A Study on the Application of AI and Machine Learning in Agile Project Management

Abstract

Aiming at the challenges of frequent requirement changes and lagging quality control faced by agile project management in dynamic environments, this study systematically explores the integration path of artificial intelligence (AI) and machine learning techniques. Through literature analysis and technology validation, the effectiveness of supervised learning, deep learning and reinforcement learning in core scenarios such as demand forecasting, defect detection and resource scheduling is revealed. The study finds that AI technology can significantly improve the risk response capability and delivery efficiency of agile projects through real-time data processing and pattern recognition, but needs to overcome the barriers of model interpretability, data silos and organisational adaptation. The study further proposes to focus on the development of dynamic adaptive algorithms, cross-modal data governance, and human-computer collaboration paradigm innovation in the future to provide theoretical support and practical guidance for the intelligent transformation of agile project management.