Generative AI Is Reshaping The Defense Of Complex Litigation

Generative artificial intelligence is reshaping the legal landscape. Complex cases that once required many hours to develop and investigate can now be researched and turned into a complaint in a fraction of the time.

For example, third-party companies are leveraging generative AI tools to identify potential lawsuits and give plaintiffs attorneys a head start before a single client has walked through the door.

As reported last August by Forbes, AI-powered startups are now scanning corporate reports, regulatory filings and news archives, and are both selling actionable case leads to plaintiffs attorneys and assisting with plaintiff recruitment through targeted digital advertising.¹

Moreover, generative AI tools can help pro se plaintiffs bring more sophisticated cases. An article published in the New York Times on May 28 shows that the proportion of pro se cases filed by nonprisoners increased from 11% of all civil cases five years ago to 16.8% in 2025.²

The same article reported that the number of pro se complaints flagged as likely containing AI-generated text rose from virtually zero in 2019 to more than 18% in 2026.

But reducing the time and resources needed to research a case and draft a complaint has profound implications beyond the plaintiffs bar and pro se litigants.

Lowering the barriers to filing new cases means the defense bar must respond to an increased wave of litigation. And if each new case requires less investment to generate, plaintiffs firms could become less risk-averse.

Cases with a higher likelihood of dismissal or lower potential damages become viable at volume, with those risks absorbed by sheer quantity of filings.

The result may be a more demanding environment for corporate defendants, which could be the prime targets of expanding litigation.

Defending against a lawsuit is expensive, document-intensive and increasingly complex. For in-house legal teams managing outside counsel and overseeing large-scale litigation — including multidistrict litigation and mass torts — the pressure to control costs while maintaining quality is constant.

Generative AI is rapidly changing that calculus. Across the full lifecycle of a case, from early research and drafting through expert battles and trial preparation, AI tools are helping defense teams work faster, smarter and more consistently.

This article surveys three areas where these tools are making the greatest difference: legal research and drafting, fact witness development, and expert witness strategy.

The tools and strategies discussed here don't work as a replacement for sophisticated counsel, but can be used to increase counsel's efficiency, freeing up bandwidth for more complex, strategic decision-making.

Legal Research and Drafting

Generative AI has become a practical drafting and research tool. For defense counsel and the in-house lawyers who oversee them, it helps turn early case analysis into draft filings, by translating case facts and prior work product into usable first drafts, and supporting the research process.

In briefing, AI can produce structured initial drafts of pleadings, motions and other filings, including motions to dismiss, summary judgment briefs and Daubert motions, by combining case-specific facts, applicable legal standards and existing templates.

These drafts still require attorney review and revision. But they reduce the time spent on repetitive sections and boilerplate language, which often results in lower outside counsel fees.

AI research tools also function as conversational assistants. Defense lawyers can ask direct, natural-language questions — such as "What are recent appellate cases excluding expert testimony for unreliable methodology?" — and receive concise summaries and citations to verify.

Because these tools can process substantial amounts of precedent quickly, they are particularly helpful for identifying comparable rulings across jurisdictions. This is a common need in cases involving claims filed in multiple states — although the completeness and relevance of results can vary, and require careful review.

In addition to general research functions, some tools can analyze how specific judges have written about recurring issues like expert reliability or causation.

This allows attorneys to tailor their framing for a particular court, although the depth and reliability of this analysis may be limited depending on the available data.

Other features, like automated outline generation that mirrors local rules or standard brief formats, improve consistency across filings and reduce the risk of procedural missteps.

The workflow is straightforward. Counsel begins by entering a short summary of the facts and procedural posture, along with a clear goal for the document needed.

The AI produces a draft organized around those inputs, often using firm-approved templates or prior briefs as a model. Attorneys then refine sections, add record citations, and adjust arguments for accuracy and tone.

For research, the process is iterative. Lawyers adjust their questions as new information surfaces, verify every authority in Westlaw, LexisNexis or Bloomberg, and use the validated results to strengthen arguments or update drafts.

Of course, attorneys must recognize that outputs may reflect gaps in jurisdictional nuance, or rely on generalized reasoning that must be tested against the specific record and governing law.

The professional obligations here are clear. AI-generated material must never be accepted without verification. Every citation must be checked, every quote confirmed and no output treated as authoritative until validated in a trusted legal database.

Only approved, secure tools should be used for case-sensitive information, and attorneys must comply with any court or ethical rules requiring disclosure of AI use. But used with appropriate review, these tools can support efficiency and consistency in legal work.

Fact Witness Preparation

Today's litigation routinely involves vast document sets, complex technical issues, and witnesses spanning corporate, scientific and regulatory domains.

Preparing fact witnesses for deposition and trial has traditionally been one of the most time consuming and labor-intensive tasks in litigation. Generative AI tools are changing that equation.

Familiar names in document review — Relativity, Everlaw, DISCO, Reveal — now offer generative AI capabilities that extend well beyond document review into strategic planning, issue analysis and document drafting.

These tools can generate first drafts of deposition outlines, defensive witness prep outlines with exhibits, direct and cross-examination questions, analyses of inconsistencies or gaps in a witness' story, transcript summaries with linked exhibits, and chronologies of case facts tied to individual witnesses.

The result is that attorneys can be better prepared in less billable time, freeing up bandwidth for higher-level strategic decisions.

One example is Opus2, which allows litigation teams to select a document subset after review, and use generative AI to surface and organize key facts for witness preparation. A real-time chatbot allows users to query document sets to locate specific types of documents, surface themes or generate draft questions.

