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Regenerative Artificial Intelligence: A Paradigm Shift in Sustainable Business Model Innovation

Author

Listed:
  • Adrian Micu

    (Dunarea de Jos University of Galati, Romania)

  • Alexandru Capatina

    (Dunarea de Jos University of Galati, Romania)

  • Angela-Eliza Micu

    (Ovidius University of Constanta, Romania)

  • Mihaela-Carmen Muntean

    (Dunarea de Jos University of Galati, Romania)

  • Iulian-Adrian Sorcaru

    (Dunarea de Jos University of Galati, Romania)

Abstract

This paper proposes a bibliometric analysis on the emerging concept of Regenerative Artificial Intelligence (Regenerative AI), on the one hand, and explores its value for business model innovation, on the other hand. Regenerative AI systems reflect the capacity for self-improvement, self-adaptation, and self-repair, empowering organizations to preserve institutional knowledge, enhance resilience, and assure long-term value creation. Our study considers Regenerative AI a strategic enabler for self-healing capabilities and circular innovation. Based on a bibliometric analysis of 637 research articles from the Web of Science Core Collection, we have identified key thematic clusters using VOSviewer software, revealing four dominant domains: technological enablers, human-AI collaboration, regenerative business outcomes, and adaptive governance. The findings highlight connections among generative technologies, innovation processes, and performance metrics, underscoring the growing academic interest in Regenerative AI. The paper provides theoretical and managerial insights, in the light of Regenerative AI as a paradigm shift in sustainable business models.

Suggested Citation

  • Adrian Micu & Alexandru Capatina & Angela-Eliza Micu & Mihaela-Carmen Muntean & Iulian-Adrian Sorcaru, 2025. "Regenerative Artificial Intelligence: A Paradigm Shift in Sustainable Business Model Innovation," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 27-33.
    • Handle: RePEc:ddj:fseeai:y:2025:i:2:p:27-33
    DOI: https://doi.org/10.35219/eai15840409507
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