Intelligent Software Project Management: A Novel Approach to Risk Analysis and Resource Allocation

The resource allocation and risk analysis in software project management continues to face challenges owing to reliance on subjective heuristics and rigid rule-based systems. The current work proposes an AI-based project management framework consisting of web-based systems with machine learning-driven risk analysis and intelligent resource allocation. The proposed Risk Analysis Component will use anomaly detection and supervised learning techniques to anticipate future project issues and enable timely intervention. The Resource Allocation Component will automatically assign tasks according to developer skill groups, workload balancing, and performance patterns using natural language processing and reinforcement learning. The system will aim to enhance decision-making efficiency, reduce failed project rates, and maximize team productivity. A full system architecture that will be integrated into existing Agile development practices, alongside a testing methodology involving benchmarking, simulation, and A/B testing to validate the approach is outlined. This paper makes a cohesive architectural framework for unifying AI-based risk management and intelligent resource allocation into software project management, bridging the previously existing chasm between these traditionally disparate issues.