By Michele Morgan, SAP
The construction industry accounts for 14.2% of global GDP. But as other industries have steadily grown their productivity in the last few decades, construction productivity has been stuck in the mud.
Between 1970 and 2020, as aggregate productivity for the U.S. economy doubled, labor productivity in the U.S. construction sector declined an average of 1% a year. Some estimates put this at $30 billion to $40 billion in losses. Meanwhile, schedule and cost overruns are the norm.
Just 8.5% of megaprojects ($1 billion or more) meet or exceed their time and budget expectations, according to one study. On top of all of that, skilled labor is growing scarce as older workers leave the industry and fewer young people enter its ranks.
While other industries have embraced the digital world to improve efficiency and performance, construction has historically been slow to adopt technology, says Dr. David Jason Gerber, director of the M.S. in Advanced Design and Construction program at University of Southern California.
It’s not hard to understand why. After all, you can’t digitize concrete. Construction is a low-margin, materials-based, physical labor-intensive field.
But AI could spur construction’s digital transformation. The industry has accumulated decades’ worth of data that, with the help of AI-driven analysis, could be tapped to boost construction productivity, prevent schedule overruns, improve cost-effectiveness, bridge labor gaps, and reduce risks. In a recent study, 92% of construction companies said they were already using or intend to use AI.
The application of generative AI specifically could be a turning point for the industry —particularly as construction projects grow more complex and demand continues to increase.
Putting generative AI to work in construction
While generative AI isn’t the only AI tool to solve the construction industry’s productivity challenges, its ability to use large language models (LLM) to create new text and images through a natural language interface makes it a good fit for construction, from planning to jobsite execution.
Jose Luis Bianco, who leads McKinsey’s engineering and construction work in North America, estimates that AI and gen AI together can unlock value of up to $18 billion for home builders alone (about 10% of industry revenues).
A recent article in Engineering News-Record listed several promising gen AI applications, including AI assistants or “co-pilots” that help employees perform tasks and quickly gather information, high-speed content generation for drafting contracts, and specialized tools to aid in decision-making, from lot selection to pricing.
Let’s examine other ways generative AI can help the construction industry improve productivity.
1. Streamlining supply chain and procurement
Construction firms manage vast, complex supply chains and networks of trade partners. By analyzing data on supplier performance, quality control, and project requirements, gen AI can identify potential bottlenecks or inefficiencies so construction companies can proactively address issues, streamline operations, and ensure timely project completion.
When facing material shortages or delayed deliveries, for example, gen AI might suggest alternative suppliers based on past collaborations or supplier quality, to keep work flowing.
Turner Construction last fall demonstrated a gen AI tool that automates contract drafting, a huge productivity tool for a company with 30,000 trade contractor deals (those contractors who supply labor and materials for a project) every year. The tool uses natural language processing (NLP) and machine learning algorithms to understand requirements, automatically generate text for statements of work or master services agreements, and speed up the overall procurement process.
2. Labor-saving digital assistants + co-pilots
Nearly one in four construction workers in the U.S. are 55 or older, according to the Associated Builders and Contractors (ABC) trade association. Meanwhile, an estimated 9 million construction workers are likely to leave the industry in 2024. The US construction industry will need an estimated 501,000 additional workers on top of the normal pace of hiring in 2024 to meet demand (and another 454,000 in 2025), according to a proprietary model from ABC.
Gen AI could help get new hires up to speed much more quickly through the deployment of digital assistants for junior project managers. By interacting with existing systems and data, these assistants can answer queries, offer suggestions, and provide step-by-step instructions in real-time.
Suffolk Construction, for instance, plans to roll out the co-pilot built into its collaboration software to find information and answer questions for workers in the field.
Co-pilots could also take on tedious tasks, boosting construction productivity. Gilbane, one of the largest privately-held real estate development and construction companies in the U.S., has been conducting gen AI proofs of concept that helps its people make the best use of their time with super-fast document retrieval.
3. Productivity boost: AI-aided project management
Construction projects involve multiple parties engaged in a wide range of discrete activities, and keeping everyone on the same page — literally — remains a challenge. It’s not uncommon to use paper blueprints, drawings and specs, and when plans change, they may not have access to the most recent information.
Many in the industry have shifted to using building information modeling (BIM) systems, whereby a digital twin of the built asset is created and managed. However, updating the digital twin can be laborious, as can pulling information from the BIM, which may contain hundreds of terabytes of data.
Gen AI can help by updating the digital twin with data gathered from AI-powered cameras and sensors, and comparing it with original plans. For example, it might find important information within the data lake of the digital twin, or even send alerts when, say, piping is scheduled for the following week, but the materials are running behind.
