Peer to Peer Magazine

38 biographies of all its employees, looking for keywords like "Abu Dhabi" or "real estate law." The problem here is that employee biographies, much like the oft-neglected document profiles discussed earlier, are rarely filled out as completely or in as much detail as a firm might like. Practical AI provides firms with a solution to the problem of expertise identification. It can find and analyze documents found on disparate systems across a law firm, and use authorship and other data embedded in these documents to determine who in the firm has expertise on a specific subject. In doing so, AI identifies subject matter experts based on implicit information and behavior. For example, if people within your firm have used templates on property law written by one specific person hundreds of different times, you can be pretty sure that particular person is very good at that particular subject. Likewise, if someone has generated 75 percent of their billable hours over the past 2 years working on international property transactions, chances are they have considerable expertise in that area. AI is able to surface that expertise simply by interpreting the various "signals" that are given off by lawyers every day as they go about their business. Identifying these experts based on their behavior – without any extra steps or effort required on their behalf – provides firms with quicker and better results than scanning biographies for keywords or "hoping for the best" with a mass email. The Next Step Practical AI provides a way to automate and improve document classification, information extraction, and expertise identification. It also allows firms to unlock the specialized knowledge and unique expertise that they have built up over decades, helping them differentiate in an increasingly competitive and commoditized market. More than giving these firms a leg up on the competition, using practical AI to handle these tasks lays the groundwork for analyzing the data that's been harvested. Classifying the elements of past matters – what documents were involved, which experts were involved, and so on – makes it easier to create predictive models around how much a similar matter might cost, or what the outcome of a similar matter might be. Think of these predictive capabilities as just one more task that practical AI will enable firms to easily accomplish moving forward. It's an exciting next step that will benefit firms and clients alike. ILTA The late, great Shirley Chisholm said "If they don't give you a seat at the table, bring a folding chair." It is with this in mind that ILTA Publications takes aim at boardroom tables everywhere and asks: What seat would you pull up to the table? Our first chair to table guest is Laurie Pierre, Information Security Analyst at McCarter & English, LLP. Today, Laurie sits down with ILTA Senior Content Manager Beth Anne Stuebe and talks about her chair, who else is at the table, and so much more. Getting a Seat at the Table L I S T E N T O P O D C A S T AT I L TA N E T. O R G / S U M M E R - 2 0 1 9 T H E R E ' S M O R E O N L I N E ! Want to learn more? You're in luck! Click the link below to listen to the audio that corresponds to this article! www.iltanet.org/summer-2019

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