Institute Project Management

In my view, the first Global Dialogue on AI Governance would be successful if it delivers a small set of clear, actionable outcomes rather than broad, non-binding statements. First, establishing a shared baseline of principlesparticularly around transparency, accountability, safety, and inclusivitywould be essential. These principles should be practical enough to guide policy and industry behavior across diverse national contexts. Second, agreement on priority risk areas and corresponding guardrails would mark real progress. This could include commitments on managing high-risk AI systems, protecting data rights, and mitigating misinformation, alongside mechanisms for ongoing review as technologies evolve. Third, creating a pathway for coordination between governments, private sector actors, and civil society is critical. A successful dialogue should result in a structured, multi-stakeholder platform or working groups tasked with advancing specific issues, rather than ending at discussion. Fourth, meaningful inclusion of perspectives from the Global South would be a key indicator of success. AI governance must reflect varied economic realities and development priorities, ensuring that emerging frameworks do not reinforce existing inequalities. Finally, defining measurable next stepssuch as timelines for follow-up meetings, pilot collaborations, or draft policy frameworks would demonstrate momentum and accountability. In short, success would be reflected not just in consensus, but in the creation of practical tools, inclusive processes, and sustained collaboration mechanisms.

AI capacity-building;

AI capacity-building is equally critical to ensure that countries especially in the Global South can meaningfully participate in, shape, and benefit from AI ecosystems. This includes technical skills, institutional readiness, and access to infrastructure. Without this, governance risks becoming exclusionary and reinforcing global disparities.

First, compute and infrastructure inequality is becoming a defining factor in AI development and governance. Access to high-performance computing, cloud infrastructure, and large-scale datasets is highly concentrated, which risks entrenching global power imbalances and limiting meaningful participation from lower-income countries. Second, data governance and data sovereignty merit sharper focus. Questions around who owns, controls, and benefits from dataespecially data originating in the Global South are not fully captured. This includes concerns about extractive data practices and the need for fair value-sharing mechanisms. Third, the environmental and energy impact of AI systems is an increasingly urgent issue. Training and deploying large-scale models requires significant energy and water resources, raising sustainability concerns that intersect with climate goals and resource constraints in vulnerable regions. Fourth, labor market disruption and the future of work should be more explicitly addressed. Beyond job displacement, there are implications for job quality, informal work, and the emergence of new forms of digital labor that may lack adequate protections. Finally, geopolitical fragmentation and regulatory divergence pose a risk to coherent global governance. Competing standards and policy approaches could create barriers to collaboration, innovation, and equitable access.

Governance gaps across safe and trustworthy AI, capacity-building, human rights, and accountability are already shaping both risks and opportunities in Sub-Saharan African context. A key challenge is limited institutional and technical capacity to evaluate, procure, and oversee AI systems. This creates dependence on external vendors, often without sufficient leverage to enforce standards on safety, transparency, or data protection. As a result, governments and organizations may adopt systems that are poorly adapted to local contexts or that embed bias and exclusion. Human rights risks are also significant. Weak or evolving data protection frameworks increase exposure to misuse of personal data, while AI-driven decision-making in sectors like finance, health, or public services can reinforce existing inequalities if not carefully governed. Limited transparency further constrains the ability of individuals to seek redress. At the same time, these gaps present opportunities. There is potential to "leapfrog" by adopting fit-for-purpose governance frameworks that integrate global best practices while reflecting local realities. Early investment in AI capacity-building across public institutions, academia, and the private sector can position the country and region to participate more actively in shaping AI ecosystems. Additionally, establishing clear accountability and oversight mechanisms can build public trust and attract responsible investment. There is growing opportunity to align AI deployment with development priorities, such as improving service delivery, expanding financial inclusion, and strengthening agricultural systems. In this context, targeted international cooperation and knowledge-sharing will be critical to bridge governance gaps while ensuring that AI contributes to inclusive and sustainable development.

First, it can serve as a coordination platform that aligns fragmented global initiatives. By mapping existing standards, frameworks, and regulatory efforts, the Dialogue can reduce duplication and help identify areas where convergence is both feasible and necessary. Second, it can facilitate practical collaboration through issue-specific working groups. These groups could focus on areas such as risk classification, auditing standards, or data governance, and produce interoperable tools, model policies, and voluntary codes of practice that countries can adapt. Third, the Dialogue can advance inclusive participation by ensuring that developing countries are not only represented but actively shaping outcomes. This includes supporting capacity-building partnerships, technical assistance, and knowledge exchange to enable more equitable engagement. Fourth, it can act as a bridge between stakeholders governments, private sector, academia, and civil society helping translate technical developments into policy-relevant insights while ensuring that governance approaches remain grounded in real-world impacts. Fifth, the Dialogue can promote accountability and continuity by establishing clear follow-up mechanisms, including timelines, reporting processes, and periodic reviews of progress on agreed priorities. Finally, it can help build trust and confidence among countries by encouraging transparency, sharing best practices, and fostering a common understanding of risks and opportunities.

Key among these is the work of the OECD, particularly its AI Principles and policy observatory, which offer widely recognized guidance on trustworthy AI. Similarly, UNESCO has advanced a global normative framework through its Recommendation on the Ethics of AI, with broad geographic representation. The Global Partnership on AI brings together governments and experts to advance applied research and best practices, while the G20 and G7 have promoted high-level policy alignment among major economies. In parallel, technical standard-setting bodies such as the International Organization for Standardization and the IEEE are developing detailed standards on AI systems, risk management, and governance. The added value of the AI Dialogue lies in its ability to bridge these fragmented efforts within a more inclusive, UN-led platform. Unlike smaller multilateral groupings, it can ensure meaningful participation from developing countries and underrepresented regions. It can also provide a neutral space for convergence, helping translate principles and technical standards into practical, interoperable governance approaches. By linking policy discussions with implementation—through pilot initiatives, capacity-building, and knowledge-sharing the Dialogue can move from alignment to action. Finally, it can strengthen system-wide coordination, ensuring that ethical frameworks, technical standards, and national policies evolve in a more coherent and mutually reinforcing way.