Kangaroo Court

Kangaroo Court: Data, Time, and AI in the M&A Process

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Whatever the type of M&A transaction, there is always the possibility that information may slip through the cracks. The growth in size and type of data exchanging hands throughout this process means that a more robust method is required for complimenting the work of M&A and Antitrust practice groups. Today, artificial intelligence can be used throughout the transaction, from early stages of preparation to post-closing analysis. It is about enhancing the work of the attorney, and respecting that effective due diligence is both an art and a science. That is to say, understanding the right questions to ask, and deploying the right tools to either assist with completing the transaction or identifying that the process is not worth continuing.

Information and Time

To begin from a note of personal admiration; I enjoy listening to the ideas that emphasize the importance of first principles. These usually appear in the form of a basic proposition or assumption that cannot be deduced from any other proposition or assumption. Examples include the first cause attitudes taught by Aristotelians and the more nuanced postulates of Kantians. Within mathematics, these are referred to as axioms. In physics, they are defined directly at the level of established science. They key is to not make assumptions that rely on empirical model and parameter fitting. The part that I enjoy is the simplicity. Removing the noise and focusing only on the essentials.

The additional appeal of first principles is in the clarity it provides. They help to remove the ego and refocus our examination on the core elements of a puzzle. In the legal world I believe it’s important to think of data in a similar way. Although we are not stripping back our understanding of a given problem to the atom, we can achieve a lot by refocusing how we think of data as a fundamental source of information, and a capital asset. It is also important to carry a healthy respect for time. These basic building blocks allows for the identification of the best methods for achieving our data goals. It also emphasizes the importance of treating data with care, so that we can readily identify the information within before utilizing it in the most valuable way.

It is equally important to consider the process we employ to turn data into something of value. We often focus on the practical aspects of data; where it lives and the form it takes. The key to thinking strategically is thinking of how to make your data work for you. This process begins by transforming data into information. When we achieve this, we can understand and leverage it to our advantage. However, in order to build a robust and competitive framework, we require a healthy respect for time. Its influence can be all powerful. It cannot be bought, adjusted, or otherwise interfered with. Therefore, it becomes a critical ingredient for measuring success. The faster a firm can turn data into information, the sooner it can find valuable insights.   

Combining human and machine intelligence

Apart from having access to intelligent minds, law firms and corporate legal teams alike have access to large amounts of data. The availability of this data is an opportunity for an organization to understand more about their internal risks, including regulatory concerns and core business relationships, as well as potential opportunities to litigate. However, accessing it requires a robust information governance framework that eliminates data silos, encourages analysis and shared intelligence, all while reducing ROT and unnecessary hosting costs. For law firms, the volumes of historical archived data provide a catalogue of potential insights about both their clients and individual practice groups.

Within the legal world, the need for comprehensive data intelligence is most evident when time is in short supply. Aside from data privacy and DSAR, a request for additional information and documentary materials made by the Federal Trade Commission or the Antitrust division is a critical example of why data intelligence matters. Managing and understanding data is enough of a challenge without tightly imposed deadlines and a scope of discovery rigidly defined by the requesting government agency. Despite less than 5 percent of filed transactions incurring a second request, the onerous nature of this process can be effectively reduced by proactively implementing robust information governance protocols. For the law firm’s M&A and Antitrust practice groups, proactively building a catalogue of AI models can ensure both the initial due diligence process and subsequent second request are addressed as smoothly as possible.

The key is using a predictive coding solution that understands language the same way humans use it. This is in order to maximize contextual accuracy of the end product (in this case, a model trained on the data). Once you’ve partnered with a service provider that understands the different results various AI tools can deliver, it is important to clearly define where the training data is located and what the M&A or Antitrust group wants to find. Successful mergers and acquisitions are a process which require advanced preparation and comprehensive due diligence in order to ensure the best possible outcome for both the buyer and seller. Incorporating AI models into this process can help both parties efficiently reduce both the data size and review time.

The scope of the second request is generally not up for negotiation. Once the requesting agency has defined the deliverables, both parties must act in a timely and comprehensive manner in order to maximize the opportunity for a positive outcome. Preparation that covers all aspects of information governance, including clear understanding of cloud ownership, third party held data, and a clear preservation and legal hold process is a critical starting point. Incorporating AI models will only serve to benefit these efforts throughout the process, especially when complexity increases with mobile and cloud data as well as rolling collections.

Given the limited timeline of a second request, proactively integrating AI models into the standard workflows of M&A and Antitrust practice groups will provide some breathing room where traditional review methods fall short. The wealth of experience law firms deliver can be supercharged when their subject matter expertise is filtered through AI models. Some non-obvious examples may include identifying data for specific industries that reveal problems which might be transactional turn-offs for a prospective buyer. In addition, using models to understand the data early in the process will assist firms in preparation of an anticipated second request, including whether multi-lingual data sets are involved. To address this, AI models should be developed in advance to cover relevant languages.

Artificial Intelligence is a tool that helps us overcome two key hurdles when we attempt to manage and understand data. It reduces the amount of time required to find relevant documents, and it accelerates our ability to reveal hidden patterns and insights within the data itself. Bringing together subject matter expertise, relevant datasets, and the right combination of technology will go a long way to improving how we think of data itself. Understanding that data is a source of intelligence and an asset can help reframe how we think about existing methodologies and challenge prevailing assumptions.

The key is to enable M&A and Antitrust practice groups with the ability to not only respond to a second request in a timely manner, but also improve the due diligence process before both parties file a notification and report form with the FTC and Antitrust division. Firms which refuse to explore these options risk running up against an opposing counsel with faster access to better information. It is no longer enough to hire smart people. Today, practice groups need to supercharge their talent with AI.  

Chip Delany on Email
Chip Delany
Strategy Director at Lineal Services, previously worked as a strategist for Legal AI tech firm NexLP and before that as a consultant in continuous improvement and labor modelling. Australian National and US permanent resident.

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