AI’s Expanding Role in Antitrust: How LLMs Are Changing Regulatory Reviews

Summary: Learn how strategically deploying large language models in both predictive and generative AI can help streamline your processes in an increasingly complex regulatory environment.

‍

Regulatory changes in the antitrust sector over the last two years are highlighting a critical role that AI (and specifically large language models) play in helping legal teams analyze and cull the vast amounts of data increasingly requested by regulators during M&A reviews.

Among the regulatory shifts, significant updates to the Hart-Scott-Rodino (HSR) Act premerger notification requirements went into effect in January. These changes aim to improve the transparency and rigor of merger reviews, but they also impose substantial new demands on filing parties. The new presidential administration also brings its own set of priorities to antitrust enforcement, which may cause further uncertainty.

As organizations create more, and increasingly complex, data each year, the pressure is ratcheting up on filing parties. In today’s environment, legal teams (and the professionals who support them) must not only understand the evolving antitrust regulations and requirements; they also must understand how to bring together the right technology and people to comply with those regulations under a growing deluge of complex datasets.

Specifically, there is a growing need to understand how strategically deploying different types of LLMs, including in both generative and predictive AI, can streamline the processes involved when companies face antitrust regulatory reviews. Moving forward, expanded use of newer AI models may mean the difference between closing a deal quickly and efficiently or risking a deal falling through because legal teams failed to comply with regulatory requests in a timely manner.  
‍

The growing role of AI in antitrust compliance

Deals that are facing antitrust scrutiny from regulators, including those requiring responses to Second Requests under the Hart Scott Rodino act, often involve an enormous amount of data and information that must be produced within extremely short timeframes. This has often required expensive, time-consuming manual work.

Legal teams are increasingly pivoting from this approach to use predictive and generative LLMs in antitrust matters. AI-backed by LLMs is incredibly adept at handling the high data volumes and tight deadlines inherent to many of these deals. With LLMs, teams can streamline these processes, allowing legal teams to identify and deliver the information that regulators are looking for.

“In a very short amount of time, we’ve seen different AI tools being strategically deployed for work beyond simple responsiveness review to much more complex tasks,” said Harsha Kurpad, Antitrust eDiscovery Counsel at Latham & Watkins. “These advancements are not only saving a significant amount of time in what have been traditionally highly labor-intensive tasks. They are allowing legal teams to manage the data involved in antitrust matters far more consistently.”

It’s important to understand the different types of AI and which type is best for which situation. LLMs are advanced AI systems that analyze language as it is used in real-world contexts. While traditional TAR marked a major progression in using technology in eDiscovery, the increased use of LLMs represent an even further advancement. While classical machine learning tools like TAR use approaches such as word frequency analysis to interpret data, LLMs interpret words as interconnected data that can analyze how their meanings change based on context. LLMs provide the foundation for both predictive and GenAI.

And while many people equate LLMs with GenAI, they are also a critical aspect of predictive AI. Predictive AI analyzes extensive data sets to generate probability-based outcomes, which allows users to forecast truths about the present or future. On the other hand, GenAI is trained on a data set that it then uses to create new content. Since GenAI is designed on creation, rather than accuracy, its output needs to be thoroughly reviewed–as several lawyers have found out the hard way.
‍

Advantages of AI tools during antitrust regulatory reviews

There are numerous advantages to using LLM-backed AI tools during antitrust regulatory reviews. Those include:
‍

Enhancing review efficiency

AI can automate key aspects of document review, including identifying relevant content and flagging privileged communications. By reducing reliance on manual review processes, organizations can achieve significant time and cost savings.
‍

Improving accuracy and consistency

Advanced AI algorithms are designed to maintain high levels of accuracy, minimizing errors that could lead to regulatory setbacks. Consistency in review processes is particularly valuable when dealing with large volumes of data.
‍

