Red Dart Consulting

A successful first Global Dialogue on AI Governance should deliver outcomes that move beyond convening toward coordinated global action. First, the Dialogue should produce a clear set of actionable "decision signals", identifying key policy trade offs, areas of convergence, and priority directions for governments and institutions. This would ensure that discussions translate into usable guidance, particularly in contexts where decision-makers must act under uncertainty. Second, success would include the establishment of a shared global baseline for AI governance principles, grounded in existing frameworks such as the OECD AI Principles and UN processes, to strengthen interoperability and reduce fragmentation across jurisdictions. Third, the Dialogue should result in concrete mechanisms for implementation and continuity, including: 1. a global observatory to track AI governance developments and outcomes, 2. regional platforms or "decision labs" to test approaches in diverse contexts, particularly in developing regions, and 3. a commitment to ongoing reporting on implementation progress. Fourth, meaningful success requires demonstrable progress on capacity-building and inclusion, ensuring that developing countries are not only represented in dialogue, but enabled to participate in shaping and applying governance frameworks. Finally, the Dialogue should reinforce a shared commitment to human rights, transparency, accountability, and human oversight, ensuring that AI governance advances innovation while safeguarding human dignity and agency. In sum, success will be defined not only by the quality of discussion, but by the Dialogue's ability to function as a catalyst for sustained, coordinated, and equitable global AI governance.

• Interoperability of governance approaches
• Transparency, accountability, and human oversight
• Protection and promotion of human rights
• AI capacity-building

he selected priorities reflect the need to balance global coordination with local capability and accountability. Interoperability of governance approaches is critical to avoid fragmentation and regulatory misalignment across jurisdictions. As AI systems operate across borders, coherence between national and regional frameworks will be essential to ensure both effectiveness and enforceability. Transparency, accountability, and human oversight are foundational to building trust in AI systems. These principles ensure that decisions made by or with AI remain explainable, contestable, and aligned with public interest, particularly in high-impact sectors. AI capacity-building is essential to address structural inequalities between and within countries. Without targeted investment in skills, infrastructure, and institutional capability, many regions risk being excluded from both the development and governance of AI systems. Capacity-building must therefore be treated not as a parallel effort, but as a core pillar of governance. Protection and promotion of human rights provides the normative foundation for all AI governance efforts. Ensuring that AI systems respect human dignity, privacy, and agency is particularly important in contexts where regulatory systems are still evolving and safeguards may be unevenly applied. Together, these priorities support a governance approach that is coherent, implementable, and inclusive, enabling global alignment while ensuring that all regions can meaningfully participate in and benefit from AI development and oversight.

Yes, while the listed themes are comprehensive, there are several cross-cutting and emerging issues that warrant more explicit attention. First, the rise of agentic and autonomous AI systems introduces new governance challenges beyond traditional model-based frameworks. As AI systems increasingly act with delegated authority, there is a need to clarify accountability, liability, and human oversight mechanisms in contexts where decision-making is partially or fully automated. Second, there is a growing gap between governance design and implementation capability. Many regions, particularly in the Global South, face constraints in institutional capacity, technical expertise, and infrastructure. This creates a risk where governance frameworks exist in principle but are difficult to operationalize in practice. Addressing this requires greater focus on implementation pathways, institutional readiness, and context-sensitive governance models. Third, the importance of data governance as a foundational layer of AI governance could be further emphasized. Issues such as data access, ownership, quality, and cross-border data flows are central to both innovation and risk mitigation, yet are often treated as adjacent rather than integral to AI governance frameworks. Fourth, there is an emerging need to address economic concentration and market dynamics in AI ecosystems, including the implications of compute access, platform dominance, and unequal participation in AI value chains. These factors have direct consequences for inclusion, competition, and long-term global equity. Finally, stronger emphasis could be placed on measuring real-world impact, including mechanisms to assess whether AI systems are delivering social and economic value while upholding human rights and minimizing harm. Addressing these cross-cutting issues will be critical to ensuring that AI governance remains adaptive, implementable, and responsive to rapidly evolving technological and societal dynamics.

