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National AI Index (NAII)


Added by:   OECD analyst
Added on:   29 Oct 2026
Updated by:   OECD analyst
Updated on:   29 Oct 2026

The National Artificial Intelligence Index (NAII) is a strategic initiative to assess and monitor the level of AI adoption readiness among government entities in Saudi Arabia. The index launched its initial phase this year with 30 government entities and it provides a structured measurement framework built on three main pillars covering seven core dimensions and twenty-three sub-domains. The Index classifies the maturity level into six main levels, each level reflects a specific stage in AI.

Initiative overview

The National AI Index (NAII) aims to assess the readiness level of government entities to adopt AI technologies, monitor their progress on a periodic basis, and provide practical recommendations on strengths and potential areas for improvement for each entity, thereby supporting the development and use of these technologies at the national level. The Index consists of three main pillars, seven sub-pillars, and twenty-three domains to ensure a comprehensive measurement process. The design of the Index considers current challenges and future opportunities faced by government entities in adopting AI. The most notable of these challenges include weak alignment between AI initiatives and national priorities, limited availability of suitable data for training, variations in the readiness of technical infrastructure across entities, a shortage of specialized national talent, and a lack of unified and clear measurement standards.
Index Objectives:
 - Aligning government efforts and priorities in the field of AI.
 - Providing and improving the necessary enablers for AI adoption within government entities. 
- Enabling the launch and adoption of AI products in key sectors and ensuring impact realization. 
The Index delivers results that reflect each entity's adoption level while also providing the necessary support and recommendations to strengthen their capabilities in developing effective and sustainable AI solutions and applications that directly contribute to achieving the objectives of Saudi Vision 2030. Additionally, the Index aims to empower government entities to develop AI products in priority sectors, provide the essential enablers to support the sustainability of these efforts, and maximize their impact.

The Index consists of three main pillars: Directions, Enablers, and Outputs. These pillars include seven main sub-pillars, divided into 23 sub-domains covering strategic, regulatory, technical, operational, and human aspects. These components were carefully selected to provide a comprehensive and integrated view of entities’ readiness levels in the field of AI. The National AI Index classifies the maturity level of government entities in AI adoption into six main levels. Each level reflects a specific stage in the entity’s practices and development of AI capabilities, and helps define a roadmap for continuous improvement. The levels are:
Level 0 -  Absence of Capabilities: Score: 0% – 4.9%
Level 1 – Establishing: Score: 5% – 24.9% 
Level 2 – Activated: Score: 25% – 49.9%
Level 3 - Managed: Score: 50% – 79.9%
Level 4 – Excellence: Score: 80% – 94.9%
Level 5 - Pioneer: Score: 95% – 100%
 

Other relevant details

Results, outcomes and impacts: The National AI Index is expected to enhance evidence-based policymaking, improve institutional readiness, and accelerate AI adoption by providing a unified, data-driven view of capabilities across sectors. It will also strengthen transparency, benchmarking, and targeted investments aligned with national priorities. Impact will be measured through baseline vs. follow-up assessments, improvement in index scores across pillars, sectoral adoption rates, compliance with AI governance frameworks, and correlation with productivity and service quality indicators over time. Challenges and lessons learned: Current challenges in developing the National AI Index include varying maturity levels across entities, inconsistent definitions and methodologies for AI systems, and reliance on external vendors, which may limit transparency and comparability. Additional risks include data availability and quality gaps, fragmented governance practices, and limited internal capabilities. Lessons learned highlight the need for standardized definitions, clear measurement criteria, strengthened internal capacities, and enhanced coordination to ensure consistent, reliable, and comparable assessments across entities.