Artificial intelligence (“AI”) is becoming an increasingly common presence in the construction industry. From drafting scopes of work and flagging safety issues to evaluating subcontractors and reviewing contracts, AI is beginning to shape how construction professionals plan, manage, and deliver work.
While these tools can make projects more efficient and data-driven, they also introduce legal, contractual, and operational risks that aren’t always obvious at the outset. As AI use grows, contractors need to understand how it works, where it creates exposure, and how to manage those risks.
This article highlights common ways AI is being used in construction today, examines the legal and operational risks it can create, and outlines practical steps contractors can take to reduce their exposure.
Key Legal and Operational Risks
Inaccuracy and Authorship
Generative AI tools like ChatGPT are now being used to draft key project documents like scopes of work, RFIs, meeting notes, and change order narratives. While these tools save time, they can also be unreliable. For instance, they sometimes generate false, misleading, or fabricated information without any clear warning. Important context is also frequently missed, and the tools often make assumptions that haven’t been stated. These types of errors create significant risks for users. For example, an AI-generated scope of work may mistakenly rely on outdated regulatory requirements resulting in costly compliance issues. Perhaps most concerning, these AI tools will present incorrect information with confidence, making errors difficult to catch.
Contractual Misinterpretation
Natural language processing tools are being used to help review and summarize lengthy and complex contract documents. While they can perform these tasks quickly, they also have significant limitations. These tools may overlook or misinterpret important provisions or misunderstand how certain clauses relate to one another, especially in custom or negotiated contracts. If a project manager relies on an inaccurate contract summary, the consequences can be serious. They might miss a deadline, misunderstand a payment obligation, or overlook a clause that shifts risk to their company, leading to waived rights, breach claims, or unexpected financial losses.
Data Dependency and Model Bias
Machine learning and predictive tools are often used to assess project risk, forecast delays, and evaluate contractor performance. However, their effectiveness depends entirely on the accuracy and completeness of the data they use. If the provided source data is outdated, incomplete, or biased, the results will reflect those same problems. For example, a subcontractor may be flagged as high risk based on flawed historical records, or a delay forecast might rely on assumptions that no longer match field conditions. These errors can distort decision making and create legal exposure, especially if certain contractors or groups are disproportionately impacted.
Lack of Transparency and Traceability
Many AI tools function like black boxes, relying on complex or proprietary algorithms that offer little visibility into how their outputs are generated. This can become an issue when AI is used to support a decision that must later be explained or defended. If the reasoning behind an AI-generated recommendation isn’t clear, the risk of being challenged on fairness or compliance increases significantly. That risk is further compounded if there is no record of how the AI tool was used. This creates problems in litigation, arbitration, and regulatory review, where decision-makers must often demonstrate that they acted consistently and followed proper procedures.
How To Start Mitigating Risks Now
Despite these risks, using AI doesn’t have to be a liability. By taking a few practical steps, contractors, project managers, and in-house counsel can manage exposure and put AI to work in a responsible, effective way.
Select Tools Deliberately
Before adopting any platform, companies need to clearly define the task they want to automate or augment. From there, they should carefully evaluate the tools that are available and determine which one is best suited for their intended use. Using the wrong tool for a job can lead to errors that are hard to detect and even harder to undo. So, the first step to using AI effectively is selecting the right tool for the task.
Establish Clear Policies and Boundaries
Companies should take a structured approach to AI implementation. Rather than allowing AI to be used on an ad hoc or trial-and-error basis, leadership should define clear rules and expectations from the outset. This includes identifying approved platforms and specifying for what tasks AI can be used. Policies should also require human review before any AI-generated content is shared externally.
Train Staff to Use AI Responsibly
Next, companies need to provide clear training to employees on how to use AI tools. This includes guidance on how to input information effectively and evaluate output critically. Employees need to be able to spot potential errors, flawed assumptions, or other issues that could affect the accuracy or reliability of the generated materials.
Maintain a Defensible Record
To mitigate liability, it is critical to maintain a clear and complete record of how AI tools are used. When an AI tool is provided by an external vendor, companies should ask them how input and output data is stored, whether logs are retained, and what procedures exist for retrieving that information if needed. A well-documented process not only supports internal accountability, but also puts the company in a stronger position if a decision is later scrutinized.
Involve Legal Early
Lastly, if you believe your company has been exposed to liability because of AI usage, involve legal counsel as soon as possible. AI-related issues often raise novel legal questions, and there is room to shape strong, strategic arguments—but only if legal is involved early enough to help. Waiting until the situation has escalated limits your options and increases the risk of unnecessary exposure.
As AI use increases in construction, so does the potential for costly mistakes. Those risks, however, do not lie with the technology itself, but rather with those who fail to manage it. Ultimately, the companies who will benefit from AI are not the ones adopting it the fastest, but the ones integrating it with discipline and good judgment.
Smith Currie Oles provides comprehensive legal services to all parts of the construction industry across the nation. Smith Currie lawyers have decades of demonstrated success representing construction and federal government contracting clients “From the Ground Up,” including procurement matters, contract formation and negotiation, project administration, claims prosecution and, when necessary, in litigation and other forms of dispute resolution.
The views expressed in this article are not necessarily those of ConsensusDocs. Readers should not take or refrain from taking any action based on any information without first seeking legal advice.