ITU releases second edition of AI readiness assessment framework
A new report by the International Telecommunication Union examines how countries and organisations can assess their preparedness to adopt and scale artificial intelligence. The document presents an analytical framework designed to measure AI readiness across infrastructure, skills, data, governance, and innovation systems, based on empirical use cases and expert input.
Published in January 2026 by the International Telecommunication Union (ITU), AI Ready. Analysis Towards a Standardized Readiness Framework – Report 2.0 builds on earlier work launched in 2024. The report draws on a bottom-up methodology, analysing real-world AI projects submitted through ITU’s AI Readiness Plugfest and consultations with 88 experts from 38 countries, a majority from developing economies.
The framework identifies six foundational factors for AI readiness, including data availability and governance, digital infrastructure, skills, research and innovation capacity, standards, and sandbox environments for experimentation. These factors are further developed into 13 analytical dimensions, supported by metrics and indices that allow countries, enterprises, and other stakeholders to conduct self-assessments tailored to local priorities and contexts.
The report highlights persistent global disparities in connectivity, computing capacity, data quality, and digital skills, noting that these gaps directly affect the ability to deploy trustworthy and scalable AI systems. It also points to the role of open-source ecosystems, standards development, and coordinated public and private investment in strengthening national AI capabilities.
In addition to the analytical framework, the report introduces a pilot AI Readiness Enablement Toolkit, described as a living tool that supports self-evaluation, gap analysis, and the generation of context-specific recommendations. ITU presents the framework as an input to capacity development and international cooperation efforts, rather than as a benchmarking or ranking mechanism, with further refinement planned in future iterations
