Artificial Intelligence Adoption and Sustainable Transformation in <scp>E7</scp> Economies: Evidence From Education, Economic Growth, and Emissions

ABSTRACT Although the concept of artificial intelligence (AI) is gaining increased attention as a factor that can drive regional socio‐economic changes, its implementation in emerging economies is not evenly distributed and utilized. The current paper analyzes the factors determining the adoption of AI in the E7 economies with the help of the quantile regression (MMQREG) and Driscoll–Kraay estimators from 2004 to 2024. The empirical study indicates that education spending can be the most effective determinant of AI adoption, and the effects are especially elevated in its lower quantiles, suggesting the significance of human capital in creating absorptive capacity. Foreign direct investment (FDI) shows that it has a negative relationship with AI adoption, meaning that the existing flows of investment are not aligned adequately with the needs of digital transformation, and they may even crowd out domestic innovation. Growth in the economy supports the acceptance of AI, with more pronounced impacts in the higher quantile, which demonstrates a self‐reinforcing effect. Higher levels of economic development are more advanced in engaging in technology uptake. On the whole, the paper highlights the complexity of AI adoption that is multidimensionally oriented and introduces an interaction between educational, investment flow, and environmental results to define sustainable socio‐economic change in emerging economies. The findings will also add to the current discussions about how technological change can be exploited to achieve inclusive and sustainable regional development by attending to the opportunities and risks of AI. These findings have a significant policy implication; by enhancing education and digital literacy, targeting further internalization of FDI into knowledge‐based industries, and combining the use of AI with the environmental governance approach, E7 economies could experience an inclusive and sustainable technological transition.