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Artificial Intelligence Adoption in the Manufacturing Sector: Challenges and Strategic Framework

Author

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  • Muhammad Yusuf Bin Masod

    (Department of Printing Technology, College of Creative Arts, UiTM Selangor Branch, Puncak Alam Campus, 42300 Bandar Puncak Alam, Selangor, Malaysia)

  • Siti Farhana Zakaria

    (Department of Printing Technology, College of Creative Arts, UiTM Selangor Branch, Puncak Alam Campus, 42300 Bandar Puncak Alam, Selangor, Malaysia)

Abstract

In today’s competitive business landscape, manufacturing organizations are increasingly recognizing the potential of artificial intelligence (AI) to enhance productivity, efficiency, and cost-effectiveness. Despite AI’s transformative applications across various sectors, its adoption within the manufacturing industry remains underexplored, with many firms facing unique challenges such as organizational complexity, legacy systems, and a shortage of specialized digital skills. This study conducts a comprehensive literature review to identify the key factors influencing AI adoption in manufacturing, categorizing them into technological, organizational, and external dimensions. Technological factors include perceived benefits, system compatibility, data quality, cost and IT infrastructure, while organizational factors encompass top management support, employee competencies, and organizational readiness. External influences involve government regulations, competitive pressures, and vendor support. By synthesizing findings from multiple empirical studies, we develop a conceptual framework based on the Technology-Organization-Environment (TOE) model, highlighting how these dimensions interact to shape AI adoption decisions. The proposed framework highlights the critical role of leadership commitment, strategic alignment of AI initiatives, and the necessity for robust technological infrastructure. It also emphasizes the impact of external factors such as supportive government policies and market competitiveness on accelerating AI integration. The study’s implications are significant for academics seeking to fill research gaps, industry practitioners aiming for successful AI implementation, and policymakers interested in fostering an environment conducive to technological advancement. While the framework offers a structured approach to understanding AI adoption in manufacturing, the study acknowledges the need for empirical validation. Future research should test the framework across different manufacturing sectors and regions to account for industry-specific factors and regional variations. By addressing these areas, organizations can better navigate the complexities of AI adoption, enhancing competitiveness and innovation in the manufacturing sector.

Suggested Citation

  • Muhammad Yusuf Bin Masod & Siti Farhana Zakaria, 2024. "Artificial Intelligence Adoption in the Manufacturing Sector: Challenges and Strategic Framework," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(10), pages 150-158, October.
    • Handle: RePEc:bcp:journl:v:8:y:2024:i:10:p:150-158
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    References listed on IDEAS

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    3. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    4. Kinkel, Steffen & Baumgartner, Marco & Cherubini, Enrica, 2022. "Prerequisites for the adoption of AI technologies in manufacturing – Evidence from a worldwide sample of manufacturing companies," Technovation, Elsevier, vol. 110(C).
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