Artificial intelligence (AI) technology is gradually integrating into the entire process of green supply chain management (GSCM), providing a systematic solution for enterprises to improve productivity and performance. This paper focuses on Chinese manufacturing enterprises, aiming to explore the multi-factor synergistic mechanism influencing differences in GSCM levels from a temporal perspective under the drive of AI. Based on 2019–2023 panel data of enterprises, this paper innovatively integrates the random forest algorithm with dynamic qualitative comparative analysis (QCA) to reveal the configurational effects of technological, organizational, and environmental factors in enterprises’ GSCM practices. The findings demonstrate that no single factor is a necessary condition for enterprises to implement GSCM; configurational analysis identifies two driving models: “AI technology innovation-driven (Configuration 1 and Configuration 2)” and “strategic resource-driven (Configuration 3)”; Configuration 1 combines research and development (R&D) investment and green awareness among executives with the enabling role of government subsidies; Configuration 2 couples R&D Investment with strong funding capacity, again facilitated by the presence of government subsidies; Configuration 3 combines AI technology adoption and green awareness among executives, supported by the necessary funding capacity and government subsidies. Additionally, inter-group analysis reveals no significant temporal effect among configurations but shows phased evolutionary characteristics. This paper has thoroughly explored the complex paths for enhancing GSCM of manufactory enterprises under the influence of AI. It is recommended that the government refine and strengthen targeted subsidy policies to better support the adoption and integration of AI in advancing GSCM within the manufacturing sector. Concurrently, manufacturers must align technology, organizational structure, and external factors, specifically through core AI technology improvements, enhanced executive green awareness, and the mobilization of government and external funding. These advancements have led to high-level GSCM within enterprises, allowing them to achieve high-quality and sustainable development.

The progress of artificial intelligence (AI) technology has significantly impacted economic growth. Research into the correlation between AI technology and total factor productivity (TFP) in enterprises enhances our understanding of how AI fosters economic efficiency and effectiveness. This study, based on 40,960 data points from 4,395 listed Chinese companies between 2003 and 2022, identifies the beneficial impact of AI technologies on corporate TFP, demonstrating that each additional unit of AI technology increases TFP by 0.046 units. The proposed approach using a Random Forest approach further refines these findings. The empirical results of Random Forest model include correlations of variables, trends in TFP over time, and a comparison of actual versus predicted TFP values, offering deeper insights into the factors driving productivity. Further analysis reveals that corporate innovation capabilities mediate this relationship, accounting for 22.176% of the total effect. Additionally, the infusion of youth into the top management team (TMT) positively moderates the impact of AI technology on TFP. The study’s findings provide a comprehensive understanding of the mechanisms through which AI technologies enhance corporate operations and growth. This pioneering study offers valuable insights for policymaking and business management, outlining a robust framework for leveraging AI to improve enterprise productivity.