Implementing Artificial Intelligence for Scrum Teams Management

Abstract

The paper addresses the optimization of Scrum team management in application development, where traditional frameworks suffer from human cognitive biases, estimation errors, device fragmentation, and complex app store release lifecycles. The authors propose a novel Hybrid AI-Agile Framework that integrates predictive machine learning models, natural language processing, and multi-agent Large Language Model (LLM) architectures to automate backlog refinement, sprint planning, risk assessment, dynamic resource allocation, and retrospective feedback. The operational mechanics feature specialized virtual role agents (Product Owner, Developer Agent, Technical Code Reviewer, and Scrum Master Facilitator), supported by mathematical models for real-time risk scoring and linear-programming resource allocation, and execute locally via an Ollama server to ensure enterprise data privacy. The proposed system establishes a reproducible paradigm for integrating cognitive automation with human-in-the-loop oversight to reduce IT project failure rates in information systems engineering.