“These guys are like astronauts”! That was possibly an overstatement—but that’s how Patrick Dundas, Knowledge Management (KM) Counsel at Schulte Roth & Zabel, described of a panel of legal technologists working on artificial intelligence (AI)-powered KM applications.
But the description is apt. Legal tech is moving beyond the theoretical, rocket science phase, and is progressing into the ‘launching rockets into space’ phase—with the same perilous balance of success and failure that the early astronauts faced.
At this year’s ILTACON 2019—the annual conference of the International Legal Technology Association—this shift to discussing the actual implementations of new technologies was on display. Examples included talks about how AI can be applied to automate work product and in KM applications, data analytics, and new forms of data visualisation. Where two or three years ago the conversation might have been: “What is AI”? “What is data analytics”? or “How might this be applied to the practice of law”?. Now the conversation simply is “Here’s how I’m doing this today”.
Session: AI-Powered Knowledge Management
AI has proven useful in eDiscovery and in contract due diligence. Those applications involve extracting insights from all kinds of unstructured data, much faster and more accurately than humans can, allowing firms to greatly speed up the early stages of an important legal processes. This session at ILTACON looked at whether that same technology could be used for KM purposes by extracting information from unstructured documents to build and maintain deal and experience databases.
- Megan Kelly, Practice & Knowledge Manager at Katten Muchin Rosenman, described a deal-terms database based on LexPredict’s Contract Suite. The firm used to rely on a form that would capture data at the end of each deal, except the problem was that the data entry just wasn’t getting done. The new system extracts data from the deal documents, populates a form that is sent to the lead attorney, who then modifies or adds to the data before approving the form. The system also provides better metrics through visualisations on the back-end that help provide deeper insights into the deals in aggregate.
- Nicola Shaver, Director of Knowledge Management at Paul Hastings, discussed a couple of use cases, where the firm was able to build “what’s market” tools, in one case from subscription contracts, and in another from credit agreements. She urged others to look for use cases in situations like this, where there is high value in being able to extract the right data from the contracts, but where it’s not a good use of time to have lawyers or others do the work manually.
- Heath Harris, Director of Legal Operations Innovation at Fenwick & West, described Fenwick’s convertible debt survey. Convertible debt is a popular method of fundraising for startups in Fenwick’s client base, but one of the difficulties in extracting insight from the transactions is that they are private deals and data about them is hard to come by. The survey was based on deal terms harvested from more than 130 documents using Kira, and the system is now capable of reviewing about 50 documents per day. The result is that the lawyers engaged in this practice can spend more time delivering insights and value to clients rather than tracking down the statistics on the deals. Harris emphasized the important of integrating subject matter experts—those lawyers who understand the underlying data and what it means—on these teams. The data analysts can bring the numbers to life but training the system and interpreting the data does require legal expertise.
- Simon Wormwell, Chief Knowledge & Innovation Officer at Osler, Hoskin & Harcourt, described a dial summary project that the transaction teams use to provide information to clients, stressing the importance of that kind of legal expertise. The project was lawyer-led, and it included design input from clients.
Session: The Rise of the Legal Data Science Team
Whether it’s AI-driven KM solutions or new way of visualising financial, practice, or business development data, it’s clear that most firms are moving toward a greater understanding that data—both internal and external—is one of their most valuable assets.
Managing that data takes teams of professionals—some lawyers, some not—to ensure that data is collected, managed, and governed properly. This panel offered a fascinating look at how different types of firms and companies are staffing their organisations to manage and leverage data, each in their own way and in their own contexts.
- Michael Klastava, Global Head of Legal Data & Analytics in American International Group’s Office of the General Counsel, described how his organisation includes a data reporting team, an analytics team that serves as ‘translators’ between the data and the lawyers who consume it, and a data management group that is responsible for data governance. A recurring theme in the data-oriented sessions at ILTACON was the importance of that governance function—processes and teams devoted to maintaining the integrity of data with the goal of always having “one version of the truth”. Klastava also noted a change in the drivers of analytics activity in recent years, as saving money has become relatively less important compared to using data to provide better insights, service, and quality.
- Eric Felsberg, National Director of the Analytics Group at Jackson Lewis, has a data-driven approach to his firm’s labour & employment law practice. The need to analyse workflow trends, pay equity, and other factual data points in clients’ operations has led to the establishment of a separate data analytics department. This client-facing organisation includes lawyers and four types of specialists: i) data specialists, who are good with working with the Excel files that are often the raw materials for analysis; ii) analysts who are good at the math; iii) statisticians who can code data up into insights; and, iv) data scientists who often are engaged in visualisations to bring the data to life.
One recurring theme in this session is that recruiting for the legal industry is a challenge. Data analysts are typically more attracted to working in technology organisations, or at least other industries where the use of data in all aspects of operations is more widespread and part of the culture.
Legal Engineering is Catching On
These weren’t the only ILTACON sessions where an engineering approach to legal innovation was evident. Other examples of topics include workflow automation using rules-based AI systems; enterprise data management; data visualisation using widely available tools; the use of APIs to move data between applications; and, even law firm finance departments were discussing the staffing, process management, and analytics aspects of their work.
What do all these new ventures and experiments have in common?
- They are delivered by cross-disciplinary teams that always include lawyers but leverage the skills of other fields;
- They focus on process and what needs to change for innovations to be successful;
- They include clients in their process;
- They focus on metrics and results — they measure current outcomes, then measure progress against that base to show return on investment (ROI); and
- They are never done. Most of these developments are not big-bang innovations that transform a legal organisation overnight. Rather, they are incremental steps to be built upon as new techniques and approaches are learned.
Back to the Dundas’ astronaut analogy. We didn’t put a man on the moon the day after President John F. Kennedy said we would. We had to work through the Mercury and Gemini programs, including some spectacular failures, to build the infrastructure for the Apollo program.
Today’s legal data analysts, process engineers, project managers, statisticians, and design engineers are the legal industry equivalent of those white-shirted technicians in Mission Control. The astronauts didn’t get into space without those teams that planned for the long run but also responded to changing facts on the ground, inventing as they went.
True, legal tech is probably still at the equivalent of the Mercury phase. However, putting an astronaut in orbit was a significant accomplishment—just as many of the smaller, incremental innovations that we see today will be built upon.
We are in an infrastructure phase, and tomorrow’s legal tech astronauts will likely travel much farther on top of it.
