AI-Driven forecasting in BRICS infrastructure investment: impacts on resource allocation and project delivery

Abstract

This study explores the role of artificial intelligence (AI)-driven forecasting in improving resource allocation, cost prediction, and project delivery in infrastructure investment within the BRICS nations (Brazil, Russia, India, China, South Africa). Through an analysis of 100 infrastructure projects, the study evaluates the effectiveness of AI tools in addressing common challenges such as cost overruns, project delays, and inefficient resource utilization. Using machine learning models, optimization algorithms, and predictive analytics, the study demonstrates that AI can significantly enhance cost prediction accuracy, reduce project completion time deviations, and optimize resource allocation, resulting in overall cost savings. The results show an average prediction error of 5.00% for cost forecasts and a 5.42% deviation in project timelines. AI-driven optimization led to an average cost saving of 5.45%. Additionally, AI tools identified 25% more risks compared to traditional methods, contributing to more proactive risk management. However, the study also highlights the challenges of implementing AI in countries with varying levels of technological readiness, data quality, and organizational resistance. The findings suggest that AI can play a critical role in transforming infrastructure development in BRICS nations, provided that barriers to adoption are addressed.