This chart compares pillar strengths across low, medium, and high-readiness countries. High-readiness countries consistently score higher across all pillars, especially in governance and infrastructure, while low-readiness countries are behind in every category.

From the results, it can be seen that the differences in the Government pillar are the greatest, indicating that it is the most capable of distinguishing different levels of readiness. Secondly, there is Data and Infrastructure, while the differences in the Technology Sector are relatively small. We find from Demaidi’s article that infrastructure maturity is often the largest bottleneck for countries in transition, whereas technological capacity alone rarely explains national readiness. We know that highly prepared countries not only have an advantage in technology, but more importantly, their policy environment is more transparent. Countries in the medium group are mostly at a stage where infrastructure is still uneven, while those with low preparedness lack a technological environment.

Among the comparisons of the ten dimensions, Infrastructure, Governance and Ethics, and Digital Capacity show the most obvious differences, which is consistent with the previous analysis: What drives the development of AI is not only technical resources, but also public trust and a series of social systems. Qian and others researchers emphasize that governance frameworks, ethical oversight, and regulatory trust are essential components of AI development, explaining why these dimensions diverge so strongly across countries (Qian et al., 2024). By comparing the pillar models of countries at different levels, we conclude that enhancing AI readiness is not simply about increasing technological investment, but rather requires the simultaneous construction of a complete infrastructure and ensuring the sustainability of technological development.