As AI Hits E-Discovery, Lawyers Move From Biglaw To A Boutique
As AI Hits E-Discovery, Lawyers Move From Biglaw To A Boutique
As e-discovery grows more complex, expect more specialization in the field.
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Many moons ago, when I was a litigation associate at a law firm, I spent a fair amount of time on discovery—yes, discovery and not e-discovery, because the process had not yet gone electronic. I would physically go to clients’ offices, review boxes and file cabinets brimming over with (paper) documents, and figure out how to extract from those documents the information needed for our case.
It was not fun. And it was stressful.
Today, of course, discovery is so much easier. Instead of doing what we did—sitting in windowless conference rooms, pawing through wall-to-wall boxes of documents (with rubber thimbles to avoid paper cuts), sometimes playing music to make the (many) hours pass more quickly—associates today review documents simply by clicking. And that’s assuming they’re even reviewing original documents for responsiveness and privilege, instead of having the work done by technology (or contract attorneys).
At the same time, discovery today is far more complicated. Cases can involve tens of millions of documents—vastly more data than what we had to deal with 25 years ago. Instead of taking paper form, these documents might be texts or WhatsApp messages, YouTube or iPhone videos, or team chats in Slack. And instead of being found in boxes and file cabinets, they might be found in laptops, on cellphones, or somewhere in the cloud.
Not surprisingly—and quite happily, for clients struggling to manage massive amounts of data—there are now lawyers, law firms, and alternative legal service providers (ALSPs) focused on e-discovery. And earlier this month, there was some significant news in that world.
Last week, Redgrave LLP, a leading e-discovery and information-law boutique, announced the arrival of three new partners—Robert Keeling, Ray Mangum, and Kristen Knapp—along with seven other lawyers in Washington, D.C. The group came from Sidley Austin, where Keeling founded and co-led the e-discovery practice.
Earlier this week, I interviewed Jonathan Redgrave, who co-founded the firm in 2010, and Keeling. We discussed the arrival of Keeling’s group—major news in the e-discovery world, where both Keeling and Redgrave are prominent practitioners. We also covered broader trends in e-discovery and information law—including, of course, the impact of generative artificial intelligence (Gen AI).
I began our conversation with a question: does the move of Keeling’s team from Sidley to Redgrave reflect a shift in the e-discovery space away from Biglaw firms and toward boutiques?
“First, on behalf of our entire team, we’re all very grateful for our time at Sidley,” Keeling said. “The firm has amazing leadership. It’s where we built our practice, and we hold our former colleagues in the highest regard. In fact, we hope to continue to collaborate with them going forward.”
“But to answer your question,” he continued, “we are seeing increased specialization in e-discovery, especially when helping clients address complex issues at the intersection of law and technology to meet the e-discovery needs in their most demanding cases.”
Here’s how specialization works at Redgrave. Unlike large, full-service firms, Redgrave doesn’t have a wide range of departments such as antitrust, commercial litigation, or white-collar defense. Instead, Redgrave partners with other firms, which it calls “merits counsel,” and those firms—Biglaw firms, midsize firms, or other boutiques—handle the relevant substantive law. Meanwhile, Redgrave handles the e-discovery and information-law aspects of the matter.1
This intense focus on e-discovery and information law gives rise to certain advantages for Redgrave. As Jonathan Redgrave told me, “Because we focus on this, we can be better at it.”
“E-discovery is getting more and more complex, with the volume and complexity of data growing exponentially, year after year,” he said. “For firms already covering so many other areas of law, does it make sense for them to also focus on what we do?”
Redgrave works with dozens of Am Law 200 firms that are eager to benefit from its e-discovery expertise. And unlike working with another full-service firm, working with Redgrave doesn’t create the same level of risk in terms of losing work to a rival. As Redgrave put it, “We’re not a threat: we’re not going to steal their antitrust or products-liability or white-collar work.”
Sometimes Redgrave is brought into a matter by full-service firms. More frequently, however, it’s hired directly by clients—often those that previously worked with Redgrave on another matter.
Clients appreciate not only the firm’s expertise in e-discovery and information law, but its competitive rates. It’s not simply that Redgrave’s billable-hour rates are lower than those of Biglaw (although they are). The firm is also flexible in its approaches, offering various alternative-fee arrangements as well as hourly billing.
