İnnovation and Digital Development Agency under Ministry of Digital Development and Transport

A successful first Global Dialogue on AI Governance should deliver concrete, actionable, and inclusive outcomes that move beyond high-level discussions toward implementation. First, it should result in a shared set of guiding principles for trustworthy and human-centric AI, aligned with existing international frameworks while allowing flexibility for national contexts. These principles should address ethics, transparency, accountability, data protection, and safety. Second, the Dialogue should establish a practical cooperation mechanism—such as a multi-stakeholder platform or working groups—bringing together governments, private sector leaders, academia, and international organizations. This mechanism should focus on priority areas including AI standards, interoperability, and cross-border data governance. Third, it should produce a clear roadmap for capacity building, particularly for developing and emerging economies. This includes knowledge-sharing, technical assistance, and access to digital infrastructure to ensure no country is left behind in the AI transformation. Fourth, tangible progress should be made toward harmonization of regulatory approaches, reducing fragmentation and enabling innovation-friendly environments while managing risks. Pilot initiatives or regulatory sandboxes across countries could be launched as part of this effort. Fifth, the Dialogue should promote real use cases of AI for public good, especially in areas such as public services, healthcare, education, climate action, and smart governance—demonstrating how AI can deliver measurable societal impact. Finally, success would be marked by a formal outcome document or declaration, endorsed by participants, with defined follow-up actions, timelines, and accountability mechanisms. In essence, the Dialogue should shift global AI governance from fragmented discussions to coordinated, results-driven international collaboration, ensuring that AI development remains inclusive, safe, and beneficial for all

• Interoperability of governance approaches
• Open-source software, open data and open AI models
• Safe, secure and trustworthy AI

Our selection reflects a balanced approach between innovation enablement, regulatory alignment, and responsible AI deployment, which are core to our institutional mandate. First, safe, secure and trustworthy AI is a fundamental priority, as ensuring public trust and system integrity is essential for scaling AI solutions across government services and the broader digital economy. This includes robust cybersecurity frameworks, risk management mechanisms, and ethical AI deployment standards. Second, interoperability of governance approaches is critical in today's fragmented global landscape. As a country actively developing digital public infrastructure and cross-border digital services, we prioritize alignment of standards, regulatory frameworks, and data exchange mechanisms. This enables smoother international cooperation, facilitates innovation, and reduces compliance complexity for both public and private sector stakeholders. Third, open-source software, open data and open AI models are key enablers of inclusive digital transformation. Promoting openness supports innovation ecosystems, empowers startups, and enhances transparency. It also allows countries with emerging digital capacities to access and adapt advanced technologies without prohibitive costs, fostering equitable participation in the global AI economy. Collectively, these priorities support a pragmatic and scalable AI governance model-one that ensures safety and trust, promotes international collaboration, and accelerates innovation through openness. This approach is particularly relevant for building resilient digital ecosystems and advancing AI adoption in public services, while maintaining alignment with global best practices

Yes. While the listed themes are comprehensive, several cross-cutting and emerging issues merit stronger emphasis to ensure future-ready AI governance. First, AI infrastructure inequality is becoming a defining global challenge. Access to compute power, cloud infrastructure, and high-quality datasets remains uneven across countries. Without targeted international cooperation mechanisms, this gap may widen, limiting the ability of developing economies to meaningfully participate in AI development and governance. Second, governance of foundation models and frontier AI systems requires more focused attention. Issues such as model evaluation, safety testing, accountability across the AI value chain, and responsible deployment of highly capable systems are evolving faster than regulatory frameworks, necessitating agile and coordinated global responses. Third, data governance in the age of AI is an increasingly complex area that goes beyond traditional data protection. Questions around data ownership, cross-border data flows, data quality, and the use of synthetic data are central to both innovation and trust, yet are not always fully integrated into AI governance discussions. Fourth, the environmental impact of AI is an emerging concern. The growing energy consumption of large-scale AI models and data centers requires alignment with sustainability goals, including green computing standards and energy-efficient AI development practices. Finally, public sector readiness and institutional transformation is a critical cross-cutting issue. Governments need not only regulatory frameworks but also internal capacity, agile procurement models, and digital infrastructure to effectively adopt and govern AI. Addressing these issues alongside existing themes would strengthen the inclusiveness, sustainability, and long-term effectiveness of global AI governance efforts.

