Integrating Artificial Intelligence with Earned Value Management for Enhanced Performance in Smart Renewable Energy Projects

Abstract

Renewable energy projects in Jordan continue to face persistent cost overruns and schedule delays, largely due to the limitations of traditional project control systems that rely on manual progress reporting, subjective assessments, and delayed decision cycles. This study evaluates the integration of Artificial Intelligence (AI) with Earned Value Management (EVM) as a comprehensive approach to improving performance monitoring and forecasting in smart renewable energy initiatives. A mixed-methods framework was adopted, combining an industry survey of 394 practitioners with an instrumental case study of a solar photovoltaic system upgrade modeled in Autodesk Revit and scheduled in Primavera P6. The case study revealed early and significant deviations, with an SPI of 0.75 and a CPI of 0.56 at Week 4, followed by a sharp decline in cost performance to approximately CPI 0.31, ultimately indicating an unavoidable budget overrun and a negative TCPI. Survey findings strongly support the potential of AI–EVM integration: 61.7% of respondents reported substantial improvements in decision-making, 57.1% emphasized enhanced real-time tracking, and over 55% highlighted gains in forecasting accuracy through AI techniques such as machine learning, computer vision, and natural language processing. Despite these positive perceptions, key barriers—including poor data quality, regulatory limitations, high implementation costs, and organizational resistance—remain significant. Overall, the findings demonstrate that AI-enhanced EVM offers a transformative shift from reactive project control to predictive, data-driven management. The study concludes that adopting such an integrated framework is essential for improving cost efficiency, schedule reliability, and overall project outcomes in Jordan’s rapidly expanding renewable energy sector.