DAWY Lab

A successful first Global Dialogue on AI Governance should move beyond discussion into actionable alignment. Key outcomes should include: 1. A shared global baseline for AI governance principles that respects national sovereignty while enabling international interoperability. 2. Practical frameworks for implementation, especially for governments, covering AI risk classification, accountability models, and public-sector deployment standards. 3. Mechanisms for inclusive participation, ensuring that developing and emerging economies are not only represented but empowered to adopt and scale AI solutions. 4. Clear pathways for public-private collaboration, recognizing that innovation is largely driven by industry while governments ensure responsible deployment. 5. Commitments to capacity-building, including funding, knowledge transfer, and infrastructure support for countries with limited AI readiness. From my perspective as a founder of an AI solutions company working closely with government entities in Saudi Arabia, success also means bridging the gap between policy and execution—ensuring that governance frameworks are practical, scalable, and innovation-enabling rather than restrictive.

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

My selected priorities reflect the need to balance innovation, governance, and equitable access: • Safe, secure and trustworthy AI is foundational, especially in government applications where public trust is critical. • AI capacity-building is essential to ensure that countries can actively participate in the AI ecosystem rather than remain consumers. • Transparency, accountability, and human oversight are key to aligning AI systems with societal values and regulatory expectations. • Interoperability of governance approaches is necessary to avoid fragmentation and enable cross-border collaboration and scalable solutions. Through my work at DAWY Lab, I have seen that governments require not only policies, but also operational models that integrate these principles into real systems. My selections prioritize areas that directly impact implementation and long-term sustainability.

Yes, several critical cross-cutting issues should be further emphasized: 1. AI sovereignty and national strategic alignment - Countries need frameworks that allow them to adopt global standards while preserving control over their data, infrastructure, and priorities. 2. Public sector readiness and procurement models - Many governments lack agile mechanisms to adopt AI solutions efficiently and responsibly. 3. Economic transformation and workforce transition - AI governance must address job displacement while enabling new economic opportunities. 4. Data governance and localization challenges - Especially for government use cases, balancing data protection with innovation is a growing concern. 5. AI for public good and national development - Governance discussions should focus more on how AI can directly contribute to national visions and societal impact, not only risk mitigation. From my experience working with government stakeholders, these issues are essential to ensure that AI governance is not only protective, but also a driver of national growth and global competitiveness.

In Saudi Arabia and the broader Gulf region, rapid advancements in AI are creating both significant opportunities and pressing governance gaps—particularly in the public sector. One of the most critical challenges is the gap between policy ambition and operational execution. While national strategies are highly advanced and forward-looking, many government entities still face difficulties in translating governance principles—such as accountability, transparency, and risk management—into deployable AI systems. This creates inconsistencies in implementation across sectors. Another key challenge is fragmentation in governance approaches globally, which impacts cross-border collaboration and limits scalability of AI solutions. For a region actively positioning itself as a global AI hub, alignment with international standards while maintaining national priorities is essential. Additionally, data governance complexities—including data sharing, localization, and privacy—pose barriers to unlocking the full value of AI, especially in government-led initiatives where sensitive data is involved. However, these gaps also present major opportunities. Saudi Arabia is uniquely positioned to lead in building practical, government-first AI governance models that are scalable and exportable. There is a strong opportunity to design frameworks that integrate policy with execution, supported by national infrastructure, regulatory sandboxes, and public-private partnerships. Furthermore, AI capacity-building and talent development are accelerating, enabling the region to shift from technology adoption to innovation leadership. From my perspective at DAWY Lab, working closely with government stakeholders, the region's strength lies in its ability to move quickly from vision to implementation—making it a potential global benchmark for effective, future-ready AI governance.

The AI Dialogue can play a pivotal role as a neutral, action-oriented platform that bridges the gap between global principles and national implementation. First, it can align diverse governance approaches by facilitating practical convergence—not necessarily uniformity—across countries. This is essential to reduce fragmentation and enable cross-border AI systems, data flows, and innovation ecosystems. Second, the Dialogue can act as a connector between policymakers, industry leaders, and technical experts, ensuring that governance frameworks are informed by real-world implementation challenges. Too often, policies are developed in isolation from execution realities. Third, it can drive inclusive global participation, particularly by elevating the voices of emerging economies and ensuring they are active contributors—not just adopters—of AI governance standards. Fourth, the Dialogue can serve as a platform for knowledge exchange and capacity-building, enabling countries to share best practices, regulatory models, and lessons learned in deploying AI responsibly. Finally, it should move beyond discussions into structured outcomes, such as toolkits, reference models, and pilot collaborations that countries can directly adopt. From my perspective working with government entities in Saudi Arabia, the real value of the AI Dialogue lies in its ability to transform international cooperation from high-level alignment into practical, scalable, and implementable governance solutions.

