AI Powered Workforce Management System

AI Powered Workforce Management System

PA. Dhanyasree, Dr. R. Madhan Mohan

Department of Computer Science and Engineering, Annamalai University, Chidambaram-608002, Tamil Nadu, India

Abstract – 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.

Keywords – Workforce Management, Artificial Intelligence, Face Recognition, LBPH, Attendance System, Chatbot.

1. INTRODUCTION

   Workforce management is a critical factor in organizational productivity and operational efficiency. With the expansion of enterprises and the adoption of distributed work environments, traditional attendance and task management mechanisms are increasingly inadequate in terms of accuracy, security, and scalability. Artificial Intelligence (AI) offers significant potential to automate repetitive processes, reduce human error, and support intelligent decision-making. This research focuses on the design and implementation of an AI-powered workforce management system that addresses key challenges such as proxy attendance, inefficient task allocation, and limited employee support mechanisms.

2. RELATED WORK

   Automated workforce management and attendance systems have been an active area of research, with several studies exploring different methods for improving efficiency, accuracy, and usability. Mamun et al. [1] proposed a smart receptionist system integrating speech, speaker, and face recognition to automate reception services, highlighting the potential of AI in handling real- time human interactions. Foundational face recognition techniques, such as Local Binary Patterns (LBP) [2,3], Viola-Jones object detection [4], and multimodal detection approaches [5], have laid the groundwork for automated attendance systems. Comprehensive overviews of face recognition algorithms, including eigenfaces [8,9] and histogram-based methods [10], provide further context for implementing robust facial recognition in workforce management. In recent years, automated attendance systems have been implemented in organisational environments. Sharma and Kaur [11] and Arora et al. [12] developed systems using OpenCV for face recognition-based attendance, demonstrating improvements in accuracy and time efficiency over manual methods. Similarly, AI- based employee performance evaluation systems [13] integrate task tracking and performance metrics to provide administrators with actionable insights, emphasizing the importance of intelligent workload monitoring. The integration of AI chatbots has further enhanced employee interaction and task management. Super-Agent [14] and intelligent knowledge-based chatbots [15] have shown how conversational agents can reduce administrative burden and provide real-time guidance. Studies on conversational agents [16,17] and large language models [18,19] demonstrate how natural language processing can be leveraged for complex query handling, enabling systems to assist employees efficiently. Advanced NLP-based chatbots for technical universities [20] highlight the application of AI in domain-specific workforce management and employee support.

   Although these systems have contributed significantly to automated attendance, task tracking, and employee interaction, most prior works are limited by either manual attendance processes, basic task assignment logic, or static query handling. The proposed AI- Powered Workforce Management System builds upon these advancements by integrating facial recognition attendance, intelligent task allocation, automated performance reporting, and an AI chatbot, creating a unified platform that improves accuracy, efficiency, and usability while addressing the limitations of previous systems.

