Governance & Policy Consultants

A successful Global Dialogue on AI Governance would be defined by its ability to deliver practical, inclusive, and implementation-oriented outcomes. First, the Dialogue should advance consensus on a set of minimum governance principles that are adaptable across diverse national contexts, particularly reflecting the priorities and capacities of developing countries. In parallel, it should catalyze meaningful partnerships and implementation pathways among governments, academia, multilateral institutions, and the private sector. These principles should translate into actionable guidance that can be embedded within national systems, including education sectors where AI is already reshaping research and student learning. This includes promoting structured frameworks for the responsible use of AI in universities, enabling innovation in research and academic work while safeguarding integrity, transparency, and accountability through clear standards and oversight. Second, the Dialogue should establish a coherent roadmap for sustained global cooperation, aligned with existing frameworks on governance, human rights, and financial integrity. Drawing on experience in anti-corruption, AML/CFT, and institutional strengthening, it is important that AI governance be integrated into broader accountability ecosystems. This includes addressing emerging risks such as AI-enabled fraud, corruption, and illicit financial flows, while also leveraging AI to enhance detection, oversight, and enforcement capacities. Strengthening institutional coordination and regulatory readiness will be essential to ensuring that governance responses remain effective across both domestic and cross-border contexts. Ultimately, success will be reflected in the Dialogue's ability to move beyond deliberation towards practical, scalable, and context-sensitive implementation, ensuring that AI contributes to innovation, public trust, institutional integrity, and equitable development.

• Safe, secure and trustworthy AI
• AI capacity-building
• Protection and promotion of human rights
• Transparency, accountability, and human oversight

The selected priorities reflect the importance of anchoring AI governance in capacity, accountability, and rights-based frameworks, while enabling responsible innovation across sectors. AI capacity-building is foundational. Many countries continue to face constraints in technical expertise, institutional readiness, and regulatory coordination. Strengthening capacities across government, academia, and oversight institutions is essential to ensure that AI is deployed effectively and responsibly. In the education sector, this includes equipping students and educators with the skills to use AI in research and academic work in a structured and ethical manner, promoting innovation while preserving critical thinking and academic integrity. Transparency, accountability, and human oversight are critical to maintaining trust in AI-enabled systems. These principles are particularly relevant in public sector functions and academic environments, where clear standards for disclosure, explainability, and oversight are necessary to mitigate risks of misuse and ensure responsible application. Protection and promotion of human rights remains central, given the potential of AI systems to impact privacy, equality, and non-discrimination. Embedding human rights safeguards helps ensure that AI adoption reinforces, rather than undermines, democratic governance and social inclusion. Finally, safe, secure and trustworthy AI provides the foundation for all other priorities. This includes managing systemic risks, preventing misuse, and promoting resilience. Together, these areas support a balanced and implementation-focused approach that advances innovation while safeguarding integrity, trust, and equitable outcomes.

Several cross-cutting and emerging issues warrant further attention. First, the use of AI within education systems, particularly universities, requires more explicit focus. While AI offers significant opportunities to enhance research, learning, and knowledge production, there is a need for clear governance frameworks that guide its responsible use in academic work, including disclosure standards, ethical guidelines, and assessment approaches that reinforce critical thinking while enabling innovation. Second, the intersection between AI and financial integrity risks-including corruption, fraud, and illicit financial flows-remains underexplored. AI has the potential both to strengthen detection and oversight, and to introduce new vulnerabilities, such as automated fraud and synthetic identities. This underscores the need for integrated governance approaches that link AI oversight with anti-corruption and AML/CFT frameworks. Third, greater emphasis is needed on the inclusive use of AI, particularly for women, disadvantaged populations, and individuals in remote, rural, or transitional contexts. Without deliberate policy and design choices, AI risks reinforcing existing inequalities in access, participation, and outcomes. Governance frameworks should therefore promote equitable access to AI tools, inclusive datasets, gender-responsive approaches, and targeted capacity-building, ensuring that the benefits of AI are broadly shared and contribute to social and economic inclusion. Fourth, the governance of data ecosystems, including issues of data access, ownership, and cross-border flows, remains central to equitable AI development and requires greater coordination at the global level. Finally, capacity asymmetries across countries continue to present structural challenges. Addressing these disparities will require sustained investment in capacity-building, regional collaboration, and knowledge-sharing platforms to ensure that global AI governance frameworks are inclusive, context-responsive, and implementable.

