MODDULA TECNOLOGIA S.A.C.

The first Global Dialogue on AI Governance should be considered a success if it delivers concrete, actionable outcomes that move the global community from discussion to implementation—particularly in developing economies. First, it should produce a Global AI Capacity Acceleration Framework focused on enabling countries to build the foundational capabilities required to participate in the AI economy. This includes access to infrastructure, talent development, open ecosystems, and institutional readiness. Without this, governance risks remaining disconnected from real-world adoption. Second, the Dialogue should curate and promote a portfolio of replicable use cases, especially in MSMEs and public services. These use cases should demonstrate how AI can drive productivity, improve decision-making, and enhance service delivery in practical, scalable ways. For most organizations—particularly small businesses—what is needed is not more theory, but clear pathways to implementation. Third, it should catalyze multi-stakeholder pilot initiatives that can be deployed and scaled across regions. These pilots should bring together governments, private sector, academia, and civil society to test solutions in real environments, generating evidence, refining approaches, and accelerating learning cycles. Importantly, these outcomes should be regionally relevant and adaptable, particularly for the Global South, where constraints and opportunities differ significantly from more advanced economies. For example, we are currently exploring how agentic AI can democratize strategic decision-making for small businesses—making capabilities traditionally available only to large corporations accessible at scale. Initiatives like this illustrate how AI governance can move beyond principles to enable inclusive economic transformation. Ultimately, the success of the Dialogue should be measured not only by the quality of its discussions, but by its ability to unlock adoption, scale impact, and expand participation in the AI economy globally.

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

I have prioritized these four areas because they collectively address what I see as the most critical gap in today's AI landscape: the disconnect between global AI governance frameworks and the ability of countries-particularly in Latin America and the Global South-to translate them into real capabilities, adoption, and inclusive economic impact. First, AI capacity-building is foundational. Without access to infrastructure, talent, and applied knowledge, most countries and organizations cannot meaningfully participate in the AI economy. This is especially relevant for MSMEs, which represent the majority of businesses and employment but remain largely excluded from AI adoption. Second, open-source software, open data, and open AI models are essential enablers of democratization. In resource-constrained environments, openness is not only a technical choice but a strategic necessity to accelerate access, innovation, and local adaptation. Third, safe, secure, and trustworthy AI is critical to ensure that adoption happens responsibly and sustainably. Trust is a prerequisite for scaling AI solutions across sectors such as finance, healthcare, and public services, particularly in regions where institutional trust may already be fragile. Fourth, addressing the social, economic, ethical, cultural, linguistic, and technical implications of AI is key to ensuring that governance frameworks are context-aware and inclusive. Latin America, for example, requires approaches that reflect its diversity, informality, and structural inequalities. Together, these priorities reflect a shift from viewing AI governance purely as a regulatory exercise to understanding it as an enabler of participation, productivity, and development. From my work advising organizations and promoting AI adoption, I believe that governance must not only mitigate risks, but also actively expand access and opportunity-ensuring that AI becomes a tool for inclusive growth rather than a driver of further inequality.

Yes. While the listed themes are comprehensive, there are several cross-cutting and emerging issues that deserve greater emphasis to ensure AI governance is both effective and inclusive. First, the "last-mile adoption gap" is not sufficiently addressed. Much of the current AI governance discourse focuses on principles, regulation, and high-level capabilities, but there is limited attention to how AI is actually adopted at scale-particularly by MSMEs and public sector organizations in developing countries. Bridging this gap requires practical frameworks, tools, and incentives that translate governance into real implementation. Second, the rise of agentic AI systems introduces new governance challenges. These systems can autonomously make decisions, interact with other systems, and execute complex tasks. This raises important questions around accountability, control, auditability, and alignment that go beyond traditional AI governance models. Third, there is a growing need to address economic concentration and asymmetry in value creation. AI development and benefits are currently concentrated among a small number of actors and geographies. Without deliberate mechanisms to broaden participation, AI risks reinforcing global inequalities rather than reducing them. Fourth, organizational and leadership readiness is an often-overlooked factor. Governance is not only a policy issue but also a management challenge. Leaders need frameworks to integrate AI into strategy, decision-making, and operations responsibly and effectively. Finally, contextualization of AI governance is critical. Global frameworks must be adaptable to regional realities, including informality, cultural diversity, linguistic differences, and institutional capacity constraints-particularly in Latin America and other emerging regions. Addressing these cross-cutting issues would help ensure that AI governance evolves from a predominantly normative exercise into a practical enabler of inclusive adoption, innovation, and sustainable development.

