CAPAI TECH

First, it should establish a shared baseline of principles not just high-level ethics, but actionable commitments around transparency, accountability, data protection, and inclusive access. These principles must reflect both global standards and regional realities, particularly ensuring that emerging economies are not excluded from shaping the rules of AI. Second, success would mean the formation of a multi-stakeholder governance mechanism. This could take the form of a standing taskforce or working groups that bring together governments, private sector leaders, academia, and civil society to co-develop policies and technical standards. Continuity beyond the dialogue is critical. Third, the dialogue should catalyze concrete pilot initiatives. For example, launching cross-border AI projects in areas such as digital identity, healthcare, or public service delivery would demonstrate how governance frameworks can be operationalized in real-world settings. Fourth, it should address the growing gap between developed and developing nations by advancing AI equity and capacity-building commitments. This includes support for sovereign infrastructure, skills development, and access to compute ensuring that countries, particularly in Africa, can participate as creators, not just consumers, of AI.

• AI capacity-building
• Transparency, accountability, and human oversight
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Safe, secure and trustworthy AI

1. Safe, Secure and Trustworthy AI Ensuring AI systems are safe and trustworthy is foundational to building public confidence and enabling government adoption across Africa. From my perspective, this includes not only technical robustness and cybersecurity, but also data sovereignty, resilience of infrastructure, and protection against external dependency. For African nations, trustworthy AI must be locally governed, securely deployed, and aligned with national interests. 2. AI Capacity-Building Capacity-building is one of the most urgent priorities. Many African countries face a significant gap in technical expertise, institutional readiness, and policy implementation capabilities. My work focuses on supporting governments through skills development, institutional frameworks, and leadership-level understanding of AI, ensuring that countries can design, deploy, and regulate AI systems independently and sustainably. 3. Social, Economic, Ethical, Cultural, Linguistic and Technical Implications of AI AI systems must reflect the realities of the societies they serve. In Africa, this includes addressing language diversity, cultural context, economic inclusion, and historical inequalities. Without deliberate intervention, AI risks reinforcing exclusion. My priority is to ensure that AI development is inclusive, context-aware, and aligned with human development goals, particularly for underserved communities. 4. Transparency, Accountability, and Human Oversight Strong governance mechanisms are essential to ensure that AI systems remain accountable and aligned with public interest. This includes clear regulatory frameworks, explainability of AI systems, and human oversight in critical decision-making processes. For governments, this is key to maintaining trust, protecting rights, and ensuring ethical deployment at scale.

Yes while the listed themes are critical, several cross-cutting and emerging issues require more explicit attention. 1. AI Infrastructure & Compute Sovereignty Beyond safety and governance, there is a growing need to address who owns and controls the underlying AI infrastructure including data centers, cloud environments, and compute capacity. Without local or regional control, many countries risk long-term dependency on external providers, limiting their ability to shape their own digital futures. 2. Data Sovereignty & Cross-Border Data Governance AI systems are only as strong as the data they rely on. There is an urgent need for frameworks that ensure data ownership, protection, and equitable sharing, particularly across borders. For regions like Africa, this includes preventing data extraction without local value creation and ensuring that data is used in ways that benefit local populations. 3. Financing & Equitable Access to AI Development A key gap is the financing of AI infrastructure and ecosystems in developing regions. Without innovative funding models and global partnerships, the benefits of AI will remain concentrated in a few countries. Bridging this gap requires alignment between governments, development finance institutions, and private sector actors. 4. AI for Public Sector Transformation There is increasing demand for AI to improve public service delivery, yet many governments lack the systems and frameworks to implement AI effectively. This includes the integration of AI into healthcare, agriculture, education, and social protection systems in a way that is scalable and ethical. 5. Geopolitics of AI AI is rapidly becoming a strategic and geopolitical asset. Emerging global dynamics around technology control, standards, and supply chains will significantly impact developing regions. Ensuring inclusive participation in global AI governance is therefore essential. These issues cut across all thematic areas and are central to ensuring that AI development is equitable, sovereign, and globally balanced.

