While most firms are exploring generative AI in some capacity, it’ll likely take large investments in talent and technology to fully realize the potential of artificial intelligence.
That realization could be incentivizing law firms to get bigger more quickly, whether it’s through lateral hires, group acquisitions or law firm mergers.
“The investments that are required to keep up with the technology changes are extremely high and extremely challenging. And to have the professional talent, not just the legal talent, the professional talent, inside their firms that can figure out how to capitalize on all these generative AI changes and the technology space, there’s an increased motivation [for mergers],” said Laura Saklad, vice president of the legal industry vertical at InTapp and a former chief operations officer at Orrick.
It can be a motivating factor for lateral moves, too.
“I had one partner saying the move was a lack of technology and efficiency at his firm, so he wanted to move to a place that was perhaps a little more progressive and efficient,” said Susan Mendelsohn, a Chicago-based legal recruiter. “AI is definitely something that’s helpful for firms [in recruiting]. But it’s still in the early stages.”
As merger interest has ramped up since the pandemic, firms have also been reshaping their staff. Last year, firms reversed the longer-term decline in library and research roles, according to a report from Thomson Reuters, with costs per lawyer devoted to those roles escalating nearly 13% and full-time equivalents per lawyer growing 5.3%. The report pointed to “preparation for increased use of generative artificial intelligence (GenAI) tools” as a potential cause.
“There are leaders of smaller firms that we are advising on M&A who have told us that part of their interest in combining with a larger firm is to have the scale necessary to properly invest in technology, including AI and other forms of tech-driven innovation,” noted law firm merger consultant Kent Zimmermann.
Still, he noted that the leading drivers of merger conversations are often tied “to the increasing need to have the benefits of scale necessary” for talent compensation in firms’ desired practice areas.
And there are many other drivers of law firm merger talks, from acquiring deeper practice benches to increasing geographic reach. Even peer pressure or fear of missing out may be part of the reason firms are exploring combinations. Meanwhile, increasing client demand and rising billing rates now also give firms more buying power for tech capabilities.
But Mark Medice, a law firm consultant and principal at LawVision, said he also thinks AI will accelerate what is already an active cycle for law firm mergers.
“It’s possible law firms will have to make much more substantial capital investments if this stuff really does take hold, and that will obviously lead to a need to have a different kind of capital model, and scale in that case will be valuable,” he said. “A larger platform, more money to spend—that will give you a competitive advantage.”
Despite the investment so far and the potential for more, a recent report from LawVision found “nearly 100% of survey respondents indicated no material productivity gains over the past two years from the use of AI.” The report collected responses from 80 mostly U.S.-based firms ranging in size from 75 lawyers to 2,000 lawyers.
Even law firms like Seyfarth Shaw, which prides itself on its approach to innovation and its exploration of AI, will tell you they’re still trying to figure things out with AI.
“I’m not sure if I can answer your question whether AI is responsible for productivity increases in our firm or not. I can say that we’ve had productivity increases in our firm, so perhaps they are linked and I just haven’t done the analysis to be able to tell you that’s why,” said Lorie Almon, the firm’s chair, in an interview last week.
Still, Medice, who authored the LawVision report, remains bullish on the future of generative AI in the legal sector. “I’m a believer,” he said, adding that the industry is still working through what is sometimes called the final-mile problem.
“It’s one thing to have some of these impacts [from AI] on a smaller scale. It’s another to actually have it in the ordinary course where you achieve critical mass and can trust the technology is correct enough to the standards of our industry, which is a pretty high standard, in order to build on the business proposition,” he said. “But you’ll need to price differently, pay people differently. You won’t need fewer people, you’ll just need people with a different skill set.”