Predictive Analytics for Strategic Workforce Planning: A Cross-Industry Perspective from Energy and Telecommunications


  • Md Atiqur Rahaman Department of Management and Information Technology, St. Francis College, New York, USA
  • Md Hasanujamman Bari Graduate Researcher, Management Information Systems, Lamar University, Texas, USA


predictive analytics, strategic workforce planning, artificial intelligence, machine learning, HR technology integration, long-term business strategy, cross-industry applications, workforce trends, organizational agility


This paper explores the integration of predictive analytics into strategic workforce planning, highlighting the transformative impact of artificial intelligence (AI) and machine learning advancements. With a focus on the energy and telecommunications sectors, the study examines the incorporation of predictive models into HR systems and processes, underlining the strategic benefits for long-term business planning. The evolving nature of work, including remote work paradigms and the gig economy, underscores the necessity for adaptable workforce strategies. The research anticipates a significant role for predictive analytics in shaping future organizational competencies and competitive advantages across various industries. The study concludes with the potential for cross-industry application of predictive analytics, suggesting an expansive future for data-driven strategic planning in navigating the complexities of the modern workforce.



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How to Cite

Rahaman, M. A. ., & Bari, M. H. (2024). Predictive Analytics for Strategic Workforce Planning: A Cross-Industry Perspective from Energy and Telecommunications. International Journal of Business Diplomacy and Economy, 3(2), 14–25. Retrieved from