AI in Litigation Practice
- Colin Levy
- Apr 27
- 2 min read
Updated: May 19
I wrote AI in Litigation Practice for the working litigator and the people who support her. The premise is simple: AI has changed the daily craft of litigation faster than the guidance has caught up, and lawyers at law firms of every size now need a practical, integrated treatment of how AI runs through the entire arc of a case. The book takes that arc seriously, walking from the first preservation letter to the post-trial motion and showing where AI helps, where it hurts, and where it has already produced sanctions.
The opening chapters tackle the discovery reckoning, where generative AI has unsettled a settled state of predictive coding, and the Mata problem in brief writing, where uneven hallucination rates defeat the kind of casual spot-checking that experienced lawyers used to rely on. From there I move into deposition preparation, real-time transcript analysis, demonstratives, jury selection, damages modeling, and the deepfake authentication questions that are now the most active area of evidentiary AI law. Each chapter offers a workflow that law firms can actually adopt rather than a survey of vendors.
The back half of the book is about the conditions under which AI use survives scrutiny. I cover the patchwork of standing orders and verification rules, the privilege and work-product analysis after United States v. Heppner, the supervisory duties under Model Rules 5.1 and 5.3, the fee implications under Rule 1.5, and the vendor diligence that separates responsible AI tools from problematic ones. The closing chapter sets out what a defensible litigation AI stack looks like for law firms that want to move past ad hoc adoption: centralized approval, written workflows, recurring training and audit, and proactive client communication.
The throughline is the principle I keep coming back to in this series. The lawyer is responsible for the work that bears her name. The tool that produced the text does not change that responsibility. Litigators who internalize that principle and build their AI practice around verification habits will be in a far stronger position than those who treat AI as an oracle. This guide is meant to make that practice concrete.