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Circular economy (CE) catches worldwide attention, and business model innovation plays an important role in enabling organizations that focus on resource efficiency, material recycling, waste reduction, and material reuse, repair and recycle inside their organizations. This study explains the difference between an online store and a brick and mortar store. Circular Business Models (CBM) reimagined by Artificial Intelligence (AI) that helps organizations to implement sustainable practices with advanced data insights. This study reviewed 32 peer reviewed journal articles published between 2015 to 2025 from ScienceDirect, using keywords such as ‘Artificial Intelligence’, ‘Machine Learning’, ‘Circular Economy’,’ Circularity’,’ Closed loops’,’ Business Model’. The selection criteria consisted of full text, English language, papers concerning the influence and impact of AI on Sustainability efforts, definitions of relevant terms, and papers from the perspective of firms and the discussion of AI capabilities. Dropping duplicates, conference proceedings which address digitalization without integrating AI/ML, or focus on algorithms without specifying impact on firms or CBM, are excluded. This analysis is concerned with the thematic scope of identifying factors, internal and external, that enable, and hinder AI enabled CBM synthesize AI capabilities, barriers to adopting AI enabled CBM programs, and studies, conceptual and empirical, which examine the contribution of AI to CBM implementation. CBM is found to be a key enabler of operations across different business formats. AI technologies like machine learning, forecasting, generative AI, digital twins, data driven design and decision support systems and data analytics are used in CBM for business analytics. There is some CBM that requires the descriptive, predictive, diagnostic, and perspective capabilities to prolong product life cycle, encourages steadier production, and decreases downtime. John Deere’s integration of Blue River technology for sustainability shows how AI orchestration is a key enabler of CBM success. The main obstacles to this implementation include organizational resistance, the absence of collaboration, unclear regulations, as well as algorithmic bias.

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AI-Driven Circular Business Models: A Comprehensive Review of AI’s Role in Advancing Circular Economy Practices

  • 会议论文

  • 发表日期：

    2025年

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  • W. A. D. C. S. Perera,
  • K. Keerthana,
  • K. M. C. Tharupath,
  • T. A. N. T. Perera,
  • G. Y. Jayasinghe

摘要

Circular economy (CE) catches worldwide attention, and business model innovation plays an important role in enabling organizations that focus on resource efficiency, material recycling, waste reduction, and material reuse, repair and recycle inside their organizations. This study explains the difference between an online store and a brick and mortar store. Circular Business Models (CBM) reimagined by Artificial Intelligence (AI) that helps organizations to implement sustainable practices with advanced data insights. This study reviewed 32 peer reviewed journal articles published between 2015 to 2025 from ScienceDirect, using keywords such as ‘Artificial Intelligence’, ‘Machine Learning’, ‘Circular Economy’,’ Circularity’,’ Closed loops’,’ Business Model’. The selection criteria consisted of full text, English language, papers concerning the influence and impact of AI on Sustainability efforts, definitions of relevant terms, and papers from the perspective of firms and the discussion of AI capabilities. Dropping duplicates, conference proceedings which address digitalization without integrating AI/ML, or focus on algorithms without specifying impact on firms or CBM, are excluded. This analysis is concerned with the thematic scope of identifying factors, internal and external, that enable, and hinder AI enabled CBM synthesize AI capabilities, barriers to adopting AI enabled CBM programs, and studies, conceptual and empirical, which examine the contribution of AI to CBM implementation. CBM is found to be a key enabler of operations across different business formats. AI technologies like machine learning, forecasting, generative AI, digital twins, data driven design and decision support systems and data analytics are used in CBM for business analytics. There is some CBM that requires the descriptive, predictive, diagnostic, and perspective capabilities to prolong product life cycle, encourages steadier production, and decreases downtime. John Deere’s integration of Blue River technology for sustainability shows how AI orchestration is a key enabler of CBM success. The main obstacles to this implementation include organizational resistance, the absence of collaboration, unclear regulations, as well as algorithmic bias.