ThinkAhead Advisory

A successful first Global Dialogue on AI Governance should move the international conversation from principles to practical pathways for implementation. Over recent years, important progress has been made in articulating ethical frameworks and high-level policy objectives for artificial intelligence. The true measure of success now lies in whether this dialogue helps translate these shared intentions into operational governance approaches applicable across diverse institutional, economic, and cultural contexts. First, success would mean building greater convergence around leadership accountability for AI oversight. Clear expectations for the roles of boards, executive teams, regulators, and public institutions are essential to ensure that governance is embedded in decision-making processes rather than treated as a purely technical or compliance issue. Second, the dialogue should help strengthen implementation capacity, particularly in emerging and developing economies. This includes facilitating knowledge exchange, supporting skills development, and encouraging partnerships that enable responsible adoption of AI technologies. Without addressing capability gaps, there is a risk that AI could widen existing inequalities between organizations and regions. Third, a successful outcome would be the identification of practical mechanisms to enhance transparency, trust, and stakeholder engagement. Public confidence will be a critical determinant of AI's long-term societal and economic impact. Governance approaches that promote explainability, accountability, and inclusive dialogue can help align innovation with broader development priorities. Finally, the dialogue should lay the foundation for ongoing global cooperation, ensuring that insights generated translate into coordinated action and continuous learning. If the Global Dialogue helps catalyse concrete governance practices, strengthen institutional readiness, and foster sustained collaboration, it will represent an important step toward ensuring that artificial intelligence supports sustainable and inclusive progress worldwide.

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

My selection reflects a governance- and implementation-focused perspective on artificial intelligence, shaped by experience in organizational leadership, sustainability strategy, and institutional transformation. Safe, secure, and trustworthy AI is a foundational priority because public confidence and long-term societal acceptance of AI systems depend on their reliability, resilience, and alignment with ethical standards. Ensuring that AI systems are designed and deployed responsibly is essential for mitigating systemic risks and enabling sustainable innovation. AI capacity-building is equally critical. Significant disparities exist across regions and sectors in terms of digital infrastructure, skills, and institutional readiness. Strengthening implementation capability, through knowledge exchange, partnerships, and investment in human capital, is necessary to ensure that the benefits of AI are broadly shared and do not exacerbate existing inequalities. Interoperability of governance approaches represents another key priority in an increasingly interconnected global economy. Organizations and governments operate across multiple regulatory environments, making coherence and alignment between governance frameworks essential. Greater interoperability can support cross-border cooperation, reduce fragmentation, and facilitate responsible scaling of AI solutions. Finally, transparency, accountability, and robust human oversight are central to effective AI governance. As AI systems increasingly influence strategic and operational decisions, clear accountability structures and explainable processes are required to maintain trust, support informed decision-making, and safeguard human agency. Together, these priorities reflect the need to move from high-level principles toward practical governance mechanisms that strengthen institutional capacity, foster coordinated global action, and ensure that artificial intelligence contributes to sustainable and inclusive development.

Yes. While the listed themes address core dimensions of AI governance, there are several other cross-cutting and emerging issues that merit further attention to ensure governance approaches remain forward-looking and effective. First, there is a growing need to address leadership and governance readiness at the organizational level. As AI adoption accelerates, many institutions lack the strategic oversight structures, risk management capabilities, and decision-making processes required to guide responsible deployment. Strengthening leadership competencies and integrating AI governance into broader corporate and public governance frameworks is therefore essential. Second, the environmental sustainability implications of AI represent an emerging concern. The energy intensity of large-scale computing infrastructure, data centres, and model training raises questions about alignment between digital transformation and climate objectives. Governance discussions would benefit from greater integration of environmental impact assessment and principles of sustainable innovation. Third, attention should be given to the long-term economic and labour-market transitions associated with AI adoption. Beyond immediate efficiency gains, AI is reshaping demand for skills, organizational structures, and productivity patterns. Governance frameworks should therefore consider strategies that support workforce adaptation, inclusive growth, and social resilience. Finally, there is a need to strengthen mechanisms for translating global dialogue into coordinated implementation. While multilateral discussions are essential, practical pathways for knowledge transfer, institutional learning, and policy experimentation will determine whether governance commitments generate tangible outcomes. Addressing these additional issues can help ensure that AI governance evolves holistically - balancing innovation with sustainability, competitiveness with inclusion, and technological progress with long-term societal well-being.

