The Domino Effect of Labor, Construction and AI

Tech can improve productivity, creating less waste and reducing the need for large crews.
By Richard Harpham
September 27, 2022

Before and since the world was hit by the COVID-19 pandemic, the construction industry has been beset by a struggle to fill positions of all shapes and sizes. Though many industries throughout the United States have had ongoing labor issues in this time, construction has felt it more acutely than others, as McKinsey found that by October 2021, the industry had 402,000 open positions, the second highest level since December 2000. This is problematic because the report also found that construction will need to add anywhere from 300,000 to 600,000 new employees every year for the next decade due to the passing of the Biden administration’s infrastructure law. This need is further complicated by the fact that by 2031, approximately 41% of the current workforce will retire—a trend that has accelerated since the pandemic hit.

Kill Two Birds with One Stone

Though the report offers a variety of ways the industry can better secure the new talent pipeline, one area that rings true is the suggestion McKinsey makes about technology advances in the industry—namely, how they can help alleviate the hiring strain. Technology can help the industry improve productivity and execution, which ultimately means less waste and lowering the need for large amounts of labor that it is struggling to acquire. And waste in construction is an issue that has plagued the industry well before the labor market got overheated, as many studies over the years seem to agree that as much as 30% of all construction efforts can be caused by rework, which always leads to wasted product and wasted labor. The running analogy/joke in construction is that for every four buildings completed, at least one more ends up in the trash.

Much waste in construction often comes down to teams working with either the wrong information or not enough of it to make sound decisions that can increase site productivity. According to a report from Autodesk/FMI, “Poor project data and miscommunication on projects” can be blamed for 52% of all rework in construction in America, leading to $31.3 billion in rework costs. The key to solving this problem is for construction companies to find this information/data they are missing and, if able, access it in real time to assist with the day-to-day decisions at any given construction site. By doing so, it can kill two birds with one stone—cutting down on waste and easing labor constraints at the same time.

Dark Data and the AI Solution

Like many industries, construction suffers from an unstructured data problem: data that is not readily available or accessible. This is the definition of dark data: data that is stored in silos and software, scattered throughout a company’s on-prem or cloud servers that, when discovered and contextualized, brings extraordinary value to choices, whether it be a “lessons learned” recap report or punch lists from previous construction projects.

To use this dark data, construction companies need to continue to invest in AI/machine learning technologies, technologies that have been available in the industry for years now (in everything from scheduling to concrete processes to safety protocols). If a company is using newer software for these things, there is a very good chance that there is an AI element baked into it. The reason why is because these tools are computational, thus having advantages with data assessment that humans don’t have and can reveal valuable situational context and insights in dark data.

The Dominoes Fall

As AI technology is integral to many ConTech startups entering the industry, it’s only a matter of time before this tech migrates downstream to the smallest of mom-and-pop construction companies—not only something with which multinational corporations can experiment. As this happens, software will become more accessible, cheaper and pervasive enough that it will unlock and use tons of dark data. This will cause a domino effect that will help the industry make better decisions based on access to real-time data and in the right context, increase productivity and decrease waste. Combined with concurrent HR efforts to make the industry more attractive to new recruits, more efficient construction sites will lessen the overwhelming labor needs that are currently stressing the industry, thus returning balance back to the construction labor equation. And that is something you can’t put a price on.

by Richard Harpham
Richard Harpham is the Chief Revenue Officer of Slate Technologies, an AI platform that maximizes efficiency and costs for the construction industry. Prior to Slate, he led the software commercialization efforts for construction startup Katerra, which was a technology-driven off-site construction company.

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