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AI-Powered Workforce Analytics Forecasting Labor Market Trends and Skill Gaps for U.S. Economic Competitiveness

Author

Listed:
  • Foysal Mahmud

    (College of Business, Westcliff University, Irvine, CA 92614, USA)

  • Mohammad Abdul Goffer

    (School of Business, International American University, Los Angeles, CA 90010, USA)

  • Partha Chakraborty

    (School of Business, International American University, Los Angeles, CA 90010, USA)

  • Sharmin Sultana

    (School of Business, International American University, Los Angeles, CA 90010, USA)

  • Evha Rozario

    (School of Business, International American University, Los Angeles, CA 90010, USA)

  • Md Alamgir Miah

    (School of Business, International American University, Los Angeles, CA 90010, USA)

  • Md Аsikur Rаhmаn Chy

    (School of Business, International American University, Los Angeles, CA 90010, USA)

  • Urmi Haldar

    (Department of Management, Glasgow Caledonian University, London, UK)

Abstract

This paper discusses how AI-enabled analytics are used to detect emerging relative skill shortages, track labor market patterns. It improves the competitiveness of the economy in the United States. The intense use of Artificial Intelligence in workforce analytics has revolutionized how governments and industries forecast the labor market needs the study throws light on the role of real-time data variables and prediction modelling in making workforce development meet changing industry demands. The research project has adopted quantitative research design. A systematic questionnaire was sent to a sampling of 300 participants comprising HR analysts, labor economists and policymakers in different industries of the U.S. Variables that were measured included the AI Integration Level, Labor Market Responsiveness, Real-Time Data Utilization, Predictive Accuracy and the dependent variable, Economic Competitiveness. The findings showed that there were significant correlations among AI integration (r = 0.71, p

Suggested Citation

  • Foysal Mahmud & Mohammad Abdul Goffer & Partha Chakraborty & Sharmin Sultana & Evha Rozario & Md Alamgir Miah & Md Аsikur Rаhmаn Chy & Urmi Haldar, 2024. "AI-Powered Workforce Analytics Forecasting Labor Market Trends and Skill Gaps for U.S. Economic Competitiveness," Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 6(5), pages 265-277, October.
    • Handle: RePEc:alk:jcsts0:v:6:y:2024:i:5:id:10491
    DOI: 10.32996/jcsts.2024.6.5.21
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