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By Suzanne McGee
The artificial intelligence tool ChatGPT has taken the world by storm since its November 2022 launch. But in many sectors of the economy, this was simply generative AI’s public debut. In the legal world, for example, law firms and others have been examining AI’s potential for a long time – putting it to practical and sometimes game-changing use (beyond asking ChatGPT to write courtroom speeches in the style of Beyoncé).
The potential applications for AI in the legal world are immense and include composing client briefs, producing complex analyses from troves of documents, and helping firms with limited resources compete with the largest groups. AI can help to conduct due diligence in corporate mergers and significantly aid legal education and knowledge acquisition in complex and fast-moving areas.
One high-profile demonstration of generative AI’s legal capabilities came when the latest iteration (GPT-4) recently passed the bar exam with flying colors. But lawyers and law students are already significantly more aware than the general population of generative AI’s potential beyond such eye-catching stunts. According to the results of a survey released by LexisNexis in March 2023, 84 percent of respondents in the legal field believe generative AI tools will increase their efficiency, while a majority believe it could advance and revolutionize the entire practice of law.
Mike Walsh, CEO of LexisNexis Legal & Professional, part of RELX, began thinking about how generative AI might transform the profession long before the advent of ChatGPT. LexisNexis was aware that AI-based technologies had tremendous potential by using it for many years, and had started to study how to draw on its vast repository of legal information to develop a reliable proprietary language model, a specific type of generative AI model designed to process and generate language. Generative AI “makes all kinds of things possible,” he says.
The first AI-linked tools have been around in the legal world for well over a decade. But so far many lawyers are cautious, however exciting they may find its potential. Greg Lambert, chief knowledge officer at Jackson Walker LLP, a Texas-based firm with almost 500 attorneys, recalls being at a conference in early April with in-house counsel from an array of companies. The day passed without a formal mention of generative AI or large language models in the presentations and Q&A sessions. But in the hallways, during coffee breaks and over lunch, the buzz was all about the latest AI breakthroughs, and what it might do for (and to) the legal profession, he says, adding: “There’s an expectation that this will change everything.”
But even if his peers believe that generative AI will make legal services “better, faster, cheaper” to deliver, Lambert says they are not envisaging that “someone will just go flip a switch and use generative AI tools and suddenly bills will be cut in half.” It’s going to take time and experimentation to understand the full horizon of possibilities, he argues.
To varying degrees, that experimentation is already underway, with giant global partnerships moving rapidly up the learning curve. Just over a year before the release of ChatGPT, Danielle Benecke was named founder and head of Baker McKenzie Machine Learning, an entirely new kind of role. “We work on questions of how to combine machine learning and other types of AI with our expertise to create new services,” Benecke explains. Her team studies how to apply generative AI and machine learning to the strategic decision-making process. The ultimate goal? “To figure out what the high-value service of law firms of the future looks like,” Benecke says.
Baker McKenzie has examined critical tasks that are time-consuming for lawyers to tackle using traditional research tools. For instance, a perennial issue for Baker McKenzie’s clients is understanding global trade sanctions and identifying related risks. So Benecke’s team undertook a pilot study to figure out how generative AI and data science might help them better advise those clients. “We looked at client supply chains that were thought to be vulnerable to sanctions and other trade restrictions and used data provided by the client and from public sources to identify risks – at scale and rapidly,” she says. “When you do that at scale, you discover things that humans on their own might not recognize.”
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