Awoke Technologies

In my view, a successful first Global Dialogue on AI Governance should go beyond principles and deliver practical, inclusive, and implementable outcomes. First, we need a shared global foundation for AI governance—anchored in transparency, accountability, fairness, and data protection—but flexible enough to reflect different national realities. However, what would truly define success is how we address the growing divide between developed and developing nations. Today, the conversation often focuses on access to compute and infrastructure. While important, this is not sufficient. If countries only consume AI built elsewhere, the gap will continue to widen. We need to shift the focus from access to capability and ownership. This means enabling countries to develop, adapt, and deploy AI solutions locally, supported by skills development, open and affordable technologies, and strong knowledge transfer. Countries should not just use AI—they should be able to shape it. A critical part of this is data sovereignty. Nations must have the ability to host, manage, and govern their data within their own trusted environments, including local or regional data centers. This allows them to apply governance and privacy frameworks aligned with their legal and cultural contexts, while also ensuring that the value generated from their data benefits their own economies. Success would also require clear implementation pathways—practical guidance on risk management, auditing, and responsible AI use—along with sustained capacity-building efforts. Ultimately, success means moving from discussion to equitable global action, where AI is not only accessible, but empowering—enabling all countries to build, govern, and benefit from it on their own terms.

• Social, economic, ethical, cultural, linguistic and technical implications of AI
• AI capacity-building
• Protection and promotion of human rights
• Open-source software, open data and open AI models

In my view, the success of the first Global Dialogue on AI Governance will depend on how effectively it translates key priorities into practical and inclusive outcomes. Advancing safe, secure, and trustworthy AI, supported by transparency, accountability, and human oversight, is essential to building trust. However, governance should not only focus on managing risks-it should also create the conditions for broader and more equitable participation in AI development and use. A central measure of success will be meaningful progress in AI capacity-building. This must go beyond access to infrastructure and instead enable countries to develop, adapt, and apply AI within their own contexts. Leveraging open-source software, open data, and open AI models can play a critical role in lowering barriers to entry, supporting innovation, and reducing dependency on a limited number of global providers. At the same time, it is important to fully consider the social, economic, ethical, cultural, linguistic, and technical implications of AI. AI systems should reflect the diversity of the communities they serve, ensuring that different languages, cultures, and societal needs are represented. Without this, AI risks reinforcing existing inequalities rather than addressing them. In this context, strengthening local capacity to manage and govern data responsibly becomes equally important. Enabling countries to operate within trusted data environments allows them to align governance and privacy approaches with their own frameworks, while supporting sustainable and locally relevant AI ecosystems. Ultimately, success will be defined by whether these efforts come together to ensure that AI is not only safe and accountable, but also accessible, inclusive, and supportive of local development across all regions.

While the listed themes cover many critical areas, there are a few cross-cutting issues that deserve more explicit attention. One key area is data sovereignty and data governance infrastructure. While elements of this are implied under human rights and accountability, there is a growing need to explicitly address how countries can store, manage, and govern their data within trusted environments, including local or regional data centers. This is essential not only for privacy and security, but also for ensuring that the economic value generated from data remains within local ecosystems. Another important issue is the unequal distribution of compute and digital infrastructure. AI capacity-building is highlighted, but without addressing structural disparities in access to compute, connectivity, and energy resources, many countries will remain dependent rather than empowered. This raises questions of long-term sustainability and equitable participation in AI development. A further emerging area is operationalizing governance. While interoperability of governance approaches is important, there is a gap in translating principles into practical, implementable mechanisms-such as auditing frameworks, risk classification standards, and enforcement models that can be adopted across different contexts. Additionally, there is a need to consider the concentration of AI capabilities among a small number of actors. This has implications for competition, innovation, and global equity, and intersects with the role of open-source and open models in democratizing access. Finally, the environmental impact of AI systems-including energy consumption and resource use-is an emerging concern that cuts across technical and policy discussions. Addressing these cross-cutting issues would strengthen the dialogue by ensuring that AI governance is not only principled, but also practical, equitable, and sustainable in the long term.

