Although the construction industry is growing rapidly to keep pace with demand for new infrastructure and housing, the sector is plagued by labor shortages and escalating material costs— both of which often lead to project delays and cost overruns. To solve these challenges, companies are beginning to use emerging technologies, like AI, across the lifecycle of construction projects to streamline requests for information, synthesize vast amounts of data for design purposes, improve project timelines, and monitor for potential safety hazards onsite.
Indeed, the market size for AI in construction is now projected to hit nearly $10 billion by 2028, at a compound annual growth rate of over 24 percent. As such, it’s more important than ever for leaders in the industry grasp the full spectrum of capabilities that AI can offer, from design through construction and maintenance, as well as necessary legal considerations.
Imagine a property owner that comes to an architect and says, “Here are my conceptual designs for a single-family house.” The owner might have a photo and presentation with basic renderings, but that’s likely the extent of it. With generative AI, however, design teams could instead offer multiple conceptual designs and quickly create 3D design renderings by leveraging vast datasets of existing architectural designs. In addition, a variety of AI tools could be deployed to quickly analyze each element’s functionality within the project, the source of materials, lead times, and potential supply chain disruptions to identify cost-effective material selections, optimize resource allocation and delivery, estimate the project timeline, and comply with environmental and safety requirements. Once the initial plans are laid, AI can be used to identify areas of overlap and resolve clashes between the 3D models rendered by various sub-teams (e.g., architects, engineers, mechanics, electricians, and plumbers). From this point, machine-learning platforms can help a design team explore and compare alternative designs.
Once the project is underway, project progress trackers ingesting data from robots scanning the project site (e.g., with 360-degree cameras and LiDAR scanning) can help construction leaders not only map their progress but make decisions that can save time and money. For instance, the data collected by these robots could be fed into generative AI models to answer questions such as: How much of the requiring piping was installed? How much could we save if we decreased the height of the ceilings? Such trackers and visualization tools can make it much easier for different teams to come together and implement tweaks to the project as they go.
Notably, according to a recent study, regular preventative maintenance can enhance the lifespan of equipment by up to 60 percent. By analyzing equipment data throughout the duration of a given project, AI tools can provide real-time analytics of equipment longevity and periodic risk assessment reports—empowering managers to pay attention to their maintenance needs, and cutting down significantly on costs and delays in the process.
Since the release of ChatGPT in November 2022, the legislative and regulatory landscape governing AI has been developing at an incredibly fast pace. Recently, there have been a number of significant developments at a global level, including the UK AI Safety Summit, the White House’s AI Executive Order, and, of course, a monumental political agreement being reached on December 8, 2023 regarding the European Union’s AI Act—the most comprehensive law governing AI to date. At the same time, federal regulators have been stepping up their focus on the use of AI and a patchwork of AI-related state and local laws has been developing in the United States across sectors.
When it comes to the use of AI within the construction industry, there are myriad potential intersection points with existing legal frameworks. Areas of law that should be considered before AI deployment include the below.
Data privacy law. AI systems deployed on a construction site often collect and analyze large amounts of data, including personal information about workers. Consequently, such tools may trigger responsibilities under a number of data privacy laws imposing requirements such as notice and consent to workers, as well as potential limitations on the use of the data collected to train the AI model.
For example, under the California Consumer Privacy Act (as amended by the California Privacy Rights Act), employees have rights regarding the personal information their employer collects and maintains about them, including the right to request deletion of personal information and the right to opt out of sharing personal information. Further, before biometric data is collected, the Illinois’ Biometric Information Privacy Act requires, among other things, informing individuals that a biometric identifier (e.g., retina or iris scan, voiceprint, fingerprint, facial geometry scan) or biometric information is being stored or collected and obtaining a written release from the individuals subject to the storage or collection.
Employment law. Of course, if the data collected about workers is later used to make an employment decision (e.g., a promotion or termination) downstream, a company may find itself within the scope of several employment-related laws at the federal, state, and even local levels. For instance, the use of an automated employment decision tool for an employee’s promotion in New York City could be subject to the City’s Local Law 144, which would require notice of the use of the tool to the employee and public posting of the results of an annual bias audit of the tool.
Intellectual property (IP) law. The use of AI in construction also requires careful consideration of IP law more broadly. For example, at a foundational level, the datasets used to train AI models are often subject to copyright and licensing agreements. In recent months, a slew of lawsuits have asserted violations of copyright law based on generative AI models using training data that is allegedly subject to copyright protection. While the viability of these and other IP-related claims remains to be seen, companies should ensure that contractual agreements with AI developers are carefully reviewed and drafted to help protect and assign IP rights.
These examples are a fraction of the array of legal considerations when deploying AI in construction. Accordingly, companies leveraging or considering the use of AI should carefully assess the specific use case for potential risks and mitigation measures, particularly in light of the rapidly evolving AI legal landscape.
The views and opinions expressed in this article are those of the authors and do not necessarily reflect the opinions, position, or policy of BRG, Gibson, Dunn & Crutcher LLP, or their other employees, affiliates, or clients.
The information provided in this article is not, is not intended to be, and shall not be construed to be, either the provision of legal advice or an offer to provide legal services. The content here is intended as a general overview of the subject matter covered. Gibson, Dunn & Crutcher LLP is not obligated to provide updates on the information herein. Those reading this article are encouraged to seek direct counsel on legal questions.
The views and opinions expressed in this article are those of the authors and do not necessarily reflect the opinions, position, or policy of BRG or its other employees and affiliates.
