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A successful first Global Dialogue on Artificial Intelligence Governance should establish a credible foundation for sustained international cooperation on AI governance, particularly by ensuring that discussions move beyond principles toward practical pathways for implementation. First, success would mean achieving a shared understanding of priority governance challenges—including safety, transparency, accountability, and interoperability—while recognizing that countries are at different stages of AI readiness. The Dialogue should help identify common minimum principles that can guide national and sectoral AI frameworks without imposing a one-size-fits-all model. Second, it should produce concrete commitments on capacity-building, especially for developing countries. This includes support for digital infrastructure, compute access, technical training, institutional readiness, and regulatory capability. Bridging AI divides must be treated as a core governance issue, not a parallel development issue. Third, the Dialogue should strengthen the multi-stakeholder model of AI governance by creating structured channels for governments, industry, academia, civil society, and technical communities to contribute to policy development. AI governance will only be effective if it reflects practical realities across sectors and geographies. Fourth, success would involve identifying mechanisms for greater interoperability across existing AI governance initiatives, reducing fragmentation and promoting coherence across regional, national, and sectoral frameworks. Finally, the most important outcome would be establishing a forward-looking cooperation agenda—with clear follow-up mechanisms, knowledge-sharing platforms, and implementation-focused workstreams. The value of the first Dialogue will ultimately be measured not by the quality of discussion alone, but by whether it catalyzes sustained collaboration and tangible institutional progress across countries.

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

The urgency of addressing safe, secure and trustworthy AI stems from the rapid integration of AI into critical sectors such as finance, healthcare, education, and public services. Without robust safeguards, AI systems can amplify risks including bias, misinformation, privacy violations, and operational vulnerabilities. Building trust in AI is essential for ensuring responsible adoption, protecting citizens, and sustaining confidence in digital transformation. AI capacity-building must be prioritized to address widening global disparities in AI readiness. Many developing countries continue to face gaps in infrastructure, compute access, technical expertise, and regulatory preparedness. Without targeted investments in capacity-building, AI risks deepening the digital divide and concentrating economic gains within a limited number of advanced economies. Strengthening institutional and human capacity is critical for inclusive participation and long-term resilience. Addressing the social, economic, ethical, cultural, linguistic, and technical implications of AI is equally important because AI systems increasingly shape decisions affecting livelihoods, access to services, and public discourse. If these dimensions are overlooked, AI may reinforce inequalities, marginalize underrepresented languages and cultures, and create unintended societal consequences. A broader understanding of AI's impact is necessary to ensure fairness, inclusion, and context-sensitive deployment. Finally, advancing the interoperability of governance approaches is essential in an increasingly interconnected digital economy. Fragmented regulatory frameworks can create uncertainty, raise compliance costs, and hinder innovation across borders. Greater alignment on core governance principles can improve cooperation, promote consistency, and support safer and more effective deployment of AI systems globally. Together, these priorities are foundational to ensuring that AI development remains inclusive, responsible, and beneficial across countries and communities.

Yes. While the listed themes provide a strong foundation, several cross-cutting and emerging issues merit greater attention. First, AI and labour market transformation requires dedicated focus. Beyond broad economic implications, AI is fundamentally reshaping employment structures, skills demand, and workforce transitions. Policymakers need stronger frameworks for reskilling, job redesign, and social protection in AI-affected sectors. Second, data governance and data sovereignty deserve explicit recognition. Access to quality, representative, and lawful data underpins AI development, yet questions around ownership, cross-border flows, consent, and equitable data-sharing remain unresolved, particularly for developing countries. Third, environmental sustainability of AI is emerging as a critical issue. Large-scale AI systems require significant energy, compute, and water resources, creating environmental and infrastructure pressures. Sustainable AI development should be integrated into governance discussions. Fourth, sector-specific governance frameworks are increasingly important. AI risks and opportunities vary significantly across sectors such as finance, healthcare, education, agriculture, and public administration. Sector-sensitive governance approaches can complement broader horizontal principles. Fifth, inclusion of developing economies in AI value chains requires stronger emphasis. Beyond capacity-building, there is a strategic need to ensure developing countries participate as creators, innovators, and contributors-not only consumers-within the global AI ecosystem. Finally, AI for public interest and development outcomes could be elevated as a distinct theme. The role of AI in advancing financial inclusion, healthcare access, education quality, climate resilience, and public service delivery deserves focused global cooperation. Addressing these emerging issues would strengthen the Dialogue by making AI governance more implementation-oriented, development-sensitive, and responsive to long-term global challenges.

