LEVERAGING ARTIFICIAL INTELLIGENCE AND PREDICTIVE ANALYTICS TO ENHANCE ACCURACY IN PROJECT CONTROLS

Abstract

This study looked into how to use Artificial Intelligence (AI) and Predictive Analytics to make project controls more accurate. It was sometimes hard for traditional project management methods to predict cost overruns, schedule delays, and risk exposures. The study used previous project data and machine learning methods like Lasso Regression and XGBoost to create predictive models that made forecasts far more accurate and helped find risks early on. Standard performance criteria were used to check the models, and they showed that the accuracy of cost and schedule predictions improved by more than 18% and 25%, respectively. Also, clustering algorithms made it easier to group risks into categories, which made it possible to come up with proactive ways to reduce them. Project managers' feedback verified that the solution was useful and easy to use. The results imply that adding AI-based prediction tools to project controls can greatly improve decision-making, lower uncertainties, and improve project delivery outcomes in all fields. This study helps to close the gap between traditional project management and new AI technologies. It calls for these technologies to be used more widely in complicated project settings.