Paper 3: An AI-Driven System for Monitoring and Enhancing Remote Work Productivity
Authors : Tushar Singh, Prashant Srivastava, Saif Siddiqui, Nitin Singh, and Bibhuti Bhushan Singh
Doi: https://doi.org/10.63920/tjths.42003
Abstract
The transition to remote work has intensified the need for effective productivity tracking solutions that balance employee performance monitoring with engagement and wellbeing. Traditional productivity monitoring systems suffer from significant limitations, including reliance on subjective assessments, lack of contextual understanding, and limited actionable insights. This research investigates the development and implementation of an AI-enhanced remote work productivity tracker that addresses these challenges through automated data collection, real-time insights, personalized recommendations, and predictive analytics. Our approach leverages machine learning algorithms to process data from multiple work applications, communication platforms, and project management tools, providing a comprehensive overview of employee productivity without compromising privacy or autonomy. Preliminary findings suggest that AI-powered productivity tracking can significantly improve performance management, accountability, and transparency in remote work environments while simultaneously enhancing employee engagement and wellbeing through stress reduction mechanisms, collaboration support, and personalized development opportunities. The research concludes that AI-driven productivity tracking represents a transformative solution for remote workforce management, offering organizations the ability to make data-driven decisions regarding resource allocation, goal alignment, and team dynamics in the evolving landscape of distributed work.
