The release and popularity of programs like Chat GPT, Bard and Dall-E have brought to light the power of generative artificial intelligence (generative AI), which is a form of artificial intelligence capable of creating information in response to prompts. While standard AI systems can only synthesize existing information, generative AI can produce new text, images, videos and code. This trait enables generative AI to “think” and “act” similarly to humans. As such, the practical uses for generative AI are widespread and are beginning to redefine how tasks are completed. In the field of law, generative AI has the ability to alter the workflow of attorneys and law firms and hence impact the client experience. With respect to mergers and acquisitions specifically, in time, generative AI likely will impact almost all steps of the deal-making process.
On the front end of a potential transaction, generative AI has the potential to significantly improve the screening process for identifying potential target companies. For example, certain generative AI platforms have the capability to identify company objectives, analyze business performance and predict future company success. This technology could generate return-on-investment values for different acquisition candidates or provide relatively accurate probability-of-success numbers for hypothetical mergers. In time, generative AI platforms should be able to perform these tasks at a significantly faster pace than deal-sourcing professionals utilizing traditional methods. Therefore, using generative AI to identify strategic and profitable acquisitions should enable potential acquirers to ultimately increase both the volume of potential target companies they can scan and the likelihood of locating target companies whose acquisition would complement the acquirer’s objectives.
In addition, generative AI is beginning to alter the way in which due diligence may be conducted. Nongenerative forms of AI (like machine learning platforms and simple computer algorithms) only collect data and analyze given information, yet the use of such nongenerative AI can significantly lessen the time required for a law firm or in-house attorney to review, analyze and summarize the information received as part of the due diligence process. For example, nongenerative AI may have the ability to quickly assess which contracts in an electronic data room include “anti-assignment” provisions – a task that is traditionally handled by junior attorneys. Given the current state of such technology, however, nongenerative AI may miss anti-assignment provisions which include unconventional wording. This could prove problematic for an acquirer who needs to know whether third-party consent is required. As clients begin to have the option to utilize nongenerative AI for certain aspects of due diligence, clients may be faced with important decisions regarding whether the accuracy of results or the costs of legal fees are more important. At present, generative AI has only begun to emerge as a due diligence tool, but it has promising capabilities, such as synthesizing information from various folders in an electronic data room to create graphs and visualizations or even providing insight into possible business and legal risks associated with a potential transaction.
Finally, generative AI can make purchase agreement drafting and review less laborious and time-consuming. Both generative and nongenerative AI systems use algorithms that provide the capability to scan documents and help with formatting and structure. Called “contract AI,” there is a growing demand for such products in the legal industry. Generative AI, on the other hand, is not widely used currently in contract drafting, but efforts to enhance the intuitive nature of this technology have the ultimate goal of assisting lawyers in creating contracts, such as purchase agreements, from start to finish. For example, an attorney could ask a generative AI platform to create a purchase agreement for company X using the basic structure from template Y but with certain conceptual deal-specific revisions, which would then be converted to precise contractual verbiage by the generative AI program. Or an attorney could ask the AI system to analyze language within the contract to determine whether it favors a particular party. While it will take time for generative AI systems to become advanced enough to accurately draft complex contracts, in the interim, lawyers may increase efficiency by using this technology in tandem with their own expertise to create rough drafts and compare contracts.
Some law firms have already begun to see the impact of emerging generative AI platforms in the M&A space. Harvey AI was the first comprehensive generative AI legal tool created and is currently in beta testing. Harvey AI is designed to act as an intuitive and comprehensive legal interface, helping with research, due diligence, contract drafting and analysis. While Harvey AI is one of the most advanced legal generative AI platforms available, other algorithms have also proven to be applicable to M&A. For instance, during beta testing, the generative AI platform roBERTa was recently used to predict the effectiveness of hypothetical mergers. While systems like Harvey AI and roBERTa are still in beta testing, they are showing promising progress toward being implementable in M&A transactions.
Given the emerging applications of generative AI in M&A transactions, strategic acquirers and private equity firms can expect generative AI to gradually provide increased opportunities to source transactions and more efficiently complete due diligence by offloading time-consuming work to AI systems. Ultimately, utilizing such AI during the deal process should have multiple positive effects, such as shortening the time between signing a letter of intent and closing the transaction and lessening the aggregate amount paid for M&A attorneys’ time. However, given the learning curve associated with utilizing the new technology incorporated in generative AI, it will take time for strategic acquirers, private equity firms and attorneys to efficiently integrate generative AI into routine deal processes. Also, in its current state, AI can provide misinformation, inaccurate answers or incomplete responses and therefore is only appropriate to use as a complement to attorney work product rather than as a replacement. Despite these obstacles, when implemented in a measured manner, generative AI will undoubtedly have multiple positive impacts on the ability of dealmakers to effectively and efficiently consummate successful M&A transactions.
Meet the Author
Anastasia Sheffler-Wood focuses her practice on corporate law, representing public and private companies in mergers, acquisitions, restructurings and cross-border transactions. Additionally, she handles private equity and venture capital investments. Anastasia also provides general corporate counseling to clients in numerous industries, including manufacturing, aerospace, transportation and logistics, food and beverage, financial services and information technology.
The author would like to thank Unnati Gupta, Legal Intern, for her contribution to the article.