OECD AI Policy Toolkit: How 47 Countries Plan to Bridge the Global AI Divide

The Global AI Transformation and the Widening AI Divide

Artificial intelligence is reshaping economies worldwide, driving innovation across industries from healthcare and agriculture to financial services and education. Yet the benefits of this transformation are accruing unevenly. Advanced economies are pulling ahead in AI development, investment, talent acquisition, and enterprise adoption, while emerging and developing economies face a growing set of barriers—financial constraints, limited digital infrastructure, and critical skills gaps—that threaten to transform the AI revolution into an AI divide.

The stakes are enormous. For developing economies, AI represents both a threat and an unprecedented opportunity. On one hand, falling behind in AI capability means falling behind in economic competitiveness, productivity growth, and the ability to deliver public services effectively. On the other hand, AI offers the potential to leapfrog traditional development pathways—using AI-powered solutions to address challenges in healthcare delivery, agricultural productivity, educational access, and government efficiency in ways that would take decades through conventional development approaches.

It is against this backdrop that the OECD has developed its most ambitious practical intervention yet: the AI Policy Toolkit. Designed to translate high-level AI governance principles into actionable, context-sensitive implementation guidance, the toolkit represents a critical bridge between aspiration and execution in international AI policy.

OECD AI Principles: The Foundation for Trustworthy AI Policy

The OECD AI Principles, adopted in May 2019, established the first intergovernmental standard for responsible AI governance. Since then, 47 countries plus the European Union have adhered to these principles, creating a common foundation for international AI cooperation that spans advanced, emerging, and developing economies alike.

The principles are organized into two complementary structures. Five values-based principles establish the ethical and societal guardrails for AI: inclusive growth, sustainable development, and well-being; respect for human rights, democratic values, fairness, and privacy; transparency and explainability; robustness, security, and safety; and accountability. Five policy recommendations then translate these values into government action areas: investing in AI research and development, fostering an inclusive AI-enabling ecosystem, shaping interoperable governance frameworks, building human capacity for labor market transformation, and promoting international cooperation.

The integration of the Global Partnership on AI (GPAI) with the OECD in July 2024 amplified both the demand for implementation guidance and the institutional capacity to deliver it. With GPAI’s network of member and non-member countries now unified under the OECD umbrella, the need for practical, adaptable tools that help diverse countries move from principle to practice became urgent. Understanding how these AI governance frameworks evolve is essential for any organization operating across borders.

Why AI Implementation Guidance Is the Critical Missing Piece

Having principles is necessary but insufficient. The gap between adopting a set of AI governance principles and translating them into effective national policy is where most countries struggle—and where the OECD identified the most critical unmet need. Three specific demand drivers emerged from consultations with adhering countries.

First, countries need context-tailored guidance for values-based principles. What “transparency and explainability” means in practice for a country with a mature AI ecosystem differs fundamentally from what it means for a country still building basic digital infrastructure. The principles are deliberately high-level; the implementation must be specific to local capacity, institutions, and priorities.

Second, countries need capacity-sensitive policy guidance. A policy recommendation to “invest in AI research and development” lands very differently in a country with world-class research universities than in one where the primary challenge is reliable electricity and internet connectivity. Implementation guidance must account for these radically different starting points without being condescending or unrealistic.

Third, countries need sector-specific implementation pathways. The OECD has identified four priority sectors where AI implementation guidance is most urgently needed: the public sector, agriculture, healthcare, and education. These sectors represent both the areas of greatest potential impact in developing economies and the domains where practical case studies and implementation models are most valuable.

Inside the OECD AI Policy Toolkit Architecture

The AI Policy Toolkit comprises two interconnected components, each serving a distinct function while reinforcing the other. The Self-Assessment Tool enables governments to evaluate their current alignment with the OECD AI Principles and identify specific areas requiring attention. The Implementation Guidance provides practical, non-prescriptive policy options and case studies that countries can adapt to their specific contexts.

Both tools are designed as interactive online applications integrated into the OECD.AI platform, freely accessible to all adhering countries. The design philosophy is explicitly non-prescriptive—the toolkit presents options, examples, and frameworks rather than mandating specific policy choices. This approach respects national sovereignty in policy design while providing the evidence base and practical examples that make informed choices possible.