Once the project is complete, construction firms could use gen AI to take all the data in the BIM and package it up as a digital manual, including links to warranties and troubleshooting information.
4. Improve construction productivity with dynamic scheduling
Scheduling has historically been the purview of industry pros using traditional project planning tools — and years of experience — to build out their schedules in a linear fashion. They usually produce a single schedule since exploring alternatives would be too time-consuming and costly.
Project scheduling could be done more quickly and accurately using AI algorithms to analyze historical company data, project requirements, and resource availability. These tools could quickly develop multiple scheduling options and run “what-if” analyses to explore how changes in variables (e.g., number of cranes used) impacts timelines and costs. Such automation and optimization could yield significant time and cost savings.
Building the schedule, though, is only half the battle. Adjusting it based on changing conditions is the other. LLMs can be used to assess project risks, considering factors like weather, labor availability, and supply chain disruptions.
These models can also continuously monitor a project’s progress and make changes to schedules in real-time. If a task is delayed or completed early, the algorithm can automatically reschedule dependent tasks to optimize project timelines.
5. Better quality control and compliance
Keeping on top of complex, confusing and frequently changing building codes is another construction productivity drain gen AI is particularly well suited to solve. Regulations vary by jurisdiction, year, and project type. They often include exceptions and can be challenging to interpret.
Some vendors are introducing AI-powered building codes co-pilots to answer building code queries such as how many exits are required for a restaurant and minimum ceiling height for a habitable room. Gen AI could also be used to analyze building schemes and blueprints to identify elements that don’t meet standards, enabling early corrections and adjustments.
Once building is underway, gen AI could also help in the execution phase to keep projects on track by monitoring progress versus plan, spotting potential delays, and recommending steps to avoid or recover from such issues. Some startups already offer drones and hard hat or crane-mounted cameras to capture information on a project’s status during execution phase. Gen AI can then compare the data gathered on-site to original plans to flag problems, like deviations from plans to minimize rework or delays.
Building an AI foundation for construction productivity
Construction businesses face multiple hurdles when it comes to taking advantage of gen AI, most of which are centered on the industry’s low levels of tech adoption to date. This is combined with a propensity to favor short-term cost concerns over long-term tech investment benefits.
Nonetheless, with some of the biggest industry players moving forward with gen AI, it’s increasingly less of an option to sit on the sidelines. Here’s how construction businesses can get the best results and better productivity with the emerging tech:
- Make gen AI a business priority. Construction companies need to coalesce around a vision and commitment to using the tech, determine how much they’re willing to invest, and empower a senior leader to take charge of the effort, says McKinsey’s Bianco. Large construction firms can build their own teams of data and AI experts to work with business leaders to define use cases, while smaller firms may lean more heavily on external partners to drive strategy and adoption.
- Get serious about data. Contractors tend to think each project is unlike any they’ve done before. And, indeed, for many, each project actually is a one-off. This can result in siloed construction data, often owned by different entities who are reticent to share their information because of competitive concerns. So, while the industry sits on an enormous cache of data, many are just at the starting point of harmonizing the data and providing consistent access to it. Tasks like data cleansing, normalizing, and transforming are essential to remove noise, standardize formats, and make the data suitable for training generative AI models.
- Pilot with care. “It’s all about de-risking the pilots,” USC’s Gerber says. Construction companies can do this be choosing projects where gen AI can be explored without adding risk—maintaining parallel processes, for example, and keeping humans in the loop. They also can partner with vendors and academic institutions, and have strategies to measure, monitor, and manage their test cases. One of the benefits of construction is that—because it’s project-based—businesses can try out new tech on a single project. Like testing out a new material, if it’s useful, they can scale it.
- Establish responsible AI policies and governance. Organizations need to fully assess the risks, such as data privacy, AI bias, and model hallucinations and establish AI policies and risk mitigation practices before rolling out new capabilities. Responsible AI frameworks include ethical guardrails, accountability for managing risks, an organization-wide structure for controls and governance, and ongoing reassessment, Bianco advises.
- Bring the culture along. Construction employees, of course, will have to be comfortable using gen AI for it to be effective.
Get going now
Gen AI has great potential to enable a new wave of productivity and efficiency within the construction industry and could well be a key driver in some long-overdue digitization and data integration and governance. As more industry players experiment with and adopt the technology, the pressure is on others to adopt as well.
At Clark Construction, Mathur says company leaders are convinced that a proactive approach to gen AI adoption is critical to continued competitive advantage.
“The real risk is to not invest and be left behind,” Gerber says.
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This story also appears on The Future of Commerce.
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