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A successful first Global Dialogue on AI Governance should move beyond discussion into actionable alignment and implementation. First, it should establish a clear global baseline for AI safety and ethics, defining minimum standards that all countries can adopt while respecting national sovereignty. This creates trust without enforcing a one-size-fits-all model. Second, the Dialogue should prioritize capability-building alongside safeguards. Governance that focuses only on risk control creates dependency. True resilience comes when countries are empowered to design, deploy, and govern their own AI systems securely. Third, success would include the development of reference architectures for sovereign AI infrastructure. These models would guide nations in building AI systems locally—ensuring data protection, regulatory compliance, and contextual relevance. Fourth, the Dialogue should produce practical collaboration mechanisms, such as cross-border sandboxes, shared research initiatives, and public-private partnerships that accelerate responsible AI adoption. Finally, measurable outcomes are essential. This includes clear timelines, pilot programs, and accountability frameworks to ensure that discussions translate into real-world impact. In essence, success is not defined by consensus alone, but by the ability to transform global dialogue into national capability, trusted systems, and scalable implementation.

• Safe, secure and trustworthy AI
• AI capacity-building
• Interoperability of governance approaches
• Transparency, accountability, and human oversight

The selected priorities reflect a balanced approach between protecting societies and enabling nations to build and govern AI independently. Safe, secure and trustworthy AI is essential to establish global trust and mitigate risks, particularly as AI systems become embedded in critical infrastructure and decision-making processes. However, safety alone is not sufficient. AI capacity-building is equally critical. Without local capabilities, countries remain dependent on external systems, limiting their ability to govern data, ensure security, and adapt AI to their societal context. Interoperability of governance approaches is necessary to enable international collaboration while respecting national differences. Countries should be able to align on shared principles while maintaining sovereignty in implementation. Finally, transparency, accountability, and human oversight ensure that AI systems remain aligned with human values, institutional responsibility, and public trust. Together, these priorities reflect a dual approach: building safeguards while simultaneously building sovereign capability. This balance is key to achieving sustainable, inclusive, and globally coordinated AI governance.

One critical cross-cutting issue not sufficiently emphasized is AI sovereignty and infrastructure ownership. Current discussions often focus on ethics, safety, and regulation, but less attention is given to who builds, owns, and controls AI systems. Without this, governance risks becoming theoretical rather than practical. Another emerging issue is data sovereignty and cross-border data flows. As AI systems rely heavily on data, clear frameworks are needed to balance global collaboration with national control over sensitive data. Additionally, there is a growing need for AI governance for decision systems, not just models. Many AI applications today directly influence policy, resource allocation, and public services. Governance frameworks must evolve to address AI as a decision-making infrastructure, not only as a tool. Finally, workforce transition and economic restructuring should be treated as a core governance issue. AI will reshape labor markets, and without proactive reskilling strategies, inequality may increase. Addressing these cross-cutting issues will ensure that AI governance is not only protective, but also strategic, future-oriented, and grounded in real-world implementation.

In Saudi Arabia and the wider Gulf region, rapid AI adoption—driven by national transformation agendas—has outpaced the maturity of governance frameworks in several key areas. One major gap lies in AI capacity-building. While investments in AI are significant, there remains a shortage of deeply specialized local talent capable of designing and governing complex AI systems end-to-end. This creates a reliance on external technologies, which can limit data sovereignty and long-term strategic independence. Another challenge is the fragmentation of governance approaches across sectors and institutions. Different entities are advancing at varying speeds, leading to inconsistencies in standards, risk management practices, and implementation readiness. This highlights the need for interoperable governance models that align nationally while remaining globally compatible. In terms of transparency and accountability, many AI systems are being adopted operationally without fully developed frameworks for explainability, auditability, and human oversight—especially in high-impact sectors such as finance, healthcare, and public services. However, these gaps also present significant opportunities. Saudi Arabia is uniquely positioned to lead in sovereign AI development, leveraging its strong institutional structure, centralized policymaking, and long-term strategic vision under Vision 2030. There is a clear opportunity to build national AI infrastructure models that prioritize security, localization, and scalability. Additionally, the region can become a global benchmark for integrating governance with implementation, by not only setting policies but actively building and deploying compliant AI systems. Ultimately, the challenge is not the lack of ambition, but the need to align capability, governance, and execution into a unified, scalable model.

The AI Dialogue can play a critical role as a neutral global coordination platform that bridges fragmented efforts in AI governance and translates them into aligned, actionable outcomes. First, it can establish shared principles and minimum standards for safe and ethical AI, while allowing flexibility for national implementation. This balance between global alignment and sovereignty is essential to build trust across countries with different priorities and capacities. Second, the Dialogue can act as a connector between policy and practice. Many existing discussions remain high-level, but there is a gap in translating governance frameworks into deployable systems. The Dialogue can facilitate the exchange of practical models, pilot projects, and real-world use cases. Third, it can enable inclusive participation, particularly for emerging economies. By providing access to knowledge, tools, and reference architectures, the Dialogue can reduce the risk of a global AI divide where only a few countries control development and deployment. Fourth, the Dialogue can support cross-border collaboration mechanisms, such as regulatory sandboxes, joint research initiatives, and interoperable governance frameworks that allow countries to work together without compromising national interests. Finally, it can serve as a platform for accountability and continuity, ensuring that commitments are tracked, progress is measured, and cooperation evolves over time. In essence, the AI Dialogue should not only convene discussions, but actively enable alignment, capability-building, and coordinated implementation at a global scale.

