agchouston.org Spring 2025 Cornerstone 17 I F YOU WANTED TO LAY A ROAD in 1918, the year the Associated General Contractors of America (AGC) was founded, you’d start with a team of workers using shovels, picks and perhaps a mule-drawn grader to level the earth. Steam-powered rollers would fol- low, compacting layers of crushed stone or gravel all under the watchful eye of engineers armed only with blueprints and experience. Today, the process looks almost noth- ing like it did more than a century ago. And that’s because the tools of the trade have evolved along with the industry’s willingness to adopt and master new technology. “The construction industry is renowned for always looking for that better tool to get the job done faster, more accurately, more precisely,” Sameer Merchant, AVP at construction technol- ogy company Autodesk, said on a recent episode of the AGC’s ConstructorCast. Over the last several years, advances in artificial intelligence (AI) and machine learning (ML) have required the industry to adapt rapidly and rethink traditional workflows. The speed and scope of these changes may seem overwhelming, but as Merchant noted, “when you explain [AI] is not a tool to replace what you’re doing but to help you do it faster and better, that’s when the industry starts to embrace it.” In 2025, 44% of commercial contrac- tors plan to increase their investments in artificial intelligence (AI), according to AGC’s 2025 Construction Hiring and Business Outlook report — a clear sign that the industry is embracing AI, and it’s already having a profound impact on nearly every level. What’s more is this is just the beginning. In a recent survey of Trimble custom- ers, 59% of respondents see the use of AI and ML as one of the most significant trends in the industry this year. Brian Perlberg, a construction attor- ney and executive director of Consen- susDocs, has seen this firsthand. “A lot of people say that construction is ripe for disruption,” he remarked to Con- structorCast recently. “We’re going to see more change in the next five years than we have seen in the past 50.” What Are AI and ML for the Construction Industry? What is AI? The term is thrown around in nearly every industry and application, and on the surface, it is fairly broad. According to McKinsey and Company, “Artificial intelligence is a machine’s ability to perform some cognitive func- tions we usually associate with human minds.” Machine learning, according to the same source, “is a form of artificial intel- ligence that can adapt to a wide range of inputs, including large sets of historical data, synthesized data or human inputs.” When applied to construction industry tasks and practices, both AI and ML can streamline processes, increase produc- tivity and make work sites and human workers safer. In general, there are a few different types of AI that you might encounter as the tools and practices become more mainstream: ] Generative AI. Models like ChatGPT or Claude create (or gener- ate) content. This can include images, text, audio or code. The models are trained on large datasets, which allows them to produce content that mimics human writing and speech patterns. There are virtually unlimited applica- tions for generative AI in the industry. ] Predictive AI. This type of AI uses statistical analysis and ML algorithms to predict patterns. It’s often used in project management and deci- sion-making applications for design, planning and safety. ] Computer Vision AI. This is used to help computers understand and process visual data. It can be used to compare images in the field and map out workflows as well as improve safety and efficiency based on what it sees. ] Robotics AI. When AI is integrated into machines, like autonomous vehicles, it enables robots to perform work with minimal supervision from humans. Robotics AI uses a combi- nation of generative, predictive and computer vision. How AI Is Reshaping Construction Processes Construction is often seen as a hands-on industry, but the actual building is only one part of an intricate system that Advances in artificial intelligence (AI) and machine learning (ML) require rapid adaptation and rethinking of traditional workflows.
