Impact of Artificial Intelligence Tool Usage on Employee Productivity and Work Efficiency: An Empirical Analysis

Abstract

This study examines the impact of artificial intelligence (AI) tool usage on employee productivity and work efficiency, focusing on task automation, manual workload, error rates, and focus levels. The applied quantitative, cross-sectional research design was based on a secondary dataset, which was collected on Kaggle, comprising of 5,600 employee-level observations. Python was used to perform descriptive statistics and Pearson correlation and multiple linear regression to assess relationships  between AI usage and productivity outcomes. The findings reveal that the use of AI tools largely automate a task and decreases the number of man hours (hours) compared to manual work thus substantiating how the tool is playing a good role in improving operational effectiveness. Nonetheless, the application of AI and automation only had a small yet significant positive impact on the error rates implying that there could be risks related to over-dependence on automated systems. Moreover, the use of AI did not imply a substantial effect on the concentration of the employees, with the hours of meetings becoming one of the factors that influenced the concentration in a negative way. To enhance cognitive performance and reduce mistakes, AI tools should be embraced by organizations but with a human supervision to enhance efficiency. This study provides empirical, employee-level evidence on the multidimensional effects of AI, contributing to a deeper understanding of human-AI collaboration in the workplace.