Abstract

The growing complexity of software development environments has intensified the need for data-driven managerial practices, particularly within Agile teams where adaptability and rapid delivery are critical. This study investigates the managerial determinants of developer productivity by analysing a dataset of Agile IT developers using predictive analytics as a supporting tool rather than a primary research focus. The analysis evaluates how experience, workload allocation, risk assessment, real-time resource prediction, and AI-assisted optimization contribute to performance outcomes. Descriptive statistics, correlation analysis, and feature importance modelling reveal that experience level, resource allocation hours, and risk-related indicators are the strongest predictors of productivity metrics and project completion rates. These findings emphasize the managerial necessity of optimizing task distribution, reducing operational risk, and strategically leveraging AI-based insights to improve team effectiveness. The study contributes to Agile management literature by demonstrating how analytical tools can enhance decision-making processes, enabling managers to promote balanced workloads, anticipate overruns, and allocate resources more effectively. Overall, the results highlight the value of integrating predictive analytics into Agile managerial practices to improve developer performance and project outcomes.

Keywords: Agile Management, Developer Productivity, Predictive Analytics, Resource Allocation, Data-Driven Decision Making

How to Cite:

Prastyo, P. & Ramadani, N., (2026) “Optimizing Team Performance in Agile Environments: A Managerial Assessment of Developer Productivity Supported by Predictive Analytics”, Agile Management 1(4), 258-271. doi: https://doi.org/10.63913/am.v1i4.140