AI-Powered Workforce Management for Automated Appraisals and Task Allocation

Any organization needs a powerful and efficient force for success. But, in most cases, current systems only work on human resource operations and pay little attention to the problems the employees and team leaders face. This research puts forward an AI-Enhanced Workforce Management System to facilitate automation in the key processes of creating appraisals, distribution of projects, and personal performance tracking. The central objective of the proposed system is that this should minimize the level of manual tasks, improve accuracy, and enable data-driven decisions at all organizational levels. The proposed method utilizes advanced AI models and frameworks on specific workforce management problems. For appraisal generation automation, RAG is used. A fine-tuned T5 model finds, condenses, and structures annual employee performance data into an appraisal report. For project assignments, the evaluation of employee skills, their availability, and the needs of a project are conducted using Skill2Vec and NLP-based models to recommend the best-fit candidates. The performance assessment is done using DistilBERT, which categorizes the employees into actionable categories based on the progress of their work and attendance. Outcomes are visualized using Chart.js, which aids in creating dashboards that can be easily user-friendly for better decision-making. Significant insights are the reduction in manual labor, improvement in the allocation of projects, and transparency of employee performance assessments. The system improves organizational efficacy as it empowers employees, simplifies the responsibilities of team leaders, and enables human resource managers to concentrate on strategic objectives. This is a pioneering approach to workforce management, combining AI-powered automation with practical insights in a unified and scalable model. Real-world implementations show how artificial intelligence can transform conventional methods of workforce management into an effective, accurate, and growth-focused system.