making discussions more interactive, practical, and outcome-oriented. One effective approach would be the use of scenario-based simulations, where participants collaboratively respond to realistic AI-related incidents. This allows stakeholders to test coordination mechanisms, identify gaps, and better understand interdependencies across sectors and regions. Another valuable format would be structured "decision labs," where small, multidisciplinary groups work through specific governance challenges and produce concrete recommendations within a defined timeframe. In addition, interactive digital platforms could support continuous engagement beyond formal sessions, enabling participants to share insights, track developments, and collaborate asynchronously. Rotating facilitation across regions and stakeholder groups could further enhance inclusivity and ensure diverse perspectives are actively integrated into discussions. By combining simulations, collaborative problem-solving formats, and continuous digital engagement, the AI Dialogue can move beyond traditional consultation models and foster more meaningful and dynamic participation.

Several existing policies and frameworks already contribute to effective AI governance. For example, the EU AI Act establishes a risk-based regulatory approach, while standards such as those developed by ISO and IEEE provide guidance on trustworthy and responsible AI development. In addition, operational resilience frameworks, such as those used in critical sectors (e.g. financial services and telecommunications), offer valuable practices for managing complex, high-risk systems. These include structured incident management processes, clear escalation paths, and continuous monitoring mechanisms. However, one of the key challenges remains the integration of these approaches into a coherent, end-to-end governance model. A promising practice would be to combine regulatory frameworks with operational methodologies that define how AI-related incidents are detected, classified, and managed in real time. This includes establishing shared incident taxonomies, coordination protocols, and feedback loops between operational teams and governance bodies. Platforms that enable cross-organizational information sharing and coordinated response would also be valuable, particularly in environments where AI systems operate across multiple jurisdictions and infrastructures. By linking policy, standards, and operational practices into a unified approach, AI governance can evolve from compliance-focused frameworks into resilient, adaptive systems capable of addressing real-world challenges effectively.