AI as Litigation Infrastructure | Webinar for CLE Credit
AI is moving beyond isolated research and drafting tasks toward an integrated litigation infrastructure that connects evidence, strategy, witnesses, workflow, client communication, budgets, and risk. This program explains how litigation teams can build AI-assisted workflows from trusted case materials, use strategic prompting to map legal elements to facts and evidence, stress-test case themes and narratives, and expand witness and trial preparation through repeatable simulations.
The speakers also examine AI mock juries, predictive tools, litigation dashboards, and the changing economics of the trial team. Throughout the program, they emphasize the limits of AI output and the lawyer’s continuing responsibility for confidentiality, privilege, work product, competence, supervision, source verification, and final legal judgment. Attendees will leave with a practical framework for selecting a defined use case, assigning ownership and review responsibility, measuring quality and usefulness, and expanding AI workflows only after they have earned confidence.
Key Learning Points
• Identify litigation tasks where AI can assist with case mapping, factual synthesis, theory development, witness preparation, and trial strategy.
• Distinguish strategic, case-specific prompting from open-ended searching and unverified output.
• Evaluate the uses and limits of AI simulations, mock juries, judicial analytics, and predictive tools.
• Recognize confidentiality, privilege, work-product, competence, supervision, and verification issues.
• Design a more transparent workflow for client reporting, budgeting, risk assessment, and lawyer accountability.
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Adam focuses on helping clients navigate complex disputes involving technology, business competition, and intellectual property, and he has become an outspoken advocate for using artificial intelligence to improve trial preparation, litigation strategy, client communication, and case management. He earned his J.D. from the University of Denver Sturm College of Law and his B.A. from Penn State University.
Daniel now focuses on applied AI and on making artificial intelligence practical and useful for lawyers, including improving legal work, litigation strategy, and client service. He earned his J.D. from Harvard Law School and his B.A. in Sociology from Grinnell College.