HYBRID AI-AUGMENTED PROJECT METHODOLOGY

Abstract

This study presents the Hybrid AI-Augmented Project Methodology (HPM-AI), a data-driven framework intended to incorporate artificial intelligence into hybrid project management settings. The method uses predictive analytics, natural language processing, anomaly detection, and sentiment analysis to combine governance discipline with adaptive execution. The proposed model utilizes two fundamental formulations: the AI-Enhanced Performance Index (AEPI), which measures multidimensional efficiency, and a regression-based Velocity Predictor, which correlates delivery speed with quality, communication, and effort metrics. We did empirical validation on twelve consecutive project sprints by comparing performance before and after integration across normalized velocity (VV), quality index (QQ), stakeholder satisfaction (SS), communication efficiency (CC), and effort index (EE). The results show that after AI was put into use, AEPI went up by 34%, velocity went up by 31%, and quality went up by 17%. The regression model got an adjusted R² of 0.91, which shows that it was very accurate at predicting. Statistical tests confirmed significance at p < 0.01 for all principal indicators, except for effort, which remained stable as anticipated. The results show that adding AI to hybrid project management changes it from a descriptive framework to a prescriptive, self-optimizing system that can keep changing thanks to automated feedback cycles. The research provides a scalable and replicable framework for AI-driven decision-making and performance enhancement within distributed project ecosystems.