Matrix Analysis of Structural Convergence of Energy-Relevant and Policy-Relevant AI Research: Implications for Energy Policy

The rapid expansion of artificial intelligence (AI) research does not automatically imply its structural integration into industry governance systems. In the energy sector, this raises the question of whether a policy-relevant AI regime has already emerged or whether a structural gap persists between technological development and institutional integration. This study is based on a dataset of 792,417 publications indexed in Scopus (1981–2025). Using the AI-Assisted Research Methodology, a piecewise linear phase segmentation of the AI corpus publications was applied. A matrix model was developed to analyze the distribution of energy relevance (Y) and policy relevance (X) in X–Y coordinates. The results indicate that AI research entered a phase of unstable growth after 2017 and a phase of methodological acceleration after 2021. Despite the growth of both indicators (X, Y), the structural concentration of research related to energy and policy remains moderate (zone 2). The adoption of AI in policy is significantly faster than its integration into energy, suggesting an institutional lag. This study introduces the concept of a “synchronization zone” as an indicator of structural convergence and proposes a framework for assessing the degree of AI integration in energy governance. The findings shift the analytical focus from the growth of publications to the structural configuration and contribute to the development of more coordinated strategies in digital and energy policy.