As a practicing litigator, you’ve probably used AI to summarize a deposition, condense a document set or draft a client update. Those uses are practical and increasingly common. But they are not where AI delivers its greatest value to a litigation team.
Key Takeaways
- The Strategy Gap: 87% of experienced litigators see AI as a competitive advantage, yet only 23% currently use it for case strategy.
- Strategic Narrative: Your team can move beyond summaries to identify evidentiary gaps and “pressure-test” your trial story against massive document sets.
- Advanced Analysis: You can also use sentiment analysis and pattern recognition to detect “between-the-lines” nuances in depositions and internal investigations.
- Client Value: Corporate counsel are looking for firms that use AI to improve legal outcomes, not just reduce administrative hours.
According to Thomson Reuters’ latest Future of Professionals Report, 80% of respondents believe AI will have a high or transformational impact on their work within five years. Yet only 54% feel confident articulating its value to clients beyond efficiency. For litigators, especially first-chair trial attorneys and the associates who support them, that gap should raise a strategic question:
Are we using AI merely to move faster, or to think better?
Recent research conducted by Ari Kaplan Advisors, which interviewed attorneys at large law firms across the United States, reported that the most meaningful impact of AI in litigation is in case strategy: 87% of experienced litigators said that AI-enhanced case strategy provides a competitive advantage. One participant was blunt about the urgency: “It will give us a competitive advantage until all of the firms catch up.”
Notably, while only 23% reported currently using AI specifically for case strategy, far more identified strategic tasks, such as reviewing and analyzing documents, connecting relevant facts to address evidentiary gaps, and crafting outlines, as the areas where AI could deliver the greatest value. This gap warrants closer examination, in addition to suggesting more AI use cases that trial attorneys and other litigation partners and associates can implement to hone their competitive edge.
Early Case Assessment: Accelerating Understanding
As one participant in Kaplan’s research explained, “Learning and assembling the facts into a coherent story of what happened and why” is a top priority in any matter. Another noted that with generative AI, “It is unbelievable how easily and quickly you can create a narrative from a large, unstructured set of records.”
Modern AI tools can analyze large document sets and quickly extract core elements: timelines of events, recurring themes, key communications, and the cast of characters. Instead of manually building a chronology over days or weeks, litigators can generate a structured outline of events in hours and then refine it with human judgment.
This does not replace legal analysis. It accelerates it. By locating connections among documents, identifying gaps in evidence, and flagging potentially problematic communications early, AI helps litigators shape strategy sooner. That earlier clarity informs everything from motion practice to settlement posture.
One experienced litigator observed that AI is particularly helpful in serial litigation involving similar motions and datasets, where it can reduce duplicative work and help develop a narrative more quickly. In matters where speed to insight is important, that advantage compounds over time.
Internal Investigations: Seeing Patterns Faster
Internal investigations present another opportunity for AI. They often involve high volumes of email, chat messages and other communications across multiple custodians. The task is not simply to review documents, but to detect patterns such as who knew what, when, and how information moved.
AI-assisted document analysis can cluster related communications, extract references to key events and identify frequently mentioned individuals or entities. Rather than relying solely on keyword searches, litigators can use semantic analysis to surface conceptually related materials that traditional searches might miss.
In fast-moving investigations, this ability to map relationships and identify anomalies early can materially change outcomes. It helps counsel brief executives more effectively, advise on disclosure obligations, and prepare for potential regulatory scrutiny.
Reviewing Incoming Productions: Moving Beyond Volume
Every litigator has faced the challenge of a massive incoming production. The instinct is to focus on review: getting through the volume. But strategic advantage lies in analysis, that is, understanding how the documents support or undermine your theory.
Kaplan’s research found that 90% agreed that gaining quicker access to insights or evidence provides a competitive edge. As one lawyer put it, “The largest pain point is document analysis, so we derive the greatest benefit from using generative AI to support this process.”
AI can help identify clusters of “hot” documents, connect communications to specific legal issues and highlight areas where proof may be thin. For associates, this means contributing more substantively to strategy discussions. For trial counsel, it provides a faster way to pressure-test whether the evidence truly supports the story you intend to tell.
Sentiment Analysis: Reading Between the Lines
A notable development is sentiment analysis in deposition and transcript review. AI can now categorize testimony as positive, negative or neutral. In some cases, it can detect more nuanced tones such as evasiveness or passive-aggressive language.
At ILTACON, Caroline Sweeney, Chief Innovation Officer at Dorsey & Whitney, shared that a partner was impressed when the firm’s AI tool identified “Minnesota Nice” testimony, meaning polite on the surface but negative in substance. As she noted, AI can sometimes reveal insights lawyers did not even realize they were looking for.
While sentiment analysis should never substitute for a lawyer’s judgment about credibility, it can serve as an additional lens. It may help identify areas to probe further, highlight inconsistencies or flag testimony that warrants closer scrutiny.
Developing a Narrative for Trial
Ultimately, winning in litigation is about storytelling. The most effective trial teams present a clear, compelling narrative grounded in evidence. AI’s ability to connect facts, issues, and documents supports that objective.
Case strategy platforms increasingly allow litigators to map events to underlying evidence, link witnesses to specific issues, and visualize timelines. AI can assist by suggesting connections among documents, identifying themes, and helping teams outline what must be proven or rebutted.
As one participant in Kaplan’s research put it, “I believe that two brains are better than one, and I would always be open to a lawyer’s brain receiving supplemental support from an AI’s brain.” That framing is instructive. AI is not the strategist. It is the second set of eyes that helps sharpen the strategist’s thinking.
From Efficiency to Advantage
Clients are watching. As litigation spending rises and cases become more consequential, corporate counsel expect outside firms to deploy technology in ways that go beyond cutting hours. “The clients are paying lawyers for strategy, not administrative tasks,” one Kaplan report participant said.
The firms that stand out will be those that can explain how AI improves outcomes: by identifying evidentiary gaps sooner, strengthening narratives earlier, and enabling better-informed decisions throughout the lifecycle of a case.
Drafting and summarization will remain valuable. But the real opportunity lies in using AI to enhance how litigators learn, analyze and strategize. Those who embrace that broader vision will not just work faster. They will work smarter and be better positioned to articulate that value to clients.
Image © iStockPhoto.com.
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