Comprehensive AI-Driven Agile Project Management and Resource Optimization Platform

Abstract

Agile Software development is an iterative and flexible methodology that empowers teams to continuously adapt to changing requirements, collaborate closely, and deliver high quality software through rapid feedback loops. Traditional project management in Agile environments remains predominantly manual, leading to time consuming documentation, imprecise task breakdowns, reactive risk assessments, and inconsistent developer evaluations. This research introduces an AI driven project management platform that leverages Natural Language Processing, Machine Learning, and Predictive Analytics to automate critical workflows and enhance decision-making. The proposed system integrates four key components, automated documentation and summarization, an NLP-based speech-to-text module, abstractive summarization combined with sentiment analysis for generating structured project plans and meeting minutes, and an AI-based task extraction and prioritization engine that processes Software Requirement Specification documents using Named Entity Recognition, dependency parsing. And topic modelling. A hybrid predictive risk assessment module combining supervised and reinforcement learning approaches improves story point estimation and effectively predicts sprint risks. Developer evaluation is an automated system for analysing GitHub pull requests using machine learning-based metrices extraction and quality prediction, ensuring higher code standards and better collaboration. The platform utilizes data from open-source repositories and industry datasets. Integrating AI significantly enhances workflow efficiency, task accuracy, and early risk identification, ultimately improving team productivity and overall project success.