In the legal profession, data is used by two separate, yet equally important groups: the lawyers who handle court cases, and the paralegals who use AI to augment their data workflows. These are their stories. DUN DUN
Law & Order jokes aside, AI and machine learning are becoming important parts of the data workflows and basic processes at boutique law firms. Emerging technologies like natural language processing are allowing these firms to process important data much faster.
Like many other businesses, law firms have a ton of data. Not every business has the resources necessary to process and use all of that data effectively. However, AI and machine learning are allowing smaller firms to catch up and make the most of their human capital.
The role of AI in legal applications is the same as it is in every other AI use case: it augments the skills and abilities that humans already possess, leaving them with more time to focus on the important aspects of their business like talking to clients and pleading cases in court.
There are several areas to which AI can be applied in the legal profession. In this article we’ll look at three ways that boutique law firms are leveraging AI to get more done with less time and effort.
Contract review is an essential aspect of any law practice. When lawyers review contracts, it requires time and concentration. Ensuring that the contracts are exactly as they should be can take hours, and as humans are susceptible to wandering minds, that leaves them prone to mistakes.
Contracts, in general, are tricky. According to KPMG, contracts are prone to “value leakage, duplicated effort, increased risk, and non-compliance.” They go on to point out that value leakage can typically cause a contract to lose between “17 to 40 percent of a contract’s value.”
Machine learning in contract review leverages natural language processing technology to analyze contracts, extract relevant data, define the problematic parts, and then provide suggestions and approval.
An article called “How AI is Changing Contracts” in the Harvard Business Review points out that “AI contracting software can […] identify contract types based on pattern recognition in how the document is drafted.”
The article points out how firms will be able to have smaller, more agile teams to review documents and offer advice on the potential changes and/or outcomes of those contracts. A well-designed machine learning workflow means that contract compliance can be performed by smaller teams whose expertise is more technical in nature.
Lawyers, then, are able to shift their focus to work that is more valuable to the firm, like “shaping strategies and navigating complex legal problems.”
The Harvard Business Review also points out that when firms have the ability to easily access similar contracts going back years, or even decades, it allows the firm to prioritize industry, assess the wording that is most often used, which gives firms an easier contracting workflow at every step of the process.
This is technology that is useful to every law firm, and is helping the firms who are making use of AI and machine learning to be more efficient and to deliver more value to their clients. For smaller law firms, especially, technology like this enhances the ability of the firm’s lawyers to do the work that means the most to their clients.
Legal research is one of the most crucial tasks that any lawyer performs. The difference between adequate and stellar research can make a huge difference in the ability to win a case. Finding the relevant laws and legal decisions that relate to a case can lead to the desired outcome for a lawyer’s client.
The process of legal research, though, can be a truly arduous task. The process of finding the right information in a timely fashion will make all the difference as one sifts through massive amounts of data to find the legislation, regulations, and case law that is necessary to litigation.
To this end, lawyers are using AI to help them with this laborious task. AI is used to conduct research on laws and regulations, provide legal opinions for cases, and inform legal departments with similar cases.
The benefit to adding machine learning to this process is that it increases efficiency and accuracy in research. A study on AI usage in law firms conducted by the National Legal Research Group found that attorneys using AI finished research projects almost 25% faster than other attorneys making use of traditional means.
This means that the average attorney would save anywhere from 132-210 hours of legal research every year. Moreover, the results that those attorneys got when using an AI platform for research were on average 21% more relevant than the traditional group. “Indeed,” the study states, “results […] were on average better in every dimension of relevance judged in the study, including legal relevance, factual relevance, similar parties, jurisdiction, and procedural posture.”
The combination of speed and quality of research when using AI allows lawyers to outperform their competitors during their crucial stage of the litigation process and its value can not be overstated.
Legal discovery is a part of the pretrial phase of lawsuits. Lawyers assess materials that may or may not be admissible during the case, and also classify those cases. One of the tools available to lawyers is something called electronic discovery. It can be done with algorithms that search for specific ideas, or even by processing the absence of certain terms.
Imagine a situation in which there are millions of cases waiting to be litigated in one country. Law firms need to classify legal proceedings at speed in order to assess the validity of cases and to classify those cases. Firms unable to get this done quickly are simply not performing the volume and something necessary to get by in this kind of environment.
This is the case (no pun intended) in Brazil, where there, astonishingly, there more than 80 million lawsuits awaiting litigation. The reason for this is the backlog of legal classification needing to be done by electronic discovery.
Using AI workflows provided by Kepler, the law firm can process thousands of cases in just minutes, as opposed to around 600 per day when done by junior lawyers and paralegals without the help of machine learning.
By connecting to a 3rd-party API, legal texts are retrieved and then processed using natural language processing in order to classify the cases. The AI workflows predict the classification with a 90% accuracy, vastly improving the time that they would have previously spent on this task.
By automating this aspect of electronic discovery, law firms are, once again, able to focus on the aspects of their business that really matters and provide greater value to their clients.
With the help of AI and natural language processing, customer Marcelo Tostes was able to help our paralegal team become more efficient and better manage a large caseload
AI allows boutique law firms to perform critical tasks in a fraction of the time they would spend doing them using traditional methods. Machine learning workflows allow those firms to continuously improve their process with results becoming more accurate as more data is added and the “machine” begins to learn more about the processes and texts.
With improved accuracy and reduced time spent on these processes, smaller law firms will be able to get a lot more done with a lot less investment in time and people. And while all of this is true, the people are still the most important part of this process. AI is only there to enhance the already hard-won skills and abilities of lawyers.
With all of the benefits listed above, it’s not hard to make a case for adding AI to a law firm’s technology stack.