Exploring AI-Powered Sprint Planning Optimization Using Machine Learning for Dynamic Backlog Prioritization and Risk Mitigation

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into agile project management has ushered in transformative capabilities for sprint planning optimization. This review explores the role of AI-powered solutions in enhancing sprint planning through dynamic backlog prioritization and proactive risk mitigation. By leveraging supervised and unsupervised machine learning models, project teams can analyze historical sprint data, stakeholder feedback, and realtime project dynamics to predict task complexity, optimize resource allocation, and adapt backlog items based on shifting priorities. The paper systematically examines various ML algorithms, such as random forests, support vector machines, and neural networks, that support decision-making in backlog grooming and risk forecasting. Additionally, it evaluates AI-driven tools and platforms capable of automating sprint estimations, velocity tracking, and identifying potential blockers before they impact delivery timelines. Key challenges in data quality, model explainability, and integration with existing agile tools are also addressed. Through comparative analysis and case study synthesis, this review underscores the value of embedding AI intelligence into agile frameworks to drive efficiency, enhance team responsiveness, and reduce delivery risks. Ultimately, the paper provides a roadmap for researchers and practitioners aiming to implement intelligent sprint planning systems within modern software development lifecycles.