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Artificial Intelligence (AI) is being leveraged in a range of legal technology products to help law firms with a wide range of tasks, from billing automation to contract review. But currently one of the most innovative uses of AI is to assist litigators with their strategy by predicting likely outcomes.

How does it work?

Before explaining how AI can help with litigation outcomes, it’s important to first understand the meaning of Artificial Intelligence in this context. The term AI within lawtech essentially refers to sophisticated software which has been programmed to automate routine and often rather mundane tasks. It generally involves Machine Learning (ML) which is software infused with the ability to “learn” how to perform certain tasks and improve in line with human direction and feedback. In turn, ML deploys Natural Language Processing (NLP) software which can understand written or spoken commands from people who may not have any computer programming knowledge. This combination of AI, ML and NLP basically enables lawyers who do not understand computer code to interact with and train legal technology software to assist with their work.

Predicting litigation outcomes

AI tools can help litigators to predict outcomes of cases by analysing vast troves of historical judgments, looking at the facts in each particular case and decisions reached by the judge. This type of software can also spot trends - for example if specific types of claims are increasingly successful at trial, or conversely if judges are more likely to rule against the plaintiff. Although these types of AI prediction tools won’t be able to explain exactly why certain litigation strategies are more successful than others, their focus on the raw data ensures an unbiased approach which helps to inform the decisions of litigators.

Two products which use AI to sift through thousands of court judgments and predict likely litigation outcomes are and Solomonic. They can be particularly helpful for more junior litigators who may not have as much of a “feel” for the way a judge is likely to lean if they take a case to court. The information gleaned from a litigation outcome AI tool can reduce the likelihood of wasting resources on going to trial where a case is unlikely to succeed, help lawyers to decide on an optimal settlement, and generally reduce risk when forming litigation strategies.

Contract review

Since litigation often sparks from problematic contractual arrangements, it is also worth mentioning that AI tools can also be used to automatically review contracts. These tools analyse the wording of contracts and check for any clauses which need to be updated in light of new legislation or case law, to reduce the possibility of contractual disputes. Rather than manually going through client documentation, AI can perform extensive searches in a fraction of the time, enabling lawyers to focus their efforts on more valuable work.

Are any firms using the tech?

Many firms have adopted AI and ML to help with litigation strategies, as well as to bolster efforts to streamline legal processes and improve efficiency in other areas.

Womble Bond Dickinson has published a paper entitled: . It notes that “whether firms use AI varies greatly depending on firm size, with users tilted heavily towards larger law firms”. It quotes a study which found that: “100% of law firms of 700 or more lawyers either were using AI tools or pursuing AI projects”.

In terms of the use of AI to predict case outcomes, it mentions another study which found that one such “algorithm reportedly predicted with 70% accuracy the outcomes of 7,700 cases that the U.S. Supreme Court handed down from 1953 to 2013.” It cautions against reliance on such tools but nevertheless argues that “AI may play a crucial role in retrieving and analysing … [litigation] data to enhance a human lawyer’s case predictions.”

The paper states that of “all the law-related applications of AI, litigators probably are most familiar with TAR in discovery.” TAR (technology assisted review) software tools deploy AI to assess the relevance of high volumes of documents for purposes of electronic disclosure. This process is commonly known as predictive coding and its use has previously been by the High Court.

Any other examples of other uses of AI to make better business decisions?

Other types of AI tools can be used to help practice managers make business and marketing related decisions. For example, analytics software can be deployed which automatically monitors the practice management system, linking specific types of legal work undertaken with consequent billing and profit margins. Based on the results of such analysis, a decision could be made to focus on specific areas of law which prove most lucrative for the firm.

As well as looking at internal firm data, AI software can also scour the wider web, detecting useful trends in the form of news stories or other public data, which may reveal certain niche legal areas which are rising in prominence. This type of information can assist the firm to prepare specific teams to take advantage of forecasted spikes in work.

What does the future hold?

Although litigators will likely use AI prediction software much more commonly than at present, it will probably continue to form just one element which helps them to decide how to handle a case, rather than becoming an arbiter of litigation strategies.

More generally, AI tools are likely to proliferate over the next few years, becoming increasingly sophisticated and providing many forms of automation which can assist lawyers and practice management teams. But it will probably be several decades at least before AI becomes truly intelligent; in the meantime it will simply form part of the toolkit for human lawyers.