Generative artificial intelligence is no longer on the sidelines of litigation.

Used thoughtfully, it can sharpen how attorneys manage complex matters, accelerate routine tasks, synthesizing disparate record evidence to identify common themes, patterns, and connections, and enhance trial readiness without compromising legal judgment. Used carelessly, it can hallucinate citations, expose proprietary information, and hand opposing counsel privileged content on a silver platter.

The upside is real. So is the downside.

In a recent Taft Intellectual Property Webinar Series program, “Best Practices and Key Considerations When Using AI in Litigation,” Tom Bejin and I traded war stories and hard-won lessons on using AI throughout the litigation lifecycle, and the guardrails every attorney needs before turning a tool loose on a live matter. The recorded program is now available to view (click here to jump to the recording).

Where AI Earns Its Keep in Litigation

Think of AI as a very fast, very literal junior associate—not a replacement for the lawyer who masterminds and directs the work.

Depending on the matter, AI tools can help litigation teams:

• Build preliminary chronologies of facts and organize case materials in minutes, not days.
• Digest large sets of documents, pleadings, and deposition transcripts and other record materials into usable summaries.
• Flag issues early, before they become expensive surprises.
• Produce first-pass task lists, outlines, and work plans.
• Speed up drafting, editing, proofreading, and formatting.
• Surface potentially relevant witnesses, documents, facts, and themes for an attorney to run down.
• Power document review workflows, including platforms such as Relativity and other AI-enabled technologies.
• Strengthen trial preparation, including organizing impeachment material, supporting jury-instruction work, and informing juror research.

These uses matter most in document-intensive cases, where teams need to get their arms around an evolving record fast. Take a deposition cross-reference: an attorney can prompt an approved tool to comb multiple witnesses’ transcripts against a set of interrogatories and return, for each responsive passage, the topic, the verbatim excerpts, and a pinpoint citation. That turns a weekend of transcript-hunting into an afternoon. But the attorney still has to open the transcript, confirm the quotes, ensure completeness, and decide what it means for the case. The discipline that keeps this reliable has a name: the AI sandwich—human framing, then AI assistance, then human review and decision. Skip the bread on either end, and you are just trusting a machine with your case.

AI can clear the underbrush so lawyers reach the real work faster. It cannot do that work for them.

What Can Go Wrong (And Already Has)

AI output can look confident, polished, and right—even when it is slop. For litigators, that is not a hypothetical risk. In July 2026, the USPTO issued its first discipline order for reckless AI use after a practitioner’s claim construction chart turned out to contain hallucinated citations. The practitioner’s defense that he had another AI tool check it was rejected outright. That is not a verification protocol; it is passing the buck to a second machine. A hallucinated case citation, a garbled summary of a key document, or a misstated procedural fact can sink a filing, torch your credibility with the court, and hand your client an unforced error.

Confidentiality and Privilege: Non-Negotiable

Before typing case-related information into any AI tool, know where that text goes: the tool’s terms, data-use practices, retention settings, security measures, and whether your firm or client has already drawn a line on this. Confidential client information, attorney work product, trade secrets, and personally identifiable information do not belong in a consumer-grade chatbot just because it is convenient.

Confidentiality concerns are showing up in protective orders too. In Morgan v. V2X, a Colorado federal court amended a protective order to require enterprise-only AI tools after recognizing that consumer-grade chatbots can retain and reuse whatever you upload.

Privilege is where this gets sharp. A prompt is not a casual question to a digital assistant—it can be a record of facts, legal theories, mental impressions, or client communications. The case law is already drawing lines, and they cut in different directions. In U.S. v. Heppner, a court held that a criminal defendant’s own chats with a public AI platform were neither attorney-client privileged nor protected work product: the tool is not a lawyer, and there is no expectation of confidentiality on an open platform. But in Tremblay v. OpenAI and Concord v. Anthropic, courts went the other way for counsel’s own prompts, holding that they reflected counsel’s mental impressions and strategy and were therefore protected work product.

