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Artificial intelligence enabling circular business model innovation in digital servitization: Conceptualizing dynamic capabilities, AI capacities, business models and effects

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  • Sjödin, David
  • Parida, Vinit
  • Kohtamäki, Marko

Abstract

This study explores the potential of AI to enable circular business model innovation (CBMI) for industrial manufacturers and the corresponding AI capacities and dynamic capabilities required for their commercialization. Employing an analysis of six leading B2B firms engaged in digital servitization, we conceptualize the perceptive, predictive, and prescriptive capacities of AI, which enhance resource efficiency by automating and augmenting data-driven analysis and decision making. We further identify two innovative classes of AI-enabled CBMs – augmentation (e.g., optimization solutions) and automation (e.g., autonomous solutions) business models – and their main circular value drivers. Finally, our research reveals novel dynamic capabilities underpinning the innovation of AI-enabled business models – value discovery, value realization, and value optimization capabilities – which enable manufacturers to make economic and sustainable values come to life in collaborating with customers and ecosystem partners. This study represents an important step in our understanding of how AI can drive circularity and sustainable innovation in industrial digital servitization. Overall, our study contributes to practice and the academic literature on AI, circular business models, and digital servitization by highlighting the potential of AI to empower CBMs for industrial manufacturers and the underlying processes of this digital transformation.

Suggested Citation

  • Sjödin, David & Parida, Vinit & Kohtamäki, Marko, 2023. "Artificial intelligence enabling circular business model innovation in digital servitization: Conceptualizing dynamic capabilities, AI capacities, business models and effects," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    • Handle: RePEc:eee:tefoso:v:197:y:2023:i:c:s0040162523005887
    DOI: 10.1016/j.techfore.2023.122903
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