What If the Defense Bar Is Fighting the Wrong AI Battle?
Plaintiff lawyers are investing aggressively in artificial intelligence, leaving insurers, self-insureds, and defense counsel with good reason to wonder whether they're falling behind.
But as the defense bar considers its response, is it focusing too narrowly on matching plaintiff firms' productivity gains, when the greater opportunity may lie in improving how litigation decisions are made?
In a recent Above the Law article, Stephen Embry, a former defense lawyer and longtime observer of legal technology, argues that the defense bar faces a growing competitive disadvantage. Borrowing from Lewis Carroll's Red Queen, who must run ever faster just to remain in the same place, Embry describes an environment in which plaintiff firms benefit from AI and financial incentives that encourage technology investment, while defense firms struggle with hourly billing, constrained rates, and pressure to control costs.
The argument deserves attention, but it also raises another question: Who is best positioned to turn AI into a competitive advantage for the defense?
Insurance carriers and large enterprise claims organizations have resources, information, and opportunities that individual defense firms often lack. Properly deployed, AI could help them make more informed decisions throughout the life of a case, changing the competitive equation in ways that go well beyond faster legal work.
Why the Plaintiffs' Bar May Have an AI Advantage
Plaintiff firms working on contingency have strong incentives to accomplish more with less. If AI reduces the time needed to investigate claims, draft demands, or prepare briefs, they benefit directly. Their lawyers can handle more matters or devote greater attention to promising cases.
For defense firms compensated predominantly by the hour, the incentives are more complicated. AI may reduce the time needed to complete a task without giving the firm a way to capture the value of that efficiency. Carriers and self-insureds have good reasons to control defense expenses, but traditional billing arrangements can leave firms with limited financial incentives to invest in technology that benefits their clients.
Large plaintiff firms can also spread technology investments across many matters, develop standardized processes, and use their scale to build specialized capabilities. Defense firms, operating independently and serving multiple carriers with varied requirements, may find comparable investments harder to justify.
AI alone will not determine outcomes, but favorable economics and scale have allowed plaintiff firms to adopt it faster.
A natural response is to ask how defense firms can acquire comparable capabilities. That's a legitimate question, but it may be too narrow.
Faster Defense Work Doesn't Necessarily Mean Better Defense Outcomes
Much of the enthusiasm for generative AI has focused on productivity. AI can help defense lawyers draft documents, research legal issues, summarize depositions, review discovery, and prepare reports more efficiently. These are valuable applications in insurance defense, where legal teams manage large volumes of information under considerable cost pressure.
Consider a defense firm that uses AI to prepare litigation status reports in a fraction of the time previously required. The attorney spends fewer hours preparing them and the carrier may realize a reduction in legal expense.
Now consider a different application of AI in the same case. New deposition testimony, analyzed alongside other evidence gathered throughout the litigation, reveals a potentially significant issue concerning the plaintiff's theory of liability. AI identifies the relevant connections and provides links to the supporting evidence, allowing defense counsel to evaluate the findings and account for them in the case assessment and settlement strategy before an upcoming mediation. Both applications provide value, but the second may materially change the assessment of exposure and the resulting settlement strategy, with financial consequences that could far exceed the savings from faster reporting.
Of course, technology doesn't determine whether evidence is credible, admissible, or persuasive to a jury. Those judgments still require experienced counsel working with claims professionals. AI's contribution is to help ensure that material information is identified, connected, and delivered before those judgments are made.
For claims organizations, the economics of litigation encompass both defense expense and the indemnity cost of resolving claims. An AI strategy focused predominantly on reducing attorney hours addresses only part of that equation.
The Biggest Opportunity May Not Be in the Law Firm
Insurers and large self-insured enterprises manage litigation across jurisdictions, lines of business, and numerous defense firms. Their scale, institutional experience, and direct financial exposure give them opportunities that individual firms may lack.
Yet much of the discussion about using AI in litigation defense assumes that law firms will be the principal buyers, users, and beneficiaries, with insurers and self-insureds encouraging AI adoption and hoping for lower bills.
