AI for Entrepeneurs / Panamenian Observatory on AI

In my opinion, the first Global Dialogue on AI Governance would be successful if it delivers outcomes that go beyond high level principles and translate into practical, actionable frameworks that can be implemented across different regions. From my work with entrepreneurs, professionals, and SMEs in Latin America, one of the biggest gaps is not awareness of AI, but the lack of structured guidance on how to adopt it responsibly and effectively. Therefore, a key outcome should be the creation of simple, adaptable implementation frameworks that countries and organizations can localize based on their realities.

• Social, economic, ethical, cultural, linguistic and technical implications of AI
• AI capacity-building
• Safe, secure and trustworthy AI
• Protection and promotion of human rights

My selection reflects a strong focus on practical implementation, responsible adoption, and inclusive growth of AI, particularly in emerging regions such as Latin America. AI capacity-building is a top priority because the main gap today is not access to technology, but the lack of structured knowledge on how to use it effectively. Without this, businesses, especially SMEs and entrepreneurs, cannot fully benefit from AI, which risks widening existing economic gaps. Safe, secure, and trustworthy AI is essential to ensure long-term adoption. If users and organizations do not trust these systems, adoption will slow down. Trust must be built not only through regulation, but also through practical education and real-world application. The social, economic, ethical, cultural, linguistic, and technical implications of AI are particularly important in regions like ours, where global solutions do not always align with local realities. AI must be adaptable to different cultural and economic contexts to avoid exclusion and unintended consequences. Finally, the protection and promotion of human rights is critical as AI becomes more integrated into daily life. Ensuring that AI systems respect fundamental rights while enabling innovation is key to sustainable progress. Overall, these priorities reflect the need to balance innovation with responsibility, while ensuring that AI becomes a tool for empowerment and growth, not inequality.

Yes, one important cross-cutting issue that is not fully captured is the gap between AI governance principles and real-world implementation. Across many regions, particularly in Latin America, there is a growing disconnect between high-level discussions on AI and the practical realities faced by professionals, entrepreneurs, and small and medium-sized businesses. While frameworks, ethics, and policies are being developed, there is often limited guidance on how these translate into day-to-day use.

In my region, particularly in Latin America, governance gaps in AI are most visible in the disconnect between policy development and practical implementation. One of the main challenges is that many businesses, especially small and medium-sized enterprises, are adopting AI without clear guidance on responsible use, data management, or risk mitigation. This is not due to lack of interest, but rather a lack of accessible, practical frameworks tailored to their realities. As a result, adoption can be either unstructured or delayed, limiting the potential impact of AI. Another challenge is the uneven distribution of capabilities. While some organizations are rapidly integrating AI into their operations, many others lack the skills, tools, or support systems to do so effectively. This creates a growing gap between those who can operationalize AI and those who cannot. At the same time, there are significant opportunities. AI has the potential to accelerate productivity, improve access to services, and enable new business models across the region. In countries like Panama, there is also an opportunity to position ourselves as regional hubs for AI adoption, innovation, and collaboration. However, to unlock these opportunities, governance efforts must go beyond principles and focus on enabling implementation. This includes capacity-building, localized support, and continuous engagement with local ecosystems. If addressed correctly, these governance gaps can become an opportunity to build more inclusive, practical, and adaptive AI ecosystems that reflect the realities of emerging regions.

The AI Dialogue can play a key role by bridging the gap between global principles and local implementation. It should go beyond a one-time discussion and enable continuous collaboration between governments, the private sector, academia, and civil society. This is essential given the fast pace of AI development. The Dialogue can also help translate high-level frameworks into practical, adaptable guidance that different regions can apply based on their realities, especially in emerging markets. Finally, it can ensure more inclusive participation by amplifying underrepresented regions and fostering the exchange of best practices and capacity-building efforts. If focused on continuity and execution, the Dialogue can drive real, coordinated global impact.

The AI Dialogue should build on existing multistakeholder initiatives led by the UN, regional organizations, and collaborations between the private sector, academia, and civil society. However, a key gap is the limited connection between high-level frameworks and practical implementation. The added value of the AI Dialogue would be to act as a coordination layer, aligning existing efforts and translating them into actionable, adaptable guidance for different regions, especially emerging markets. It can also strengthen the inclusion of underrepresented actors, such as entrepreneurs and SMEs, who are critical for real-world adoption but often excluded from global discussions. By focusing on coordination, localization, and execution, the Dialogue can turn existing initiatives into real, scalable impact.

Stakeholders should contribute based on their strengths: governments (policy), private sector (implementation), academia (research), and civil society (ethics and social impact). The AI Dialogue should follow three phases: Pre-dialogue: collect structured inputs at the country level. Dialogue: combine high-level discussions with practical use cases. Post-dialogue: ensure follow-up, clear outputs, and regular updates. This keeps the process inclusive, practical, and continuous.

Entrepreneurs, professionals, and small and medium-sized businesses are among the most underrepresented voices, especially in emerging regions like Latin America. Local perspectives that reflect different cultural and economic contexts are also often missing. To include them, it is important to create accessible participation mechanisms at the country level, such as structured digital consultations and spaces to share real use cases. Their participation should also be promoted through local communities, networks, and partnerships with the private sector. Without these voices, AI governance risks remaining theoretical and disconnected from real-world adoption.

Traditional panels are not enough. The AI Dialogue should include more interactive and execution-focused formats. First, small working groups based on real challenges, where participants collaborate on specific use cases and propose solutions. Second, live case sessions where organizations share how they are actually implementing AI, including challenges and lessons learned. Third, structured feedback loops, such as real-time digital input and voting, to capture diverse perspectives during the sessions. Finally, short follow-up sprints after the Dialogue to turn ideas into actionable outputs. These formats make the Dialogue more dynamic, grounded, and outcome-driven.

Effective AI governance can be supported by approaches that focus on structured implementation, not just principles. For example, in my work with entrepreneurs and SMEs, we use step-by-step frameworks that guide organizations from defining objectives to testing and adjusting AI solutions. This helps ensure responsible and measurable adoption rather than unstructured use. Another practical approach is building communities and platforms that provide access to tools, training, and real use cases, allowing users to learn and apply AI in a controlled environment. These models can also create strong partnerships with academia and the private sector, enabling collaboration to support communities while allowing businesses to continue operating and growing. This shows that effective AI governance is not only about regulation, but about enabling practical, collaborative, and scalable implementation.