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I L T A S U R V E Y R E S U L T S | A R T I F I C I A L I N T E L L I G E N C E & M A C H I N E L E A R N I N G 3 I L T A S U R V E Y R E S U L T S | A R T I F I C I A L I N T E L L I G E N C E & M A C H I N E L E A R N I N G 3 Almost two thirds of respondents said AI-powered software is already creating fundamental change in the legal industry or will do so in the next 1-3 years. If that's true, then the 4% who believe it will be 10 years+ before that change takes place, may need to rethink their strate. A broad use for Legal AI is to streamline, enhance, or extend the delivery of legal services. Leveraging Legal AI for operations teams addresses real business problems. Two broad categories of AI tools are those that create efficiencies and those that are predictive. There's no surprise then that respondents said the top legal business uses where AI-powered software will have an impact deal with those two categories. First in the list are Contract Analysis; eDiscovery (that's where this all started years ago) and where the sheer volume of documents makes it rife for these technologies; Legal Research, where the volume of data is not humanly consumable; and Outcome prediction for legal cases. Only a few respondents believe that more than 25% of the work currently done by attorneys will be replaced by AI tools in the next five years. At a conservative estimate, the 1.3 million lawyers in the U.S., who work 1,800 hours a year @ $100/hour, will result in more than $58 billion a year of work being done by AI tools. That's a staggering amount of work that will not be done by lawyers. Focusing on how respondents are incorporating AI into their strate and business operations, almost one third say AI is integral to their current strategic planning. There's a broad range of business cases and ROI/budget development approaches being taken by organizations. Almost half have a specific budget line or at least combine with one or two other items. Just over half of firms still do not budget for AI projects or are thinking about starting in the next year. The largest element listed in determining the ROI for AI tools is "Innovation", followed by KM, marketing, internal cost management, creating assets for the organization and R&D. That seems to mean that to persuade decision-makers to proceed with AI projects, you have to show more than an improvement in efficiencies or good predictive outcomes. Instead, you have to show the real business improvement that will come from implementing a tool. The biggest challenges in advancing adoption of AI in organizations is understanding the need for the tools, change management and cost. No different than really any other new technolo or tool needed to answer business problems. Looking at the eight categories of legal AI-powered tools on the market today, ranking the relevancy or importance of these categories of AI-powered software to organizations, from highest to lowest - E-Discovery/ Document Review was seen as the most relevant, then Legal Research, Security, Insight/Predictive Tools, Contract (Pre-Execution), Billing/Spend Management, Risk Assessment, Expertise Automation, and Contract (Post-Execution) was sen as least relevant. There is lots of discussion around who is developing or purchasing legal AI-powered software to solve internal problems and/or to solve business problems for clients/customers. Exactly 50% said they had a tool in production or a POC for external use. The percentage is higher for AI tools for external use, showing again that impact for clients is a more important motivator to start the legal AI-powered software journey. Commentary related to the questions:

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