Judges Are Figuring This Out Too

Judges are not immune to the pull of these tools. Judge Kevin Newsom of the Eleventh Circuit asked ChatGPT about the ordinary meaning of “landscaping” while writing a concurrence in Snell v. United Specialty Insurance—the case turned on whether a trampoline installation counted. He published his prompts and the AI’s answers in an appendix, and even admitted he could have prompted more thoughtfully. Judge Scott Schlegel of Louisiana’s Fifth Circuit Court of Appeal uses AI to search voluminous records and summarize testimony, with every output linked back to the record; his publication, AI in Chambers, warns that “a complete refusal to understand or use GenAI may undermine competence and diligence.” In other words: sitting this one out is not an option anymore.

The Judicious Judge’s Guide to Generative Artificial Intelligence and Large Language Models is a forthcoming article in the Columbia Science and Technology Law Review (Vol. 28, No. 2, May 2027), co-authored by a group of sitting judges and legal scholars, offering guidance for judges and their chambers on using GenAI tools grounded in the ABA Model Code of Judicial Conduct. It cautions that  “a complete refusal to understand or use GenAI may undermine competence and diligence,” acknowledging a growing “judicial technology competence” norm. The guide identifies roughly nineteen judicial GenAI use cases across six categories: building a curated collection to prompt against for style-matching, case-specific research, and chambers-wide knowledge management; legal research and authority checking; drafting and editing; summarizing briefs, motions, transcripts, and evidence, including building chronologies and timelines and querying the record for evidentiary objections; court administration and analytics such as discovery and exhibit organization, handwriting-to-text conversion, and workload studies; and translation, transcription, and accessibility support, including plain-language drafting and preliminary translation triage for self-represented litigants.

As the bench gets more fluent with these tools, judges will spot AI slop faster than ever. Assume your reader knows the technology as well as you do, because increasingly, they do.

Know Your Judge’s Rules Before You Prompt

Courts are not waiting around. Within the Northern District of California alone, AI disclosure requirements range from no certification at all to a requirement that lead trial counsel personally verify AI-generated content and preserve every prompt. Same district, opposite rules.

The Five-Point Checklist

Five principles cut through the noise:

1. Match the tool to the task. AI excels at administrative, organizational, and first-pass analytical work. It is not  suited for unsupervised legal analysis, final factual determinations, or strategic calls that require human judgment. And use platforms vetted for security, confidentiality, contractual protections, and the intended use case.

2. Verify every material output. Treat every output as first draft. Check every citation, quotation, factual assertion, record reference, calculation, and procedural statement against the primary source every time; no exceptions.

3. Protect confidential information. Do not input sensitive information until you know where it goes, how it is stored, whether it trains the model, and who controls access and retention.

4. Preserve authenticity. The advocacy stays yours. AI can streamline drafting and preparation, but every filing, communication, and argument has to reflect your own analysis and professional judgment.

5. Maintain human oversight. The tool answers to no one. The lawyer answers to the client, the court, opposing counsel, and every professional obligation that comes with the job.

Watch the program recording

The webinar goes deeper on all of it: how judges are using AI, evolving disclosure rules, protective-order fights, privilege landmines in chat prompts, the tools attorneys are actually using, and real applications and use cases.

You can access the full webinar recording here, or use the links below to navigate directly to a topic of interest:

• How Judges Use AI — 2:53
• AI Disclosure Rules — 12:25
• Protective Orders — 14:38
• Privilege Issues Associated with Chat Prompts — 18:15
• AI Tools and Use Cases — 23:46
• Practical Uses for AI in Trial Preparation — 55:35

AI’s footprint in litigation will only grow. Responsible adoption takes more than knowing which button to click. It takes understanding what the technology can and cannot do, building real safeguards, and exercising the judgment that clients and courts are counting on you to bring.