Exploring the nexus of artificial intelligence in talent acquisition: Unravelling cost-benefit dynamics, seizing opportunities, and mitigating risks


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The rise in talent management complications led organizations to rely on the latest technologies to automate their routine HRM tasks through AI. This study proposed to examine fundamental aspects of AI in talent acquisition (cost-benefit, opportunities, and risk factors) from the context of strategic analysis and decision-making. 52 respondents from HRM and the information technology departments from fifteen large dairy enterprises, each with more than one thousand employees, were included in the focus group discussion. Both departments were included in the focus group discussion as they heavily employ AI in talent acquisition. The opinions were collected in multiple rounds based on the cost, benefit, opportunity, and risk criteria using the analytical hierarchy process, a multi-criteria decision-making framework. The findings demonstrated that most respondents opinioned AI supports talent acquisition with many opportunities (38.7%) that involve the identification of the best applicants (18.7%) and different benefits (33.2%) to the organization in the form of saving time and cost (16.1%) leading to higher efficacy. The study infers that the application of AI in HRM significantly contributes to talent acquisition, streamlining processes, improving efficiency, and enhancing decision-making. The study recommends that implementing AI in talent acquisition requires a strategic approach, and organizations need to consider factors such as data privacy, ethical use of AI, and ongoing training to ensure successful integration into their hiring processes. Additionally, regular monitoring and adjustments are essential to optimize the effectiveness of AI tools in talent acquisition.

The authors of this article would like to thank Prince Sultan University for its financial and academic support for this publication.

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    • Figure 1. Theoretical framework for AI-based HRM applications
    • Figure 2. Priority weight for criteria of AI-based application
    • Figure 3. Priority weight for alternatives of AI-based application
    • Figure 4. Performance sensitivity analysis
    • Figure 5. Performance sensitivity analysis
    • Table 1. Measurement scale used in the questionnaire
    • Conceptualization
      Sania Khan, Shaha Faisal, George Thomas
    • Formal Analysis
      Sania Khan, Shaha Faisal
    • Investigation
      Sania Khan, Shaha Faisal
    • Project administration
      Sania Khan, Shaha Faisal
    • Resources
      Sania Khan, George Thomas
    • Software
      Sania Khan
    • Supervision
      Sania Khan
    • Visualization
      Sania Khan, Shaha Faisal
    • Writing – original draft
      Sania Khan, Shaha Faisal, George Thomas
    • Writing – review & editing
      Sania Khan
    • Data curation
      Shaha Faisal
    • Methodology
      Shaha Faisal
    • Validation
      Shaha Faisal
    • Funding acquisition
      George Thomas