Governance gaps across the selected thematic areas are already shaping outcomes across Africa's public sector and digital economy. Challenges. First, the absence of harmonised regulatory frameworks for safe, secure, and trustworthy AI creates fragmentation across countries, slowing deployment and increasing compliance uncertainty. Limited standards on cybersecurity, model validation, and data protection expose governments to operational and reputational risk. Second, weak capacity-building at both technical and policy levels constrains adoption. Many institutions lack the skills to procure, deploy, and oversee AI systems, leading to reliance on external vendors and limited local ownership. Third, insufficient attention to the social, economic, and cultural implications of AI risks reinforcing inequality. Underrepresentation of local languages and contexts in datasets can produce biased outcomes, particularly in sectors such as healthcare, agriculture, and financial services. Finally, gaps in transparency, accountability, and human oversight reduce public trust. Limited mechanisms for explainability, auditability, and redress make it difficult for governments to scale AI responsibly. Opportunities. These gaps also present a strategic opportunity. Africa can design fit-for-purpose governance models that integrate policy, infrastructure, and ethics from the outset—rather than retrofitting them later. There is strong potential to build sovereign AI ecosystems, combining local data governance, regional standards, and secure infrastructure. With the right frameworks, governments can shift from reactive to data-driven public service delivery, improving outcomes in health, agriculture, and social protection. Regional coordination can further enable interoperability and scale, while partnerships with global stakeholders can unlock financing and technology transfer. Addressing these governance gaps holistically will be key to ensuring that AI development in Africa is inclusive, trusted, and aligned with long-term development priorities.

The AI Dialogue can serve as a neutral, multilateral bridge between countries at different stages of AI development, helping to translate high-level principles into practical, coordinated action. First, it can support policy harmonisation by facilitating convergence on core governance standardssuch as safety, data protection, accountability, and human oversight while allowing for regional adaptation. This reduces regulatory fragmentation and enables cross-border collaboration. Second, the Dialogue can advance equitable participation by ensuring that developing regions, particularly Africa, have a meaningful voice in shaping global AI norms. This is critical to avoid a governance landscape dominated by a few technology-producing nations and to ensure that global frameworks reflect diverse social, cultural, and economic contexts. Third, it can act as a platform to align financing, technology, and capacity-building efforts. By bringing together governments, development finance institutions, and the private sector, the Dialogue can help structure partnerships that support AI infrastructure, skills development, and public sector adoption in underserved regions. Fourth, the AI Dialogue can promote knowledge-sharing and best practices, enabling countries to learn from each other's regulatory approaches, deployment models, and risk mitigation strategies. This accelerates implementation and reduces duplication of effort. Finally, it can help establish trust and accountability mechanisms at a global level, including shared approaches to auditing AI systems, managing cross-border data flows, and addressing emerging risks. Overall, the AI Dialogue has the potential to move beyond discussion into coordinated global action, ensuring that AI governance is inclusive, interoperable, and aligned with sustainable development objectives.

The AI Dialogue should build on a set of existing global, regional, and implementation-focused initiatives, while adding coordination and execution value. At the global level, frameworks such as the United Nations system's work on AI governance, the OECD AI Principles, and multi-stakeholder platforms like the Global Partnership on Artificial Intelligence provide important normative guidance on trustworthy and human-centric AI. These initiatives establish shared principles, but often require stronger pathways for implementation across diverse national contexts. At the regional level, institutions such as the African Union, including African Union Development Agency – NEPAD, and the Smart Africa Alliance are advancing digital transformation, policy alignment, and capacity-building across African countries. These platforms are critical for translating global principles into regionally relevant strategies. In parallel, emerging platforms such as the Conference of African Presidents on AI (CAPAI) are focused on advancing sovereign AI policy, infrastructure, and governance at a presidential level, creating a bridge between high-level political leadership and practical implementation across the continent. In addition, development finance and infrastructure initiatives—particularly those led by institutions such as the World Bank and the African Development Bank—play a key role in funding digital infrastructure and enabling large-scale deployment. The added value of the AI Dialogue lies in its ability to connect these efforts into a coherent ecosystem. It can: Bridge the gap between principles and execution, linking policy frameworks with funding and infrastructure Enable cross-regional coordination and interoperability Elevate underrepresented regions and leadership platforms such as CAPAI within global governance discussions Facilitate multi-stakeholder partnerships that combine policy, finance, and technology By acting as a convening and coordination mechanism, the AI Dialogue can accelerate the transition from fragmented initiatives to integrated, scalable, and inclusive AI governance systems globally.

Stakeholder Contributions Governments: Lead on policy development, regulatory alignment, and national AI strategies, while ensuring public interest, inclusion, and accountability. Regional and multilateral bodies (e.g., African Union and African Union Development Agency – NEPAD): Coordinate cross-border frameworks, harmonise standards, and support regional implementation. Development finance institutions (e.g., World Bank, African Development Bank): Provide funding mechanisms for AI infrastructure, capacity-building, and public sector deployment. Private sector and technology providers: Deliver scalable solutions, infrastructure, and innovation, while adhering to governance and ethical standards. Academic and research institutions: Contribute evidence-based insights, technical expertise, and evaluation frameworks. Civil society: Ensure inclusivity, human rights protection, and community-level accountability. Emerging platforms such as CAPAI: Bridge political leadership and implementation by convening Heads of State and aligning policy with infrastructure deployment.

