Research on the Enhancing Effect of Artificial Intelligence and Machine Learning on the Productivity of Remote Workers

The rise of remote work has highlighted the need for tools and technologies that can enhance employee productivity outside of the traditional office setting. Artificial intelligence (AI) and Machine Learning (ML) have demonstrated potential for optimizing remote work environments by automating tasks, controlling workflows, and offering insights into worker performance. Though, the unpredictability of remote work conditions across different industries and geographic regions pose some challenges affecting the applicability of the result. This research aims to examine the impact of AI and ML on remote workers' productivity. It seeks to assess how these technologies can improve productivity by examining employee behavior and performance patterns. A novel method called Refined Random Natural Gradient Boosting (RR-NGboost) technique is implemented, to develop predictive models for analyzing productivity changes. These methods are trained to recognize patterns in workplace behavior and forecast productivity trends. Data is gathered from remote workers in various places (city, town, and village), covering factors like work hours, task completion rates, and time management. The data is cleaned (by removing inconsistencies and missing values) and Z-score normalization is used to scale the data and develop model performance. Principal Component Analysis (PCA) is used to minimize dimensionality and highlight the most important traits. According to the results, the proposed RR-NGboost method is quite accurate in predicting production fluctuations, achieving a Mean Squared Error (MSE) of 0.3958 and a Mean Absolute Error (MAE) of 0.4234, demonstrating its strong predictive capability and minimal deviation from actual productivity scores. RR-NGboost is the best in terms of feature importance and prediction reliability. The research indicates that AI and ML approaches can significantly improve remote worker productivity by giving real-time insights and automating time management operations, which benefits workers as well as managers.