Governance gaps in AI are becoming increasingly evident when moving from high-level principles to real-world implementation, particularly in developing regions such as Latin America. One of the most significant challenges is the gap between policy frameworks and operational capacity. While many countries are advancing guidelines on trustworthy AI, they lack the infrastructure, talent, and institutional capabilities required to implement them effectively. This creates a risk of "paper governance" without real impact. A second major challenge is the limited accessibility of AI for MSMEs and public sector organizations. Despite their central role in employment and service delivery, these actors often lack the resources, knowledge, and tools to adopt AI responsibly and productively. This reinforces existing productivity gaps and limits inclusive growth. Third, there is a growing concern around concentration of technological power and value creation. A small number of global players dominate access to advanced models, compute, and data, which can exacerbate global inequalities if not addressed through more open and collaborative approaches. At the same time, there are important opportunities. Advances in open-source models, cloud infrastructure, and agentic AI are lowering barriers to entry and enabling more scalable and affordable solutions. These developments can significantly accelerate adoption if supported by the right governance frameworks. There is also an opportunity to shift toward more practical, use-case-driven governance, where policies are informed by real implementations in sectors such as healthcare, education, finance, and small business development. Finally, the increasing global attention to AI governance creates a unique window to build multi-stakeholder collaboration models that combine policy, technology, and implementation. To fully realize these opportunities, governance must evolve from a predominantly regulatory focus to a more balanced approach that also prioritizes capacity-building, adoption, and equitable participation in the AI economy.

The Global Dialogue on AI Governance can play a pivotal role in advancing international cooperation by acting as a bridge between global principles and coordinated, practical action—particularly across regions with differing levels of readiness. First, it can help establish a shared but flexible foundation for AI governance, promoting interoperability across national and regional frameworks while allowing for contextual adaptation. This is critical to avoid fragmentation and enable collaboration across borders, especially in areas such as data flows, standards, and responsible AI practices. Second, the Dialogue can serve as a platform to align priorities between developed and developing economies. For many countries in Latin America and the Global South, the central challenge is not only managing AI risks but enabling participation in the AI economy. International cooperation must therefore include mechanisms for capacity-building, technology access, and knowledge transfer. Third, it can facilitate multi-stakeholder collaboration at scale, bringing together governments, private sector, academia, and civil society to co-design and implement solutions. This includes fostering partnerships that move beyond discussion into pilot initiatives and scalable programs. Fourth, the Dialogue can play a key role in translating technical expertise into policy-relevant and actionable insights, particularly by connecting the work of the Scientific Panel with real-world use cases and decision-making processes. Finally, it can help promote a shift toward implementation-oriented cooperation, where countries and organizations share not only principles and frameworks, but also practical experiences, tools, and lessons learned from deploying AI in different sectors. By combining alignment, inclusion, and action, the Global Dialogue has the potential to become not just a forum for exchange, but a catalyst for coordinated global progress in AI governance—ensuring that its benefits are more equitably distributed across regions and stakeholders.

