Intelligent team work planning: a model for increasing sprint value

Abstract

Agile software development methodologies are currently the dominant approach to IT project management. However, sprint planning and decision-making processes within Agile teams still largely rely on manual input and intuitive judgments, often leading to suboptimal resource allocation and unmanaged risks. A critical factor in successful project execution is the quality of requirements and tasks in the backlog. Tasks formulated in natural language frequently contain ambiguities or inconsistencies, complicating their interpretation and the team's ability to plan effectively. Such defects in task descriptions can result in misunderstandings, increasing the likelihood of errors and product defects. An analysis of existing approaches reveals the absence of an integrated model that simultaneously considers task formulation quality, planning mechanisms, and personalized task assignment. This paper introduces an intelligent sprint planning model that formalizes the selection of tasks based on their characteristics, team member preferences, business value, and associated risks. The decision-making process is supported by machine learning algorithms and large language models. Experimental evaluation of the proposed model on benchmark datasets confirmed its sensitivity to task description quality: reducing the clarity of just two task descriptions increased the aggregated defect risk by 50% and decreased the integral sprint value by 10–15%. In contrast, the use of stable task assignment preserved sprint value under similar conditions. Therefore, the proposed approach enables the enhancement of sprint outcomes by incorporating task text quality and defect risk into the planning process.