3. PROPOSED SYSTEM

   The proposed AI Powered Workforce Management System is envisioned as a unified, intelligent, and web-based platform designed to automate key workforce operations, including attendance management, task allocation, performance monitoring, and employee interaction. The primary objective of this solution is to replace manual and semi-automated workforce processes with an accurate, secure, and scalable AI-driven system that minimizes human intervention and operational errors. The system employs a role-based access mechanism, categorizing users as administrators and employees, each with a dedicated dashboard offering role-specific functionalities and real-time information. Administrators can manage employee profiles, monitor attendance, create and assign tasks, and analyze workforce performance through automated reports. Employees, in turn, can view attendance status, access and submit tasks, track task history, and interact with the system via an AI-powered Chatbot interface. This approach ensures secure, controlled access while maximizing operational efficiency. A key component of the system is the facial recognitionbased attendance module, which replaces traditional manual or biometric systems. Employees facial images are captured during registration using a webcam and securely stored in the database. Feature extraction is performed using the Local Binary Pattern Histogram (LBPH) algorithm, generating a unique facial representation for each employee. During attendance marking, live video input is processed and matched against stored templates. Upon successful recognition, attendance is automatically recorded along with the date, time, and image evidence. This contactless method eliminates proxy attendance, reduces fraudulent practices, and significantly enhances accuracy and security. Central to the proposed work is the intelligent task management module, which automates workload distribution and ensures optimal resource utilization. Administrators can create tasks manually or rely on the AI-driven system to dynamically allocate assignments based on employee skills, roles, historical performance, and current workload. Employees can view, accept, and submit tasks through their dashboards, with real-time tracking of progress. This module directly benefits both administrators by saving significant time and reducing errors in manual task allocaionand employeesby providing clarity on responsibilities, deadlines, and accountability. Complementing the task module is the performance monitoring and reporting system, which continuously evaluates employee performance based on attendance, task completion, quality of submissions, and adherence to deadlines. Automated performance metrics and analytical reports provide administrators with actionable insights for appraisals, promotions, training, and workload adjustments, while employees gain a clear view of their progress and areas for improvement. This transparency enhances productivity and promotes a performance-driven organizational culture. To further improve usability and responsiveness, the system integrates an AI-powered Chatbot module. The Chatbot interacts with employees in natural language, assisting with queries related to attendance, task details, deadlines, and system navigation. Leveraging natural language processing, it provides instant, accurate responses, reducing administrative workload, minimizing delays, and improving overall user satisfaction. Together, the task management, reporting, and AI Chatbot modules create a synergistic effect: tasks are efficiently assigned and tracked, performance is continuously monitored and analyzed, and employees receive immediate supportresulting in a highly intelligent, user-friendly, and productivity-enhancing workforce management solution.

4. METHODOLOGY

   The proposed AI-Powered Workforce Management System adopts a role-based, modular methodology to automate attendance tracking, task management, performance evaluation, and user interaction within an organization.

   Fig. 1. Flowchart of the Proposed AI-Powered Workforce Management System

   The system begins with a centralized home page that allows users to either register or log in. Upon successful authentication, the system performs role verification to determine whether the user is an administrator or an employee. Based on this role, the user is redirected to the corresponding dashboard, ensuring secure and controlled access to system functionalities.

   1. Administrator Workflow

      After authentication, administrators are redirected to the Admin Dashboard, which serves as the control centre for workforce management. The administrator can create tasks and assign them to employees either manually or through an AI-based task allocation mechanism. The AI-based approach considers employee skills and current workload to ensure balanced and efficient task distribution. The administrator can monitor task progress by viewing task statuses categorized as pending or completed. Performance evaluation is supported at both the organizational level and the individual employee level, allowing administrators to assess productivity and identify improvement areas. In addition, the administrator can view detailed employee profiles, which include personal information, assigned tasks, task completion history, and attendance records. This feature enables better decision-making for task assignment and performance analysis. To enhance system usability, an AI-powered chatbot is integrated into the admin dashboard to assist with operational queries and system navigation. Administrators can also modify their profile information through the profile management module.

      Fig. 2. Admin Dashboard

   2. Employee Workflow

   Employees are redirected to the Employee Dashboard after successful login. As the first mandatory step, employees must mark their daily attendance using a face recognition-based attendance system, which ensures accuracy and eliminates proxy attendance. Once attendance is recorded, employees can view the tasks assigned to them along with their current status. Employees are allowed to complete assigned tasks and submit them through the system. During task execution, an AI Chatbot provides assistance, guidance, and resource recommendations to improve task completion efficiency. The employee dashboard also includes a personal performance report section that displays the number of tasks completed, pending tasks, and attendance details such as the number of days present. This promotes transparency and allows employees to track their own performance. Employees are also provided with profile modification functionality to update their personal details. Both administrators and employees can securely log out of the system after completing their activities. This ensures proper session termination and protects the system from unauthorized access.

