Research on the optimization of ESG internal control in manufacturing enterprises driven by artificial intelligence: taking Prince Holdings as an example
As global regulatory pressure on corporate sustainability intensifies, manufacturing enterprises face growing challenges in building effective Environmental, Social, and Governance (ESG) internal control systems. Traditional approaches, reliant on manual data collection and fragmented reporting, are increasingly inadequate for the scale and complexity of modern manufacturing operations. This paper investigates how Artificial Intelligence (AI) technologies can systematically optimize ESG internal control in manufacturing enterprises. Employing a combination of longitudinal case study analysis, literature review, and panel regression, this study uses Prince Holdings (Oji Holdings Corporation) as the primary case and draws on a panel dataset of 15,623 firm-year observations from 3,358 A-share manufacturing companies over 2018–2023. The findings demonstrate that AI adoption is significantly and positively associated with ESG internal control quality (β = 1.051, p < 0.001), with data governance capability identified as a partial mediator and organizational readiness as a positive moderator. Based on these findings, a five-layer AI-ESG optimization model aligned with the Committee of Sponsoring Organizations of the Treadway Commission (COSO) framework is proposed as a replicable blueprint for the manufacturing sector.