Nuclear verdicts have long been associated with venue, jury attitudes, plaintiff-friendly narratives, litigation funding, and evolving trial tactics. Artificial intelligence may now help plaintiffs’ firms analyze and amplify those drivers—changing how cases are selected, valued, and prepared for trial. A recent Insurance Journal article describes how some plaintiffs’ firms are using AI, large-scale mock juror testing, social media analytics, and other data-driven tools to support litigation strategy. These tools can be applied to evaluate arguments, research prospective jurors, and test legal theories before trial.

For manufacturers and defense counsel, the emerging concern is not simply that plaintiffs are adopting AI, but how the use of these data-driven technologies may make litigation strategies more targeted and scalable across cases of varying size.

The Shift from Litigation Experience to Litigation Analytics

Extensive jury research and case testing were once largely associated with high-stakes litigation. According to the Insurance Journal article, technology and large-scale mock juries may now help some plaintiffs’ firms evaluate legal theories and trial themes more efficiently and extend those capabilities to smaller matters. The article describes a growing “Moneyball” approach in which data informs case selection, resource allocation, juror evaluation, argument development, and legal strategy. These tactics may help plaintiffs’ firms refine narratives, strengthen settlement leverage, and pursue higher resolution values.

This raises an important question: Are defense strategies evolving at the same pace? For defendants managing large claim portfolios, the challenge is to identify the risk indicators materially affecting exposure and use those insights to guide litigation strategy, case valuation, and settlement decisions.

Why Product Liability Litigation May Be Particularly Susceptible

Product liability cases often involve complex scientific or technical issues that can be difficult for jurors to evaluate. Outcomes may therefore be shaped by factors beyond the underlying science, including perceptions of corporate conduct and trust. Public reporting on major product liability verdicts frequently highlights allegations involving product testing, warnings, consumer safety, and corporate credibility. Because these themes are not reducible to a single technical fact, AI-enabled jury research may help firms test which narratives resonate most strongly before entering the courtroom.

The results may be divergence among cases that appear similar from a scientific or technical perspective that generate very different jury responses due to these other non-technical factors. While historical outcomes may remain relevant, firms may not want to rely on past precedent alone to predict future exposure.

The Emerging Data Gap

Litigation is becoming increasingly data driven. As plaintiffs’ firms are using data systematically to shape litigation strategy, defendants may need a comparable analytical discipline to assess the factors influencing exposure. That means looking beyond individual case files to identify patterns across venue, counsel, allegations, settlement behavior, trial outcomes, and other portfolio-level indicators. If these trends continue, competitive advantage may increasingly depend on how effectively legal teams translate those insights into legal strategy.

Manufacturers and defense counsel should be asking whether they have the visibility to identify emerging exposure before they become the sole defendant in a case or the matter approaches trial. Portfolio-level analysis can help legal teams distinguish matters that warrant early resolution from those that should be defended—and prevent increasingly targeted plaintiff tactics from dictating settlement strategy. The future of product liability defense may depend not only on how individual cases are litigated, but also on how effectively risk is identified, measured, and managed across the portfolio long before trial.