In February 2022, Litera convened a roundtable conversation among leading North American M&A lawyers around the topic, "How AI is Transforming M&A Due Diligence."
Several times during the roundtable, the use of AI in M&A was compared to its use in eDiscovery, where it has a longer track record and has acquired a much wider acceptance. The consensus of the roundtable participants was that lawyers applying AI in M&A are on the same path as their litigation colleagues in the general acceptance of AI (with some firms still a few steps behind).
The participants freely shared some of the challenges that the current M&A environment presents. AI doesn't solve all those challenges, but it certainly plays a role in how some participants address them. Here's a quick review of some of the hurdles that the participants identified:
The sheer volume of documents to be reviewed. Today's organizations are optimized to generate huge volumes of documents, including contracts at the core of most due diligence efforts. Several participants acknowledged that the abundance of documents and the limited time frames that the current market conditions present are driving them towards due diligence practices that are scalable and can rapidly and accurately assess large volumes of data. Dealing with that scale is something that AI is good at – for example identifying change of control provisions across a vast body of contracts that may not ordinarily all be flagged for review.
The disorganized state of documents in many deals. Many deal lawyers face less-than-ideal data practices on the part of the target companies they are evaluating. Important material contracts might be collected in one folder, but employment contracts or other important classes of contracts might be scattered across many folders managed by multiple departments. Several participants in the roundtable see a lot of value in using AI to sort and categorize contracts across unruly document repositories. Even before lawyers get to the important work of analyzing the documents for risk, deal teams need to know exactly what documents they are dealing with, along with being able to easily review late entries to the virtual data room and omit duplicates.
Finding needles in haystacks. The participants identified several situations where technology can help find critical risks that might otherwise go undiscovered via manual review. One example is identifying rogue employees' actions by looking for unusual patterns in documents related to employee behavior or transactions. Another: Identifying the core provisions in upstream agreements that can cause severe risk or even lead to renegotiations if they are not discovered early. Several participants said they are starting to see the actual value of using AI in new, creative ways to find otherwise elusive risks.
The sheer cost of traditional manual due diligence. The volume of documents involved in a deal is often directly related to costs. At a time when the volume of documents is up, and the cost-sensitivity of clients is also high, comprehensive manual diligence is becoming untenable. Some participants shared that they are proactive about using AI-based contract review software to pre-empt the cost discussion with clients. It's a challenge to price correctly, but some are finding success with alternate fee structures for AI-based diligence work, while others are simply becoming better at estimating their costs when using AI to streamline previously traditional manual phases of review.
The collaboration imperative. The question of using AI or not on diligence projects is driving an increased need for open collaboration between teams on either side of a deal. Discussions around the scope of review and desired diligence schedules are best taken up at an early stage. One participant expressed a desire to leverage AI-based analysis to organize and prepare documents before settling on the scope. Another noted that their firm ties the use of Kira's document analysis software to various collaboration tools, which allow buyers, sellers, and other parties to get information in one place in a secure way prior to beginning the analysis.
The challenge of divergent expectations. The roundtable discussed the divergent expectations of the lawyers who encounter AI-based tools in their practices. The skeptics distrust AI and assume that using AI for document review removes human judgment from the process. The adopters have a more realistic view of what AI does – that AI can speed up the initial organization of documents and the extraction of key provisions, as well as to allow budget to increase review scope where necessary. They see the technology as replacing some workflow bottlenecks in diligence work but not replacing the value of human analysis of the AI's outputs. Several participants noted that supporting associates with this technology is an important "quality of life" issue in an age when it's challenging to retain associates who perceive their work as unnecessarily tedious. This gap in expectations can be a land mine for firms if partners who control technology decisions don't understand the nature of the benefits it brings to their teams.
No legal technology is a silver bullet for any work process. However, the voices heard in this roundtable show that many of the world's leading law firms are having robust discussions about leveraging AI in their organizations’ due diligence processes. They are doing so not because AI is an all-encompassing solution that replaces existing processes or the value that humans provide, but because it helps address so many of these discrete challenges that today's accelerating M&A market presents.
Posted in Artificial Intelligence