Nile University

Success means the Dialogue produces actionable governance architecture, not just declarations. Three outcomes matter most. First, a shared framework that acknowledges asymmetric capacity realities. Most governance conversations are shaped by actors with mature AI infrastructure. A successful Dialogue centers developing economies as co-architects of the rules, not recipients of norms written elsewhere. Second, concrete capacity-building commitments tied to measurable milestones. Universities and innovation hubs in the Global South are natural nodes for responsible AI diffusion but remain chronically under-resourced. The Dialogue should produce binding pledges on technology transfer, open AI models, and institutional support. Third, a legitimate multi-stakeholder process that goes beyond governments and large tech firms. Academia, civil society, and emerging-market entrepreneurs need a structural seat, not a consultation footnote. A framework built without these voices will overlook a huge potential enabled by AI.

Safe, secure and trustworthy AI;AI capacity-building;Social, economic, ethical, cultural, linguistic and technical implications of AI;Interoperability of governance approaches;

My priorities reflect the reality of building AI-enabled ventures at an Egyptian research university. AI capacity-building is the foundation; without it, governance norms stay theoretical. The social, cultural, and linguistic dimensions matter because AI systems trained on Western data systematically disadvantage underrepresented populations. Safe and trustworthy AI is non-negotiable for adoption and harm prevention. Interoperability matters because fragmented regulatory regimes create compliance burdens that hurt smaller economies most.

A critical gap is AI governance for deep tech commercialization and research spinoffs in emerging markets. Regulatory environments are designed around large incumbents, creating compliance burdens that price out early-stage startups before they reach scale. University-based ventures operating at the intersection of research and market face a double disadvantage: limited resources and governance frameworks that were never built with them in mind. The Dialogue should produce proportionate, tiered governance instruments that distinguish between frontier AI developers and research-stage spinoffs building applied solutions for local challenges. Without this distinction, well-intentioned regulation becomes a barrier to entry that concentrates AI development further in the hands of a few dominant players.

In Egypt and across the MENA region, governance gaps in AI are felt most acutely at the institutional level. Universities and research centers are accelerating AI-related work but operating without clear national frameworks governing data use, model deployment, or liability. This creates a chilling effect on innovation precisely where it needs to grow. The capacity gap is the most immediate challenge. There is no shortage of talented researchers and entrepreneurs, but the infrastructure to translate AI research into governed, market-ready applications is thin. Accelerators and university venture builders are filling this gap by default, without the policy scaffolding or funding instruments that would make their work sustainable and scalable. The cultural and linguistic gap compounds this. AI tools dominating the market are built on datasets that underrepresent Arabic language, Egyptian context, and African economic realities. Startups building for local markets face a structural disadvantage from day one, competing with models that have orders-of-magnitude more training data in dominant languages. The risk is that without deliberate governance intervention, AI adoption in countries like Egypt will remain consumption-led rather than production-led.

For countries like Egypt, the Dialogue must create structured pathways for emerging economies to bring their governance realities, local contexts, and development priorities into the global framework. In other words, the dialogue's most important role is ensuring developing economies help write the rules, not just receive them. Examples: the Dialogue can advance cooperation in three ways. First, by establishing a common baseline for AI governance interoperability, so that countries building national frameworks are not starting from scratch or inadvertently creating incompatible systems. Second, by institutionalizing knowledge exchange between countries at different stages of AI development, moving beyond donor-recipient dynamics toward genuine peer learning. Third, by creating accountability mechanisms that are light enough to be adopted broadly but meaningful enough to drive behavior change.

There is strong groundwork to build on: the OECD AI Principles, UNESCO's Recommendation on AI Ethics, GPAI, the UN AI Advisory Body, and the G7 Hiroshima AI Process. At the same time, regional efforts such as the EU AI Act and the African Union's emerging AI strategy are shaping localized governance approaches. What is missing is alignment across these layers, especially when it comes to implementation.

Governments should contribute by sharing regulatory priorities, testing policy approaches, and opening pathways for cross-border alignment. Industry players, especially frontier AI companies, should bring technical insights, safety practices, and real deployment challenges. Academia should anchor the Dialogue in evidence, evaluation methodologies, and long-term societal implications. Startups and venture builders should provide a ground-level perspective on innovation dynamics, constraints, and opportunities. Civil society should ensure accountability, ethics, and societal relevance. A second layer should focus on regional nodes, particularly in emerging ecosystems, to ensure local realities are reflected in global discussions. Finally, there should be a strong feedback loop between discussion and implementation. Outputs from working groups should be tested through pilots and sandboxes, with lessons fed back into the Dialogue. This structure ensures that participation translates into tangible outcomes rather than remaining at the level of principles.

Today, much of the global AI governance agenda is shaped by advanced economies, large technology companies, and established research institutions. While their role is critical, this creates a structural imbalance where countries that are rapidly adopting AI, but lack strong representation, are positioned as rule-takers rather than contributors. Beyond geography, there is also a gap in representation from startups, venture builders, SMEs, and applied researchers. These actors are closest to how AI is actually being deployed in sectors such as agriculture, healthcare, manufacturing, and education. Their insights are essential to ensure that governance frameworks are practical and innovation-enabling. To address this, inclusion needs to be designed intentionally. This can be done through regional representation mechanisms, targeted fellowships, and funded participation for stakeholders from underrepresented regions. Establishing regional Dialogue nodes or hubs can also help bring local perspectives into global processes.

The Dialogue can adopt working sessions with clear deliverables. Instead of open discussions, each session should aim to produce something tangible such as a draft guideline, a policy comparison, or a pilot concept. Participants should know in advance what they are expected to contribute and what the outcome will be. Second, the Dialogue should include problem focused tracks. For example, a track on AI in healthcare or agriculture where participants work through a real use case, identify governance challenges, and propose practical solutions. This makes discussions grounded rather than abstract.

In my view, the most effective models are those that integrate regulation, experimentation, and ecosystem development into a coherent system. That is where AI governance becomes both practical and scalable. A strong example is the EU AI Act, particularly its risk-based approach. What makes it effective is not just the classification of AI systems, but the clarity it provides to developers and deployers on compliance pathways. This creates predictability while still allowing room for innovation. Another important practice is the use of regulatory sandboxes, as seen in countries like the UK and Singapore. These allow companies and regulators to test AI applications in controlled environments, reducing uncertainty and accelerating responsible deployment. This is especially relevant for emerging markets where regulatory frameworks are still evolving.