Governance gaps in AI are already having tangible implications across public sector governance, education systems, and financial integrity frameworks, particularly in developing country contexts. A key challenge is the mismatch between the rapid adoption of AI technologies and limited institutional readiness to govern their use. Even where policies, regulations, or guidelines exist, there are often insufficient operational frameworks, tools, and technical capacity to implement them effectively, resulting in underutilization or inconsistent application. This is further compounded by the fluid and rapidly evolving nature of AI technologies, which outpaces traditional regulatory approaches and complicates oversight. In the education sector, the widespread use of generative AI in research and academic work presents both opportunities and risks. While AI can enhance learning and innovation, the absence of clear, operational guidance—such as disclosure standards, verification mechanisms, and assessment redesign—creates challenges around academic integrity, reliability of outputs, and equitable access. Verification failures, including difficulties in distinguishing human-generated from AI-assisted content, further complicate governance responses. From a financial integrity perspective, emerging risks include AI-enabled fraud, synthetic identities, and the amplification of illicit financial flows, particularly in contexts where AML/CFT systems are still developing. At the same time, AI offers opportunities to strengthen detection, risk analysis, and enforcement, provided governance frameworks are effectively operationalized. Opportunities lie in leveraging AI to enhance institutional effectiveness, improve data-driven decision-making, and expand access to services, including in underserved areas. Realizing these benefits, however, requires practical implementation mechanisms, sustained capacity-building, and adaptive governance frameworks that can respond to evolving risks. Addressing these gaps is essential to ensure AI supports institutional integrity, public trust, and inclusive, sustainable development.

The AI Dialogue can play a pivotal role in advancing international cooperation by serving as a convening and coordination platform that bridges global principles with practical implementation. First, it can facilitate alignment of governance approaches by promoting interoperable principles that are adaptable to different national contexts, while minimizing fragmentation. This is particularly important for developing countries, where clarity and coherence are essential for effective adoption. Second, the Dialogue can strengthen knowledge-sharing and peer learning, building on existing global and regional networks such as the G20, Financial Action Task Force and its regional-style bodies, as well as asset recovery and financial integrity networks. Leveraging these established platforms can support cross-border cooperation, standard-setting, and practical exchange of tools and experiences, particularly in managing emerging risks such as AI-enabled financial crime. Third, it can connect and amplify sector-specific and thematic dialogue platforms, including initiatives focused on AI and workforce readiness, as well as those advancing AI for women and inclusive participation. Integrating these perspectives will ensure that AI governance frameworks are responsive to real-world applications and social priorities. Fourth, the Dialogue can catalyze targeted technical assistance and capacity-building, including South–South and triangular cooperation, enabling countries to translate policies into operational frameworks and strengthen institutional capacity. Finally, it can contribute to a sustained global cooperation architecture, linking diverse initiatives into a coherent ecosystem with mechanisms for coordination, follow-up, and accountability. In this way, the Dialogue can move beyond normative discussions to support practical, inclusive, and coordinated global action on AI governance.

The AI Dialogue should build upon and connect with a range of existing initiatives, regional platforms, and partnership mechanisms that are already advancing AI governance and capacity-building. At the national and regional level, several countries have begun establishing AI hubs and digital innovation centers that provide infrastructure for skills development, experimentation, and access. For example, Jitume Initiative in Kenya has established digital hubs across all 47 counties, offering an important foundation for last-mile access, training, and inclusive participation in the digital economy. Such models provide scalable infrastructure that can be leveraged for responsible AI adoption and governance. The Dialogue should also connect with peer-to-peer technical assistance and regional cooperation mechanisms, where countries are already sharing expertise, tools, and lessons learned. This includes collaboration through multilateral institutions, regional bodies, and practitioner networks, which support capacity-building in areas such as governance, financial integrity, and digital transformation. In addition, existing global and regional initiatives led by organizations such as United Nations Educational, Scientific and Cultural Organization, World Bank Group, and the African Union provide important normative frameworks, policy guidance, and technical support that can be further aligned and operationalized. The added value of the AI Dialogue lies in its ability to connect, harmonize, and scale these efforts, reducing fragmentation and enhancing coherence. It can serve as a platform to link infrastructure with governance, policy with implementation, and global standards with local realities, while promoting inclusive access, coordinated technical assistance, and sustained collaboration. Ultimately, the Dialogue can amplify existing efforts by providing a coherent, action-oriented ecosystem for global AI governance.

Inclusive and effective participation in the AI Dialogue requires a multi-stakeholder, structured, and action-oriented approach. Governments and regulatory bodies can contribute by sharing policy experiences, supervisory practices, and implementation challenges, particularly in operationalizing AI governance across sectors. Academia and research institutions can provide evidence-based insights, innovation pathways, and guidance on responsible AI use, including in education systems. The private sector can offer technical expertise, scalable solutions, and responsible innovation practices. Civil society organizations play a critical role in ensuring that human rights, inclusion, transparency, and accountability considerations are embedded in governance frameworks, while also amplifying community-level perspectives and impacts. To maximize impact, the Dialogue should be structured around thematic working tracks (e.g., governance, capacity-building, inclusion, financial integrity), supported by multi-stakeholder working groups tasked with producing actionable outputs such as policy toolkits, regulatory guidance, and implementation frameworks. A hybrid format combining high-level plenaries with smaller, technical sessions would enable both strategic alignment and practical problem-solving. Dedicated sessions for regulators, civil society, and sector practitioners can ensure deeper engagement on oversight, accountability, and real-world application. Regional consultations and peer-to-peer exchanges should be embedded to ensure context-specific perspectives, particularly from developing countries. Importantly, the Dialogue should incorporate implementation and follow-up mechanisms, including communities of practice, monitoring frameworks, and sustained collaboration platforms, ensuring that stakeholder contributions translate into measurable and lasting outcomes.

