AI in Agile Project Management: Enhancing Sprint Planning and Delivery

Abstract

The aim was to investigate how Artificial Intelligence (AI) has contributed to the development of Agile Project Management, particularly in sprint planning, task prioritization, and delivery management. The work was guided by three research questions addressing the efficiency of AI-supported sprint planning, its role in task allocation and prioritization, and the challenges of implementing AI within Agile environments. The study was conducted using an integrative literature review methodology, combining theoretical frameworks and empirical studies. A total of seven peer-reviewed sources were selected based on relevance, diversity of methodology, and alignment with Agile performance domains derived from the PMBOK® 7 framework. The analysis revealed that AI technologies, such as machine learning and natural language processing, have been used to improve estimation accuracy, forecast sprint risks, and automate repetitive tasks. Results also indicated that AI has enabled dynamic task assignment and predictive sprint analytics across various platforms. Despite these advantages, several challenges were identified, including limited compatibility with legacy systems, concerns over data privacy, and cultural resistance to change. Recommendations were made to promote gradual AI integration, improve team literacy, and address ethical considerations. It was concluded that AI can enhance Agile practices when implemented in alignment with team dynamics, ethical standards, and existing workflows.