Technological University Dublin

A successful first Global Dialogue on AI Governance should deliver concrete, actionable outcomes that move beyond high-level principles. This includes advancing practical, globally relevant governance approaches that organizations can adopt to manage AI risks across the system lifecycle. A key outcome should be greater alignment and interoperability between existing governance frameworks to reduce fragmentation and support coherent global compliance. The Dialogue should also promote clear mechanisms for transparency, accountability, and auditing, enabling AI systems to be effectively monitored and governed in practice. Equally important is ensuring inclusive participation from governments, industry, academia, and civil society across all regions, supported by targeted capacity-building efforts. Ultimately, success would be defined by a clear and actionable roadmap that translates dialogue into measurable, real-world progress on responsible and sustainable AI governance.

• Safe, secure and trustworthy AI
• AI capacity-building
• Interoperability of governance approaches
• Transparency, accountability, and human oversight

Current AI governance efforts remain largely principle-driven, with a clear gap in operational implementation. Prioritizing safe, secure, and trustworthy AI must therefore be coupled with enforceable mechanisms for transparency, accountability, and human oversight, enabling continuous monitoring, auditing, and real-world risk management. Interoperability of governance approaches is increasingly critical as fragmented regulatory landscapes create inefficiencies and uncertainty for global deployment. Greater alignment is necessary to ensure that governance is both effective and scalable across jurisdictions. At the same time, AI capacity-building is essential to avoid widening global inequalities, ensuring that all regions can actively participate in shaping and enforcing AI governance. Together, these priorities emphasize a shift from abstract principles to measurable, implementable governance, enabling AI systems to be managed responsibly at scale.

A key cross-cutting issue not sufficiently captured is the operationalization of AI governance within organizations. While many frameworks define principles, there remains a gap in translating these into enforceable processes such as lifecycle risk management, continuous monitoring, and independent auditing of AI systems in real-world deployments. Another emerging issue is the environmental sustainability of AI. As AI systems become increasingly resource-intensive, governance frameworks must integrate considerations such as energy consumption, emissions, and resource use to ensure that responsible AI also includes environmental responsibility. In addition, the governance of general-purpose and generative AI models introduces new challenges, especially around downstream use, accountability, and risk propagation across multiple applications and actors. Finally, there is a need to strengthen the link between policy and technical implementation. Without mechanisms that connect regulatory intent to system-level enforcement, governance risks remaining aspirational rather than effective. In my opinion, addressing these cross-cutting issues is essential to ensure that AI governance is practical, enforceable, and aligned with real-world system behavior.