Indeed, governance gaps in safe and trustworthy AI, capacity-building, interoperability of governance approaches, and transparency and human oversight are already shaping developments across sectors in my region, particularly in emerging and transition economies. One of the most significant challenges relates to implementation capacity. While awareness of AI's strategic importance is growing among organizations and public institutions, many lack the technical expertise, governance frameworks, and risk-management structures needed to deploy AI responsibly. This creates uneven adoption patterns, where a small number of advanced actors move quickly, while others struggle to assess risks or capture value. As a result, there is a risk of widening productivity and competitiveness gaps both within and across sectors. A further challenge concerns regulatory and governance fragmentation. Organizations operating across European and global markets must navigate multiple evolving frameworks, which can create uncertainty and complicate compliance. Limited interoperability between governance approaches may slow innovation, particularly for smaller firms and public bodies with constrained resources. At the same time, trust and accountability considerations are becoming increasingly central. Stakeholders — including employees, customers, and regulators — are seeking greater clarity on how AI systems influence decisions, how data is used, and who remains responsible for outcomes. Without clear oversight mechanisms, organizations may face reputational and operational risks that affect long-term adoption. These gaps also create important opportunities. Strengthening governance capabilities can support more strategic and sustainable use of AI, enabling organizations to enhance productivity, improve service delivery, and participate more actively in global value chains. Increased alignment with international governance standards can also foster investment, innovation partnerships, and knowledge exchange. Addressing these challenges through coordinated policy action and institutional learning will be critical to ensuring that AI contributes to inclusive economic development and long-term resilience.

The Global Dialogue on AI Governance can indeed play a pivotal role in advancing international cooperation by serving as a trusted platform for alignment, practical exchange, and collective learning across diverse governance traditions and levels of technological readiness. First, it can help foster greater convergence around core governance principles and implementation approaches. By bringing together governments, international organizations, the private sector, academia, and civil society, it can facilitate the identification of common priorities and reduce fragmentation across emerging regulatory and policy frameworks. This is particularly important in a rapidly evolving technological landscape where inconsistent governance approaches may create uncertainty and slow responsible innovation. Second, it can support knowledge sharing and capacity building, especially in emerging and developing economies. Structured exchanges of experiences, case studies, and governance tools can strengthen institutional readiness and enable more balanced participation in the global digital economy. Encouraging partnerships between regions and sectors can also help accelerate responsible adoption and reduce capability gaps. Third, it can act as a catalyst for translating global commitments into coordinated action. By highlighting practical governance models, encouraging pilot initiatives, and promoting continuous monitoring of progress, it can help ensure that discussions lead to tangible outcomes rather than remaining at the level of high-level consensus. Finally, it can help build mutual trust and legitimacy in AI governance. Inclusive participation and transparent processes can reinforce confidence that governance approaches reflect shared societal values and development priorities. By strengthening alignment, enhancing institutional capabilities, and promoting sustained cooperation, the AI Dialogue has the potential to support a more coherent and effective global governance landscape — one that enables innovation while safeguarding long-term social and economic resilience.

The Global Dialogue on AI Governance can build on an existing and growing ecosystem of international initiatives shaping norms, regulatory approaches, and practical implementation pathways. By building on existing frameworks while strengthening coordination and inclusiveness, the AI Dialogue can contribute to a more coherent and effective global governance landscape that supports responsible innovation and sustainable development. Key reference points include the OECD AI Principles, which have provided an early foundation for responsible AI policy alignment; UNESCO's Recommendation on the Ethics of Artificial Intelligence, which offers a comprehensive values-based framework; and the G7 Hiroshima AI Process, which is advancing discussions on generative AI governance. In the European context, developments around the EU AI Act and related digital policy initiatives are also contributing to more structured regulatory approaches and operational guidance for organizations. In addition, partnerships among multilateral institutions, regional organizations, industry alliances, and academic networks are increasingly supporting knowledge exchange, capacity building, and experimentation with governance models. Initiatives focused on digital public infrastructure, responsible innovation, and skills development are particularly relevant for emerging economies seeking to strengthen institutional readiness. The added value of the Global Dialogue lies in its ability to connect these diverse efforts within an inclusive, global platform that bridges policy development and practical implementation. Unlike many existing initiatives that are regionally anchored or sector-specific, the Dialogue can facilitate cross-regional learning, promote interoperability between governance approaches, and encourage more coordinated responses to shared challenges. Furthermore, the Dialogue can support translation of high-level principles into actionable governance practices by highlighting case studies, fostering pilot collaborations, and encouraging continuous monitoring of progress. It can also help ensure that perspectives from developing economies and underrepresented stakeholders are meaningfully integrated into global discussions.