“As a boutique, we can adjust and adapt, in real time, to the different ways that clients want to do business,” Redgrave said. “We don’t have 17 different committees that need to sign off on a billing arrangement or certain financial metrics we need to reach.”
Since its founding, Redgrave has expanded along with the size and scope of the e-discovery industry. After the arrival of Keeling’s group, the firm has almost 50 lawyers, plus 18 other legal professionals with expertise and advanced degrees in fields such as software engineering and information science. Their expertise is increasingly valuable in e-discovery, especially as generative AI reshapes the field.
“Gen AI is a transformative technology, and the impact on e-discovery over the next several years will be significant,” Keeling predicted. “We’ve already seen that Gen AI-based document-classification technologies have the potential to outperform traditional machine-learning-based approaches to Technology-Assisted Review (TAR).”
Keeling said that if generative AI is used correctly, it can find a higher percentage of relevant documents and reduce the percentage of irrelevant documents sent to review. And Gen AI can also provide contextual information about its decisions, such as excerpts from the document and the rationale for its determination.
“The main barriers at present are cost and speed—at this point, I don’t think Gen AI is practical for most large-scale reviews,” he said. Although reviewing documents using generative AI tools currently on the market is faster and cheaper than human review, it’s still not as fast and as cheap as TAR, according to Keeling. And if the user of generative AI gets the prompt or instructions wrong, leading to unreliable results, the tool will need to be rerun through the document set—using up additional time and incurring additional costs.
“But we’re also seeing lots of competition and innovation in this space,” Keeling said. “I expect rapid improvement on both of those fronts. In addition, Gen AI will have lots of other uses in e-discovery, from generating privilege log entries, to summarizing key documents, to helping attorneys search for relevant documents. It’s an exciting time to be in this field.”
So generative AI creates opportunities for e-discovery—but it also creates challenges. For example, take deepfakes and other documents that can be generated using AI. How do lawyers and judges authenticate evidence when it’s increasingly difficult to separate the genuine from the fabricated?
“It’s really going to be a hard problem,” Keeling said. “The deepfakes continue to get better and better.”
Keeling doesn’t expect that deepfakes will be a significant issue in a typical discovery response from a corporate defendant in civil litigation, which will largely consist of documents generated in the ordinary course of business. But there are going to be cases where a key piece of evidence is questioned and there are doubts about its authenticity, he said. “You can imagine the need to bring in competing experts to help the court resolve the dispute.”
Privacy is another challenge for lawyers working in e-discovery. Numerous laws and regulations focused on privacy, from the EU General Data Protection Regulation to the California Consumer Privacy Act, make it more difficult to work with data—and create potential legal liability for mishandling data, for both law firms and their clients.
“One of the biggest difficulties is that collecting documents in places with strict privacy laws can be more complicated and take longer as a result—for example, when dealing with EU custodians in a U.S. matter,” Keeling explained.
Ensuring that everything is done correctly when handling data, as e-discovery continues to become more complex and challenging, explains why Keeling isn’t worried about generative AI reducing the demand for e-discovery services or the opportunities for lawyers in the field.
“It’s very tempting to make a bold claim that Gen AI will eliminate first-pass responsiveness and privilege review, such as that often performed by contract attorneys today,” Keeling told me. “But if you look historically at other transformative technologies in the legal space, it hasn’t always played out like that,” he said.
The creation of case-law databases eliminated the need for law firm associates to go to the library and scour physical books, Keeling said. “But the ease of searching vast numbers of cases also changed expectations: associates were suddenly expected to find all the relevant cases, not just those their library had a copy of. It’s not that AI will replace humans; instead, humans who are efficient with AI will replace those who are not.”
“Gen AI machines are not separate, intelligent animals yet,” Redgrave said. “We are far from the singularity. While these machines have a lot of computing power, there’s still an incredibly valuable role for the human element.”
“Might there be, in the future, fewer jobs at the base level in e-discovery? Probably. But there will be a greater volume of work, as well as a greater demand for services at higher levels—which will bring greater rewards and satisfaction to lawyers.”
Working with merits counsel was also the general model of Keeling’s group when they were at Sidley, except their group had more of a regulatory focus—e.g., antitrust, especially second requests—while Redgrave historically has worked more in the litigation space. So their practices are complementary and synergistic.
A version of this article originally appeared on Bloomberg Law, part of Bloomberg Industry Group, Inc. (800-372-1033), and is reproduced here with permission.
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