Abstract

This study explores the use of data-driven analysis to identify distinct managerial profiles that influence the success of Agile project management. Using a dataset of Agile project performance indicators, the research applied machine learning clustering techniques, dimensionality reduction, and explainable artificial intelligence to uncover patterns in managerial behavior and project outcomes. The analysis revealed three unique managerial archetypes: Efficient & Strategic, Risk-Controlled, and Agile-Driven. Each archetype represents a different balance between leadership involvement, operational efficiency, and strategic control. The results show that Agile project success can emerge from multiple managerial configurations rather than a single standardized model. Projects emphasizing governance and cost efficiency perform well under structured oversight, while those focusing on risk management achieve stability in complex environments. The most mature Agile projects demonstrate strong leadership engagement and team collaboration, resulting in high adaptability and consistent performance. These findings highlight that organizational agility depends on aligning management practices with contextual and strategic needs. The study contributes to Agile management research by demonstrating how machine learning can serve as a decision-support tool for identifying managerial maturity, guiding process improvement, and optimizing leadership strategies in dynamic project environments.

Keywords: Agile Management, Machine Learning, Data-Driven Decision Making, Managerial Archetypes, Leadership

How to Cite:

Andes, J. & Latina, E. J., (2026) “Data-Driven Identification of Managerial Archetypes to Enhance Leadership Effectiveness in Agile Project Management”, Agile Management 1(2), 139-154. doi: https://doi.org/10.63913/am.v1i2.132