Report from the CLOC Institute: A Measured Approach to Artificial Intelligence

Topics: Artificial Intelligence, Business Development & Marketing Blog Posts, Client Relations, CLOC, Efficiency, Legal Innovation, practice engineering

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LAS VEGAS —The CLOC Institute, the annual meeting of the Corporate Legal Operations Consortium (CLOC), is underway this week, and the growth of this event is pretty remarkable by legal industry standards.

In just a few short years, CLOC has grown from an informal regional organization in Northern California, to the host of a high-energy event that has taken over a good chunk of the conference center at the Bellagio resort. More than 1,000 attendees — representing in-house legal departments but also law firms, technology providers and alternative legal service providers — attended sessions and networked. Next year, CLOC has said it is already planning for 2,500 attendees.

That’s not bad for a group that represents what some might say is the less sexy side of law — operational functions and the core competencies that support them, such as data management, litigation support, communications, knowledge management and data analytics. But the fact is that those functions are where the action is in legal today, and more often than not it’s the buy side — these corporate in-house legal teams — that are leading transformation in the industry.


For more of our coverage of the CLOC Institute event, click here.


An early session on “Practical Applications of AI in Today’s Law Department” provided a taste of the pragmatic approach. Moderator Paul Lippe of Elevate and panelists Mary O’Carroll of Google, Steve Harmon of Cisco, and Sylvie Stulic of Electronic Arts offered a range of insight around the implementation of AI in their organizations. Most of their advice and perspective reflected the real-world concerns of people who don’t have time for the hype surrounding artificial intelligence in legal, but who do understand that AI is already here and is being put to work in corporate legal departments. A sampler:

  •        Start with problems and questions, not specific tools — O’Carroll had some good tips on looking for opportunities to leverage AI. She and her team always approach it from the perspective of problems and processes, and they ask the question, “Is this work going to the right resource?” If not, then that’s a likely application for AI. Harmon, similarly, sees opportunity in the work that’s now being outsourced, with the idea that the work addressed by outside labor is more suitable for AI.
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  •        Entry strategies — There’s a distinction between work that’s related to “Managing” an in-house legal department, and work that’s more about “Performing”, which is the very core of lawyering. While the panel offered several examples of cases where AI is actually working on the Performing side, Harmon noted that the Management activities — such as billing compliance, legal spend analytics, pricing and budgeting, etc. — provide a less threatening “entry strategy” for legal departments who want to dip their toes in AI.
  •        AI and services — Harmon recognized a need for AI in cases where legal departments face “bursts of work” or sudden demands on an organization, especially when staffing up just to throw bodies at the work isn’t an option. That work can involve large data sets that create an opening for leveraging AI, but likely in conjunction with a services offering. In fact, he noted, “Going forward, I think we are more likely to be purchasing AI through service providers and service offerings.” This is consistent with the Legal Executive Institute’s findings in our study of alternative legal services providers, where one of the largest growth areas anticipated by our survey respondents was the integration of services and technology by ALSPs.

The entire panel was in agreement that there are many applications for AI in in-house practice and operations, but Harmon had the last word. “No new golf clubs” was his motto — “focus on your swing, not the tools.”

In other words, it’s important to not simply look around for AI offerings and try to fit them into operations. Better to focus on operations, find the weak spots where there is room for improvement, and only then think about what tools might be best for the job.