Sugeng Riyadi, Mohamad Halilintar

Measuring Employee Productivity with AI: Opportunities and Challenges for HR

Introduction

Measuring employee productivity with ai: opportunities and challenges for hr. AI revolutionizes HR's employee productivity measurement. Explore opportunities like automation & real-time analysis, plus challenges like data privacy & employee resistance.

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.

Review

This paper addresses a highly pertinent and timely topic: the application of artificial intelligence in measuring employee productivity within human resource management. The abstract clearly outlines the research's objective to explore AI implementation, identifying both opportunities and obstacles for HR departments. Employing a qualitative methodology through in-depth interviews and documentation studies with 15 HR practitioners in Indonesia, the study grounds its insights in practical, industry-specific experiences. This approach provides valuable context on how AI is perceived and utilized in a developing economy, making a useful contribution to the growing body of literature on HR technology adoption. The findings presented are compelling, particularly the claim of up to a 67% improvement in productivity measurement accuracy compared to conventional methods – a striking figure for a qualitative study that warrants further methodological scrutiny in the full text. Key opportunities identified, such as automation of evaluation processes, real-time data analysis, and personalized performance assessments, highlight the transformative potential of AI. Equally important are the significant obstacles unearthed, which include critical concerns around data privacy, employee resistance to new monitoring methods, and the inherent complexity of technology implementation. The explicit mention of strategic recommendations signals the paper's practical utility for organizations considering AI adoption. While the abstract promises valuable insights, a full review would need to delve deeper into the methodological underpinnings, especially regarding the derivation of the 67% accuracy improvement figure within a qualitative framework. The focus on Indonesia offers a specific cultural context, prompting questions about the generalizability of employee resistance findings to other regions. Future research could explore a mixed-methods approach to quantitatively validate perceived benefits and challenges, or conduct comparative studies across different cultural and economic landscapes. Nevertheless, this research provides a solid foundation for understanding the dual nature of AI's impact on HR productivity measurement, laying crucial groundwork for both practitioners and academics.

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