The AI Dialogue should build upon and connect with existing global and regional initiatives to avoid duplication and accelerate impact. Key initiatives include the OECD AI Principles, UNESCO Recommendation on the Ethics of AI, and ongoing efforts under the Global Digital Compact. These frameworks provide strong foundations in ethics, human rights, and policy alignment. In addition, national strategies—such as those led by countries in the Gulf region under long-term transformation agendas—offer valuable examples of rapid implementation and state-led coordination. Institutions like SDAIA in Saudi Arabia demonstrate how governance can be integrated with execution at scale. The Dialogue should also connect with industry and research ecosystems, including public-private partnerships, AI labs, and cross-border innovation platforms. These actors play a key role in translating governance into deployable technologies. However, the added value of the AI Dialogue lies in what is currently missing: First, it can act as an integration layer, linking fragmented initiatives into a coherent global framework. Second, it can provide reference models for sovereign AI infrastructure, helping countries move from principles to implementation. Third, it can enable practical collaboration mechanisms, such as shared sandboxes, pilot programs, and interoperable regulatory approaches. Finally, it can ensure that governance is not only about compliance, but also about building capability and long-term strategic independence. By connecting existing efforts and focusing on implementation, the AI Dialogue can shift global governance from guidelines to grounded, scalable systems.

Effective AI governance requires coordinated contributions from multiple stakeholders, each bringing distinct capabilities. Governments should lead in setting national priorities, regulatory frameworks, and ensuring alignment with public interest. Private sector and AI developers should contribute technical expertise, real-world deployment insights, and innovation capacity. Academia and research institutions can provide evidence-based analysis, risk assessment, and long-term foresight. Civil society plays a critical role in representing societal impact, ethics, and inclusion. To enable meaningful collaboration, the AI Dialogue should adopt a structured, multi-layered format: First, a strategic plenary level to align on global priorities and shared principles. Second, thematic working groups focused on key areas such as safety, capacity-building, and governance interoperability. Third, implementation tracks where stakeholders co-develop pilot projects, regulatory sandboxes, and deployable models. The Dialogue should also include regional tracks to reflect different contexts and levels of readiness, ensuring that outcomes are globally aligned but locally adaptable. To maintain continuity, a year-round mechanism should be established, with defined milestones, reporting frameworks, and measurable outputs. Ultimately, the goal is to shift from discussion to execution—creating a system where stakeholders do not only contribute ideas, but actively co-build practical, scalable governance solutions.

Despite the global nature of AI, several critical voices remain underrepresented in current governance discussions. First, emerging economies and developing countries are often included symbolically but lack meaningful influence in shaping frameworks. Their priorities—such as infrastructure readiness, local talent development, and economic impact—are not always adequately reflected. Second, non-technical professionals and practitioners—including policymakers, operators, and public sector implementers—are underrepresented. AI governance is often discussed at a highly technical level, while those responsible for real-world implementation are not sufficiently included. Third, youth and future workforce voices are largely absent, despite being the most affected by long-term AI transformation. Fourth, women and underrepresented groups, particularly from regions where participation in advanced technology sectors is still emerging, remain under-engaged in global platforms. To address this, the AI Dialogue should move beyond open invitations and adopt targeted inclusion mechanisms: • Dedicated seats or quotas within working groups • Regional representation frameworks • Fellowship and sponsorship programs to support participation • Structured channels for practitioner and youth input In addition, contributions should be valued not only based on technical depth, but also on implementation experience and societal perspective. True inclusion is not about presence alone, but about enabling diverse voices to shape outcomes, influence decisions, and co-create solutions.

To foster meaningful and dynamic engagement, the AI Dialogue should move beyond traditional conference formats and adopt more interactive, outcome-driven approaches. First, co-creation labs can bring together governments, developers, and researchers to collaboratively design solutions to real governance challenges. These sessions should produce tangible outputs such as policy prototypes, system designs, or pilot frameworks. Second, live policy and technical simulations can allow participants to test governance models in real-time scenarios—such as AI deployment in healthcare, finance, or public services—helping bridge the gap between theory and practice. Third, cross-border regulatory sandboxes can enable multiple countries to experiment jointly with interoperable governance approaches, accelerating learning and alignment. Fourth, the Dialogue can introduce challenge-based tracks, where participants work on defined problems over a set period, with clear deliverables and evaluation criteria. Fifth, digital collaboration platforms should complement in-person sessions, enabling continuous engagement, knowledge sharing, and progress tracking throughout the year. Finally, short, high-impact interventions—such as expert lightning rounds—can ensure diverse perspectives are heard without losing focus. These formats shift engagement from passive discussion to active participation, experimentation, and co-development, ensuring that the Dialogue produces not only insights, but implementable outcomes at scale.

Effective AI governance is best demonstrated through approaches that combine policy with real-world implementation. One key example is the development of national AI strategies that integrate governance, infrastructure, and capability-building. Countries that align regulatory frameworks with deployment-rather than treating them separately-are better positioned to ensure both innovation and control. Another effective practice is the use of regulatory sandboxes, particularly in sectors like finance and healthcare. These environments allow governments and developers to test AI systems under controlled conditions, enabling iterative governance and risk mitigation before full-scale deployment. In addition, data governance frameworks that prioritize data classification, localization, and controlled access are essential. These approaches ensure that sensitive data is protected while still enabling innovation and cross-sector collaboration. From a technical perspective, the adoption of auditability and explainability mechanisms-such as model documentation, decision logs, and traceability systems-provides transparency and strengthens accountability in AI-driven decisions. Emerging platforms that integrate AI governance into operational systems also offer promising solutions. These systems move beyond static policies and embed governance directly into workflows, enabling continuous monitoring, compliance checks, and real-time risk detection. Finally, public-private collaboration models play a critical role. When governments partner with technology providers and research institutions, they can accelerate the development of scalable, compliant AI solutions while maintaining oversight. The most effective approaches share a common principle: governance is not an external layer, but an integrated system embedded within the design, deployment, and operation of AI.