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Forward-looking legal departments have made big strides over the past decade using advanced technology applications to automate processes and create new cost efficiencies. Powerful new tools allow us to manage complex, high-volume legal matters more effectively. A prominent example is technology-assisted review (TAR) in e-discovery, which has proven to be transformative for legal teams tasked with sorting through hundreds of thousands or even millions of documents to find the most relevant information. With TAR, we can do that work many times faster and much more accurately than was possible just a few years ago, and with significant reductions in cost.

But TAR is only one example in which artificial intelligence (AI) technologies like analytics, predictive modeling and machine learning are making it possible for legal departments to control both cost and risk with unprecedented granularity—and AI is not just another application in the legal department.

The next stage in the evolution of legal technology will have less to do with the development of smarter applications than with applying AI to the data generated by those applications, and it will extend well beyond e-discovery to the entire legal workflow. Many GCs already know this, and they are using AI to develop a thoroughly data-driven view of their processes and performance, from e-discovery and internal investigations to multi-matter litigation and portfolio management.

AI and analytics are very good at finding patterns in data. At any point in your legal workflows where you are using technology applications to improve efficiency, you are also generating large amounts data that can be analyzed by machines over time to identify patterns and provide previously unattainable insights that inform better decision-making and provide strategic direction. There are virtually no limitations to what this data can tell you. Here are just a few of the possible scenarios:

Internal investigations: Organizations subject to frequent internal investigations understand that recidivism is a risk—even if they lack effective processes to deal with it. Data analysis can help you quantify and mitigate the risk with greater precision. Are there departments, product areas or individuals who figure into such investigations more frequently than others? The data will tell you. If leaks are regularly part of the picture, data analysis may be able to indicate that the leaks tend to originate from, say, your products team as opposed to your R&D team. Having this information will give you direction as you move to tighten up internal policies and procedures.

For many companies, it’s likely that at least some of your investigations will make it to full-blown litigation. In that case, you can track the litigation over time and identify the custodians from whom data is most often requested. Depending on the frequency and value of such cases, you might choose a more proactive route, creating a “ready data” repository for e-discovery from certain “hot” custodians. Analytics will not only tell you whose data to focus on, but also whose data is likely to be of most interest in particular litigation categories.

Analysis of Contracts: Across an organization’s litigation portfolio, certain key documents are going to come up again and again in discovery. While the specific circumstances surrounding an alleged breach of contract in one legal matter may be different from circumstances surrounding breach of contract in another matter, these cases may well refer to the same contract or to the same contract template. If there is a weak clause in a contract or a template, advanced analytics applied to litigation data can help you quickly pinpoint the problem and address it—whether that takes you back to the organization’s document management system, a contract template engine, or perhaps to a specific partner or practice group or outside firm.

Analysis of billing data: There is a wealth of information in the invoices you get from outside counsel. Which associates are most cost-effective at specific tasks? AI-driven data analysis might reveal that a firm’s second-year associates are routinely achieving the same results in specific kinds of work as its fourth-year associates who bill at a higher rate. This presents an opportunity to negotiate with outside counsel about who performs the work in question and/or the applicable rates. Should contract attorneys be doing the work that a firm’s associates have been doing up to now? Should certain tasks be insourced or perhaps diverted to an alternative legal services provider (ALSP)? Cross-analysis of performance metrics and billing details can provide concrete answers, and those insights may provide a bargaining chip as you negotiate fees with firms and ALSPs going forward.

Analysis of litigation patterns: Data analysis may uncover an emerging pattern in the coming year’s litigation portfolio. Let’s say your organization is suddenly facing a lot more antitrust work and fewer corporate disputes. Having such data presents an opportunity to approach outside counsel and work out an alternative fee arrangement for the kinds of litigation you anticipate seeing more of. You may have a great relationship with the corporate law group, but maybe now is the time to push for an AFA in antitrust at reduced cost.

The proliferation of applications over the past decade or so has taken us a long way toward more efficient processes, but it has also resulted in a fragmented technological ecosystem. Many corporate legal departments are already moving to unify these diverse applications within a single platform and interface, and with AI technologies built in. The beauty of AI is that it can be applied across multiple applications and ecosystems, whether they pertain to e-discovery, litigation, investigations, matter management, invoicing, specific practice areas or any other function within the department. Wherever you generate data, AI can detect meaningful patterns and provide data-based insights to help you achieve better outcomes with more efficiency and at lower cost.

David Carns is the chief strategy officer of Casepoint LLC. He joined Casepoint as a Director of Client Services in 2010, rose the ranks to executive vice president until his most recent promotion in 2017. In addition to being a recovering attorney, Carns possesses a lifelong passion for technology and its advancements. His career has always found him at the intersection of technology and the legal field given his intimate knowledge of both.