First, stakeholders from developing countries, particularly those working at the operational level in government institutions, regulatory bodies, and oversight agencies, are often insufficiently represented. Their practical experience in implementing governance frameworks is essential to ensuring that global approaches are realistic, context-responsive, and implementable. Second, women, youth, and marginalized populations, including those in rural, remote, and transitional contexts, continue to face barriers to participation. Without their inclusion, AI governance risks reinforcing existing inequalities in access, representation, and outcomes. Third, educators, students, and academic institutions who are at the forefront of AI adoption are not consistently engaged in governance discussions, despite facing immediate challenges related to responsible AI use in research, learning, and assessment. Fourth, practitioners in anti-corruption, financial integrity, and public sector accountability, including regulators and civil society organizations, bring critical insights on emerging risks such as AI-enabled fraud, governance vulnerabilities, and accountability gaps, yet remain underrepresented in AI policy forums. To address these gaps, the Dialogue should promote targeted inclusion mechanisms, including regional consultations, dedicated participation tracks, and financial and technical support for underrepresented groups. Leveraging local institutions, civil society networks, and existing digital infrastructure (such as community hubs) can further broaden access and participation. Ensuring inclusive representation will strengthen the legitimacy, relevance, and effectiveness of global AI governance efforts.

Innovative engagement formats will be essential to ensure that the AI Dialogue is interactive, inclusive, and solution-oriented. First, problem-solving labs or "implementation clinics" could bring together governments, regulatory bodies, civil society, academia, and the private sector to address real-world challenges, such as developing frameworks for responsible AI use in education or managing AI-enabled financial risks. These sessions should produce practical, context-specific outputs. Second, peer learning exchanges and country-led showcases can facilitate the sharing of experiences, lessons learned, and scalable models, including initiatives that expand access and capacity at the local level. Third, multi-stakeholder simulation exercises can be used to explore complex governance scenarios—such as responding to AI-related risks or regulatory gaps—helping participants understand trade-offs, coordination challenges, and oversight requirements. Fourth, hybrid and digital participation platforms should be integrated to enable broader and more inclusive engagement, particularly for stakeholders in developing countries and underserved areas. Fifth, thematic roundtables and cross-sector dialogues—including on AI and education, AI and financial integrity, and AI for inclusion—can ensure focused and relevant discussions. Finally, establishing ongoing communities of practice and follow-up working groups, with sustained engagement from regulators, civil society, and sector practitioners, will be critical to maintaining momentum and translating dialogue into measurable action and impact.

Several emerging policies, practices, and platforms provide practical insights into advancing effective and inclusive AI governance. First, risk-based governance frameworks as reflected in initiatives such as the European Union's AI regulatory approach offer a structured model for categorizing AI systems based on their potential impact, enabling proportionate oversight and safeguards. Such approaches can be adapted to different country contexts, particularly when complemented by implementation guidance and capacity-building. Second, national and regional capacity-building platforms, including digital and AI hubs, provide scalable models for strengthening skills and access. For example, Jitume Initiative in Kenya has established community-based hubs across all 47 counties, offering infrastructure for training, innovation, and inclusion. These platforms demonstrate how existing infrastructure can be leveraged to support responsible AI adoption at scale, particularly in underserved areas. Third, multi-stakeholder governance approaches that integrate governments, regulatory bodies, private sector actors, academia, and civil society have proven effective in embedding accountability and inclusivity. This includes the development of guidelines for responsible AI use in education, such as disclosure standards and assessment redesign, which help balance innovation with integrity. Fourth, peer-to-peer technical assistance and regional cooperation mechanisms including those supporting governance, financial integrity, and digital transformation enable countries to share practical tools, lessons learned, and operational models. These approaches are particularly valuable in addressing implementation gaps and ensuring context-relevant solutions. Finally, integration of AI governance into broader accountability frameworks, including anti-corruption and AML/CFT systems, offers a practical pathway to manage emerging risks such as AI-enabled fraud and illicit financial flows. Together, these approaches highlight the importance of linking policy with implementation, infrastructure with capacity-building, and innovation with accountability. If you want, I can now do a final polish of all answers together so your submission reads like a high-level policy brief.