OECD has introduced OECD.AI Index as a composite measurement framework designed to evaluate national AI ecosystems based on their adherence to the OECD AI Recommendation. The Index is intended to provide a comparable overview of national AI ecosystems and support evidence-based policymaking for trustworthy AI.
To ensure cross-country comparability, the Index utilizes the OECD.AI Normalization Process, which standardizes diverse datasets—ranging from compute power to research output—onto a common scale.
The Index assesses five critical pillars: AI Research & Development, AI Enabling Infrastructure, AI Policy Environment, Jobs and Skills, and International Co-operation.
Utilizing a rigorous methodology involving Principal Component Analysis (PCA) and k-means clustering for data imputation, the Index provides cross-country comparisons to help policymakers track progress toward trustworthy AI. While currently led by the United States, United Kingdom, and Switzerland, the framework is expanding annually to include indicators like AI business adoption and environmental impact.
For nations like India, the Index serves as a high-fidelity benchmarking tool to align domestic strategies with global standards for ethical and responsible AI development.
Key Pillars of the OECD.AI Index Framework
AI Research & Development: Measuring research output, model innovation, and high-impact software projects.
AI Enabling Infrastructure: Assessing high-quality digital connectivity, cloud compute availability, and GPU cluster density.
AI Policy Environment: Evaluating government investment, public-sector AI use, and the establishment of regulatory sandboxes.
Jobs and Skills: Focusing on AI talent attraction, public digital literacy, and the supply of specialized AI competencies.
International Co-operation: Tracking participation in standardization efforts, global research collaborations, and initiatives like GPAI.
What is the “OECD.AI Normalization Process”? The OECD.AI normalization process is a technical methodology used to make diverse datasets comparable across different countries and metrics. Because AI indicators—such as “number of AI models” vs. “broadband capacity”—have different scales, the Index uses population scaling to account for country size, followed by log transformations to handle extreme outliers. Finally, a “Min-Max Scaling” is applied to bring all values onto a standardized 0-to-1 scale. This ensures that the equal weighting approach is mathematically robust and that a country’s score reflects its relative implementation performance rather than just its raw geographic or economic size.
Policy Relevance
For India, the OECD.AI framework represents a transition from “Ad-hoc AI Planning” to “Metric-Driven Governance,” providing a conceptual roadmap to scale its position as a regional AI leader.
Benchmarking Strategic Hubs: India can utilize the “Jobs and Skills” pillar to evaluate the effectiveness of its Regional AI Hubs in attracting global talent and building a “Viksit Bharat” ready workforce.
Standardizing Ethical Compute: Aligning with the “Enabling Infrastructure” metrics allows India to measure its sovereign compute density (GPU clusters) against global top performers like Switzerland.
Operationalizing GPAI Leadership: The “International Co-operation” pillar provides a checklist for India to deepen its engagement in global AI standardization efforts, ensuring its domestic innovations are globally interoperable.
Federal Data Maturity: The methodology’s focus on data availability and official statistics supports the need for India to upgrade its national cultural and economic statistics to meet the 15% missingness threshold.
Implementation Fidelity via Sandboxes: Following the Swedish case study, India can identify gaps in its regulatory sandboxes, ensuring that startups have the “Techno-Legal” certainty needed to test high-risk AI applications.
Follow the full report here: The OECD.AI Index Framework 2026
