Digital White Papers

© ILTA and Thomson Reuters 2026. A I G U I D E L I N E S E R I E S | A I G U I D E F O R L E G A L P R O F E S S I O N A L S : A F O U N D A T I O N A L O V E R V I E W 4 Applicability to Practice of Law Research Legal research is one of the highest-volume, most time-intensive tasks in legal practice — and one where AI can meaningfully reduce the time required to familiarize oneself with the relevant body of law, identify authority, and assess precedential weight. Some relevant examples: • Generating an initial orientation to an unfamiliar area of law — identifying key concepts, major statutes, and doctrinal frameworks. Outputs need to be independently verified. • Summarizing judicial opinions, regulatory guidance, or agency rulings for internal attorney consumption. Not for external transmission without review. • Conducting citation analysis to evaluate precedential value, subsequent judicial treatment, and current validity, enabling researchers to identify controlling authority and assess whether cited cases remain good law. Drafting Work Product AI can be helpful for routine, high-volume, or templated documents where speed and consistency matter and the attorney's role is review and judgment, not initial composition. Examples include: • Routine transactional documents where parameters are well-defined (e.g., NDAs, engagement letters, basic commercial agreements, board resolutions, standard form pleadings). • Templated correspondence (e.g., demand letters, cease-and-desist letters, status updates). • Administrative and workflow tasks (e.g., timekeeping narratives, billing summaries, due diligence checklists, closing checklists, meeting agendas). Document Review AI can significantly reduce review time and improve consistency across large document sets: • Generating summaries of individual documents for attorney review in high-volume transac- tions or productions. • Pattern recognition across large document sets — identifying all documents containing a par- ticular clause, obligation, or factual issue; or those lacking a required provision. Ediscovery & Fact Development GenAI-assisted workflows are evolving from document classification into areas such as substan- tive fact development: • Prioritizing and classifying documents for relevance, privilege, or responsiveness. • Document summarization, issue spotting, and timeline construction. • Continuous Active Learning (CAL) workflows, where the model is refined as reviewers code documents, improving accuracy over the course of a review.

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