This chart shows three clusters of countries based on their pillar averages and total AI readiness scores. The grouping reveals clear tiers—low, medium, and high performers, indicating that countries share distinct structural patterns in AI readiness.

As Lazăr and others note, socioeconomic and cultural factors often create distinct tiers of AI development, which supports the validity of our 3 cluster structure (Lazăr et al., 2024). After we visualize these outputs, we observed that there are 3 different types of distinct profiles, which are a high-readiness cluster, a medium-readiness cluster, and a low-readiness cluster. The countries in the high-readiness cluster are performing very well in almost every dimension, which indicates that these countries have stable governance, a high level of education, an advanced digital environment, and well-developed policy. The medium-readiness cluster shows uneven development trends; they might have advantages in some of the dimensions, but in other dimensions, they are relatively weak. This indicates that, for these countries, their AI environment is in the process of developing or on transition. For the countries in the low-readiness cluster, they usually have system weaknesses or defects in governance, technology or infrastructure. As we make these outputs into visualisation, we can clearly see trends of areas, Europe, North America and East Asia occupied most of the high-readiness cluster, while Africa, South Asia, and Latin America fall into the lower clusters. Based on our collected data, we found that differences in governance models and strategic coordination often lead to strong regional clusters in the development of artificial intelligence, which is highly consistent with our research results. These patterns represent that AI readiness is regional, not only national. It will be affected by their shared history, economy, and governance policy system. Cluster Analysis helps us to reveal how social conditions can predict AI readiness and development in the world.