Artificial Intelligence In Future Performance Management: A Conceptual Framework

  • Mahendrakar S
  • Soundararajan D
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Abstract

In this competitive scenario, collecting and analyzing precise data for organizational growth,is highly essential. Artificial Intelligence (AI) aids, to finish the work in a very fast and efficient way, whereas it enters into various departments with AI systems. AI is a technological revolution,changes human perspectives, scope, complexity, and transformation. Purpose: The  study investigates applications and usage of Artificial Intelligence on performance management activities and further analyses the future of PM in the HR department in the organization. Methodology: The study is descriptive in nature, used secondary data where the data were collected from research papers, publications, websites, HR blogs, survey reports, etc.  Finding: Employee performance measured and changed perspectives by information technology,big analytics and artificial intelligence.The quality of employee performance perpetually develops with the  integration of AI that enables the adoption of predictive analytics in accentuating employee performance. Research Implication: Digital performance management system will reduce the time, efforts of management and lead to transparency and honesty in employee performance appraisal. The outcome of the study is based on the practical applicability of performance management system. Theoretical Implication: The findings of the study endows with HR managers, academicians, IT professionals  and practitioners with insight into how AI usage for performance management might look like, and what implications it holds for their functioning. Also, a contribution is made to the connection with the PMS strategy adopted by the Indian and global industries on  performance management and Artificial Intelligence.

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APA

Mahendrakar, Smt. R., & Soundararajan, Dr. K. (2025). Artificial Intelligence In Future Performance Management: A Conceptual Framework. International Journal of Environmental Sciences, 11(10s), 162–176. https://doi.org/10.64252/0fe78m77

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