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A path to follow to overcome foundational barriers to the adoption of artificial intelligence within the manufacturing industry: a conceptual framework

Author

Listed:
  • Moacir Godinho Filho

    (Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School)

  • Sofia Vieira Queiroz de Almeida

    (UFSCar - Federal University of São Carlos)

  • Murís Lage Junior

    (UFSCar - Federal University of São Carlos)

  • Lauro Osiro

    (UFTM - Universidade Federal do Triângulo Mineiro [Uberaba, Brazil])

  • Bruna Lima

    (UFSCar - Federal University of São Carlos)

  • Mario Henrique Callefi

    (Chemnitz University of Technology / Technische Universität Chemnitz)

Abstract

Despite growing interest, many industries face foundational barriers to AI adoption, especially in emerging economies. This study systematically analyzes these barriers in manufacturing, addressing a critical gap in the literature. Unlike prior research on application-specific challenges, we focus on foundational issues that must be resolved for effective AI implementation. Using Interpretive Structural Modeling (ISM) and fuzzy MICMAC, we develop a four-level framework identifying 20 key barriers. Our framework provides actionable steps for managers, emphasizing workforce reskilling, Enterprise Information Systems (EIS), and Industry 5.0 principles. This study offers practical insights to help industries navigate AI adoption challenges.

Suggested Citation

  • Moacir Godinho Filho & Sofia Vieira Queiroz de Almeida & Murís Lage Junior & Lauro Osiro & Bruna Lima & Mario Henrique Callefi, 2025. "A path to follow to overcome foundational barriers to the adoption of artificial intelligence within the manufacturing industry: a conceptual framework," Post-Print hal-04991946, HAL.
    • Handle: RePEc:hal:journl:hal-04991946
    DOI: 10.1080/17517575.2025.2458685
    as

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