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Solo & Small Law Firms

PART II : AI Tools for Solo and Small Law Firms

Generally, today’s AI tools for solo and small law firms break down into three categories: (1) legal research and issue spotting; (2) law practice automation and marketing tools and (3) substantive legal issues arising out of the use of algorithmic, AI-driven platforms in legal matters ranging from criminal defense, employment, insurance, custody defense and others that solo and small firm lawyers tend to handle.  One caveat: many commercial tools bill themselves as using AI are at best, display the “weak AI” characteristics described in Part I where a machine detects patterns or applies rules but is not yet thinking at a level of human logic.    

Legal Research and Issue Spotting Tools

 Brief Contextual Tools

As an appellate practitioners, one of the most exciting AI advancements for me has been its application to legal research. For too long, legal research advancements stood still.  From the time that I graduated law school in 1988 until just recently, I ran searches using the same Boolean logic that I’d been taught back in law school. Sure, natural language search emerged – but it sucked. And while the cost of legal research finally began to decline as the WEXIS duopoly faced competition from inexpensive but effective tools like  VersusLaw, Fastcase and Google Scholar, the capabilities of these systems weren’t all that different from earlier iterations.

But now we’ve reached a point where AI and data analytics are helping to innovate the way we do legal research.  One of the first developments out of the gate was Casetext’s  CARA, where lawyers can upload a brief and generate find relevant or omitted cases that pertain to the subject of the brief. In other words, it’s a form of contextualized search. Of course, this being the legal industry where a great idea generates dozens of copies – and so now, at least a half dozen companies – most recently Westlaw, and Bloomberg, offer some type of brief analyzer tool.  Unfortunately, I’m not familiar with the cost of most of the brief analyzer services but Casetext’s entire research system with CARA included comes in at just $65/month making it eminently affordable for solo and small firms.

Analytics

Increasingly, analytics are becoming an important component of legal research because they may give insight into the outcome of a particular case or line of argument.  Fastcase founder Ed Walters has spoken about the importance of  using analytics to advise clients about likely outcomes as well as the costs and risks involved in a particular case.   Two research oriented analytics programs are Lex Machina and Docket Alarm which lawyers can use to sort data to figure out the average duration to resolve a case, percentage of cases disposed of on summary judgment or wins on a given issue before a particular judge.

Analytics can also be used by law firms internally to determine the average cost of handling a particular case. This article describes how practice management tool Smokeball helps firms harness the power of their data to determine the time and cost of preparing certain documents – and presumably, many of the other LPM tools include similar features. On this point, however, I’m a skeptic because even a high-volume small practice can’t possibly have enough data to offer meaningful analytic results. Another firm – Atrium harvests data from its startup clients on employee salaries and vesting schedules. Atrium then uses that data (if authorized by the client) to help its other clients make business decisions about salaries and stock policies.  As I’ve written here, I’m uncomfortable with law firms leveraging their position as trusted advisors who are privy to business data the using that information to advise other clients – but that’s an ethics question that hasn’t yet been addressed.

Contract Analysis

One overlooked source of legal research involves contract analysis. Many times, lawyers are asked to review a non-compete or employment contract for a client – and while they can research potential issues, applying a contract analysis tool to identify whether certain standard terms are omitted or whether any problematic terms are included offers the best starting point. Contract analysis tools have been around for a long time – I blogged about an early version of Legal Sifter’s analysis tool five years ago which ranked a contract based on its completeness and also whether the terms weighed in favor of the drafter or the other party. Legal Sifter is still around but not sure whether these tools are available. Another popular contact analytics tool is Law Geex  which analyzes various contracts for employment and NDAs. A few years back, I paid $79 to run a LawGeex contracts analysis – but not sure whether that pricing option is still available. For more details on contract analysis tools, see this ABA Journal article.

Law Practice Automation and Marketing Tools

Law Practice Automation Tools

 Legal automation tools like document automation have been around for a while.  As far as I can tell, the early iterations of these tools aren’t AI-powered but are nonetheless, highly robust.  I’ve blogged about online, self-scheduling tools and automated intake forms and recorded a 30-minute video on other tools like chatbots, invoicing and billing tools and integration tools like Zapier.

But what’s more exciting is the emergence of more sophisticated products that appear to be AI driven and that can automate routine work in meaningful ways. For example, LegalMation created a complaint analysis tool where users can upload a complaint and Legalmation will generate an answer and a complete set of discovery requests. As I blogged here, companies like Legalist and Do Not Pay have developed chatbots and forms that help consumers prepare complaints for small claims court – though there’s no reason that these tools couldn’t be used by lawyers as well.

AI Driven Marketing Tools

SEO and other online web marketing tools can be costly and it’s difficult to determine how effectively they work. One company, Lawfty is a data-driven system that determines which systems work best to draw clients. There are many other AI-powered marketing tools but haven’t yet been adopted in the legal sector. 

For additional information on AI-powered marketing tools, check out this talk, Practicing With Machines by Nicole Black. .

Substantive AI Issues

There’s a final component of AI and law practice that is important for solo and small firm lawyers to understand:  the substantive legal issues that AI raises.  I described some of these in this chapter on AI and this chapter on Algorithm Law from 41 Legal Practice Areas That Didn’t Exist 15 Years Ago.  Foremost, AI raises the problem of algorithmic bias which has reared its head in criminal defense sentencing (with reliance on racially-disparate risk classification software), differential pricing that may favor certain groups and bias in hiring algorithms. And more algorithm-based tools are in the pipeline from use of algorithms in custody disputes or online dispute resolution. Many solo and small firm lawyers are not yet aware that these algorithms can impact the cases that they handle – yet they may lack the resources or the leverage to force disclosure of these algorithms or to retain the technical experts needed to challenge the underlying assumptions. Some states like New York have adopted algorithmic transparency requirements as has the European Union GDRP which requires data controllers to provide “meaningful logic” about any automated decision-making tools that produce “legal effects” on EU data subjects.  But there’s still a need to ensure that solo and small firms have access to the means to challenge algorithms that may impact their clients. And as discussed in Part III that’s one of the many roles that law librarians can play.

For additional information on algorithmic bias, check out :

Trust But Verify: A Guide to Algorithms and the Law, (April 2017), Beyond the Information Age: the Duty of Technology Competence in the Algorithmic Society, South Carolina Law Review (2018) and Measuring Algorithmic Fairness  (July 2019).