Abstract Artificial intelligence (AI) is emerging as a powerful driver of the circular economy (CE), enabling production systems to become more resource-efficient, less waste-intensive and strategically aligned with sustainability goals. This study offers the first comprehensive mixed-methods assessment of how AI transforms industrial production ecosystems in the post-ChatGPT era. Drawing on a bibliometric network analysis of 196 peer-reviewed articles (2023–2024) and a systematic review of 104 studies, we identify the main AI-enabled mechanisms advancing CE principles in smart manufacturing, waste valorisation, supply-chain transparency, and sustainable design. The evidence shows that AI can increase resource-efficiency metrics by up to 25% and reduce production scrap by as much as 30% in documented cases. We also highlight adoption barriers, particularly for small and medium-sized enterprises and firms in emerging economies, where capability and data constraints limit impact. This research deepens theoretical understanding by integrating CE principles, Industry 4.0 architectures, green innovation theory, and lifecycle assessment into a unified conceptual framework. It also delivers targeted recommendations for policymakers, managers, technology providers, and investors. The findings position AI not merely as an operational tool but as a strategic orchestrator of regenerative production systems, offering a clear roadmap for accelerating circular transitions in line with the Sustainable Development Goals.
Lopez et al. (2026) studied this question.