Measuring Employee Productivity with AI: Opportunities and Challenges for HR

Authors

  • Sugeng Riyadi Universitas Borobudur, Indonesia
  • Mohamad Halilintar Universitas Borobudur, Indonesia

DOI:

https://doi.org/10.38035/gijea.v3i4.642

Keywords:

artificial intelligence, employee productivity, human resources, HR technology, performance evaluation

Abstract

The development of artificial intelligence (AI) technology has revolutionized various aspects of human resource management, including employee productivity measurement. This research explores AI implementation in measuring employee productivity and identifies opportunities and obstacles faced by HR departments. Through a qualitative approach using in-depth interviews and documentation studies, this research involved 15 HR practitioners from various industries in Indonesia. Results show that AI can improve productivity measurement accuracy by up to 67% compared to conventional methods. Main opportunities include evaluation process automation, real-time data analysis, and performance assessment personalization. However, significant obstacles were found in data privacy aspects, employee resistance, and technology implementation complexity. This research provides strategic recommendations for organizations seeking to adopt AI for effective employee productivity measurement.

References

Anderson, M., & Lee, S. (2023). Personalized performance metrics through artificial intelligence: A systematic review. Journal of Human Resource Technology, 15(3), 245-267.

Chen, L., & Zhang, W. (2021). Evolution of employee productivity measurement: From manual tracking to AI-powered analytics. International Review of Human Resource Management, 12(4), 112-134.

Deloitte. (2023). Global human capital trends 2023: AI and the future of work. Deloitte Insights.

Drucker, P. F. (1999). Management challenges for the 21st century. HarperBusiness.

Gartner. (2023). Market guide for AI in human resources. Gartner Research.

Johnson, R., & Brown, K. (2023). Automated data collection in performance management: Benefits and implementation strategies. HR Technology Quarterly, 8(2), 78-95.

Kaplan, R. S., & Norton, D. P. (2004). Strategy maps: Converting intangible assets into tangible outcomes. Harvard Business Review Press.

Li, X., & Wang, Y. (2023). Machine learning applications in employee performance prediction: A comparative study of algorithms. Computational Human Resources, 7(1), 34-52.

McKinsey Global Institute. (2023). The age of AI: How artificial intelligence is transforming human resources. McKinsey & Company.

PwC Indonesia. (2023). Digital transformation in Indonesian enterprises: HR technology adoption survey. PwC Indonesia.

Rodriguez, A., Martinez, C., & Lopez, D. (2022). Real-time analytics for proactive performance management: Implementation experiences from Fortune 500 companies. Strategic HR Review, 21(5), 189-205.

Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.

Smith, J., Davis, M., & Wilson, P. (2022). Natural language processing in 360-degree feedback analysis: Accuracy and implementation considerations. Applied AI in Human Resources, 4(3), 156-174.

Thompson, L., & Davis, R. (2023). Privacy concerns in AI-powered employee monitoring: Survey findings and recommendations. Privacy and Technology Law Review, 18(2), 89-107.

Williams, S., Johnson, T., & Brown, A. (2022). Algorithmic bias in HR systems: Detection, measurement, and mitigation strategies. Fairness in AI Quarterly, 3(4), 23-41.

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Published

2025-12-04

How to Cite

Sugeng Riyadi, & Halilintar, M. (2025). Measuring Employee Productivity with AI: Opportunities and Challenges for HR. Greenation International Journal of Economics and Accounting, 3(4), 542–548. https://doi.org/10.38035/gijea.v3i4.642

Issue

Vol. 3 No. 4 (2025): Greenation International Journal of Economics and Accounting (December 2025 - February 2026)

Section

Articles

License

Copyright (c) 2025 Sugeng Riyadi, Mohamad Halilintar

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