In India and across Asia, governance gaps in AI are becoming increasingly visible, particularly in the financial services sector, where adoption is accelerating across credit underwriting, fraud detection, customer service, compliance, and risk management. A key gap is the absence of fully harmonized AI governance standards across jurisdictions. This creates uncertainty for financial institutions operating across markets, especially in areas such as model accountability, explainability, and cross-border data usage. In a region as diverse as Asia, fragmented regulatory approaches can slow innovation and increase compliance complexity. Capacity gaps also remain significant. While India has a strong digital public infrastructure and growing AI talent base, institutional capacity—particularly among smaller financial institutions, regulators, and rural financial ecosystems—remains uneven. This affects the ability to adopt AI responsibly and at scale. Data governance remains a major challenge. Financial AI systems depend on large volumes of customer and transaction data, raising concerns around privacy, consent, data localization, and cybersecurity. Weak governance in these areas can undermine trust and increase operational risk. Bias and inclusion are particularly relevant in financial services. AI-driven lending and underwriting systems may unintentionally exclude underserved populations such as MSMEs, informal workers, or first-time borrowers if models are trained on incomplete or historically biased datasets. At the same time, recent advances in AI present major opportunities for India and Asia: improving fraud detection, enhancing financial inclusion, reducing customer acquisition costs, and strengthening compliance monitoring. Closing these governance gaps through clearer standards, stronger regulatory capacity, interoperable frameworks, and responsible innovation practices will be critical to ensuring AI strengthens—not destabilizes—the financial sector and broader economic inclusion.

The Global Dialogue on Artificial Intelligence Governance can play a critical role as a neutral, inclusive platform for building international cooperation on AI governance at a time when technological advancement is outpacing policy coordination. First, the Dialogue can help build shared understanding around core governance principles such as safety, accountability, transparency, human oversight, and trustworthiness. While national contexts differ, global alignment on foundational principles can reduce fragmentation and support more coherent policy development. Second, it can strengthen capacity-building cooperation, particularly for developing countries. AI readiness remains highly uneven across countries, with significant gaps in infrastructure, compute access, talent, and regulatory preparedness. The Dialogue can help mobilize technical assistance, knowledge-sharing, and institutional partnerships to bridge these divides. Third, the Dialogue can promote interoperability of governance approaches by facilitating exchange between governments, regulators, industry, academia, and civil society. This can help identify practical lessons, reduce duplication of effort, and support compatibility across emerging regulatory frameworks. Fourth, it can elevate the voice of developing economies in shaping global AI governance. International discussions are often driven by a small number of technologically advanced countries, but AI governance must reflect broader developmental, social, and economic realities. Finally, the Dialogue can support a shift from fragmented discussions to action-oriented cooperation, including joint research, open-source collaboration, standards development, and sector-specific implementation frameworks. Its long-term value will lie not only in convening stakeholders, but in creating sustained mechanisms for follow-up, cooperation, and mutual learning. In that sense, the AI Dialogue can serve as a bridge between global principles and practical implementation, helping build a more inclusive and coordinated global AI governance ecosystem.

The Global Dialogue on Artificial Intelligence Governance should build upon existing international initiatives rather than duplicate them. Several important foundations already exist, including the UNESCO Recommendation on the Ethics of AI, which provides a globally endorsed normative framework for ethical AI governance ; the OECD AI Principles and policy observatory, which have informed many national AI strategies; the G7 Hiroshima AI Process, which has advanced guiding principles and voluntary codes of conduct for advanced AI systems ; and the United Nations recommendations on global AI cooperation, capacity-building, and standards exchange . The Dialogue should also connect with regional and sectoral efforts, including national AI governance frameworks, standards bodies, and industry-led responsible AI initiatives. In sectors such as financial services, healthcare, and education, sector-specific regulatory practices offer practical lessons that can enrich broader governance discussions. The added value of the AI Dialogue lies in its ability to act as a connecting platform across these fragmented initiatives. Unlike existing forums that are often regional, thematic, or membership-based, the UN-led Dialogue offers universal participation and stronger representation from developing economies. Its value would be threefold: first, promoting interoperability across governance models; second, strengthening capacity-building and inclusion for countries with limited AI readiness; and third, translating high-level principles into practical cooperation mechanisms such as knowledge-sharing, technical assistance, and implementation partnerships. In this way, the Dialogue can help transform a fragmented AI governance landscape into a more coherent, inclusive, and action-oriented global ecosystem.

Effective AI governance requires meaningful participation from a broad range of stakeholders, and the Global Dialogue on Artificial Intelligence Governance should be structured to enable practical, balanced, and implementation-oriented engagement. Governments should contribute policy perspectives, regulatory experiences, and national priorities, particularly on safety, rights, and public-interest use cases. Their participation is critical for shaping interoperable governance approaches. Industry can provide operational insights on AI deployment, risk management, technical safeguards, and innovation pathways. As primary developers and deployers of AI systems, their practical experience is essential for realistic governance frameworks. Academia and technical communities should contribute evidence-based research, technical assessments, and foresight on emerging risks and opportunities. Their role is important in grounding discussions in scientific and technical rigor. Civil society can help ensure that governance discussions remain people-centered by bringing perspectives on rights, inclusion, equity, and social impact, particularly for vulnerable communities. International organizations and development institutions can support coordination, capacity-building, and cross-country knowledge exchange, especially for developing economies. To strengthen the Dialogue's effectiveness, the format should combine high-level plenary sessions with thematic breakout discussions that allow deeper technical and policy exchange. Each thematic track should be co-chaired by governments and non-government stakeholders to ensure balance. A dedicated capacity-building and implementation track should be included to focus on practical challenges faced by developing countries. Structured pre-submissions and issue papers can improve the quality of discussions. Finally, the Dialogue should establish clear follow-up mechanisms—such as annual progress reviews, knowledge-sharing repositories, and working groups—to ensure continuity and translate discussions into practical cooperation and measurable outcomes.

