MRI
MRI India Journals Vol. 9 No. 7 (2025): Volume 9 Issue 7 2025

Smart HR: Integrating Artificial Intelligence into Predictive Workforce Planning Models

Authors

  • Dr. Venkata Siva Varma.Ch MBA., M.D.C.H., D.HR., SET., Ph.D., Assistant Professor, Department of Management Studies, Vignan's Foundation for Science, Technology and Research, Vadlamudi (PO), Chebrolu (MD), Guntur (Dt), Andhra Pradesh, India – 522213
  • Dr. G. Vidya Sagar Rao Asst Professor, Osmania University, Hyderabad, Telangana
  • Dr. Neha Bharani Associate Professor, SOC, IPS Academy, Indore, Madhya Pradesh
  • Dr. Melanie Lourens Deputy Dean Faculty of Management Sciences, Durban University of Technology, South Africa

DOI:

https://doi.org/10.65521/ijasret.v9i7.1540

Keywords:

Predictive Workforce Planning Artificial Intelligence Machine Learning Human Resource Management Retention Strategies Employee Attrition Data-Driven Decision Making Talent Management Ethical AI HR

Abstract

AI is helping businesses in HR by allowing them to base their workforce planning on data analysis. The study looks at the IBM HR Analytics Employee Attrition dataset to see how AI helps predict staff leaving, improve recruitment and boost retention. The research shows that individuals who earn less each month (mean = $4,787 vs. $6,832 for those retained) and are younger (mean = 33.6 vs. 37.5) leave the company more often, mainly in sales (20.63%). The model shows that job satisfaction (odds ratio = 0.765, p < 0.001) and work-life balance (odds ratio = 0.790, p = 0.020) greatly influence whether someone stays at their job. AI tools for predicting outcomes can help companies save 35% on attrition and recruit 40% more employees, but they still have to deal with ethical problems from algorithmic bias. The findings highlight that effective data and AI policies are important for success in workforce planning.

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Published

2025-07-24

How to Cite

Varma.Ch, D. V. S., Rao, D. G. V. S., Bharani, D. N., & Lourens, D. M. (2025). Smart HR: Integrating Artificial Intelligence into Predictive Workforce Planning Models. International Journal of Advanced Scientific Research and Engineering Trends, 9(7), 22–28. https://doi.org/10.65521/ijasret.v9i7.1540

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