Artificial intelligence, specifically machine learning, is not a new topic. IBM’s Deep Blue beat world chess champion Garry Kasparov over a series of chess matches in 1997. Since then, artificial intelligence has drastically improved in performance and is utilized across different fields, from matching riders with drivers on ridesharing apps to predicting criminal activities in urban areas.
Many people see legal work as the final frontier for machine learning because much of the work requires an understanding of complex human language. The challenges of natural language processing were highlighted when IBM’s Watson responded “Toronto” to a question on “U.S. Cities” on the game show “Jeopardy” in 2011. Such challenges are made more complex in law by seemingly long and convoluted drafting, sometimes mired with old English and Latin phrases.
However, things have rapidly changed over the last few years, driven primarily by increasing advance of technology. By way of illustration, a recent demonstration by LawGeex, an automated contract review service provider, showed that its machine-learning AI algorithm beat 20 experienced lawyers in both time and quality in the reviewing of non-disclosure agreements, a common task among junior lawyers.
In response to disruptions from legal tech and increasing demands from clients, Clifford Chance, an international law firm with one of the most pre-eminent legal practices in the world, is one of the first traditional “BigLaw” law firms to experiment with the provision of AI augmented legal service.
To “make sure the best ideas from legal and operations are coming forward and able to percolate,” Clifford Chance appointed Bas Boris Visser, the firm’s Amsterdam managing partner at the time, as Global Head of Innovation and Business Change in 2015. He was charged to “anticipate, respond and adapt to key factors shaping the legal industry.” Since then, Clifford Chance has started experimenting with and deploying a number of new machine learning software to support its lawyers’ day-to-day jobs. The most notable examples are Clifford Chance’s partnership with Kira Systems and the launching of Dr@ft.
Kira is a “machine learning software that identifies, extracts, and analyzes text” in documents. It came with some “knowledge” out of the box (with little set-up time) and it was further trained by specialist lawyers at Clifford Chance for over one year “to deliver more effectively against the specific requirements of Clifford Chance’s clients.”
Junior associates and paralegals spend most of their time sipping through thousands, even millions, of documents as part of the legal due diligence exercise in an M&A transaction or the discovery process in a litigation proceeding. Kira is most effective in such work, whereby high volume of documents are reviewed under a short period of time. An internal research found that Kira could save about 20% of the team’s time in an acquisition due diligence exercise involving more than 4,000 documents, even though the team chose to check Kira’s findings manually. Under the traditional hourly billing model, such savings on lawyers’ time (and therefore legal fee) can be significant to clients.
Dr@ft is an automated document assembly system developed internally by Clifford Chance. It enables clients to generate contracts quickly and automatically within Clifford Chance’s private cloud after answering an online questionnaire. Dr@ft adds value by saving up to 85% of lawyers’ time, promoting consistency across documentation, and reducing burden on clients’ legal and compliance teams.
To remain competitive in the increasingly crowded legal sector, Clifford Chance must continue to improve its AI tools (through supervised learning) and adopt new tools where appropriate. Despite the lead time to train Kira, it is not difficult for other law firms to replicate Clifford Chance’s success. The basic Kira software is not proprietary to Clifford Chance and is open to partnership with other law firms. Other similar machine learning software, e.g. LawGeex and eBrevia, exist in the market, too.
Looking into the future, as more law firms adopt Kira (or similar software) in their analytical work, the legal landscape will look commoditized and undifferentiated. Also, as AI tools become more intelligent and refined, questions will arise as to whether clients can directly acquire the necessary legal services from the software developer. As such, Clifford Chance should try to acquire Kira or at least strike an exclusive deal with Kira to make it proprietary to the firm. Clifford Chance should also integrate Kira, Dr@ft, and all future AI tools more closely with the rest of the firm and consider how to price future work where AI tools (with heavy upfront cost but little variable cost) play a heavy role in the overall service provision.
In addition to the pricing dilemma, it is also worth considering how the workforce of Clifford Chance will look in the future: What role will junior lawyers play? Will the firm employ more coders than lawyers?