Market Data 
As a $2 billion construction management firm overseeing more than 150 active worksites on any given day, Shawmut Design and Construction is responsible for keeping roughly 30,000 employees, contractors, and subcontractors safe.
The Boston-based firm has employed AI for about eight years, using it for data collection, risk evaluation, worker safety compliance, and more.
Shaun Carvalho, the company’s chief safety officer, said this strategy had become invaluable for creating a safer operation overall while still prioritizing growth and efficiency.
“You’re talking about a business that, quite literally, 20 or 30 years ago was driven by paper and clipboards,” Carvalho told Business Insider. “Anything we can do to leverage technology, we’ll do it.”
Using data and AI to promote safety at construction sites
Shawmut uses artificial intelligence to predict safety-related incidents on its construction sites.
With the help of an AI tool created by a private vendor, Shawmut can pull various data points to help score the likelihood of something going awry.
Some of this data is from the National Weather Service — information about forecast temperatures, prospective weather events, and the associated fallout, such as freezing pipes. Other data is about personnel: If there’s an influx of new people to a job site, but not an influx of new leaders, the AI system will flag it as potential for disaster.
“Not having enough leaders means we probably aren’t getting enough eyeballs on the workers who are out there now,” Carvalho said. “When your job is to keep everyone safe, eyeballs are a very good thing.”
This data dump isn’t in real time, at least not yet. The drops come at least once a day, empowering leaders to speak with teams and make appropriate tweaks, Carvalho said.
Pairing GPS data with AI capabilities
Shawmut has been using AI since 2017, Carvalho said. The program really took off during the COVID-19 pandemic, when the company devised innovative ways to leverage the technology to ensure worker safety.
The firm’s chief people and administration officer, Marianne Monte, told BI that the company used GPS tracking software on workers’ phones to monitor their location on job sites. An AI engine automatically sent alerts when workers got closer than 6 feet apart.
Gradually, the company has expanded this use case to track whether workers are tied off on buildings (so they don’t fall) and whether workers are using scaffolding properly.
“These tools feel very benign to me, and they are making our job sites safer,” Monte told SAP. “It’s much less about the height of that ladder than it is about a person’s mental state when they get on it.”
Privacy rights are a key consideration in the age of AI
While AI can help construction businesses like Shawmut keep sites safe, some experts have raised ethical questions about using AI in this fashion.
Benjamin Lange, who studies the intersection of technology and ethics, said leveraging AI to essentially monitor people raises concerns about privacy, informed consent, and data security, which must be carefully managed to prevent misuse or overreach.
“Companies must be transparent about data collection practices, ensure that tracking is strictly limited to safety purposes, and provide workers with opt-in mechanisms to maintain trust and protect individual autonomy and workers’ privacy rights,” said Lange, a research assistant professor in the ethics of AI at the Ludwig Maximilian University of Munich and a researcher leader at the Munich Center for Machine Learning.
After hearing this feedback, Shawmut representatives decided to anonymize data from the jump.
What’s next for AI in construction
AI in construction faces several challenges, including data reliability and lack of human oversight.
“There are also concerns about overreliance on automation, which may reduce human oversight and accountability in critical decision-making,” Lange said.
AI systems rely on high-quality, accurate data, and any errors or biases in datasets can lead to costly mistakes or unsafe conditions, Carvalho said. Additionally, construction sites may vary widely in complexity, making it difficult for AI models trained on past projects to generalize effectively, he added.
Recent industry research indicates that bad data in the construction industry is a big hurdle for companies attempting to embrace AI. A recent report by Autodesk and FMI Consulting indicated that poor data — information that is “incomplete, inaccurate, inconsistent, or outdated,” according to the report — costs the industry $1.8 trillion annually. That same report said that 95% of all construction data goes unused.
Still, Shawmut is planning to extend and amplify its AI programs over the next three to five months, Carvalho said.
One push: real-time response. Carvalho imagines a future where every Shawmut employee wears a badge linked to a digital map of a job site. In this world, the AI system would alert managers in real time the moment an employee stepped out near something dangerous.
“What we want isn’t actually in the marketplace yet,” Carvalho said.
He added that he’d like to see AI technology that accounts for the various rules and regulations that differ by state. If, for instance, a contractor is on a Shawmut job in California and then heads to a job in Utah, this dream system would automatically update the job site policies to reflect the rules and regulations in the new municipality.
A system like this one would also have the potential to contribute to job safety.