Governance gaps and rapid advances in AI are creating both significant opportunities and structural challenges for Azerbaijan's digital ecosystem and the public sector. On the opportunity side, AI enables the acceleration of digital public services, data-driven policymaking, and more efficient service delivery through platforms such as e-government systems and digital identity solutions. The growing availability of open-source tools and global AI models lowers entry barriers, allowing startups and public institutions to experiment, innovate, and scale solutions more rapidly. Additionally, alignment with international standards on AI governance creates opportunities for cross-border digital cooperation, positioning the country as a regional digital hub. However, several governance gaps present critical challenges. First, fragmentation of global AI regulatory approaches complicates interoperability and creates uncertainty for both public institutions and private sector actors seeking to adopt or export AI solutions. Second, limited access to high-quality data, compute infrastructure, and advanced AI capabilities constrains the pace of local innovation and increases dependency on external technologies. Third, ensuring safe and trustworthy AI deployment remains a key concern, particularly in public sector use cases, where risks related to data privacy, cybersecurity, and algorithmic bias must be carefully managed. The absence of fully mature regulatory frameworks and standardized risk assessment mechanisms can slow adoption. Fourth, while open-source AI presents major opportunities, it also introduces governance and security considerations, including responsible use, compliance, and oversight. Overall, these dynamics highlight the need for a balanced approach: strengthening national capacities and governance frameworks while actively engaging in international cooperation to ensure interoperability, trust, and inclusive growth in the AI ecosystem.

Effective AI governance requires coordinated, multi-stakeholder engagement, where each group contributes its comparative advantage. Governments should provide strategic direction, policy frameworks, and enable regulatory environments, while also sharing practical experiences from deploying AI in public services. Private sector and technology companies can contribute technical expertise, innovation capacity, and insights on real-world implementation, including risk management practices. Academia and research institutions play a critical role in advancing evidence-based policymaking, standards development, and independent evaluation of AI systems. International organizations can act as neutral conveners, ensuring inclusiveness, facilitating knowledge exchange, and supporting capacity-building across regions. Civil society ensures that human-centric values, ethics, and societal impacts are fully integrated into AI governance discussions. In terms of format and structure, the AI Dialogue should move beyond a traditional conference model and adopt a hybrid, action-oriented approach: First, establish thematic working groups aligned with priority areas (e.g., AI safety, interoperability, open AI, capacity-building), each tasked with producing concrete outputs such as policy recommendations, toolkits, or pilot initiatives. Second, integrate high-level plenary sessions with technical deep-dives and roundtables, enabling both strategic alignment and operational discussions. Third, introduce pilot collaboration tracks, where countries and organizations can co-develop and test solutions (e.g., regulatory sandboxes, cross-border data frameworks, AI for public services use cases). Fourth, ensure regional representation and continuity, through follow-up mechanisms such as annual progress reviews, digital collaboration platforms, and clear accountability frameworks. Finally, the Dialogue should culminate in a practical outcome document and implementation roadmap, ensuring that discussions translate into measurable actions and sustained international cooperation.