All AI-generated content includes links to the supporting documents, with summaries of the relevant portions. For instance, in a products liability case, a key fact might be that employees warned a manager about a potential product malfunction.

The tool can locate and flag related documents, allowing the defense team to assess their significance and prepare accordingly.

Everlaw AI Assistant adds further capability by generating topical summaries for up to 20,000 documents at a time, allowing teams to review brief summaries rather than full text and only drill into the underlying documents when warranted.

The tool can surface documents on topics like "product defect," "adverse reaction," or "regulatory compliance," and offers sentiment analysis to flag documents where employees expressed negative views about regulatory requirements — the kind of document that can be highly damaging if not identified and addressed early.

A significant and underappreciated advantage of these tools is consistency across large, dispersed litigation teams. In MDLs and other complex cases, coordinating multiple outside firms is a constant challenge. Generative AI tools allow MDL teams to create and share customized templates, so that all firms are working with the same deposition outline format and covering the same key issues across all witnesses and cases.

Some sophisticated corporate defendants are now building bespoke generative AI tools specific to their litigation programs, shared across all outside counsel, with deposition summary formats tailored to in-house counsel's exact specifications.

Rough deposition transcripts can be fed into these tools for near-instant automated summaries, ensuring that all case reporting meets consistent standards.

Finally, these tools help defense teams anticipate opposing counsel's strategy. Platforms like Everlaw Deposition Analyzer, Eve and Opus2 can ingest prior trial or deposition transcripts from related litigation and surface key topics, recurring themes, emotional content and inconsistencies — giving defense teams insight into an adversary's patterns without committing weeks of junior associate time to manual review.

Expert Witness Strategy

In complex litigation, expert witnesses are often a decisive factor. Whether proving or refuting causation, challenging a design defect theory or defending a risk-benefit analysis, the quality and credibility of expert testimony can shape outcomes.

AI is giving defense teams powerful new tools to challenge opposing experts and fortify their own.

Mining Transcripts for Contradictions

In large litigations, particularly in mass torts where experts testify repeatedly across a bellwether process, identifying inconsistencies in expert testimony is one of the most valuable things a defense team can do.

Where an expert performs differential diagnoses across multiple plaintiffs, for example, contradictions in how that expert has ruled out confounding variables in different cases can provide compelling cross-examination material or grounds for exclusion.

AI-powered analytics can search thousands of pages of depositions, exhibits and trial transcripts to identify these contradictions automatically. AI tools detect inconsistencies by comparing language patterns, factual assertions and prior positions taken by the same expert.

They can also index and link prior depositions across litigations, even when stored in different formats or databases, and generate searchable summaries that allow attorneys to quickly pinpoint discrepancies for cross-examination.

While the AI tools can assist with identifying the contradictions, attorney insight and analysis is essential for identifying which points to make and how to best frame them into an effective cross examination.

Challenging Methodology Through Scientific Literature

Beyond testimony review, AI tools can attack the foundation of an opposing expert's opinions by scanning scientific databases to locate peer-reviewed studies relevant to the expert's methodology.

These tools flag literature that contradicts or undermines the expert's approach — whether due to flawed assumptions, misinterpreted data or lack of scientific consensus.

They can also mine databases to identify potentially contradictory authorities that the expert has ignored, or even identify articles published by the expert that are relevant to the subject matter.

In an era where Daubert challenges can be outcome-determinative, this capability gives defense teams a significant edge in building a record for exclusion.

Developing Jury-Friendly Demonstratives

Complex scientific and technical testimony can overwhelm jurors, and a defense team that cannot communicate its expert's analysis clearly is at a serious disadvantage.

AI-driven visualization tools translate complex datasets into compelling, intuitive graphics, making trends and relationships easier to grasp.

In cases with complex scientific arguments or lengthy technical histories, AI can generate timelines, charts and diagrams that help jurors follow the defense narrative with confidence.

These demonstratives can be prepared more quickly and at lower cost than with traditional methods, and they allow in-house counsel to review and approve presentation strategy earlier in the process.

Strengthening Your Own Experts

Importantly, all of these AI-driven tactics work in both directions. Defense teams can run the same analyses on their own experts before trial to identify vulnerabilities in prior testimony, anticipate areas of attack and uncover scientific literature that opposing counsel might deploy against them.

This proactive approach allows counsel to work with their experts to refine opinions, ensure consistency across multiple cases and prepare for likely cross-examination questions well in advance of trial.

In large-scale product liability defense programs, where the same experts may testify repeatedly, this kind of systematic expert management is an enormous advantage.

Risks and Safeguards

Generative AI's benefits come with risks that counsel must actively manage. The most significant is hallucinations, which are AI-generated content that appears credible but is fabricated, whether in the form of invented case citations, misquoted testimony or unsupported factual claims.

Courts have already sanctioned attorneys for filing briefs containing hallucinated case law, and all AI outputs must be verified against primary sources before use.

Attorneys should also stay current on evolving disclosure requirements, as courts and bar associations increasingly mandate transparency about AI use in filings.

Experts face their own exposure. Reliance on unvetted AI-generated analysis can invite methodology attacks on cross-examination and jury skepticism, making transparency and rigorous vetting essential.

Finally, sensitive case materials should only be processed through approved, secure systems. Externally hosted or cloud-integrated tools raise real confidentiality and privilege concerns that firms and clients cannot afford to ignore.

Footnotes

1. See https://www.forbes.com/sites/jeffkauflin/2025/08/21/this-ai-fueled-startup-is helping-attorneys-find-new-class-action-lawsuits/.

2. See https://www.nytimes.com/2026/05/25/us/politics/artificial-intelliegence courts.html.

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.