guides

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...

I wrote Adopting Artificial Intelligence: A Practical Guide for Law Firms for the lawyer who has heard a great deal about AI and wants a careful, plain-English explanation of what it is, what it can and cannot do inside a law firm, and how to begin using it responsibly. The guide assumes no technical background. It is meant to be read cover to cover by a managing partner, scanned by a practice group leader, or kept on the shelf as a reference for a firm’s general counsel and innovation team....

In this guide, I take seriously the concerns lawyers actually voice about using AI in legal practice, arguing that every one of them is reasonable and that most have systemic answers grounded in existing professional obligations. Drawing on ABA Formal Opinion 512, published state bar guidance on artificial intelligence, international regulatory frameworks, and a growing body of case law involving lawyers and AI tools, I show that the risk for lawyers is not in using artificial intelligence...

In this guide, I examine how judgment and AI interact in real legal work, arguing that artificial intelligence changes how lawyers process information but not the core value of human decision-making. Drawing on recent studies, I show that AI tools increase speed and sometimes accuracy, yet true judgment still depends on human reasoning, experience, and ethics. I outline what AI does well (document review, research, drafting, data extraction, regulatory monitoring) and explain that...

In this playbook, I present a practical implementation roadmap that helps legal teams move from curiosity about AI and artificial intelligence to disciplined, sustainable use across clearly defined workflows. I begin with a structured needs assessment and prioritization matrix so teams select narrow, high-impact implementation candidates instead of buying generic AI solutions in search of a problem. I then outline how to implement internal AI governance: approved tool lists, acceptable-use...

In this guide, I explore how AI and artificial intelligence have entered the courtroom, from the first headline-grabbing failures like Mata v. Avianca to the more recent and unsettling episodes where judges themselves relied on AI-generated drafts that smuggled hallucinated facts and citations into judicial opinions. I show how these incidents triggered a wave of standing orders, local rules, and certification requirements that now shape what lawyers must disclose about their AI use in the...

In this guide, I offer a curated roadmap to the people, publications, events, and tools that matter most in legal tech and legal technology, helping lawyers cut through noise and focus on resources that move their practice and operations forward. I explain why legal technology is now central to how firms and in-house teams manage documents, contracts, compliance, and risk, and how developments in cloud computing, data analytics, workflow automation, and AI have made technology strategy a core...

In this guide, I explain why AI agents built on artificial intelligence are not ordinary software, showing how autonomous agents decide at runtime which data to access, which tools to invoke, and which actions to take across enterprise systems, creating unpredictable scope and cascading risks that traditional SaaS contracts and security models cannot handle. I highlight how AI agents chain tools and APIs, cross system boundaries, and maintain persistent memory, which complicates attribution...

In this guide, I explain why AI and artificial intelligence vendor contracts differ from standard SaaS, focusing on who owns AI outputs, whether your data trains third-party models, how hallucinations are handled, and how to measure performance for systems that behave probabilistically. I highlight how boilerplate terms on training data, subprocessors, output ownership, model drift, bias, and unilateral changes usually favor vendors, and I provide concrete replacement language and checklists...

In this guide, I provide a practical roadmap for how lawyers can use AI and artificial intelligence responsibly across contract drafting, legal research, document review, and knowledge management, starting with clear explanations of how large language models work and where they fit into real-world legal workflows. I show how AI and artificial intelligence can boost efficiency while also creating new risks around accuracy and AI hallucinations, and I explain how ethics rules and opinions such...

In this guide, I explain how AI systems used in legal practice can generate “AI hallucinations,” meaning confident but fabricated or unsupported legal content that looks real, especially fake case citations, misdescribed holdings, and invented statutes. I stress that these AI hallucinations are not rare glitches but a structural result of next-token prediction, training data gaps, and architectural limits, and that even specialized legal tools still show measurable hallucination rates on...