This article is sponsored by Trellis.

Civil litigation is flooded with busy work. In 2020, a survey of general counsel published by Juro and Wilson Sonsini found that 67 percent of in-house attorneys at fast-growth companies felt buried in low-value work. And, for outside counsel, the problem only gets bigger. This same survey found that one-third of the largest law firms spend nearly one of every three hours on low-value tasks. Whether they’re rummaging for motion templates, extracting key facts from a complaint, or drafting case assessments for a client, many litigation attorneys find themselves processing the law rather than practicing it. How, then, can these attorneys return to litigation—to tackling, in a people-focused manner, the complex legal issues affecting their clients?

From Files to Algorithms

Artificial intelligence (AI) refers to a specific technological assemblage, one built to simulate human intelligence in machines. Initially developed in the 1950s, AI systems now leverage sophisticated algorithms to sift through large datasets, identifying patterns, making predictions, and recommending actions. The technology is everywhere. Our email providers use it to quarantine incoming messages into designated spam folders. Our search engines use it to reformulate our queries for more relevant search results.

These applications rely on extractive AI, a branch of AI designed to pull out information from a text and answer specific questions about its contents. This is the kind of AI that has powered contemporary civil litigation. It’s the kind that comes ready-baked into every single legal research and analytics platform. Extractive AI enables attorneys to quickly sift through thousands of court records in a single sitting, automating the compilation of strategic insights about judges and parties, expert witnesses and law firms, case outcomes and settlement amounts. This is the technology that has helped litigation attorneys identify the patterns and the trends informing the direction of each and every case. A great example of this technology is used in eDiscovery.

But there is also another type of AI, one that is a much more recent, much more uncharted, technology. Generative AI is a form of AI that deploys machine learning models to create new outputs. These new outputs are inspired by—rather than fully reliant on—an existing dataset. First formulated in the 1960s, generative AI entered into popular culture upon the launch of ChatGPT on November 30, 2022. The world suddenly witnessed how large language models can perform statistical analyses of language use, applying the patterns learned from training datasets to produce novel written content at lightning speed. On that day, the entire playing field changed within every single service sector. Legal research and analytics platforms took note.

A Case in Point

Imagine you are a labor and employment attorney. A client reaches out to you, asking if you can represent them in a wrongful termination claim filed by a disgruntled ex-employee. You start by reading the complaint. Then the long process of creating a strategy beings. Understanding each causes of action and for each, the relevant claimed facts. Next the long research begins with finding similar cases and supportive case law, all must be as relevant to your cases as possible.  After all of that, you might feel ready enough to devise a discovery plan. Quickly, though, because you still need to distill all of this information into a case assessment for your client.

Now, imagine doing this for the 50 or more other cases you currently have pending. In this context, litigation attorneys are left to either sink or swim. Legal research and analytics platforms have responded to these challenges by broadening their scope and applying new generative AI technologies to their archives of trial court data. The result? The automation of routine research and writing tasks. These platforms now offer a wide variety of one-click work products, everything from multi documents timeline creation to drafted arguments and case assessments. These work products are made possible through a blend of (1) meticulously curated trial court case data, (2) commercially available large language models, and (3) proprietary AI technologies, all of which combine to support litigation attorneys within the exact court systems where their cases are filed.

Motion Drafting

“Firms are also using [generative AI] like a sparring partner or a brainstorm idea generator,” explains Ken Crutchfield, vice president and general manager of legal markets at Wolters Kluwer. Consider, as an example, a motion drafting tool, a device that can help an attorney with something like a motion for summary judgment. After uploading the complaint into the system, the tool will identify all of the relevant details and facts about the case. It will then locate similar cases from a database containing hundreds of thousands of successful trial court motions, pulling out every relevant motion for summary judgment it can find. The tool will then draft a tailored motion, incorporating the most successful arguments and the most relevant case law from the most similar cases—tweaking everything to accommodate the specific facts of your case.

As an MSJ that can take nearly a month to produce, a one-click draft of a motion for summary judgment can save weeks of work. This is, perhaps, why 39 percent of law firm leaders named drafting documents as one of the ways their firms plan to use AI technologies. The goal of these productivity tools is to generate space for creativity. In a matter of minutes, a litigation attorney can generate a working draft of a motion for summary judgment, leaving them room to stretch the molds that have constrained conventional legal argumentation. That is to say, litigation attorneys now have the time to experiment with all of those analogous—but not directly related—precedents, interpreting and applying the seemingly irrelevant or otherwise overlooked elements of case law in new and meaningful ways. “I see this as a tremendously exciting time in our profession,” begins Ilona Logvinova, associate general counsel at McKinsey & Company. “It releases that time to let us do more creative, tailored, and innovative work that will allow us to ramp up our expertise and craft unique value.”

Case Assessment

“I use [generative AI] all the time to help me have the first go of things if I’m creating the business case for something,” explains James Arnold, head of legal project management at Dentons. While it’s important to examine how AI technologies are helping litigation attorneys obtain desired case outcomes, it’s equally as important to consider how these same technologies are shaping the client experience. Clients want to know that you understand their world and their industry, that your insights align with what they have experienced. More often than not, this means minimizing their litigation spending and exposure, all within an environment where it can be notoriously difficult to predict when a lawsuit may arise, how long it may last, and how costly it may be.

Litigation attorneys periodically author case assessments to help their clients navigate through this murky, complex, and unpredictable landscape. As a work product, a case assessment typically communicates general case information, providing a complate case strategy from sumamrizing the complaint and listing the causes of action and claimed facts, to key witnesses and missing facts, to relevant strategy, jury pool information, and suggested next steps. The document will typically conclude by outlining the next steps of the litigation process and identifying any necessary deadlines, litigation holds, or discovery procedures. While all of this information is crucial to communicate to a client, it places litigation attorneys in a taxing metasphere, where they are forced to spend an inordinate amount of time summarizing their work process rather than performing it. What if things could be different?

Generative AI is now in a position to fulfill these general narration tasks. Legal research and analytics platforms have trained their large language models to produce original case assessments in response to a given complaint. Sophisticated versions of these assessments go beyond summarizing the document. They also articulate that which is missing, identifying the elements—whether it be a party, a witness, a document—that would be crucial for solidifying a legal claim. By doing so, generative AI technologies are helping litigation attorneys communicate the strengths and weaknesses of an action, providing analyses that are essential to the development of potential defense strategies and the estimation of their costs. “As of today, it’s an amazing personal productivity tool,” begins William Gaus, chief innovation officer at Troutman Pepper Hamilton Sanders. “We want to leverage that in overall operations of the firm and overall delivery of legal services from the firm.”

Concluding Thoughts

AI is having its moment. According to Arati Prabhakar, the director of the White House Office of Science and Technology Policy, it’s “one of the most consequential technologies of our times.” The advances in both extractive AI and generative AI are quickly rearranging our relationships with nearly everything and everyone in the world. For the legal sector, the automation of low-value, routine research and writing projects has pushed litigation attorneys to focus their energy and creativity on strategic decision-making and client advocacy tasks, showing us that AI is neither a passing trend nor a harbinger of doom. It is a force-multiplying tool, one that has redefined what it means to practice the law. Echoing the words of Jordan Furlong, “when you can no longer sell the time it takes to achieve a client’s outcome, then you must sell the outcome itself and the client’s experience of getting there.”

Trellis AI is a new legal productivity platform that leverages the largest repository of state court docket data to help litigators evaluate cases, automate brief drafting, suggest winning strategies, and more. Click here to learn more or request a demo today.