The Global Dialogue on AI Governance should build on and connect existing initiatives that are already advancing AI principles, innovation, and impact, while addressing the current fragmentation across efforts. Key initiatives include platforms such as AI for Good (ITU), which has been instrumental in showcasing practical AI solutions aligned with the Sustainable Development Goals; UNESCO's Recommendation on the Ethics of AI, which provides a normative global framework; and the OECD AI Principles, which have guided policy development across multiple countries. Additionally, emerging collaborations such as the Global Partnership on AI (GPAI) and various regional strategies offer valuable insights and networks. However, these efforts often operate in parallel, with limited coordination between policy frameworks, technical communities, and real-world implementation. The added value of the Global Dialogue lies precisely in its ability to act as a convergence platform—not by duplicating existing initiatives, but by connecting them in a more structured and action-oriented way. First, it can create bridges between principles and practice, linking global frameworks with deployable solutions and use cases across sectors and regions. Second, it can promote greater inclusion of underrepresented regions, particularly Latin America and other parts of the Global South, ensuring that their priorities—such as capacity-building, MSME adoption, and access to infrastructure—are integrated into global agendas. Third, it can facilitate interoperability and alignment across governance approaches, reducing fragmentation and enabling more coherent international cooperation. Finally, the Dialogue can help catalyze joint pilot initiatives and partnerships, bringing together governments, private sector, academia, and civil society to test and scale solutions in real-world contexts. By positioning itself as a connector and accelerator, the Global Dialogue can transform a landscape of valuable but dispersed efforts into a more coordinated ecosystem capable of delivering tangible, inclusive, and scalable impact.

Different stakeholders bring complementary capabilities to the AI Dialogue, and its effectiveness will depend on how well these contributions are structured and integrated. Member States should provide policy direction, enable regulatory environments, and identify national and regional priorities. They also play a key role in committing to and supporting pilot initiatives. The private sector can contribute scalable technologies, investment, and real-world implementation experience. Their participation is essential to move from principles to deployable solutions. Academia and the technical community should provide research, evidence-based insights, and support the development of open tools, standards, and methodologies. Civil society plays a critical role in ensuring inclusion, accountability, and representation of diverse societal perspectives, particularly for vulnerable and underrepresented groups. International organizations can act as conveners, coordinators, and enablers of global alignment, ensuring continuity across initiatives and supporting capacity-building efforts. To fully leverage these contributions, the Dialogue's format and structure should be designed for interaction, co-creation, and execution, not only exchange. First, sessions should be problem-driven rather than purely thematic, focusing on concrete challenges such as MSME adoption, public service transformation, or AI capacity gaps. Second, the Dialogue should incorporate multi-stakeholder working groups tasked with developing actionable outputs—such as frameworks, pilot proposals, or policy recommendations. Third, it should include regional tracks or lenses, ensuring that discussions reflect diverse realities, particularly from the Global South. Fourth, strong emphasis should be placed on linking the Scientific Panel's insights to practical use cases and decision-making contexts, making outputs actionable. Finally, the Dialogue should establish continuity mechanisms, such as follow-up platforms or implementation coalitions, to ensure that outcomes extend beyond the event itself. By combining diverse stakeholder contributions with a more dynamic and execution-oriented structure, the AI Dialogue can become a catalyst for collaborative, scalable, and inclusive progress in AI governance.

Global discussions on AI governance continue to underrepresent several critical voices and perspectives, particularly from regions and actors most affected by the outcomes of these policies. First, there is a significant underrepresentation of the Global South, including Latin America, Africa, and parts of Asia. While these regions face some of the most pressing challenges—such as capacity gaps, informality, and inequality—their perspectives are often not sufficiently reflected in global frameworks. This creates a risk of governance models that are not fully applicable to their realities. Second, MSMEs and entrepreneurs are largely absent from the conversation. Despite representing the majority of businesses and employment globally, they are rarely included in governance discussions, which tend to focus on governments and large technology companies. As a result, their needs—such as access to affordable tools, practical guidance, and implementation support—are often overlooked. Third, there is limited participation from non-technical leaders and practitioners, including those in traditional sectors such as agriculture, construction, and small-scale services. These actors are essential for understanding how AI can be applied in real-world contexts beyond the technology sector. Fourth, local communities and culturally diverse groups, including indigenous populations and speakers of underrepresented languages, are often excluded. This limits the ability to address linguistic diversity, cultural context, and inclusive design in AI systems. To address these gaps, the Dialogue should actively design for inclusion. This includes establishing regional consultation mechanisms, supporting participation through funding and capacity-building, and creating dedicated tracks for MSMEs and non-technical stakeholders. It should also promote multilingual engagement and leverage hybrid formats to broaden access. Importantly, inclusion should not be limited to participation, but extend to co-creation of solutions and decision-making processes. Ensuring these voices are meaningfully integrated will be essential to building AI governance frameworks that are not only globally aligned, but also locally relevant and equitable.