Supporting multilingual document review

Among the changes to the HSR rules is a requirement that all foreign language documents be translated into English. With these new translation requirements, LLMs’ ability to process and translate documents in multiple languages becomes a critical asset. This capability ensures compliance while expediting the review process.
‍

Customizing workflows

LLM AI tools can be tailored to align with specific review needs and regulatory requests. Using these tools to create workflows that prioritize document populations that may require a higher level of scrutiny or populations that do not require manual review at all, enables legal teams to focus attorney resources on the most critical aspects of review, while ensuring regulatory deadlines are met.
‍

“Second Requests are among the most complex and time-sensitive exercises in legal practice. Predictive AI has been a cornerstone in managing the responses to Second Requests effectively for many years,” said Ellen Blanchard, Senior Counsel at Norton Rose Fulbright. “We are now exploring ways to utilize generative AI to further alleviate the pressures of Second Requests by enabling faster triage, smarter prioritization, and more accurate document classification at scale. When the right types of AI tools are used strategically, they can enhance the efficiency, precision, and defensibility of our responses to regulators.”
‍

Two best practices for organizations navigating the evolving landscape

To effectively manage the intersection of the regulatory landscape and technological advancements, organizations should adopt several best practices.
‍

1. Ensure good data governance from the outset

Organizations can begin acting long before M&A activity takes place to ensure that corporate data is well governed. This minimizes the risk of a deluge of irrelevant data that would need to be culled by AI during a regulatory request, while also ensuring

that data that will be required for review is maintained appropriately.

Regularly review and update internal document management and compliance policies to reflect evolving regulatory requirements. Organizations should implement clear protocols for identifying and retaining documents relevant to potential antitrust reviews.
‍

2. Invest in the right teams and tools

Organizations should also make sure they have the right teams in place, who understand how to leverage advanced AI tools in ways that align with organizational goals and compliance needs. This means finding outside counsel who not only understand how to navigate the complexity of antitrust compliance, but also understand how AI tools can help ensure compliance more efficiently.

According to Robert Keeling, Partner at Redgrave LLP, it’s also critical to engage expert support and partner with experienced eDiscovery counsel and providers who can help legal teams navigate complex regulatory data landscapes.

“Effectively handling the frenetic setting of antitrust matters today requires close collaboration and trusted relationships between outside counsel, in-house legal departments, and third-party partners,” Keeling said.

“AI tools are proving invaluable in streamlining antitrust matters. However, maximizing their benefits requires a strong partnership between outside counsel, in-house counsel, and eDiscovery providers. We’ve seen AI deliver exceptional results in antitrust cases—achievements that wouldn’t be possible with traditional tools or workflows,” Keeling said.

“But success depends on knowledgeable, experienced partners working together intentionally, ensuring each brings their unique expertise to these complex situations.”

Continuous monitoring is another important step. That involves staying informed about ongoing regulatory changes and emerging technologies in the compliance space.
‍

Moving forward with AI

Regulatory environments are dynamic in nature. While changes present new challenges, they also offer opportunities for organizations to enhance their compliance strategies and operational efficiency.

“While the antitrust environment remains in flux, there is no question that the right tools and technology will be vital to help legal teams comply with regulatory requests. For example, integrating AI into specialized attorney review workstreams holds the promise of speeding up the compliance process by minimizing the amount of data that attorneys need to manually review,” said Bryan M. Marra, Senior Antitrust Attorney at Arnold & Porter.

“This would give outside counsel more time to focus on strategic matters and more nuanced decision making, which would allow us to serve our clients more efficiently in the future.”

By embracing advanced technologies and proactive planning, businesses not only meet regulatory requirements but also gain a competitive edge in an increasingly complex marketplace. The process of antitrust and Second Request reviews tends to drive early adoption of technology that makes document review more efficient and accurate. The high data volumes and short turnaround times push legal teams towards tech that can help them meet substantial compliance.

As the regulatory landscape continues to evolve and new technologies constantly emerge, the ability to adapt and innovate will be a key determinant of success.