| Resumen |
The agile approach has driven the need for advanced tools in project management, especially in multi-project environments. This work presents the integration of a Machine Learning (ML) model into Worki, an agile tracking platform, to analyze historical Sprint data and evaluate performance. The model classifies and assesses progress and work pace, displaying the results directly within the platform. This enables a data-driven perspective, facilitating early anomaly detection and improving project management efficiency. © 2025 Elsevier B.V., All rights reserved. |