Critically, the toolkit is designed for evolution. Rather than producing a static document that becomes outdated as AI capabilities and governance challenges evolve, both components are built to allow adjustments and updates as countries’ needs change and new implementation evidence emerges. This adaptive design reflects the OECD’s recognition that AI governance is not a destination but a continuous process of learning and adaptation.

The AI Self-Assessment Tool: Evaluating National AI Readiness

The Self-Assessment Tool follows a structured four-step process designed for rapid completion while delivering actionable insights. In the first step, challenge identification, users select from a predefined list of common challenges in implementing the OECD AI Principles. This list is compiled by the OECD Secretariat based on research, co-creation workshops, and input from the GPAI Tours de Table conducted with countries across different development stages.

The second step, policy area selection, maps the identified challenges to specific policy domains where intervention is needed. This mapping ensures that countries focus their limited resources on the areas most relevant to their particular challenges rather than attempting to address all aspects of AI governance simultaneously.

In the third step, current policy assessment, users evaluate their existing policy actions using a graded scale. This honest self-evaluation—determining strengths versus areas needing improvement—provides the foundation for targeted recommendations. The graded scale allows nuanced assessment rather than simple yes/no categorization.

The fourth step generates tailored recommendations that link directly to the Implementation Guidance. This linkage between assessment and action is the tool’s key differentiator—it addresses the common “now what?” problem that plagues readiness assessments by connecting identified gaps to concrete policy options and exemplar case studies. Drawing inspiration from the OECD Innovation Playbook (2024), the tool is designed for intuitive use, enabling multiple submissions per country so different ministries and agencies can assess their specific domains independently.

AI Implementation Guidance for Diverse Economies

The Implementation Guidance component provides the substantive depth behind the Self-Assessment Tool’s recommendations. It offers practical policy options for both the values-based principles and the policy-oriented recommendations, drawing on an extensive evidence base that includes guidance from OECD member countries, the OECD.AI Policy Observatory database, desk research on policy initiatives in emerging and developing economies, co-creation workshops, and GPAI Tours de Table.

What distinguishes this guidance from generic policy advice is its commitment to geographic diversity and context sensitivity. Every recommendation includes examples from countries at different development stages and in different regions, ensuring that a developing economy in Southeast Asia can find relevant case studies alongside examples from European Union member states or North American economies. This diversity is not decorative—it reflects the fundamental design principle that effective AI governance must account for different levels of knowledge and skills, digital infrastructure, and policy approaches.

The guidance covers both enabling and governing dimensions of AI policy. On the enabling side, it addresses how countries can build the foundations for AI adoption: investing in research infrastructure, developing talent pipelines, ensuring data availability and quality, and creating business environments that attract AI investment. On the governing side, it tackles the harder questions of how to ensure AI systems are trustworthy, transparent, and accountable within diverse regulatory and institutional contexts.

Bridging the AI Divide for Developing Economies

The Implementation Guidance has what the OECD describes as a “primary focus on addressing the unique challenges faced by emerging and developing economies.” This is not a token acknowledgment—it represents a structural design choice that shapes the entire toolkit’s content and methodology.

Emerging and developing economies face a distinct constellation of barriers to AI adoption. Financial constraints limit investment in the computing infrastructure, research facilities, and talent development that AI advancement requires. Limited digital infrastructure—from broadband connectivity to cloud computing access—constrains the environments in which AI can be deployed. Skills gaps at all levels, from basic digital literacy to advanced machine learning expertise, restrict both the development and the productive use of AI systems.

Yet these same countries stand to benefit most dramatically from AI if the barriers can be addressed. AI-powered agricultural tools can optimize crop yields in regions where food security is a pressing concern. AI-enhanced healthcare diagnostics can extend medical expertise to underserved rural areas. AI-enabled educational platforms can personalize learning at scale in countries where teacher shortages limit educational quality. The toolkit’s emphasis on sector-specific guidance in public services, agriculture, healthcare, and education directly targets these high-impact application domains.