To foster meaningful and dynamic engagement, the AI Dialogue should move beyond traditional panel formats and adopt more interactive, problem-solving, and outcome-oriented approaches. First, the Dialogue should incorporate challenge-driven sessions, where participants work on clearly defined, real-world problems—such as AI adoption for MSMEs, public service transformation, or capacity-building in developing countries. These sessions should be designed to produce tangible outputs, not just exchange perspectives. Second, multi-stakeholder co-creation labs can be highly effective. In these formats, small, diverse groups—bringing together policymakers, technologists, business leaders, and civil society—collaborate to design solutions, frameworks, or pilot initiatives. This encourages deeper interaction and shared ownership of outcomes. Third, the Dialogue should include use-case demonstrations and "implementation showcases", highlighting real-world applications of AI across sectors and regions. This helps ground discussions in practice and enables participants to learn from concrete examples. Fourth, introducing regional breakouts or perspective tracks would allow participants to address context-specific challenges and opportunities, particularly for the Global South. These sessions can then feed into plenary discussions, ensuring global alignment while preserving local relevance. Fifth, interactive formats leveraging digital tools, such as live polling, scenario simulations, and AI-assisted collaboration platforms, can enhance participation and capture diverse inputs in real time. Additionally, the Dialogue could benefit from "commitment sessions", where stakeholders publicly present initiatives, partnerships, or pilot projects they are willing to advance following the event. Finally, it is critical to ensure continuity beyond the Dialogue, through working groups or implementation coalitions that carry forward the ideas generated. By combining these innovative formats, the AI Dialogue can shift from a space of discussion to a platform for co-creation, experimentation, and actionable progress in AI governance.

Several existing policies, practices, and platforms provide valuable lessons for effective AI governance, particularly when they combine principles with practical implementation. At the policy level, frameworks such as the UNESCO Recommendation on the Ethics of AI and the OECD AI Principles have been instrumental in establishing globally recognized standards around transparency, accountability, human rights, and trustworthiness. Their strength lies in providing a common foundation that can be adapted across jurisdictions. From an implementation perspective, initiatives such as AI for Good (ITU) demonstrate how governance can be linked to real-world impact by connecting stakeholders and showcasing AI solutions aligned with the Sustainable Development Goals. This model highlights the importance of bridging policy with practical applications. Open ecosystems also play a critical role. The growth of open-source AI models, open data platforms, and collaborative development environments has significantly lowered barriers to entry, enabling broader participation-particularly in developing regions where access to proprietary technologies may be limited. In addition, emerging practices around AI sandboxes and regulatory experimentation environments allow governments and organizations to test AI applications in controlled settings. These approaches help balance innovation with risk management and provide valuable insights for policymaking. From my experience working with organizations, there is also increasing value in practical frameworks that integrate AI into decision-making and operations. For example, approaches that combine strategy design with AI-supported by tools such as agentic systems-can help organizations, including MSMEs, adopt AI in a structured and responsible way. Looking forward, the most effective approaches to AI governance will be those that connect global principles with local implementation, foster open and collaborative ecosystems, and provide clear pathways for adoption. Ultimately, governance should not only define what responsible AI looks like, but also enable organizations and societies to apply AI effectively, inclusively, and at scale.