A carrier, TPA, or self-insured working with dozens of defense firms could provide access to common case-intelligence tools across its panel, establish consistent expectations for source-supported case assessments, and make the resulting analysis accessible to both counsel and claims professionals. This creates the prospect of improving litigation management across an entire portfolio rather than one firm at a time.
Through such an approach, claims professionals and defense counsel could work from a more accessible, consistently organized body of case intelligence. Significant evidence can be identified and linked to its sources, emerging issues examined collaboratively, and claims professionals better equipped to understand the basis for counsel's assessments.
The carrier's involvement would not diminish counsel's role. Indeed, it would make that role more valuable by allowing lawyers to devote more attention to legal analysis, litigation strategy, and advocacy rather than assembling and communicating basic facts.
Confidentiality, privilege, access controls, and differing law firm workflows present substantial obstacles. Addressing them will require collaboration among carriers, defense firms, and technology providers, but the problems need not be solved independently by every firm.
To realize their potential advantage, insurers and self-insureds may also need to reconsider fee arrangements so that defense firms have a reason to adopt and use these capabilities effectively. Otherwise, new tools could inherit the same misaligned incentives as the old system.
From Information Retrieval to Litigation Intelligence
A litigation file isn't simply a collection of documents to be summarized. It contains allegations, testimony, medical histories, discovery responses, expert opinions, and other evidence that may support, contradict, or qualify what is known about the case. The significance of an individual document often depends on its relationship to information found elsewhere.
A witness's testimony may change the understood sequence of an accident; an expert opinion may rest on assumptions that warrant closer examination; and new discovery responses may strengthen or weaken the defense's view of its exposure.
Identifying these relationships is an essential part of what experienced lawyers do. AI can assist by analyzing information across documents, identifying potentially significant facts and developments, and allowing users to trace findings to their supporting evidence.
Source verification is essential. Claims professionals and attorneys should not be asked to accept AI-generated conclusions without being able to examine the underlying material, determine what the record actually says, and evaluate whether the finding warrants further action.
The next step is to move beyond retrieving information when someone knows what question to ask. Intelligent systems may be able to identify issues deserving attention, suggest additional avenues of investigation, and help keep case assessments current as evidence develops.
This is research we are pursuing at esumry, supported by a research award from the U.S. National Science Foundation. Our work focuses on developing an intelligent guidance system that can connect facts and relationships across complex litigation records, identify issues warranting attention, and provide claims professionals with actionable guidance grounded in verifiable source evidence. The goal isn't to replace the judgment of claims professionals or defense counsel, but to help them exercise that judgment with a more complete and timely understanding of the case.
Changing the Scorecard: From AI Adoption to Litigation Performance
Adoption rates, documents processed, time saved, and reductions in outside counsel expenses are relatively easy to measure. They can demonstrate efficiency, but reveal less about whether AI improves litigation performance.
To establish ROI, claims organizations must examine whether AI helps identify material issues earlier, improve case assessments, support timely reserve reviews, and inform settlement decisions. For example, they could assess how quickly new evidence is reflected in evaluations, whether important issues surface before critical litigation milestones, and how clearly counsel's recommendations are supported by the record.
An AI strategy that improves speed without improving the quality of underlying decisions may produce modest savings while leaving much of its potential value unrealized.
Winning the Right Race
Embry's Red Queen analogy captures the pressure facing insurance defense as plaintiff firms invest in AI and build increasingly sophisticated capabilities. But insurers and defense firms need not respond merely by matching those investments. They can pursue a more collaborative approach that combines counsel's legal expertise with the broader perspective and resources of the claims organization.
Greater efficiency remains worth pursuing, and the defense should expect technology to reduce unnecessary legal expense. Before investing, however, carriers should ask whether the technology will improve the quality and timeliness of the decisions that shape how their claims are defended and resolved.
Schedule a demo to see how esumry helps defense teams turn case documents into structured, accessible litigation knowledge that supports trusted, faster, consistent pretrial work.
About the Author
James Chapman is a co-founder of esumry and a defense litigator. He writes about the intersection of AI, litigation strategy, and legal operations.
Using esumry, privilege is protected with ZDR (zero data retention), and case analysis is fast, strategic, and secure. Create timelines, tag testimony, assess credibility, and get ahead of how the other side will use the record—before they do.
