AI-Driven Employee Productivity Monitoring System: A Case Study in Remote Work Environments
DOI:
https://doi.org/10.71143/d0mgnd04Keywords:
Artificial Intelligence, Employee Efficiency, Teleworking, Machine Learning, Observing SystemAbstract
The dissemination of the COVID-19 pandemic has become the trigger of the shift to remote work processes, and today, organizations confront new challenges with regard to monitoring the productivity of employees. Assessing productivity based on physical presence and using manual tools for measurement is ineffective in a virtual working environment. Artificial Intelligence (AI) can disrupt the productivity monitoring industry by bringing the potential to view and monitor employee performance, working habits, and interest in real-time without constant surveillance. This paper describes the work of the author in implementing a system for monitoring employee productivity based on artificial intelligence within a remote working experience. Assisted by the machine learning algorithms, the system would be configured to run different kinds of work patterns, communication speeds, and work accomplishment rates that can give the managers a detailed insight into the amount of productivity of a particular employee, and all this will happen such that it will not interfere with the privacy of that employee. The study gauges the effectiveness of artificial intelligence surveillance in enhancing productivity, challenges that surround the installation of the systems, and fears by the workers that could crop up because of the surveillance. In accordance with the findings, systems powered by AI can potentially increase the productivity rates significantly and contribute to the development of the so-called data-culture working processes, as well as introduce feasibility in decision-making to managers. However, concerns about data privacy, employee trust, and integrations remain very important. To conclude the paper, it is noted that the fate of future workplace and organizational productivity lies in the hands of AI-driven productivity tracking and remote work.
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