The Global AI Index assesses countries' standing in artificial intelligence through three core pillars: implementation, innovation, and investment. Implementations are categorized by talent and infrastructure, focusing on the physical underpinnings of AI, such as skilled practitioners and computing power. Innovation examines a country's ability to generate new scientific knowledge and apply it in practice, measured through research publications and open source activities.
The investment pillar assesses AI efforts through both government strategies and commercial ecosystems. The Government Strategy specifically considers spending plans in areas such as training and research infrastructure. Meanwhile, the “Commercial Ecosystem” pillar focuses on the scale of private investment in AI companies and the dynamism of their overall commercial environment, highlighting financing as a key driver.
The index itself consists of 108 metrics sourced from 24 public and private datasets. These indicators are categorized into three pillars and further divided into seven sub-pillars to provide a comprehensive assessment of a nation's AI capabilities. Country profiles can be used to explore rankings across these specific sub-pillars, providing a detailed understanding of strengths and weaknesses.
AI investment and innovation
The Global AI Index is comprised of 108 indicators from 24 sources to assess national performance across artificial intelligence. It is structured around three pillars: Implementation (People, Infrastructure, Operating Environment), Innovation (Research, Development), and Investment (Government Strategy, Commercial Ecosystem). The index aims to benchmark countries on AI investment, innovation and adoption, providing a comprehensive overview of AI capabilities and efforts.
The “Investment” pillar specifically focuses on national efforts in AI through two sub-pillars. 'Government Strategy' measures planned investment and spending in areas such as training, research and infrastructure. Commercial Ecosystem assesses the scale of private investment in AI companies and the overall dynamism of the commercial sector supporting AI development. These factors highlight financial commitments to accelerate advances in AI.
The US currently ranks first overall, and also tops in 'talent', 'infrastructure' and 'commercial ecosystem'. China is in second place overall, and Singapore is in third place. The index enables detailed country profiles and allows examination of performance across each of the seven sub-pillars. This detailed data reveals not only overall rankings, but also specific strengths and weaknesses in each country's AI environment.
AI implementation and infrastructure
The Global AI Index evaluates countries based on three pillars: adoption, innovation, and investment. Implementations are further categorized by 'people' and 'infrastructure', focusing on skilled AI practitioners and physical infrastructure such as computing power and electricity. “Infrastructure” specifically measures the availability of resources needed for AI development and demonstrates its importance as a foundational element of AI progress across the nation.
Innovation is evaluated through “research” and “development.” “Research” evaluates the generation of new scientific knowledge in AI, and “Development” evaluates the practical application of that knowledge to assets and products. These sub-pillars emphasize the importance of both theoretical advances. and Practical implementation to achieve the country's overall AI status.
The index utilizes 108 indicators from 24 sources and categorizes them across these pillars and sub-pillars. “Government Strategy” and “Commercial Ecosystem” fall under the investment pillars and measure national commitment and private investment in AI. These components represent a comprehensive approach to assessing a country's AI capabilities, including talent, resources, innovation, and financial support.
Talent is focused on recruiting skilled professionals for artificial intelligence solutions, from top AI research scientists working on cutting-edge AI to developers and experts applying AI at a practical level.