1. Developing countries and regional institutions Many African, Latin American, and small island states are not adequately represented in standard-setting processes, despite being major adopters of AI systems. Regional bodies such as the African Union and African Union Development Agency – NEPAD have growing roles but remain under-leveraged globally. Inclusion: Ensure formal representation, co-chairing roles, and dedicated regional tracks within international AI dialogues. 2. Public sector implementers Mid-level government officials responsible for deploying AI in critical sectors are often excluded from high-level discussions. Inclusion: Establish practitioner-led forums and implementation labs to ensure operational realities inform policy design. 3. Local technologists and researchers from the Global South AI development remains concentrated in a small number of countries, limiting diversity in datasets and innovation pathways. Inclusion: Invest in local research ecosystems and prioritise partnerships that build in-country technical capacity. 4. Linguistically and culturally diverse communities Many AI systems do not reflect the linguistic diversity and cultural contexts of large populations, particularly in Africa. Inclusion: Support local language datasets and community-driven AI development. 5. Civil society and affected communities Vulnerable populations are often not meaningfully included in governance processes. Inclusion: Embed participatory governance models and accountability mechanisms. 6. Emerging continental leadership platforms (including CAPAI) Platforms such as the Conference of African Presidents on AI (CAPAI), which operate at the presidential and policy-implementation interface, remain underrepresented in global discussions. These platforms are uniquely positioned to bridge high-level political leadership, infrastructure development, and continental coordination, yet are not systematically integrated into global governance structures. Inclusion: Formal recognition and integration of such platforms into international AI Dialogue frameworks, including participation in agenda-setting, working groups, and implementation tracks.

1. Policy-to-Implementation Labs Structured sessions where governments, technical experts, and financiers co-design real-world use cases (e.g., AI in healthcare or agriculture). These labs should produce concrete outputs such as pilot project frameworks, governance models, and financing pathways. 2. Multi-Stakeholder "Deal Rooms" Closed, outcome-driven sessions that bring together governments, development finance institutions, and technology providers to structure bankable AI projects. These formats can accelerate alignment between policy priorities, infrastructure needs, and funding mechanisms. 3. Regional Co-Creation Tracks Dedicated tracks led by regional institutions such as the African Union Development Agency – NEPAD and the Smart Africa Alliance, ensuring that global discussions are grounded in regional realities and lead to locally implementable solutions. 4. Leadership Roundtables (Presidential / Ministerial Level) High-level, closed-door dialogues that enable decision-makers to align on strategic priorities, commitments, and cross-border collaboration, while linking directly to technical and implementation tracks. 5. Practitioner Exchange Platforms Interactive sessions where public sector implementers share lessons learned, operational challenges, and best practices, helping bridge the gap between policy design and execution. 6. AI Governance Simulation Exercises Scenario-based simulations (e.g., responding to AI system failures or cross-border data issues) that allow stakeholders to test regulatory responses and coordination mechanisms in a practical setting. 7. Continuous Digital Collaboration Platform An online workspace to sustain engagement beyond the Dialogue, enabling working groups to track progress, share resources, and coordinate implementation. These formats would transform the AI Dialogue into a dynamic, solutions-driven platform, delivering tangible outcomes rather than purely theoretical discussions.

Several existing policies, practices, and platforms provide practical foundations for effective AI governance, but there is a growing need for mechanisms that bridge policy, political leadership, and implementation this is where CAPAI plays a distinct role. At the global level, frameworks such as the OECD AI Principles and the UNESCO Recommendation on the Ethics of AI provide strong normative guidance on trustworthy, human-centric AI. Similarly, the European Union AI Act introduces a risk-based regulatory model that balances innovation with safeguards. However, these frameworks often face challenges in practical implementation, particularly in emerging markets. In this context, the Conference of African Presidents on AI (CAPAI) represents an emerging governance and implementation platform designed to operationalise AI policy at scale. CAPAI focuses on three core areas: Presidential-Level Alignment: Convening Heads of State to drive high-level political commitment and coordinated AI strategies across countries. Sovereign AI Infrastructure: Supporting the development of national and regional AI ecosystems, including data governance frameworks, cloud infrastructure, and compute capacity. Policy-to-Execution Bridge: Translating global AI principles into deployable national frameworks, including pilot projects, regulatory models, and institutional structures. CAPAI complements the work of regional bodies such as the African Union and African Union Development Agency - NEPAD by providing a focused platform for execution, while aligning with broader continental strategies. In addition, CAPAI facilitates multi-stakeholder collaboration, linking governments with development finance institutions and technology partners to enable financing and deployment of AI infrastructure and public sector solutions. This approach addresses a critical gap in AI governance: moving from principles and policy frameworks to real-world implementation, ensuring that AI systems are not only ethical and well-governed, but also locally owned, scalable, and aligned with development priorities.