AI appears to have particular relevance for dealmakers. Law firms and financial advisories are increasingly adopting AI tools for due diligence, for instance, and investment funds are beginning to use them for deal sourcing. In the foreseeable future, AI could be used to assist in valuation as well.
In order to find out how AI is being used in the deal process and how proficient dealmakers need to be in order to successfully implement and evaluate the technology, Toppan Vintage commissioned Mergermarket to interview five experts in the industry.
Toppan Vintage question: What other kinds of machine learning or artificial intelligence applications are there to assist in the dealmaking process at present? What sorts of tools can you envision being created in the future? Leading industry experts weigh in...
Dean Harvey, Partner, Perkins Coie says: When you think about what narrow AI does really, really well, it detects patterns in massive volumes of data. I think predictive intelligence is going to be an area of significant effort and growth. Predictive intelligence is when an AI algorithm is trained on existing data sets in order to identify patterns and develop a model to predict future outcomes and trends. For example, in a dealmaking context, models could be developed that analyze data to predict which companies in a sector are likely to reach certain growth targets.
Adam Robert Pah, Professor, Kellogg School of Management weighs in: There are significant opportunities in due diligence, which is something that's already going on in the law world with e-discovery. A human can read one document perfectly, but the amount of information that you're going through when you're looking at a deal, a company, or a lawsuit is massive. A single company has terabytes going into petabytes of data about themselves, and that is where artificial intelligence and machine learning can thrive.
Private equity valuations are another. That's a lot harder, though, especially if you're looking at start-up acquisitions, just because the time to actual payout is so long. What that means is that many compounding factors come into play. Solutions are going to be a lot harder in that domain, although I know there's at least one start-up working on the problem, and selling its scores and insights to other firms. One thing they've done is essentially score based on first rounds of funding, which is a much shorter time window, and not really necessarily what acquiring firms are most interested in. But that's the kind of time window you can try to make predictions for.
An acquirer may be more interested in, say, whether or not a company gets to an IPO – but so many different things go into that, and so much of it is not about the company itself. A big part of that equation is what's going on at the time. For example, there is no way that Twitter could IPO now. If Twitter was created even five years later, it wouldn't be Twitter.
Troy Ungerman, Partner, Norton Rose Fulbright adds: A number of tools such as Kira, RAVN, eBrevia and Luminance help to accelerate the due diligence process. They automate the extraction of key clauses and analyze their worth across many hundreds of contracts. In addition to finding specific built-in clause types, these AI-based applications allow users to customize the application to have it machine-learn any desired clause. In teaching the tool sample clauses, the tool gains intelligence and is part of a statistical model which is used to review later contracts.
There are also applications which help to create a first draft deal document. Tools such as Contract Express, Neota Logic and High Q make the process for drafting much easier by using a model template as the basis from which the first draft can be created. Though document automation tools have been in existence for quite some time, the level of sophistication has jumped from basic to much more complex, often involving the ability to create document creation workflows and rich features such as calculations in loan documents.
More specifically, Contract Express is used to generate first drafts of legal documents from agreed-upon model clauses, limiting human error and drastically increasing efficiency across a wide range of legal and business practices.
We are enthusiastic early adopters of AI and other advanced technology tools to enable us to deliver better service to our clients.
Natalie Pierce, Shareholder, Littler Mendelson says: At Littler, we're working with our data analytics team to pilot-test a number of AI applications, including Kira, as well as write some of our own. We're building smart libraries of data related to employment due diligence – which is what we’re often called to help with as a firm – and business restructuring in general, including post-integration strategies. While still a work in progress, it will be a very powerful database with links to access pointers and case law that will help us understand how contracts should best be drafted. We will apply AI to this platform, which is part of the reason we are vetting Kira and other AI applications.
At the same time, we’re advising clients on other types of tools that may be useful for their businesses. We are working with HR departments wanting to pilot test predictive analytics to profile their talent and to increase efficiencies and retention.
When you look back on why deals might fail or under-deliver, you often come back to due diligence failures in the area of personnel, or just not fully appreciating the people issues related to an acquisition. So firms are looking to see how AI applications can potentially assist with these challenges. Also, as AI applications become more evaluative, dealmakers will need to understand if the AI tools have a contextual basis that would influence their results — not bias in the sense of private rights but, for example, in the sense of evaluating risk.
Noah Waisberg, CEO, Kira Systems adds: There is a lot of hype around artificial intelligence right now, but the truth is that very few of the tools currently in existence have much use. To speak bluntly, one of the things that makes our system relatively unique among AI offerings is that people actually use it. We have around 2,000 active projects in a typical month.
I do think there are other potential areas for AI, but more of them are in the early stages right now. One thing that stands out to me is matchmaking platforms. For example, the other day I saw something that was almost like a Tinder for people looking to buy and sell companies. Another possibility is the use of artificial intelligence for negotiation software, to help people reach an agreement over a point. If there's a disagreement, the software can come up with something fair that the different parties can agree to. There are probably due diligence tasks that AI can do on the financial side as well, such as spotting accounting anomalies. Deloitte is one of our biggest users, for instance, and of course they aren’t a law firm at all.
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Artificial Intelligence Enters the Mainstream
Find out how AI is being used in the deal process and how proficient dealmakers