The role of artificial intelligence (AI) in achieving sustainability goals has garnered attention in academic literature. While AI has been argued to be crucial in addressing circularity challenges, organizations face challenges in configuring a business model. Designing new business calls for insights on how AI can be integrated into value creation and capture mechanisms. There is a lack of clarity on how organizations deploy AI as they transition to circular business model innovation. The purpose of the study is to explore how AI is integrated into organizational processes while adopting circular business models. We conducted online open-ended interviews with 55 participants to explore the potential role of AI in enabling the adoption of circular business models. Our findings have implications for theory building relating to AI business model innovation and provide a novel avenue for further research on business model innovation literature. Building on a natural resource-based view, the findings indicate that while implementing a circular business model is challenging, AI enables organizations to create, transfer, and capture value through resource efficiency and the reuse of resources. As AI technologies continue to evolve, organizations must develop adaptive capabilities to continually explore opportunities. AI enables organizations to reduce costs, develop novel value-creation strategies, and capture opportunities, resulting in improved efficiency. Transitioning to a circular business model requires developing routines, and organizations must adapt existing systems to ensure these systems result in pollution prevention, product stewardship, and sustainable development. It is important for managers to develop organizational resources and capabilities that enable the development of AI capabilities.

Global net-zero ambitions require transformative strategies to decarbonise carbon-intensive global value chains (GVCs). This study examines how multinational enterprises (MNEs) in sunset industries integrate carbon capture technologies (CCT) with operational and supply chain dynamics (OSCD) to advance decarbonisation. Drawing on institutional theory (InsT) and dynamic capabilities theory (DCT), we investigate how external decarbonisation pressures activate internal capability routines that shape the adoption of technological and supply chain innovations. Using qualitative insights from 55 industry professionals across energy-intensive sectors, we analyse how firms navigate regulatory fragmentation, mobilise early-stage decarbonisation investments and develop collaborative and digital mechanisms to support low-carbon transitions. The findings reveal that coercive and normative pressures primarily stimulate sensing and seizing capabilities, while transforming capabilities develop more gradually through experiential learning and organisational reconfiguration. Firms often pursue hybrid CCT–OSCD strategies, combining technological interventions with operational and supply chain adjustments to manage institutional complexity. This study contributes theoretically by offering an integrated InsT–DCT framework that explains how institutional constraints and dynamic capabilities interact to enable decarbonisation in sunset industries. Managerially, the findings identify priority capability areas, including policy sensing, digital resource mobilisation and supply chain reconfiguration that can accelerate decarbonisation across global value chains.