Modern organizations face challenges in managing workforce activities such as attendance tracking, task allocation, performance monitoring, and employee support. Traditional workforce management systems often rely on manual or rule-based mechanisms, leading to issues such as proxy attendance, administrative overhead, and inefficient workload distribution. This paper presents an AI-Powered Workforce Management System designed to improve operational efficiency through intelligent automation and secure authentication. The proposed system integrates face recognition using the Local Binary Pattern Histogram (LBPH) algorithm for reliable attendance management, AI-assisted task allocation based on employee skills and workload, and an intelligent chatbot assistant powered by Google Gemini to support employee queries and guidance. The system follows a modular web-based architecture comprising facial authentication, attendance management, task management, performance analytics, and conversational AI modules. Performance analytics are derived from attendance records and task completion data to provide actionable insights for administrators and team leaders. Experimental evaluation conducted in a simulated organizational environment demonstrates improved attendance accuracy, reduction in fraudulent attendance practices, decreased administrative effort, and enhanced task management efficiency. The results indicate that the proposed system offers a scalable, reliable, and intelligent solution for modern workforce management by combining practical AI techniques with real-world organizational requirements.
Dhanyasree et al. (Wed,) studied this question.