   Fig. 3. Employee Dashboard

5. SYSTEM MODULES

   The proposed AI-powered workforce management system is composed of several interconnected modules designed to automate attendance recording, task management, employee interaction, and performance evaluation. Each module operates independently while sharing data through a centralized backend to ensure scalability, security, and efficient information flow. The modular architecture enables easy system maintenance and future enhancements.

   1. User Registration Module

      The methodology begins with the user registration module, where new employees are enrolled into the system. During registration, users provide basic profile details such as name, employee ID, role, designation, and skill set. Simultaneously, facial images are captured using a webcam. Multiple facial samples are collected under varying lighting conditions to improve recognition robustness. These images undergo pre-processing, including grayscale conversion, face detection, normalization, and resizing. Facial features are extracted using the Local Binary Pattern Histogram (LBPH) algorithm and securely stored in the database as unique facial templates. This registration process ensures accurate identity enrolment and forms the foundation for secure authentication and attendance tracking.

   2. Login and Authentication Module

      After successful registration, users access the system through the login module, which provides secure, role-based system entry. The login process uses credential-based authentication, where users authenticate themselves using a unique username and password assigned during registration. The authentication credentials entered by the user are validated against securely stored records in the system database. Upon successful verification, the system identifies the users role and grants access accordingly. Once authenticated, users are automatically redirected to their respective dashboards:

      • Administrator Dashboard, providing access to employee management, task assignment, attendance monitoring, and performance analytics.

      • Employee Dashboard, allowing users to view attendance status, access assigned tasks, submit task outputs, and interact with the AI Chatbot.

        This separation of login authentication and biometric attendance verification ensures both system usability and security, while maintaining efficient and streamlined access control.

   3. Face RecognitionBased Attendance Module

      The face recognitionbased attendance module provides a secure and automated mechanism for marking employee attendance through real-time facial verification. When an employee initiates attendance, the webcam captures a live facial image, which is transmitted to the server in Base64 format for processing. Facial regions are detected using the Haar Cascade classifier, and recognition is performed using the Local Binary Pattern Histogram (LBPH) algorithm. During registration, employee facial images are stored and used to train an LBPH model, which is reused for future verification to reduce computational overhead. The captured face is matched against the trained model using a confidence threshold to ensure accuracy. Upon successful recognition, attendance is recorded with date, time, and image evidence. Failed recognition pompts the user to retry or re-register, ensuring secure, contactless, and reliable attendance tracking.

   4. Task Creation and Assignment Module

      The Task Creation and Assignment Module enables administrators to create, manage, and allocate tasks efficiently through both manual and intelligent mechanisms. Admins can define task title, description, required skill, and deadline, and choose either manual assignment to a specific employee or automatic assignment. In auto-assignment mode, the system analyses employee workload and distributes tasks evenly to ensure balanced utilization, marking such allocations as AI-assisted. The module also supports reassignment of pending or previously unassigned tasks, ensuring no task remains unattended. Each task maintains a clear status lifecycle (Pending, Submitted, Completed) and records whether it was assigned manually or automatically, providing transparency and control for administrative decision-making.

   5. Employee Task Execution and Submission Module

      The Employee Task Execution and Submission Module allows employees to view, accept, execute, and submit assigned tasks through a dedicated dashboard. Employees can review task details, deadlines, and priorities, formally accept unassigned tasks, and work on them accordingly. Upon completion, the module enables submission of a textual description along with optional file uploads as evidence of work performed. Task status is automatically updated upon submission, allowing administrators to track progress in

      real time. This module ensures accountability, structured task handling, and seamless interaction between employees and management, thereby improving task completion efficiency and overall workflow transparency.

   6. Task Verification and Approval Module

      The task verification module allows administrators to review submitted task outputs. Administrators evaluate task completion based on accuracy, quality, and adherence to requirements. Based on the evaluation, the task status is updated as completed, pending revision, or rejected. This verification process ensures quality control and provides reliable input for performance assessment and reporting.

