By Rob Toews
The law touches every corner of the business world. Virtually everything that companies do—sales, purchases, partnerships, mergers, reorganisations—they do via legally enforceable contracts. Innovation would grind to a halt without a well-developed body of intellectual property law. Day to day, whether we recognize it or not, each of us operates against the backdrop of our legal regime and the implicit possibility of litigation.
The legal services market is one of the largest in the world. At the same time, it remains profoundly under digitised. For better or worse, the field of law is tradition-bound and notoriously slow to adopt new technologies and tools.
Expect this to change in the years ahead. More than any technology before it, artificial intelligence will transform the practice of law in dramatic ways. Indeed, this process is already underway.
Leibniz, who is one of the grandfathers of AI, was a lawyer. He said: “It is unworthy of excellent men to lose hours like slaves in the labour of calculation which could safely be relegated to anyone else if machines were used.”
In 1673, he presented the machine for four arithmetic operations in the UK. Leibniz says “The only way to correct our reasoning is to make them as tangible as the mathematicians’ so that we can find our error at a glance, and when there are disagreements between people, let’s calculate and see who is right!”
So, let’s think, why shouldn’t it be possible for machines to complete all steps of the event chain which occurs in a lawyer’s mind while they are deciding?
The law is in many ways particularly conducive to the application of AI and machine learning. Machine learning and law operate according to strikingly similar principles: they both look to historical examples in order to infer rules to apply to new situations.
Among the social sciences, law may come the closest to a system of formal logic. To oversimplify, legal rulings involve setting forth axioms derived from precedent, applying those axioms to the particular facts at hand, and reaching a conclusion accordingly. This logic-oriented methodology is exactly the type of activity to which machine intelligence can fruitfully be applied.
Contracts are the lifeblood of our economic system; business transactions cannot get done without them. Yet the process of negotiating and finalising a contract is today painfully tedious.
Each side’s lawyers must manually review, edit and exchange red-lined documents in seemingly endless iterations. The process can be lengthy, delaying deals and impeding companies’ business objectives. Mistakes due to human error are common—no surprise given that attention to minutiae is essential and contracts can be thousands of pages long.
There is a massive opportunity to automate this process. Start-ups including Lawgeex, Klarity, Clearlaw and LexCheck are currently working toward this vision. These companies are developing AI systems that can automatically ingest proposed contracts, analyse them in full using natural language processing (NLP) technology, and determine which portions of the contract are acceptable and which are problematic.
“We believe legal professionals should be able to leverage large datasets to make more informed decisions in the same way that marketing and sales professionals have been doing for years,” said Clearlaw CEO Jordan Ritenour.
For now, these systems are designed to operate with a human in the loop: that is, a human lawyer reviews the AI’s analysis and makes final decisions as to contract language. But as NLP capabilities advance, it is not hard to imagine a future in which the entire process is carried out end-to-end by AI programs that are empowered, within pre-programmed parameters, to hammer out agreements.
While this may sound futuristic, large businesses like Salesforce, Home Depot and eBay are already using AI-powered contract review services in their day-to-day operations. Expect adoption to go mainstream before long.
“These solutions are helping legal teams offload the mundane aspects of reviewing and redlining contracts so that they can focus on more high-impact work,” said Lawgeex CEO Noory Bechor. “AI technology will ultimately broaden the lawyer’s role from a narrow focus on risk mitigation to more strategic engagement on company initiatives.”
Negotiating and signing a contract is only the beginning. Once parties have a contract in place, it can be a massive headache to stay on top of the agreed-upon terms and obligations. This challenge is particularly acute for organisations of any scale: large enterprises will have millions of outstanding contracts, with thousands of different counterparties, across numerous internal divisions.
To a remarkable degree, companies today operate in the dark as to the details of their contractual relationships. AI offers the opportunity to solve this problem. NLP-powered solutions are being built that extract and contextualise key information across a company’s entire body of contracts, making it straightforward for stakeholders throughout the organization to understand the nature of its business commitments.
The business opportunities that these solutions will unlock are numerous. Sales teams can more easily track when contracts are up for renewal and thus capitalise on revenue and upsell opportunities. Procurement teams can stay on top of the details of existing agreements, empowering them to renegotiate when necessary. Regulatory teams can maintain a comprehensive perspective on a company’s activities for compliance purposes. Finance teams can make sure they are always ready for due diligence.
The siloed, opaque contract environment in which most companies operate today will likely seem archaic a decade from now.
A handful of AI teams are building machine learning models to predict the outcomes of pending cases, using as inputs the corpus of relevant precedent and a case’s particular fact pattern.
As these predictions become more accurate, they will have a major impact on the practice of law. For instance, companies and law firms are starting to use them to proactively plan their litigation strategies, fast-track settlement negotiations and minimise the number of cases that need actually go to trial.
Toronto-based Blue J Legal is one start-up developing an AI-powered legal prediction engine, with an initial focus on tax law. According to the company, its AI can predict case outcomes with 90 percent accuracy. “We are already starting to see significant advantages being gleaned by sophisticated parties leveraging machine learning legal prediction technologies,” said Blue J Legal CEO Benjamin Alarie.
A related use case for AI is in litigation finance, a practice in which a third party funds a plaintiff’s litigation costs in return for a share of the upside if the plaintiff’s case is successful. AI is supercharging litigation finance by enabling investors to develop more sophisticated, data-driven assessments of which cases are worth backing. One start-up doing particularly interesting work in this area is Legalist.
In the words of US Supreme Court great Oliver Wendell Holmes, presciently written over a century ago, “For the rational study of the law the blackletter man may be the man of the present, but the man of the future is the man of statistics.”
A final area in which machine intelligence is increasingly making inroads is in legal research. Legal research was historically a manual process, with law students and junior firm associates consigned to searching through physical caselaw volumes to find relevant precedent.
In recent decades, with the advent of software and personal computing, this process has gone digital; lawyers now generally conduct research using computer programs. Yet beyond rudimentary search functionality, these legacy solutions do not possess much intelligence.
In the past few years a new wave of start-ups has emerged seeking to leverage advances in NLP to transform legal research. Companies like Casetext and ROSS Intelligence are building research platforms that have more sophisticated semantic understanding of legal opinions’ actual meanings. These platforms go beyond mechanical key-word matching to surface truly relevant existing law. Their semantic models enable them to provide nuanced perspectives on how different cases relate.
Consider the main functional areas in a business: marketing, sales, customer success, finance, accounting, human resources, talent, legal. In nearly all of these functions, billion-dollar-plus enterprise software businesses have been built in the past decade to enhance productivity. The glaring exception is legal.
Conventionally viewed as a cost centre and largely overlooked by entrepreneurs, the legal function has seen little innovation in recent years. Today, Microsoft Word and email remain the dominant digital tools that legal teams use to carry out their work.
Considering the size of the legal market, this represents a significant opportunity for value creation. As artificial intelligence, and in particular natural language processing, continue to mature, they will unlock massive opportunities to transform and revitalize the field of law.