E-discovery has historically held a central role in the conversation around technology in the legal industry, but 2017 has seen great traction in other technologies. Throughout the year, law firm and in-house leaders have directed their excitement toward technologies like artificial intelligence (AI) and blockchain, leaving some to wonder if and how these technologies may apply in the e-discovery space.

On December 1, dubbed “E-Discovery Day,” e-discovery software group Exterro will host a webcast titled “Updating Your E-Discovery Toolkit” to discuss some of the ways in which these new technologies have shaped the e-discovery landscape going into 2018. Many have found that the year's major transformations around e-discovery have been less about specific technologies, and more about procedures.

“I've been pleased to see e-discovery strategy become a bigger part of what we are talking about as the year comes to a close,” e-discovery blogger and computer forensics specialist Craig Ball told LTN. Ball, who will present in the webinar, cited the Jaws theme to add, “Insofar as technology strategy, the biggest focus seems to be on cybersecurity as many hear the da-DUM-da-DUM-da-DUM of [the EU Global Data Protection Regulation] swimming towards America.”

Maura Grossman, another webinar panelist and research professor at University of Waterloo, saw a similar trend among e-discovery staff, though perhaps buoyed more by increasing familiarity with the Electronic Discovery Reference Model (EDRM) than a fear of penalties.

“My impression is that requesting parties have become a lot more savvy in the past year. They are asking more detailed and sophisticated questions about ESI sources, technologies, and processes,” she noted.

AI has had a fairly mixed reception among e-discovery specialists. Ralph Losey, e-discovery specialist and principal at Jackson Lewis, finds that predictive coding, an algorithmic machine learning process, has now become something of an industry standard for e-discovery work in large cases of more than 100,000 documents. At this point, Losey doesn't see quite as much use in smaller cases, but that could change.

“It has not penetrated down into the medium and small size cases yet, but will one day. For that to happen, and for uniform quality controls to take place, an open-source methodology will have to be established and software prices will have to come down even further,” he said.

Others are seeing a far more limited use for AI in e-discovery. “More and more firms are using supervised machine learning tools for electronic discovery, due diligence, and contract review, but I would not say their use is ubiquitous,” Grossman noted.

While e-discovery leaders see increasing use of predictive coding and machine learning for e-discovery matters, they see through the hype that accompanies AI and machine learning in other fields. “Is AI an integral part of firm e-discovery processes? No way, save for firms calling what analytics they already had AI, because 'AI' sounds much cooler than 'what we already had,'” Ball said.

AI and other buzz-worthy technology providers tout price points that often put them beyond the scope of e-discovery leaders' budgetary constraints. What e-discovery teams can improve, however, is the processes that accompany them.

“Software improvement by vendors should be a constant process, but that is usually beyond the direct control of lawyers. What we can control is the methodology,” Losey noted.