AI-Based Workflow & Task Optimization System

Abstract

Modern organizations face increasing operational complexity due to fragmented tooling, manual task allocation, and reactive workflow management. Traditional systems rely on rigid rule-based automation, lacking predictive foresight and adaptive decision-making capabilities. This paper presents an AIBased Workflow & Task Optimization System thatintegrates Machine Learning (ML), Natural Language Processing (NLP), and Predictive Process Monitoring (PPM) into a unified microservices architecture. The platform features an intelligent task assignment engine powered by XGBoost, a real-time Service Level Agreement (SLA) breach prediction module using gradient boosting classifiers, and a conversational NLP interface leveraging GPT-4. Developed with Django REST Framework, React.js, PostgreSQL, and Celery, the system supports horizontal scalability, eventdriven processing, and seamless third-party integration. Experimental evaluation demonstrates a weighted F1-score of 0.887 for task assignment, an AUC-ROC of 0.921 for SLA prediction, and a 52% reduction in workflow bottlenecks. User acceptance testing yielded an overall satisfaction score of 4.4/5. The system delivers enterprise-grade intelligent automation while maintaining costefficiency through open-source technologies.