ALIGNING SCRUM TEAM PERFORMANCE INDICATORS WITH LONG-TERM GOALS USING MATHEMATICAL MODELS AND AI

Abstract

This paper explores the challenge of aligning Scrum team performance metrics with the long-term goals of an organization through the use of artificial intelligence (AI) tools and mathematical modeling. The study addresses the difficulty of harmonizing empirical metrics used in agile project management frameworks with formalized strategic indicators, particularly within the OKR methodology. As a solution, the authors propose the implementation of a customized GPT model capable of analyzing large volumes of project data, forecasting key performance indicators, and suggesting optimization actions. The research is based on the application of multiple regression analysis to determine the influence of factors such as team size, number of defects, test coverage, technical debt, and team experience on productivity (velocity) and other key Scrum metrics. The presented mathematical models enable both retrospective analysis and future scenario simulation, depending on the organization’s goals. Special attention is given to the real-world validation of the proposed methodology with Scrum teams, which confirmed its practical effectiveness and revealed the most influential metrics. Furthermore, the study demonstrates that using Custom GPT significantly reduces the cost of manual analysis, improves planning accuracy, and automates decision-making processes. A method for prioritizing metrics based on team and organizational goals is also proposed, grounded in the integration of AI analytics with strategic management systems. The paper emphasizes that, despite the power of AI as a decision-support tool, human involvement remains essential for interpreting context, project-specific factors, and potential model inaccuracies. In conclusion, the proposed approach enhances result predictability, improves team productivity, and ensures stronger alignment between team actions and long-term business objectives. The results can be applied in IT project management practices and lay the groundwork for further research into integrating AI with agile methodologies.