From the perspective of the Innovation and Digital Development Agency, global AI governance discussions still lack sufficient representation from emerging digital economies, public sector implementers, and local innovation ecosystems. First, emerging and middle-income countries are often underrepresented, despite being active adopters of digital public infrastructure and AI-enabled services. Their practical experience in scaling cost-effective, citizen-centric solutions—particularly in e-government, digital identity, and public service delivery—should be more systematically integrated into global discussions. Second, public sector practitioners and implementers (government agencies responsible for deploying AI solutions) are not always adequately included. While high-level policy and technical perspectives are present, there is a gap in operational insights related to procurement models, system integration, data governance in practice, and service delivery challenges. Third, startups and local tech ecosystems, especially from smaller markets, remain underrepresented. These actors are key drivers of innovation and often develop agile, context-specific AI solutions, yet they face barriers in accessing global platforms and partnerships. Fourth, non-English speaking communities and local data ecosystems are insufficiently reflected in AI governance debates. Linguistic diversity and locally relevant datasets are critical for inclusive and fair AI systems, particularly for underrepresented languages. To address these gaps, the AI Dialogue should adopt a more inclusive and structured participation model: – Establish dedicated tracks for emerging economies and public sector use cases; – Create mechanisms to integrate startups and SMEs (e.g., innovation showcases, funding-linked participation); – Support multilingual engagement and local content representation; – Provide capacity-building and sponsorship mechanisms to ensure equitable participation. Such an approach would ensure that AI governance becomes more practical, inclusive, and globally representative, reflecting diverse implementation realities and development needs.

To ensure meaningful and dynamic engagement, the AI Dialogue should move beyond traditional panel formats and adopt innovative, outcome-driven engagement models aligned with practical implementation and international cooperation. First, introduce co-creation labs (policy and technical sprints), where mixed groups of policymakers, technologists, and practitioners collaboratively develop concrete outputs—such as draft policy frameworks, interoperability guidelines, or pilot project concepts—within a limited timeframe. Second, organize use-case driven sessions, where countries and institutions present real AI implementations in areas like digital public services, data exchange platforms, and smart governance. These sessions should focus on lessons learned, scalability, and replicability rather than high-level narratives. Third, establish regulatory sandbox simulations, allowing participants to test AI governance approaches in controlled, cross-border scenarios. This would be particularly valuable for exploring interoperability, data governance, and risk management in practice. Fourth, create an AI solutions marketplace and innovation showcase, where startups, SMEs, and technology providers—especially from emerging ecosystems—can demonstrate solutions and connect with governments and investors. This would directly support ecosystem development and partnerships. Fifth, implement multi-track roundtables with structured outputs, where each session is required to produce actionable recommendations, feeding into a centralized outcome document and roadmap. Finally, ensure continuous engagement beyond the event through digital collaboration platforms, enabling working groups to track progress, share knowledge, and advance joint initiatives over time. Such formats would transform the Dialogue into a practical, results-oriented platform, fostering collaboration, accelerating implementation, and ensuring that diverse stakeholders actively contribute to shaping global AI governance.

From the perspective of the Innovation and Digital Development Agency, effective AI governance is best supported by integrated digital infrastructure, clear regulatory direction, and practical implementation tools. Several examples illustrate this approach: First, digital public infrastructure (DPI) plays a foundational role. Platforms such as national digital identity systems (e.g., mygovID) and unified e-government portals (myGov) enable secure, user-centric access to services while ensuring strong authentication, consent-based data sharing, and traceability. These elements are critical for trustworthy AI deployment in public services. Second, interoperability platforms such as the Digital Bridge (G2G/G2B/G2C data exchange layer) demonstrate how standardized, real-time data exchange can support AI-ready ecosystems. By enabling secure and structured data flows across institutions, such platforms reduce fragmentation and improve the quality of AI-driven decision-making. Third, cloud-based and biometric signature solutions (e.g., SİMA) support scalable and secure digital transactions, ensuring identity verification and legal validity-key prerequisites for AI-enabled automation and service delivery. Fourth, the development of national AI and digital strategies (such as AI Strategy 2025-2028 and Digital Economy Strategy 2026-2029) provides a coordinated policy framework that aligns innovation with risk management, ethics, and international best practices. Fifth, AI-powered legal and regulatory tools (e.g., qanun.ai) enhance transparency and accessibility of legislation, supporting both policymakers and businesses in navigating regulatory environments more effectively. Finally, promoting open data ecosystems and open-source approaches encourages innovation, supports startups, and ensures inclusivity, while enabling oversight and accountability. Collectively, these policies and platforms demonstrate a practical, ecosystem-based approach to AI governance-combining secure infrastructure, interoperability, strategic policy direction, and openness to drive responsible and scalable AI adoption.