   7. Performance Monitoring and Analytics Module

      The Performance Monitoring and Analytics Module enables administrators to track and evaluate employee performance using attendance and task-related data. The system automatically computes key metrics such as attendance count, completed tasks, and pending tasks for each employee. It supports report generation for specific employees and selected time periods, with exportable reports in spreadsheet format and basic visual analytics. This module helps administrators monitor productivity, identify performance trends, and make informed management decisions, thereby improving transparency and overall workforce efficiency.

   8. AI Chatbot Module Using Gemini Module

   The proposed system integrates an AI-powered chatbot using Google Gemini to support both employees and administrators through intelligent, context-aware interactions. For employees, the chatbot acts as a virtual assistant by providing task-related guidance, deadline reminders, and responses to queries regarding task objectives, submissions, and status, thereby promoting timely completion and reducing manual supervision. For administrators, it functions as a management support tool by delivering real-time summaries of task progress and responding to natural language queries about task assignments, employee status, and deadlines. Leveraging Google Geminis advanced natural language understanding, the chatbot offers accurate, role-specific assistance, reduces administrative workload, enhances communication efficiency, and improves overall transparency within the workforce management system.

6. RESULTS AND DISCUSSION

   The AI-Powered Workforce Management System was evaluated in a simulated organizational environment and demonstrated significant improvements in workforce operations. Using LBPH-based face recognition, the system reliably identified employees and recorded attendance with timestamped images, providing a secure and verifiable log, though recognition could be affected under extreme lighting or partial facial occlusion. The task management module streamlined the creation, assignment, acceptance, and submission of tasks, allowing admins to allocate work manually or based on employee skills, while employees could submit files and completion details, ensuring real-time tracking of task progress. The Gemini-powered AI chatbot offered instant support for queries related to attendance, tasks, and system navigation, reducing repetitive administrative work and enhancing employee experience. The web-based dashboards provided an intuitive interface with smooth real-time operations, and the modular design allows scalability for additional users and tasks. Overall, the system effectively integrates biometric attendance, intelligent task allocation, and AI assistance to enhance security, efficiency, and workflow management, while highlighting areas for improvement such as updating employee profiles and accommodating challenging recognition conditions.

7. CONCLUSION

   The AI-Powered Workforce Management System successfully integrates face recognition, intelligent task management, and an AI chatbot to automate and streamline key organizational operations. By leveraging LBPH-based face recognition, the system provides accurate and secure attendance tracking, reducing manual effort and enhancing accountability. The task management module enables efficient creation, assignment, and monitoring of employee tasks, while the Gemini-powered chatbot delivers real-time assistance, improving workflow and reducing administrative overhead. The web-based, modular design ensures usability, scalability, and smooth real-time operations, making the system suitable for organizations of varying sizes. Overall, this system demonstrates that combining biometric attendance, AI-driven task management, and intelligent support can significantly enhance workforce efficiency, productivity, and employee satisfaction, providing a strong foundation for future enhancements such as predictive analytics and advanced performance recommendations.

8. FUTURE SCOPE

The proposed AI-Powered Workforce Management System has improved attendance accuracy, task management, performance monitoring, and employee interaction. Future enhancements can further increase its efficiency and usability. The facial recognition module can be upgraded with deep learning models such as CNNs or Vision Transformers to improve accuracy under varied

conditions, while integrating multi-modal biometrics can enhance security. The task management module can incorporate predictive task allocation and adaptive scheduling, optimizing workload distribution and reducing employee burnout. The AI chatbot, currently handling attendance and task queries, could leverage advanced NLP models, such as large language models (LLMs), to provide personalized guidance, proactive notifications, and more complex query handling. The reporting module can be enhanced with predictive analytics, interactive dashboards, and real-time visualizations to support better decision-making. These improvements would make the system more intelligent, adaptive, and scalable, further enhancing organizational efficiency and productivity.

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