As a Chief Legal Officer (CLO), you wear a number of different hats. You’re responsible for managing your organization’s risk profile in both legal and regulatory compliance issues, overseeing current matters, and supervising the legal department—and on top of that, you need to dedicate a substantial portion of your time to strategic guidance for the rest of the business. If you feel like you’re stretched too thin, you’re not alone.
But there’s a willing assistant ready to help you out whenever you ask. This assistant doesn’t mind drudgery and will never complain about doing the same task over and over (and over). Nor does this helper shy away from massive reams of data—in fact, this assistant needs that data to do its job. We’re talking, of course, about artificial intelligence (AI), which can streamline work processes, knock out repetitive tasks with ease, and sift through piles of data in short order, highlighting the potentially interesting portions for human review.
This blog post reviews the role of a CLO and the benefits of AI before outlining six ways that CLOs can use AI to improve their eDiscovery workflows.
The role of a Chief Legal Officer
Why CLOs should embrace artificial intelligence (AI)
6 ways CLOs can leverage AI in eDiscovery
1. Completing early case assessment
2. Structuring data through concept clustering
3. Using technology-assisted review (TAR)
4. Redacting personal information
5. Generating eDiscovery analytics
6. Managing eDiscovery costs
The Chief Legal Officer (CLO) should not be solely concerned with managing legal matters. Rather, this central position encompasses four different, but overlapping, roles or personas. The CLO should serve as:
Lawyers are generally most comfortable acting in the guardian role, performing tasks such as case analysis and risk assessment that they are trained for and experienced in. However, in a Deloitte survey, company leaders indicated that they would prefer to see CLOs place a greater emphasis on the catalyst and strategist roles, guiding the company’s overall approach and integrating legal concerns with business aims. To accomplish that, CLOs should consider outsourcing legal operations to a dedicated operational manager and delegating the purely legal tasks associated with the guardian role.
As a CLO, the more you can streamline legal operations and save time on legal matters, the more time you have to think and work strategically. That’s where artificial intelligence (AI) can be a boon.
First, what do we mean when we refer to AI? Generally speaking, AI is “the ability of a computer or machine to mimic the capabilities of the human mind”—learning from information to recognize language, understand concepts, and differentiate objects. Voice-based assistants like Alexa or Siri use AI to understand cues, execute actions, or display results that match a request.
AI excels at sifting through massive quantities of data to identify specific terms or concepts, even when those concepts are expressed in different terms. Because an AI system can scan data faster than any human and doesn’t get tired or distracted, it can evaluate data sets faster and more easily than a human while maintaining accuracy. A machine can also manage repetitive, laborious tasks quickly and effectively without falling prey to boredom or wandering attention.
Legal departments can therefore use AI to streamline processes, reduce costs, and increase their productivity. Given that “nearly two-thirds (63 percent) of [legal department] respondents say recurring tasks and data management constraints prevent their legal teams from creating value at their organization,” AI offers a way for CLOs to offload those time-consuming responsibilities and focus on the strategy and growth priorities that matter to the company’s future.
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The double-edged sword of AI is that it needs vast quantities of data to learn what information is important so that it can provide useful insights. It shines when there’s a massive data set—too much for manual processes to sort through. Thankfully, there’s one area of law that is plagued by huge quantities of data: eDiscovery. In fact, eDiscovery makes an outstanding entry point for CLOs and legal departments just learning to leverage AI, as it has many different applications and uses within the varied tasks of eDiscovery.
Here are just a few of the applications for AI in corporate eDiscovery.
At the start of a matter, the legal department needs a way to gain rapid insight into the validity of a claim and its potential ramifications. Is this a minor ripple that will disappear in a few weeks or a major bet-the-company dispute that will rage on for years? At this early investigative stage, the goal is to assess the entire universe of data and home in on what matters. However, “It is practically impossible to review all the data manually since investigations regularly involve more than ten terabytes of data … the equivalent of 80 sea containers filled with paperwork.”
AI can cut through that mass of data to identify “hot documents” and other essential pieces of information, allowing the legal team to quickly assess the seriousness and worth of the claim. Note that AI does not take the place of lawyers—it simply augments their efforts, leading them to the data that is most likely to be relevant so that they can verify its importance.