In my region and sector, governance gaps in AI are creating both significant challenges and important opportunities, particularly in how AI is adopted, controlled, and scaled. One of the most pressing challenges is the gap between access and true capability. While there is growing exposure to AI tools, many organizations still lack the capacity to develop, adapt, and govern AI systems locally. This creates a dependency on external platforms, limiting control over data, models, and long-term innovation. A related challenge is data governance and data sovereignty. In many cases, data is not fully managed within trusted environments, and governance frameworks are either fragmented or still evolving. This raises concerns around privacy, security, and the ability to ensure that the value generated from data benefits local institutions and economies. There are also gaps in operationalizing AI governance. While there is increasing awareness of the need for transparency, accountability, and human oversight, organizations often lack practical frameworks for implementation—such as auditing mechanisms, risk classification, and monitoring systems. This slows down adoption and creates uncertainty. At the same time, these gaps present clear opportunities. There is strong potential to build AI capacity from the ground up, leveraging open-source software, open data, and open models to accelerate learning and reduce barriers to entry. This can support the development of locally relevant solutions that better reflect social, cultural, and linguistic contexts. Additionally, there is an opportunity to design governance approaches that are context-aware and forward-looking, rather than retrofitted. By investing in local infrastructure, skills, and data ecosystems, regions can move from being passive users of AI to active contributors and innovators. Overall, the current moment presents a critical window to shape AI adoption in a way that is equitable, locally grounded, and sustainable.

The AI Dialogue can play a critical role by moving international cooperation from alignment in principle to coordination in practice. First, it can serve as a platform to build shared understanding and trust across countries with different levels of technological maturity. By creating space for open exchange, it helps ensure that governance discussions are not dominated by a few actors, but instead reflect a broader range of perspectives, including those of developing regions. Second, the Dialogue can advance cooperation by supporting interoperability of governance approaches. Rather than enforcing uniform models, it can help identify common building blocks—such as risk frameworks, accountability mechanisms, and standards—that countries can adapt to their own contexts while still enabling cross-border collaboration. A key contribution of the Dialogue is in strengthening AI capacity-building as a shared global effort. International cooperation should not be limited to policy discussions; it should include knowledge transfer, technical partnerships, and access to open resources such as open-source tools, open data, and open models. This helps reduce fragmentation and enables more countries to actively participate in AI development. The Dialogue can also play an important role in promoting responsible data governance practices, including approaches that support trust, security, and appropriate levels of data control. This is essential for enabling collaboration while respecting national priorities and legal frameworks. Finally, the Dialogue can help translate global discussions into practical actions, such as pilot initiatives, collaborative frameworks, and ongoing working groups that continue beyond the initial engagement. Ultimately, its value lies in ensuring that international cooperation is not only about coordination among advanced economies, but about creating a more inclusive and balanced global AI ecosystem, where all countries can contribute to and benefit from AI responsibly.

There are already several important initiatives shaping AI governance, including the OECD AI Principles, UNESCO's Recommendation on the Ethics of AI, the G7 Hiroshima AI Process, and regional frameworks such as the EU AI Act. In addition, technical and multi-stakeholder efforts led by organizations like the ITU, ISO/IEC, and global research communities are contributing to standards, best practices, and capacity-building. While these initiatives provide strong foundations, they often operate in parallel, with varying levels of participation and implementation across regions. The AI Dialogue can add value by serving as a connecting platform that brings these efforts into a more coherent and inclusive global conversation. One key contribution would be to strengthen coordination and interoperability across existing frameworks. Rather than creating new principles, the Dialogue can help identify common elements and practical pathways for alignment, making it easier for countries to navigate and adopt governance approaches that fit their contexts. Another important role is to elevate capacity-building and knowledge exchange as a central pillar of cooperation. Many existing initiatives set expectations, but fewer provide the sustained support needed for countries to implement them. The Dialogue can help bridge this gap by encouraging technical partnerships, sharing of tools and expertise, and greater use of open-source software, open data, and open AI models. Additionally, the Dialogue can ensure that underrepresented regions and perspectives are more actively included, particularly in shaping how governance is applied in practice. This is critical to avoid a fragmented system where only a few actors define global norms. Ultimately, the added value of the AI Dialogue lies in its ability to move from multiple parallel efforts to a more connected, inclusive, and action-oriented ecosystem, enabling countries not only to align on principles, but to implement them effectively.