Effective AI governance requires meaningful participation from a wide range of stakeholders, and the Global Dialogue can benefit from structured contributions that reflect the complementary roles of governments, the private sector, academia, civil society, and international organizations. Governments and regulators can contribute by sharing policy experiences, regulatory approaches, and lessons learned from implementation. Their role is critical in identifying governance gaps, ensuring alignment with public interest objectives, and fostering cross-border cooperation. Private sector actors can provide practical insights into the operational realities of AI deployment, including risk management practices, governance models, and innovation challenges. Their engagement can help ensure that policy discussions remain grounded in real-world application and technological feasibility. Academia and research institutions can contribute evidence-based analysis, foresight on emerging technological trends, and evaluation of governance outcomes. This analytical perspective is important for supporting informed decision-making and long-term policy coherence. Civil society organizations play a key role in representing societal concerns, promoting ethical considerations, and strengthening accountability. Their participation helps ensure that governance frameworks remain inclusive and responsive to broader social impacts. To maximize effectiveness, the AI Dialogue could adopt a structured, multi-layered format. High-level plenary discussions can set strategic direction, while thematic working groups or roundtables can focus on specific governance challenges and practical solutions. Regular knowledge-sharing sessions, case study presentations, and capacity-building workshops could support continuous learning and collaboration. Clear mechanisms for documenting outcomes, monitoring progress, and facilitating follow-up actions would further enhance the Dialogue's impact. By combining inclusive participation with practical orientation, the AI Dialogue can evolve into a sustained platform that supports coordinated global action and responsible innovation.

One important gap concerns stakeholders from emerging and developing economies, particularly those outside major technology hubs. Limited institutional capacity, resource constraints, and uneven access to policy forums can restrict their participation. Greater inclusion could be supported through targeted capacity-building initiatives, regional consultation platforms, and financial mechanisms that enable sustained engagement in global processes. Another underrepresented perspective is that of small and medium-sized enterprises (SMEs). While large technology companies often shape policy debates, SMEs face distinct governance challenges related to compliance costs, access to expertise, and responsible deployment of AI solutions. Structured industry dialogues and sector-specific working groups could help ensure that governance frameworks reflect the realities of organizations of different sizes and capabilities. There is also a need to strengthen the participation of workers and professional communities affected by AI-driven transformation. As AI reshapes job roles, skill requirements, and organizational structures, the experiences of employees, trade associations, and professional bodies can provide valuable insights into labour market impacts and responsible transition pathways. In addition, diverse leadership perspectives, including women and other underrepresented groups in technology decision-making roles, should be more systematically included. Greater diversity can enhance the quality of governance debates by bringing varied experiences, ethical considerations, and societal priorities into focus. To address these gaps, the Global Dialogue could adopt more inclusive consultation formats, support regional networks, encourage multi-stakeholder partnerships, and facilitate knowledge-sharing mechanisms that enable continuous participation rather than one-time engagement. This way, broadening representation will strengthen trust, improve policy relevance, and contribute to a more balanced and sustainable AI governance outcome.

One effective approach is to use thematic breakout labs or policy sprints, in which small, diverse groups of participants work together on concrete governance challenges and produce short, actionable recommendations. This format encourages collaboration, practical thinking, and cross-sector learning. Case-based discussions can also foster meaningful engagement. Presenting real examples of AI deployment — including governance successes and failures — enables participants to explore trade-offs, identify transferable lessons, and connect policy principles with operational realities. Another useful format is the multi-stakeholder roundtable with facilitated dialogue, designed to ensure balanced participation among governments, industry, academia, and civil society. Structured moderation and targeted guiding questions can help move discussions from general positions toward constructive problem-solving. The Dialogue could also incorporate interactive digital platforms, such as live polling, collaborative drafting tools, or moderated online forums. These mechanisms allow broader participation, capture diverse perspectives in real time, and help maintain engagement between formal sessions. Finally, follow-up communities of practice or working groups can sustain momentum beyond the event itself. Regular virtual exchanges focused on specific governance themes can support continuous learning, progress monitoring, and the development of practical guidance. By combining inclusive participation with interactive, action-oriented formats, the AI Dialogue can enhance the quality of engagement and contribute to more tangible governance outcomes.

At the regulatory level, the European Union's AI Act represents an important attempt to introduce a risk-based governance framework. By differentiating between levels of risk and linking obligations to specific use cases, it offers organizations clearer guidance on compliance, accountability, and responsible deployment. Similarly, national AI strategies adopted in a number of countries are helping align innovation objectives with ethical and societal considerations. From a standards perspective, initiatives such as ISO/IEC guidance on AI management systems and risk management contribute to more structured governance practices. These standards can help organizations embed AI oversight into existing quality, security, and operational frameworks, thereby strengthening institutional readiness. In the private sector, leading organizations are increasingly establishing internal AI governance structures, including ethics review boards, model risk management processes, and cross-functional oversight committees. These mechanisms help ensure that AI adoption is aligned with corporate governance principles, sustainability strategies, and stakeholder expectations. Multi-stakeholder platforms also play a valuable role. Industry alliances, research partnerships, and public-private initiatives focused on responsible AI development enable knowledge exchange, experimentation with governance tools, and the scaling of good practices across sectors. Digital innovation hubs and sandboxes, for example, allow organizations to test AI solutions within controlled regulatory environments while maintaining safeguards.