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.针对敏捷项目管理在动态环境下面临的需求变更频繁、质量管控滞后等挑战，本研究系统探讨了人工智能（AI）与机器学习技术的融合路径。通过文献分析与技术验证，揭示了监督学习、深度学习与强化学习在需求预测、缺陷检测及资源调度等核心场景中的应用效能。研究发现，AI技术通过实时数据处理与模式识别，可显著提升敏捷项目的风险应对能力与交付效率，但需克服模型可解释性、数据孤岛及组织适配等落地障碍。研究进壹步提出未来应聚焦动态自适应算法开发、跨模态数据治理与人机协同范式创新，为敏捷项目管理的智能化转型提供理论支撑与实践指南。針對敏捷項目管理在動態環境下面臨的需求變更頻繁、質量管控滯後等挑戰，本研究系統探討了人工智能（AI）與機器學習技術的融合路徑。通過文獻分析與技術驗證，揭示了監督學習、深度學習與強化學習在需求預測、缺陷檢測及資源調度等核心場景中的應用效能。研究發現，AI技術通過實時數據處理與模式識別，可顯著提升敏捷項目的風險應對能力與交付效率，需克服模型可解釋性、數據孤島及組織適配等落地障礙。研究進壹步提出未來應聚焦動態自適應算法開發、跨模態數據治理與人機協同範式創新，為敏捷項目管理的智能化轉型提供理論支撐與實踐指南。