A Decision‐Making Framework to Facilitate AI in the Circular Economy: A Case Analysis From an Emerging Economy Context

The Circular Economy (CE) transition presents a sustainable alternative to linear economic models. Artificial Intelligence (AI) provides a powerful tool for accelerating this change by enabling more innovative resource management and waste reduction alongside real‐time decision‐making. However, multiple complex barriers, including technological, organisational, environmental and human (TOE‐H) perspectives, prevent the widespread adoption of AI within CE practices. Therefore, this research aims to investigate the principal barriers preventing AI from becoming part of CE efforts. The study identifies 18 barriers and 15 solutions through an extensive literature review and expert opinion. The research uses a hybrid framework that combines the Fuzzy Analytic Hierarchy Process (Fuzzy‐AHP) and fuzzy technique of order preference by similarity to ideal solution (Fuzzy‐TOPSIS) to analyse the barriers and solutions. The Fuzzy‐AHP is used to determine the barrier weights; in conjunction with this, fuzzy‐TOPSIS is also employed to rank the solutions. The study reported lack of skilled workforce and low consumer awareness as the top barriers, while ‘encouraging eco‐design’ and ‘support smart reverse logistics systems’ were defined as the top solutions. This research contributes to the scholarly literature by mapping and prioritising strategic solutions to overcome barriers to adopting AI in the circular economy, which previous studies have not adequately addressed. Additionally, the research provides practical recommendations for government officials, industry managers and environmental experts to address these barriers and facilitate a smart circular transition driven by data. This study helps to understand AI's role in CE while establishing the foundations for inclusive technological solutions that support sustainable development.