⚡ Quick Summary
This technical paper introduces the OECD.AI Index – a structured, data-driven tool designed to measure how countries implement the OECD AI Recommendation. It combines 28 indicators across five key policy areas, including R&D, infrastructure, policy environment, skills, and international cooperation.
The Index aims to move beyond fragmented AI metrics by offering a comparable, standardised view of national AI ecosystems. It blends quantitative and qualitative data, applies statistical normalization and aggregation, and produces country-level scores to support benchmarking and policy evaluation.
Importantly, the Index is explicitly governance-oriented. It does not just measure AI capacity or innovation, but links these elements to policy commitments and outcomes aligned with “trustworthy AI.” It is positioned as a living tool, updated annually, and intended primarily for policymakers but also useful for researchers and stakeholders navigating global AI development.
🧩 What’s Covered
The document is structured as a full technical specification of the OECD.AI Index, covering conceptual, methodological, and analytical layers.
It begins by positioning the Index within the broader ecosystem of AI measurement tools, identifying gaps in existing indices—especially around governance and trustworthy AI. It then defines the Index’s purpose: to operationalise the OECD AI Recommendation into measurable indicators.
The core of the paper is the conceptual framework, which is built around five components:
– AI Research & Development
– AI Enabling Infrastructure
– AI Policy Environment
– Jobs & Skills
– International Cooperation
Each component is broken into sub-components and mapped to specific indicators (28 in total). These include metrics such as AI patents, compute infrastructure, VC investment, AI talent flows, and participation in international standards.
The methodology section provides a detailed pipeline:
– Data collection from mixed sources (official stats, surveys, private datasets)
– Data harmonisation and cleaning
– Missing data imputation (forward fill + k-means clustering)
– Normalisation (per capita scaling, log transforms, min-max scaling)
– Equal-weight aggregation across components
– Robustness checks (PCA, correlation analysis, sensitivity testing)
The results section presents country rankings for 2023–2024, highlighting differences in national strategies and performance profiles. For example, strong performers like the US and UK score highly due to R&D and investment, while others excel in skills or governance.
Finally, the paper outlines limitations (data gaps, missing indicators, measurement challenges) and future development plans, including expanding geographic coverage and incorporating new governance-related metrics.
💡 Why it matters?
This paper represents one of the most serious attempts to operationalise AI governance at scale.
Most existing indices measure “AI strength” or “readiness.” This Index instead links performance directly to policy implementation—bridging the gap between strategy and measurable outcomes. That makes it highly relevant for regulators, governments, and institutions working under frameworks like the EU AI Act or OECD principles.
It also introduces a critical shift: governance is not treated as a separate layer, but embedded across infrastructure, skills, and investment. This aligns closely with real-world implementation challenges, where policy effectiveness depends on ecosystem conditions.
For practitioners, the Index provides a benchmarking tool. For policymakers, it offers evidence-based prioritisation. And for the broader AI governance field, it sets a foundation for standardised measurement of “trustworthy AI” progress.
❓ What’s Missing
The Index still struggles with some of the hardest governance questions.
First, it largely measures proxies rather than direct governance outcomes. Core principles like fairness, accountability, or human rights are not directly quantified, reflecting broader measurement challenges.
Second, there is a strong bias toward data availability. Indicators such as compute infrastructure or VC investment are well captured, while areas like AI risk management, safety practices, or real-world harms are underrepresented.
Third, the reliance on composite scoring introduces trade-offs. High performance in one area (e.g. R&D) can compensate for weak governance, which may obscure risk exposure.
Finally, coverage is still limited to OECD countries, reducing global applicability—especially for emerging AI ecosystems.
👥 Best For
Policymakers designing or evaluating national AI strategies
AI governance professionals looking for benchmarking frameworks
Researchers analysing global AI ecosystems
International organisations working on AI standards and cooperation
📄 Source Details
OECD (2026) – Technical paper developed under the OECD Working Party on AI Governance (AIGO), based on contributions from an expert group and multiple international stakeholders
📝 Thanks to
OECD AI Expert Group, OECD Secretariat, and contributing institutions including OECD Directorates, GPAI experts, and external research partners
