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Exploration and prioritisation of critical success factors in adoption of artificial intelligence: a mixed-methods study

Author

Listed:
  • Sam Solaimani
  • Reza Dabestani
  • Thomas Harrison-Prentice
  • Edward Ellis
  • Michael Kerr
  • Abhishek Choudhury
  • Naser Bakhshi

Abstract

Artificial intelligence (AI) is becoming a strategic asset for businesses across all sectors. While most large companies have taken their first steps towards AI adoption, the success has remained strikingly limited. The current underdeveloped understanding of the critical success factors (CSFs) is argued to be one of the reasons for failing AI adoption. This study applies a mixed-methods approach, in which a broad information systems (IS) literature is systematically reviewed to identify CSFs relevant to AI adoption, including management support, business casing, problem orientation, data quality, data governance, cyber security and regulations. Next, an analytic hierarchy process (AHP) survey is combined with expert interviews to empirically rank and refine the identified CSFs across a multi-stage AI adoption model. The findings contribute to the scholarly discourse on CSFs relevant to AI adoption and help firms sharpen their focus and leverage their resources efficiently towards a more effective adoption of AI.

Suggested Citation

  • Sam Solaimani & Reza Dabestani & Thomas Harrison-Prentice & Edward Ellis & Michael Kerr & Abhishek Choudhury & Naser Bakhshi, 2024. "Exploration and prioritisation of critical success factors in adoption of artificial intelligence: a mixed-methods study," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 45(4), pages 429-453.
    • Handle: RePEc:ids:ijbisy:v:45:y:2024:i:4:p:429-453
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    Cited by:

    1. Sam Solaimani, 2024. "From Compliance to Capability: On the Role of Data and Technology in Environment, Social, and Governance," Sustainability, MDPI, vol. 16(14), pages 1-16, July.

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