Unlocking the Potential of AI-driven Circular Business Model Innovation : A case study of an industrial symbiosis

Sammanfattning: Purpose – This study aims to explore and provide empirical insights into AI-driven circular business model innovation (CBMI) in industrial symbiosis. In doing so, it addresses the knowledge gap regarding how industrial companies can use AI to amplify circular business models and facilitate AI-driven circular innovation. Method – A thematic analysis was used in the study to answer the research questions. It was based on 32 interviews with informants from five companies conducting an AI innovation initiative and experts, as well as two site visits, four project meetings and 61 company documents.  Findings – The analysis showed how AI can amplify an industrial symbiosis and uncovered three principles and symbiotic facilitators for AI-driven CBMI in an industrial symbiosis. The principles and symbiotic facilitators were combined in a coevolutionary alignment framework for AI-driven CBMI in industrial symbioses. Theoretical contributions – This study contributes to prior literature by (1) depicting how AI changes business models and amplifies an industrial symbiosis, where past research only had conceptualised it; (2) identifying principles that describe how AI-driven CBMI should be approached; (3) uncovering three symbiotic facilitators that create conditions for successful AI-driven CBMI; and (4) conceptualising a coevolutionary framework based on the principles and symbiotic facilitators for aligning the innovation efforts between partners in industrial symbioses. Practical contributions – Managers in industrial symbioses can use this study to comprehend how AI can improve resource flows and the significance of efficient data sharing in collaborative AI-driven innovation. Moreover, it provides a framework to assist companies in aligning innovation initiatives among partners in order to succeed with AI-driven CBMI. Limitations of the study – The study focused on five companies involved in an AI innovation initiative in one specific industrial symbiosis. As a result, the findings’ generalisability may be limited, and validating these findings in other industrial symbioses and different industrial ecosystems or partnerships would thus be interesting for future research.