The construction industry loses up to 20% of its budget on most projects due to inaccurate estimates, delays, and poor coordination between stakeholders. A new study by Natalia Breus, PhD economist and founder of ProjectFlow AIreveals how integrating AI, IoT, and data analytics enables construction companies to forecast costs and manage risks at every stage — from design to commissioning.
The Challenge Traditional construction management often operates in silos, with fragmented data and reactive decision-making. This results in inefficiencies, cost overruns, and delayed timelines.
The Solution By combining AI-driven forecasting, machine learning algorithms, and real-time IoT data, project managers gain predictive insights into budget deviations, resource use, and schedule risks — long before they escalate.
The Model The research introduces a polynomial regression model enhanced by the least-squares method, allowing highly accurate cost prediction based on technical and financial parameters of past projects.
This approach transforms static reporting into a living financial model that continuously learns and adapts.
The Impact
Up to 3x faster decision-making
80% fewer budget overruns
Real-time visibility across resources, timelines, and financial performance
A foundation for a new generation of AI-driven construction ERP systems
In short: smarter planning, proactive control, and fewer surprises on site.
Let’s discuss how AI can redefine your construction cost management!
About the Author
Natalia Breus is a PhD economist and founder of ProjectFlow AI, an AI-powered system designed to surface real-time insights from informal communication across complex infrastructure projects. With over 15 years of experience in large-scale construction, digital transformation, and executive financial oversight, she brings a rare combination of strategic vision and operational depth.