As global energy demand continues to intensify, artificial intelligence technology innovation (AITI) offers emerging possibilities for improving energy efficiency and supporting sustainable energy transitions. At the same time, the integration of renewable energy is reshaping energy systems and influencing energy consumption patterns. This study examines the relationship between AITI and energy intensity using panel data for 42 countries over the period of 2000–2022. Analysis with the Driscoll and Kraay standard errors fixed effects regression method reveals that AITI significantly reduces energy intensity; however, the evidence of a U-shaped relationship indicates that the reducing effect of AITI may reverse beyond a threshold. Economic growth, trade openness, financial development, and government regulations reduce energy intensity while foreign investment and skilled labor increase it. Mechanism tests reveal that AITI reduces energy intensity by promoting information and communication technology development, and renewable energy enhances the reducing effect of AITI. Heterogeneity analyses reveal that the reducing impact of AITI on energy intensity is significant in developed economies but insignificant in developing nations. These findings provide empirical insight for policymakers, emphasizing the need to align AITI with renewable energy transitions through tailored strategies that reflect economic diversity and regional conditions.