The regional outreach strategy targets Asia, the Middle East, and Latin America and the Caribbean—regions where AI adoption is accelerating but governance frameworks remain nascent. Through co-creation workshops in these regions, the OECD ensures that the toolkit reflects the priorities and constraints of its intended users rather than projecting advanced economy assumptions onto fundamentally different contexts.

Co-Creation Methodology: Building AI Policy Tools With Countries

The co-creation methodology is perhaps the toolkit’s most innovative design feature. Rather than developing tools in OECD headquarters and distributing them to countries for implementation, the OECD has engaged diverse countries throughout the development process through workshops, consultations, and iterative feedback cycles.

Three Tours de Table conducted in March and April 2025 gathered input from GPAI member countries, non-OECD countries, and non-GPAI countries, ensuring representation across the full spectrum of AI development stages. These consultations informed the identification of common challenges, the prioritization of policy areas, and the selection of case studies that would be most relevant across different contexts.

The co-creation approach serves multiple purposes beyond improving the toolkit’s content. It builds ownership among participating countries, increasing the likelihood of actual adoption and use. It creates peer learning networks among countries facing similar challenges. And it provides the OECD with ground-truth intelligence about what AI governance challenges look like in practice—information that enriches the entire OECD AI knowledge ecosystem.

The methodology also includes a piloting phase where selected countries test the toolkit’s tools and provide feedback before the general launch. This validation step ensures the tools work in practice, not just in theory, and identifies usability issues that desk-based development might miss.

The OECD AI Ecosystem: Complementary Tools and Resources

The AI Policy Toolkit does not operate in isolation. It is part of a comprehensive OECD AI knowledge ecosystem that provides multiple layers of support for countries at different stages of AI governance maturity.

AI Country Reviews offer deep-dive national analysis with tailored recommendations for individual countries. While the toolkit provides broad, self-service guidance, Country Reviews deliver the intensive, country-specific assessment that complex governance challenges often require. The two approaches complement each other: the toolkit can identify areas for deeper analysis, while Country Reviews generate case studies that enrich the toolkit’s guidance.

The OECD.AI Policy Database serves as a comprehensive global repository of AI policy initiatives, restructured to align with the toolkit’s categories of interest. This database enables peer learning—countries can search for how other nations have addressed specific governance challenges and evaluate the outcomes of different policy approaches.

The forthcoming OECD AI Index, currently under development, will add quantitative benchmarking capability across key policy areas. When combined with the self-assessment tool’s qualitative evaluation and the policy database’s case study evidence, this index creates a three-dimensional view of AI governance progress that no single tool could provide alone.

Capacity building through hands-on training and workshops ensures that countries can actually use these tools effectively. The OECD recognizes that even free, well-designed online tools require supporting infrastructure—training, community engagement, and institutional support—to drive meaningful adoption.

AI Policy Toolkit Timeline and What Comes Next

The toolkit follows a 15-month development timeline spanning January 2025 to March 2026. Key phases include initiation and scoping (establishing the framework and methodology), content development (building the guidance based on research and co-creation), prototyping (developing the interactive online tools), piloting (testing with selected countries), and launch (public release on the OECD.AI platform).

Reporting milestones are aligned with major OECD and GPAI events: progress reports at the GPAI Plenary in Spring 2025, updated status at the GPAI Plenary in Fall 2025, formal presentation to the GPAI Council in November 2025, and the final launch at the GPAI Plenary and Digital Policy Committee meetings in Spring 2026.

For countries preparing for the toolkit’s availability, the implications are immediate. Governments should begin internal discussions about which ministries and agencies will participate in the self-assessment process. They should review their existing AI policies and initiatives to prepare for honest self-evaluation. And they should identify the priority sectors—public services, agriculture, healthcare, education—where implementation guidance will be most immediately valuable.

For businesses operating across borders, the toolkit’s standardizing effect on AI governance approaches creates both opportunities and obligations. As more countries align their policies with the OECD AI Principles through the toolkit, regulatory convergence increases—reducing compliance complexity for multinational operations while raising the baseline for responsible AI practices everywhere. The toolkit may be a government-focused instrument, but its effects will ripple through every organization operating in the global AI ecosystem.