The AI Dialogue should build upon and connect with several key international initiatives and frameworks to avoid duplication and maximize impact. These include efforts led by organizations such as the United Nations, OECD AI Principles, UNESCO Recommendation on the Ethics of AI, as well as multilateral discussions within the G20 and World Economic Forum. Additionally, regional and national strategies—such as those emerging from leading AI nations—offer valuable implementation models that can be shared and adapted globally. However, the added value of the AI Dialogue lies in its ability to go beyond existing frameworks by: 1. Integrating efforts across platforms into a more cohesive global ecosystem, reducing fragmentation. 2. Focusing on implementation, by translating principles into practical tools, policy templates, and deployment models—especially for governments. 3. Enabling pilot collaborations, where countries can co-develop and test governance approaches in real-world scenarios. 4. Bridging the Global North and South, ensuring equitable access to AI capabilities, infrastructure, and governance expertise. 5. Providing continuous, adaptive dialogue, rather than static recommendations, allowing governance approaches to evolve alongside technology. From my perspective, the AI Dialogue has a unique opportunity to become not just another forum, but a global execution layer for AI governance, connecting vision with real-world impact.

Effective AI governance requires structured collaboration across stakeholders, each contributing distinct value: • Governments should define policy priorities, regulatory frameworks, and national strategies. • Private sector actors should bring technical expertise, real-world implementation insights, and scalable solutions. • Academia and research institutions should contribute evidence-based analysis and forward-looking risk assessments. • Civil society should ensure inclusivity, ethics, and societal alignment. To maximize impact, the AI Dialogue should adopt a multi-layered structure: 1. High-level plenaries for strategic alignment and political commitment. 2. Thematic working groups focused on key areas such as risk, data governance, and capacity-building. 3. Implementation labs where stakeholders co-develop practical tools, frameworks, and pilot use cases. 4. Continuous engagement model (not one-off events), supported by digital platforms for ongoing collaboration. From my experience, the most critical factor is ensuring that contributions are not only discussed, but translated into clear, actionable outputs.

Despite global progress, several voices remain underrepresented in AI governance discussions: • Emerging and developing economies, which often lack the resources to actively shape global standards. • Public sector implementers (mid-level government operators), who face real execution challenges but are rarely included in policy discussions. • SMEs and local tech innovators, who drive grassroots innovation but lack global visibility. • Youth and future workforce representatives, who will be most impacted by AI-driven transformation. • Non-technical communities, whose perspectives are essential for ethical and societal alignment. To address this, inclusion must move beyond symbolic participation to structured integration: 1. Provide funding and sponsorship mechanisms to enable participation from underrepresented regions. 2. Establish regional consultation tracks feeding into the global dialogue. 3. Create practitioner-focused forums specifically for implementers, not just policymakers. 4. Leverage digital participation platforms to scale global engagement. In my view, inclusive governance is not just equitable—it is essential for building globally relevant and adoptable AI frameworks.

To foster meaningful and dynamic engagement, the AI Dialogue should move beyond traditional panel discussions and adopt more interactive, outcome-driven formats. First, AI Governance Labs can enable participants to co-design policies, frameworks, and implementation models in real time. These sessions should focus on producing tangible outputs rather than high-level discussions. Second, simulation-based exercises can be highly effective. By modeling real-world scenarios—such as AI-related risks, cross-border data challenges, or regulatory conflicts—stakeholders can collaboratively test governance approaches and identify gaps. Third, policy-to-prototype sessions can bridge the gap between theory and practice by translating governance principles into mock tools, workflows, or operational frameworks during the session itself. Fourth, multi-stakeholder roundtables with small, diverse groups (governments, private sector, and technical experts) can enable deeper, more practical discussions compared to large plenary sessions. Fifth, open innovation challenges can crowdsource solutions to specific governance problems, with pathways to support implementation beyond the Dialogue. Finally, digital collaboration platforms should complement in-person engagement, ensuring continuity, follow-up, and global participation beyond the event itself. From my experience working with government entities, the most impactful engagements are those that integrate policy, technology, and execution within the same environment—transforming dialogue into actionable outcomes.

Effective AI governance is best demonstrated through integrated approaches that combine policy, technical infrastructure, and real-world implementation. One key example is the development of national AI strategies that align governance with economic and societal goals. In Saudi Arabia, such strategies have enabled a coordinated approach to AI adoption across government entities, linking regulation with national transformation objectives. Another impactful practice is the use of regulatory sandboxes, which allow governments and private sector partners to test AI solutions in controlled environments. This reduces risk while enabling innovation and faster policy adaptation. AI risk classification frameworks are also essential, where systems are categorized based on their potential impact (e.g., low-risk vs. high-risk applications). This enables proportionate regulation and more efficient oversight. From an operational perspective, AI governance platforms-such as centralized dashboards for monitoring AI systems, compliance tracking, and auditability-are emerging as practical tools for governments to ensure accountability and transparency. Additionally, public-private partnerships play a critical role in bridging the gap between policy and implementation. Collaboration between governments and AI solution providers enables the co-development of scalable, real-world governance models. Another important approach is embedding ethics-by-design and human-in-the-loop mechanisms directly into AI systems, ensuring continuous oversight and alignment with societal values. From my experience at DAWY Lab, the most effective solutions are those that move beyond standalone policies toward integrated governance ecosystems-where strategy, regulation, technology, and execution are designed to work together seamlessly.