In the South African and broader African context, AI governance gaps are already shaping both risk exposure and opportunity pathways, particularly across public sector systems and financial services. A key challenge is the gap between policy ambition and implementation capability. While there is growing alignment with global principles around transparency, accountability, and human rights, institutional capacity to operationalize these principles remains uneven. This includes constraints in technical expertise, regulatory tooling, and the ability to effectively audit or oversee AI systems in practice. A second challenge relates to interoperability and regulatory fragmentation. As global AI governance frameworks evolve at pace, there is a risk that emerging markets must navigate multiple, and sometimes misaligned, standards without sufficient influence in shaping them. This creates complexity for both regulators and organizations operating across borders. Additionally, data governance and infrastructure limitations continue to affect the ability to develop and deploy AI systems responsibly. Issues of data quality, access, and cross-border data flows are central to both innovation and risk mitigation, yet remain unevenly addressed. At the same time, these gaps present significant opportunities. There is an opportunity for regions such as Africa to leapfrog into context-driven governance models, embedding human rights, inclusivity, and accountability from the outset, rather than retrofitting them. There is also strong potential to position the public sector as a leader in responsible AI adoption, particularly in areas such as financial systems, service delivery, and digital identity. Overall, addressing these governance gaps will require a dual focus on global alignment and local capability-building, ensuring that AI governance is both coherent across borders and implementable within diverse institutional contexts.

The AI Dialogue can play a critical role as a neutral coordination platform that translates fragmented global efforts into coherent and actionable cooperation. Currently, AI governance is being shaped across multiple forums, including multilateral organizations, regional bodies, and industry-led initiatives, often operating in parallel with limited alignment. The Dialogue has an opportunity to act as a bridging mechanism, identifying areas of convergence and facilitating coordination without duplicating existing efforts. One key role is to support the development of interoperable governance approaches, enabling different jurisdictions to align on core principles while maintaining flexibility for local context. This is particularly important for ensuring that AI systems operating across borders are subject to consistent expectations around transparency, accountability, and human oversight. The Dialogue can also strengthen cooperation by enabling structured knowledge exchange, including the sharing of policy experiences, regulatory approaches, and implementation lessons across countries at different levels of maturity. Importantly, it can serve as a platform to elevate the perspectives of developing regions, ensuring that international cooperation reflects diverse realities and capacity levels, rather than reinforcing existing imbalances in global governance. Finally, the Dialogue can contribute to international cooperation by supporting the development of shared reference points, such as common terminology, risk frameworks, and measurement approaches, which are essential for meaningful collaboration. In this way, the AI Dialogue can evolve from a convening space into a functional coordination layer, enabling more aligned, inclusive, and effective global AI governance.

The AI Dialogue should build upon and connect with existing global and regional initiatives, including frameworks such as the OECD AI Principles, UNESCO's Recommendation on the Ethics of AI, the work of the UN system, and emerging regional strategies such as those being developed across Africa and other regions. It should also engage with multi-stakeholder platforms and industry-led efforts focused on AI safety, standards, and responsible development, as well as technical bodies working on interoperability and governance tooling. While these initiatives provide a strong foundation, they often operate in silos, with varying levels of adoption, implementation, and coordination. The added value of the AI Dialogue lies in its ability to act as a connecting and synthesizing platform, rather than a duplicative one. Specifically, the Dialogue can: 1. Map and align existing initiatives, identifying overlaps, gaps, and opportunities for collaboration 2. Support interoperability between governance frameworks, reducing fragmentation across jurisdictions 3. Facilitate cross-regional engagement, ensuring that insights and practices are shared more equitably 4. Elevate implementation-focused insights, including what works in practice across different institutional contexts In addition, the Dialogue can contribute by promoting inclusive participation, particularly from regions that are underrepresented in existing global governance processes, and by supporting mechanisms that translate global principles into locally actionable approaches. Ultimately, its value will lie not in creating new frameworks, but in strengthening coherence, coordination, and practical application across the global AI governance ecosystem.