In the course of managing an eDiscovery matter, you may identify numerous relevant facts and key areas of interest. For example, even in a relatively simple contract dispute, you might need to learn more about the exact date promised for delivery, the actual date of delivery, and the agreed consequences for any delay. To that end, a review would run more smoothly if you could evaluate documents and electronically stored information (ESI) about the promised date of delivery all at once before turning to either of the other issues.
AI can enable this sort of approach through what’s known as concept clustering. In essence, an AI system can learn to recognize terms that occur together, creating “piles” of information about each separate issue. By taking random or disorganized data and structuring it into groups, AI can help the legal team rapidly make sense of the corpus of information. When ESI is presented in conceptual clusters, review teams can achieve a 15 to 20 percent increase in review speed, which saves both time and money.
Perhaps the best-known use of AI in eDiscovery is in the review stage—the most expensive and time-consuming portion of the eDiscovery pipeline. With technology-assisted review (TAR), a human reviewer begins assessing documents in a corpus, tagging them for relevance and privilege as well as any other labels of interest. As the human reviewer proceeds, an AI system effectively watches over their shoulder, learning which words, terms, and phrases may be of interest. The AI then identifies documents that share that language and floats them to the top of the pile for the human reviewer to evaluate. As the review proceeds, the TAR software continues to learn and gets more accurate in its assessments, limiting the number of documents that the human reviewer has to actually examine.
Using TAR in review can reduce the length of review by as much as 40 percent, with attendant cost savings. And it’s been well accepted by the courts since at least 2015, when U.S. Magistrate Judge Andrew J. Peck noted in Rio Tinto PLC v. Vale S.A., 306 F.R.D. 125 (S.D.N.Y. 2015), that “the case law has developed to the point that it is now black letter law that where the producing party wants to utilize TAR for document review, courts will permit it.”
During the course of discovery, one of the primary concerns is accurately identifying personal information so that it can be withheld as privileged, redacted, or otherwise anonymized and shielded from disclosure. Sifting through mounds of ESI to pinpoint every name, phone number, Social Security number, or other identifier is not only time-consuming and mindless, but it’s also prone to errors as bleary-eyed reviewers accidentally scan past important identifiers.
Fortunately, AI systems never get bored, tired, or distracted—and they can be trained to identify all types of personal information with tremendous accuracy, flagging those fields for redaction or anonymization. This is an advantage in protecting privileged information for eDiscovery, plus it enables greater compliance with privacy laws, such as the EU’s General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA).
One of the traps of eDiscovery is that it’s easy to get lost in the trees and never step back to see the forest. CLOs can lose sight of how much they’re spending on a particular type of matter or fail to notice which outside counsel are consistently underperforming. Corporate law departments generally have information about those metrics on hand, but most of them don’t routinely organize that data or consult it to guide their future decisions. In fact, a Thomson Reuters survey of in-house attorneys found that “almost two-thirds of survey respondents indicated their legal departments have access to data regarding outside counsel costs and legal costs, yet less than half (49 percent) feel they are effectively using this data.”
This is yet another area where AI shines, readily compiling information about spend, firm performance, and case results and presenting it in an easy-to-understand format. Using AI-based analytics to discover patterns in your cases or in your costs can help you address recurring problems at the source, assign matters to the most capable and cost-effective outside counsel, and better manage your data.
There’s been a recurring theme throughout the previous sections, but it’s a benefit worth calling out on its own: namely, AI can help CLOs substantially save on their eDiscovery spend. AI helps cut costs by enabling earlier and more precise assessment of potential matters so that small matters can be resolved before considerable resources are invested. It speeds the evaluation of documents by clustering concepts together, which saves on review. Technology-assisted review further slashes costs by limiting the number of documents that human reviewers must evaluate, sparing as much as 40 percent of the cost of review. AI rapidly and accurately flags information for redaction, saving additional time and money. Finally, AI-based analytics evaluate patterns in spend and case outcomes to identify areas for improvement.
AI offers a myriad of ways for legal teams to outsource repetitive, data-intensive legal tasks, saving valuable time that they can then use to focus on strategic priorities and enhance their own value to the C-suite and the board. In fact, in a survey of legal department operations managers, two-thirds of respondents (64.9 percent) stated that they expect to see law departments using AI for legal work within the next three years. Will you be among them?