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Procurement of Artificial Intelligence Systems in UAE Public Sectors: An Interpretive Structural Modeling of Critical Success Factors

Author

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  • Khalid Alshehhi

    (Department of Industrial Engineering and Engineering Management, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates)

  • Ali Cheaitou

    (Department of Industrial Engineering and Engineering Management, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates)

  • Hamad Rashid

    (Department of Industrial Engineering and Engineering Management, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates)

Abstract

This study investigates the critical success factors (CSFs) influencing the procurement of artificial intelligence (AI) systems within the United Arab Emirates (UAE) public sector. While AI holds immense potential to enhance public service delivery, its successful integration hinges on critical factors. This research utilizes Interpretive Structural Modeling (ISM) to analyze the CSFs impacting AI procurement within the UAE public sector. Through ISM, a structural model is developed to highlight the interrelationships between these CSFs and their influence on the procurement process, outlining the key elements for successful AI procurement within the UAE public sector. Based on the literature review and expert validation from the UAE public sector, ten CSFs were identified. This study found that clear needs assessment is the most influential CSF, while the long-term value of AI systems or services is the least influential. This study provides policymakers and public sector leaders with valuable insights, enabling them to formulate effective strategies to optimize the procurement process and establish a strong foundation for AI adoption. Finally, this will lead to an improved and more efficient public service delivery in the UAE.

Suggested Citation

  • Khalid Alshehhi & Ali Cheaitou & Hamad Rashid, 2024. "Procurement of Artificial Intelligence Systems in UAE Public Sectors: An Interpretive Structural Modeling of Critical Success Factors," Sustainability, MDPI, vol. 16(17), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7724-:d:1471929
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    References listed on IDEAS

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    2. Kate Crawford & Ryan Calo, 2016. "There is a blind spot in AI research," Nature, Nature, vol. 538(7625), pages 311-313, October.
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