This post is written by Steve Embry
Fascinating workshop today @legalweek2018 on AI and its use in the practice of law. AI has gotten a lot of attention recently in the legal world as being a possible solution to better predicting case outcomes, costs, fees, even a2j.
All of that may be true. But one of the big issues today is the wide gap between the have and have nots of the legal world. Big firms v. small. Those with well-healed lawyers and those without any. Query: what will be the impact of AI on these groups and issues.
Immediately before the session, we had an interesting table discussion while waiting for the AI workshop to begin on just this subject. (perhaps to state the obvious: sometimes the most interesting at big conferences like this are the things you learn not from presenters but by those in the audience who have to use and sell – the ones in the trenches). At my table this morning was an AI software vendor and an IT professional at a mid-size firm in Pittsburgh.
Somewhere along the usual small talk program, the IT vendor said, “Of course, most AI programs are not really for small firms but can only effectively be used by the really large guys.”
This sparked a good discussion and, later, thinking on my part of whether this is really true and if so, why.
We came up with three reasons why this may be the case: lawyers at small firms don’t have the time to dig into and understand what AI can do and how to use it – and they don’t have an IT staff that can help. Two, they don’t have the underlying data to make it work. Three, relatedly, they can’t justify the costs associated with a lot of AI programs.
I thought about this a lot over the remainder of the day. I had always thought and preached that small firms have an advantage over their big cousins mainly because they can be nimble and have less fear of failure. This got my ear since I have always argued that small firms, by virtue of their size and management style, can be really nimble around technology and better compete with the bigs because they use technology more efficiently.
The hypothetical I always use when talking about this is the hypothetical litigation partner who hears about a great new technology for streamlining pleading preparation. He proposes to his practice group. After a few weeks, the group kicks it to the litigation technology. After a couple of months, the committee approves and sends it to IT. IT says grace in a matter of weeks. It then goes to the firm’s technology committee. They take another month but approve it. It then goes to the managing partner who really doesn’t understand but finally after a lot of persuasion sends it to the firm’s executive committee for approval. The EC really likes the product but is afraid it won’t work perfectly. But it really doesn’t matter, since by that time the tech’s outdated.
But being nimble doesn’t help much if you can’t make use of your nimbleness.
So can small firms take advantage of AI or is this an area only for the big firms who can use it to even further their advantage? The answer is a very lawyerly, “It depends.”
First, it’s true that small firm lawyers are stretched for time. The yearly surveys by Clio of small firms using its software have repeatedly demonstrated that administrative, marketing and accounting functions stretch most small firm lawyers to the breaking point, reducing their revenue streams. For firms using the billable hour model, every non billable hour reduces revenue. Reducing revenue reduces profit, at least in the short run. And, like any technology, any AI takes time to learn to use effectively. Big firms have large and sophisticated IT staffs to understand, take care of, learn about and in many cases, use the technology on which the lawyer/owners of business can presumably rely. Small firms don’t.
Secondly, AI depends on data. Large firms and organizations have tons of it even if they don’t always use it. As Shawnna Hoffman of IBM Watson, who was on today’s opening panel, put it, only 20% of all existing data is available on internet, the rest is behind firewalls. This data can be used by the large firms and when combined with the data of their clients (assuming they are smart enough to collaborate and get and use it) creates powerful tools that small firms don’t have.
Finally, and relatedly, we talked about the fact that the lack of data problem is compounded since less data means less confidence in outcome. Less confidence means the product is not as valuable. So small firms making AI decisions will conclude that, for example, a product giving only a 50% confidence level may not be worth the price.
So what’s the answer? Are small firms doomed in the brave new world? I’m not sure I buy the argument that small firm lawyers don’t have or won’t take the time to understand and use AI. There are already a number of plug and play solutions that enable small firms to use some forms of AI. These options, such as those offered by the legal research AI product, Ross, offer an intuitive AI platform with a support system which will enable small firms to use the program without a lot of learning time and technical knowledge.
But data availability poses a more significant problem. Legal research is low hanging fruit for AI because the data is publically available. Other forms of AI need data that’s not necessarily publically available at least and until small firms figure out how to collaborate with their data. For example, a really good use of AI is to predict outcomes. But as Aaron Crews, chief analytics officer at Littler, who was on a later panel on AI today, put it: As an in-house counsel, I didn’t need you to tell me whether I’m going to win. I need you to tell me if I should settle the case and for what. That takes data about previous settlements and outcomes not publically available.
And since lawyers don’t generally like to collaborate with each other for competitive purposes, the chances of the kind of collaboration and sharing of data by firms on their own is not likely. But legal service providers may help. Clio for example collects and analyzes data from hundreds of firms. Netdocs has hinted at using doing something similar.
Short of this, small firms use of AI will remain limited. Small firm lawyers wont for example be able to use AI to determine how long it takes them to do certain tasks, to predict outcomes in similar cases based on how the firm has done in previous cases. To understand settlement values of a case. To understand the strategies to get to that settlement value.
Without better collaboration, small firms just won’t have the data and cant access. The same is true for the a2j problem. Again, for AI to be useful, the data to solve the problem has to be collected and analyzed. This requires collaboration and cooperation by people and entities who aren’t used to it.
So the true limitation of AI is not its technical capabilities. It’s not the availability of the data. It’s the access to the data that exists. Those developing AI legal need to think about and understand how to get over this problem.
I am a member of Frost Brown Todd LLC and the Firm’s class action, privacy and mass tort groups. I’m a national litigator and advisor experienced in developing solutions to complex litigation and corporate problems. I am also a member of fbtTECH, my firm’s technology industry group that focuses on the future and anticipating the ways in which technology will impact the legal system and the issues facing clients. I write frequently on the impact of technology on the practice of law. I am currently Vice-Chair of the American Bar Association Law Technology Resource Center, an Editor of Law Practice Today Webzine and a member of the ABA’s Law Practice Futures Initiative. I am also Chair of the Data Breach, Privacy and Cyber Insurance Section of the Federation of Defense and Corporate Counsel. You can find me on LinkedIn, Twitter, Facebook, Instagram and Google+. I am also a frequent speaker and writer. In addition to practicing law and tech, my passions include education, and officiating swimming on national and local levels. I am a husband and proud father. And, finally, I bleed blue: I am unabashedly and unapologetically a huge University of Kentucky basketball fan.