Different stakeholders can contribute most effectively when the Dialogue is structured to move beyond representation toward meaningful participation and co-creation. Governments can contribute by sharing policy approaches, regulatory experiences, and implementation challenges, while industry and technical actors can provide insight into system design, deployment realities, and emerging risks. Civil society and academia play a critical role in ensuring that governance remains grounded in human rights, ethics, and societal impact. To enable these contributions, the Dialogue would benefit from formats that support structured interaction rather than sequential interventions. This could include: 1. Thematic working groups focused on specific governance challenges 2. Cross-sector roundtables to explore trade-offs and align perspectives 3. Case-based discussions grounded in real-world scenarios In addition, introducing mechanisms such as facilitated breakout sessions and moderated synthesis segments would allow diverse inputs to be consolidated into actionable insights. Digital participation tools could also be leveraged to gather real-time input and feedback, ensuring broader inclusion beyond those speaking in plenary sessions. Ultimately, the structure of the Dialogue should enable stakeholders not only to share perspectives, but to jointly shape outcomes, fostering a sense of shared ownership and accountability in advancing global AI governance.

Despite growing global engagement, several voices and perspectives remain underrepresented in AI governance discussions. These include stakeholders from developing regions, particularly across Africa and parts of the Global South, as well as public sector practitioners, small and medium-sized enterprises, and communities directly impacted by AI systems but not traditionally included in policy processes. There is also a need to elevate perspectives from non-technical disciplines, including social sciences, ethics, and community-based organizations, which provide critical insight into the societal implications of AI. Inclusion should go beyond participation in dialogue and extend to influence over outcomes. This requires: 1. Targeted outreach and support mechanisms, including funding and logistical support to enable participation 2. Regional consultation processes that feed directly into global discussions 3. Capacity-building initiatives to ensure stakeholders can engage meaningfully with technical and policy content Additionally, providing opportunities for stakeholders to contribute through written inputs, case studies, and local evidence can help ensure that diverse experiences are reflected in the Dialogue. Language accessibility and culturally relevant framing are also important to ensure that participation is not limited by linguistic or contextual barriers. Ultimately, inclusive AI governance requires shifting from a model of representation to one of equitable participation and shared influence, ensuring that global frameworks reflect the realities of all regions and communities.

To foster more meaningful and dynamic engagement, the Dialogue could incorporate innovative formats that move beyond traditional plenary discussions toward interactive and decision-oriented participation. One approach is the use of scenario-based simulations, where participants engage with real-world AI governance challenges and explore different policy responses. This enables stakeholders to better understand trade-offs and implications in practice. Another effective format would be "decision labs" or facilitated workshops, where small, diverse groups work collaboratively to develop recommendations on specific governance issues, which are then presented back to the broader Dialogue. Introducing live polling and digital feedback tools can also enhance participation by capturing a wider range of perspectives in real time, including from participants who may not have the opportunity to speak. Additionally, multi-stakeholder roundtables structured around key policy tensions, such as innovation versus regulation, or openness versus control, can help surface areas of alignment and divergence more clearly. To strengthen continuity, the Dialogue could also incorporate ongoing virtual engagement tracks, allowing stakeholders to continue discussions and collaboration between formal sessions. These formats would support a shift from passive engagement to active co-creation, ensuring that the Dialogue produces not only insights, but also practical, collectively developed outcomes that can inform global AI governance efforts.

Several existing policies and approaches offer valuable foundations for effective AI governance, particularly where they combine principle-based frameworks with practical implementation mechanisms. At the global level, frameworks such as the OECD AI Principles and UNESCO's Recommendation on the Ethics of AI provide strong normative guidance, emphasizing transparency, accountability, human rights, and human oversight. Their strength lies in their broad adoption and ability to support interoperability across jurisdictions. From a regulatory perspective, the European Union's AI Act represents a structured, risk-based approach, offering a practical model for categorizing AI systems and aligning regulatory requirements with levels of risk. This provides a useful reference for translating high-level principles into enforceable measures. In practice, effective governance is increasingly supported by organizational-level mechanisms, including: 1. AI risk and impact assessments prior to deployment 2. Algorithmic auditing and monitoring processes 3. Clear documentation and transparency reporting 4. Human-in-the-loop oversight mechanisms, particularly in high-impact use cases There is also growing value in multi-stakeholder platforms and regulatory sandboxes, which allow governments, industry, and civil society to collaboratively test and refine governance approaches in controlled environments. Importantly, in developing contexts, emerging practices that focus on capacity-building, public sector leadership, and context-sensitive implementation are critical. These approaches ensure that governance frameworks are not only adopted, but effectively operationalized within local institutional realities. Overall, the most effective approaches are those that integrate global principles, risk-based regulation, and practical implementation tools, enabling AI governance systems that are both robust and adaptable across different contexts.