Global discussions on AI governance continue to be shaped largely by technologically advanced economies, major technology companies, and established policy institutions. As a result, several critical voices remain underrepresented. First, developing countries and emerging economies need stronger representation. Many of these countries are rapidly adopting AI but often lack equal influence in shaping governance frameworks. Their realities—capacity constraints, infrastructure gaps, and development priorities—must be better reflected in global policymaking. Second, small and medium enterprises (SMEs) are often absent from AI governance discussions, despite being major adopters of AI across sectors. Their perspectives on affordability, compliance burden, and practical implementation are important for creating workable governance frameworks. Third, sectoral practitioners—such as professionals in financial services, healthcare, education, and agriculture—are underrepresented. Since AI deployment risks and opportunities vary significantly by sector, operational insights from frontline implementers are essential. Fourth, linguistic and culturally diverse communities, particularly from non-English-speaking regions, remain underrepresented. AI systems increasingly shape access to information and services, yet governance discussions often overlook language inclusion and cultural context. Fifth, workers and labour representatives should have a stronger voice, as AI is directly affecting employment patterns, workplace structures, and skill requirements. To improve inclusion, the AI Dialogue should adopt structured regional consultations, multilingual participation mechanisms, and targeted representation from developing countries and underrepresented sectors. Dedicated tracks for SMEs, sector practitioners, and workforce transitions can broaden practical perspectives. Financial support for participation, especially for low-resource stakeholders, will also be important. A truly inclusive AI governance framework must reflect not only those building AI, but also those most affected by its adoption.

To make the Global Dialogue on Artificial Intelligence Governance impactful, the format should move beyond traditional statement-based plenaries and create more interactive, problem-solving-oriented engagement. First, scenario-based policy labs could be highly effective. These sessions can simulate real-world AI governance challenges—such as AI bias in lending, misinformation during elections, or cross-border data governance—to enable stakeholders to discuss practical responses rather than abstract principles. Second, multi-stakeholder roundtables organized by sector (financial services, healthcare, education, public administration) can help ground governance discussions in operational realities. Sector-specific engagement allows participants to identify practical risks, regulatory gaps, and implementation pathways. Third, regional dialogue clusters can provide space for geographically relevant perspectives, particularly from developing economies, where AI readiness and governance needs differ significantly from advanced markets. Fourth, solution showcases and implementation case clinics could allow governments, companies, and institutions to present real AI governance practices, lessons learned, and operational models. This would shift the Dialogue toward actionable learning. Fifth, "Dialogue of Dialogues" sessions—bringing together leaders from existing AI governance initiatives, standards bodies, and regional frameworks—can improve coordination and reduce fragmentation. To deepen inclusion, structured written submissions and moderated synthesis sessions should be used to ensure broader participation beyond speaking slots. Finally, establishing ongoing thematic working groups after the Dialogue would create continuity and allow discussions to evolve into concrete outputs such as guidance notes, capacity-building partnerships, or sectoral frameworks. A combination of interactive, sectoral, regional, and implementation-focused formats would make the Dialogue more dynamic, inclusive, and action-oriented, increasing its practical value for participants and policymakers alike.

Several existing policies and operational practices offer practical lessons for effective AI governance and can inform future cooperation under the Global Dialogue on Artificial Intelligence Governance. The UNESCO Recommendation on the Ethics of AI is a strong example of a human rights-based governance framework, covering fairness, transparency, accountability, and human oversight, while also providing implementation tools such as readiness assessments and policy guidance for governments. The Organisation for Economic Co-operation and Development (OECD) AI Principles and related reporting mechanisms offer a practical foundation for risk-based governance, policy benchmarking, and international comparability. These have helped shape several national AI strategies and regulatory approaches. The Group of Seven Hiroshima AI Process provides an important model for governance of advanced AI systems through voluntary codes of conduct, transparency reporting, and risk mitigation practices. Its emphasis on safety testing and incident reporting is particularly relevant for high-impact AI systems. At the institutional level, AI risk management frameworks such as internal model governance, algorithm audits, bias testing, and human-in-the-loop review mechanisms are increasingly being adopted across sectors like financial services and healthcare. Open-source platforms and collaborative ecosystems also offer concrete solutions by expanding access to models, datasets, and technical tools, particularly for developing countries. A key lesson across these examples is that effective AI governance works best when principles are paired with operational mechanisms-such as audits, transparency reports, risk classification, and accountability structures. The AI Dialogue can add value by connecting these fragmented approaches, promoting interoperability, and enabling knowledge-sharing across countries and sectors, particularly to support implementation in developing economies.