The volume of data in the world is increasing rapidly. Whereas it used to take a 50 lb hard disk drive to house 5 gigabytes of data, we now store 50 times that quantity into a small flash drive. The large quantities of data that most construction projects require would take months, or even years to process manually.

AI might be a trigger word for most people, bringing up opinions and debates across a variety of fields. However, we’d like to highlight a subset of AI that has related application the construction world. Machine Learning (ML) is a branch of artificial intelligence which involves training algorithms to progressively learn from data to inform predictions or decisions. Rather than a person programming machines, the machines themselves use software with algorithms that allow them to create predictions based on their analysis of data. Let’s break that down a bit further.

A simpler definition of machine learning is found in its name. It’s when a machine does the learning for you. Rather than simply drawing on statistics or averages, ML develops algorithms. Think of it like this: If you’ve ever unlocked your phone with facial recognition, or used voice-to-text, you’ve had a brush with machine learning. Many notable companies, such as Yelp and Google, have implemented this subset of AI into their businesses to improve systems and increase engagement.

Through using machine learning in construction, we can predict potential safety hazards and project risks. A model can collect data from millions of similar jobs to find patterns and poor outcomes such as delays or rework. For example, Mortenson uses ML models for forecasting project performance and resource planning, as well as proactive safety monitoring. Common construction platforms that already include ML features are Procore, which integrates AI and ML plugins for risk prediction and scheduling insights. Autodesk Construction Cloud also uses ML to analyze RFIs, submittals, and field data to predict risk.

Along with predicting possible construction pitfalls, machine learning can assist in cost prediction. Turner Construction, for example, partners with tech firms like Buildots and OpenSpace to compare progress versus planned budgets, enabling real-time cost risk tracking. Good cost prediction can save companies 15% or more on funds, yet inaccurate predictions lose that same company money while introducing a myriad of other problems. Cost prediction has long remained a challenge for estimating due to external factors like the economy, regulation changes, natural disasters, etc. Machine learning’s ability to process complex data allows it to deliver refined predictive models.

As construction projects grow more complex, the ability to harness data effectively is becoming a necessary advantage. Machine learning isn’t just a tech trend but a practical tool that can help companies improve safety, reduce costs, and make smarter decisions. By embracing this technology, the construction industry can move beyond reacting to problems and instead start anticipating and preventing them.

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