AI Arrives

AI isn’t coming to law. It’s already here — and in a big way — as law firms augment their corporate legal services to exploit heretofore unheard-of efficiencies
DRAMATIC PREDICTIONS about robots replacing lawyers are common enough. Yet lawyers soldier on, to the benefit of their clients on M&A and other transactions. While these lawyers have not been replaced yet, increasingly they are sharing their workload with computers, employing the practical innovations that Artificial Intelligence tools and programs can provide. In the American Bar Association Journal, Julie Sobowale defines the term: “AI, sometimes referred to as cognitive computing, refers to computers learning how to complete tasks traditionally done by humans. The focus is on computers looking for patterns in data, carrying out tests to evaluate the data and finding results.”

Lexpert-ranked lawyers told
Lexpert that they are finding several points in transactions where tools are under consideration or already in place — starting with their own internal knowledge resources. Joel Binder of Stikeman Elliott LLP says, “We are certainly using AI technology. As an example, we built and trained our own in-house repository for classifying, accessing and leveraging our knowledge tools. This is a system that we continue to hone over time understanding that the data we generate is one of our greatest assets and opens up a range of opportunities to leverage AI.”

Firms are also using it to gather and synthesize Competitive Intelligence information, which can be of enormous benefit to their clients.
Jennifer Traub of Cassels Brock & Blackwell LLP: “Clients expect me to know their businesses, the developments in their industries and advise them in a timely manner; it’s not an option. Knowing is crucial. Having access to market and competitive intelligence tools that employ AI technologies allows me to stay in the know and turn that insight into something the client needs to hear — for example, giving them the heads up about a development that could significantly impact their business.”

AI is coming to law firms across practice areas, not strictly in M&A, and certain firms are seizing the day and getting involved in development and testing. Lawyers in different practice areas are learning from each other’s experiences.
René Branchaud of Lavery, de Billy, L.L.P. says, “With the creation of our Lavery Legal Lab on Artificial Intelligence a year ago, we started rapidly introducing AI tools in some of our practice areas. More specifically, we are using AI with translation tools for patents and technical documents.”

Osler, Hoskin & Harcourt LLP’s Monica Biringer says the firm has been involved in early beta testing for software programs in Tax and in Employment and Labour. In the case of Tax, they’ve “rolled it out more broadly.”

Several lawyers told us, as did
John Emanoilidis of Torys LLP, that they are using AI “particularly in repetitive tasks like Due Diligence that can be enhanced through predictive analytics.” Cameron Belsher at McCarthy Tétrault LLP puts an estimated quantum on that: “In measuring our AI Due Diligence offerings, we have seen savings of around 60 per cent, with greater cost predictability, efficiency and accuracy.”

Companies’ data is increasingly voluminous and much of it unstructured. To the first part, many AI developers argue that AI tools are more effective at reviewing those volumes than are students or junior associates who may not be well instructed in what they are looking for, not to mention the fact that they may be sleep-deprived or bored. M&A lawyers in Canada would call that an exaggeration but generally suggest the optimal route is to have lawyers working with computerized tools to handle the volume, while adding their judgment in ways that machines do not possess. As to the matter of training, law schools are starting to take note: at both Harvard and Georgetown, they are offering coding courses so that students can learn computer and legal languages.

As to the challenge of finding problematic, possibly fraudulent, information in unstructured data during Due Diligence, AI software providers Jay Leib and Dan Roth say: “There were whistleblowers in their companies who knew what was going on, and the unstructured data contained the stories. Companies could detect potential problems early on, provide alternatives to counsel and the C-suite, and understand their exposure. It would prevent unnecessary legal spend and mitigate risk, thus protecting the company’s brand and shareholder value.”

Document reviewers have always tried to detect what a target company might be concealing, but now AI vendors are developing, according to Sobowale, “technology that can turn information into stories. [There] is a program that can read through unstructured data and summarize conversations, including the ideas discussed, the frequency of the communications and the mood of the speakers.”

This is not to say that computers can
feel the mood of speakers as humans to varying degrees can, but computers can ingest more unstructured data than we can and, within that, recognize patterns. Then enter the humans: corporate leaders will ultimately still make the decision to pursue deals are not. And they will continue to be greatly assisted by internal and external counsel when it comes to strategy. It takes wisdom to discern, “What does it all mean?”

In the meantime, are clients beginning to ask their M&A counsel about the applicability of AI tools? Belsher says, “I think clients have been conditioned over the last decade or so to limit the scope of Due Diligence or rely on in-house Due Diligence. With dedicated contract reviewers that incorporate AI tools that are scalable to meet a deadline, more clients are using us for their document review needs.”

Biringer, whose firm is also using AI tools for Due Diligence, says, “Because our lawyers are using the AI tools, we are gaining a better understanding of how we can leverage them to help clients. So we are trying to proactively reach out to clients to have those discussions and start the conversation.”

As
Ian Palm at Gowling WLG (Canada) LLP observes, “Machine learning systems will be an increasingly important element in the software we use. Forward-focused clients know this. They are eager for us to integrate AI-enabled tools where it means more efficient service.”

Mindy Gilbert, from Davies Ward Phillips & Vineberg LLP, points out that firm’s pro-activity also: “Our use of Artificial Intelligence and other technological tools has been driven by our approach to the practice of law. … Using reliable, state of the art technology is an important part of providing high-quality client service.”

The more lawyers learn about AI, and use it, it seems, the more they see its potential applicability for clients.
Leanne Krawchuk from Dentons Canada LLP, told Lexpert: “We took a big step towards this in founding Nextlaw Labs, a business accelerator focused on investing in, developing and deploying new technologies to transform the practice of law. These new technologies, several of which involve AI for tasks such as legal research and contract reviews, are focused on streamlining processes and boosting efficiency, which should translate into cost savings for our clients. I am looking forward to being able to use these AI technologies for data site reviews and search result summaries in our M&A deals once they are ready for deployment. AI is expected to speed up the pace at which we can complete a lot of the Due Diligence. Hopefully, it will also enable us to prepare more useful Diligence Reports in relation to the contracts posted to a data site in a way that that can be tailored to address or highlight certain areas of risk that a client may be most concerned about.”

But how will humans and machines learn from each other in order to advance AI for clients’ benefit? At SpendMatters.com, Pierre Mitchell defines “machine learning” in a way that bears reference to this discussion. Machine Learning, he writes, “refers to computers that ‘learn’ from the data they process rather than relying on humans for rules-based procedural programming to act upon that data. It not only discovers patterns in data but also specifically helps correlate various data inputs and key data outputs, which helps enable predictive analytics.”

The level of input from humans that goes into machine learning can be placed into two streams. Mitchell explains: “In a ‘supervised learning’ approach, human experts determine the outputs and the system ‘learns’ how to mimic the human experts, as well as uncover latent variables and interactions that humans wouldn’t have spotted on their own. ‘Unsupervised learning’ doesn’t rely on humans for direct training and stretches into the realm of deep learning.”

There will be a place for both supervised and unsupervised machine learning as AI evolves in the context of providing legal services, especially in Due Diligence and analogous voluminous elements. Humans will continue to develop and input the requirements of traditional tasks, so that they can be performed faster and more reliably. On top of that, however, there is the potential for computers to go deeper, and make discoveries that we did not know to ask for.