From Billboards to Bots: How AI Is Reshaping Mass Tort Claim Origination

Artificial intelligence has moved from the back office of mass tort firms into the front end of the claims pipeline: the ad that finds a potential claimant, the chatbot that first hears their story and the software that decides whether their case is worth taking. For anyone defending mass tort claims, understanding that pipeline is becoming as important as understanding the medicine or the exposure history.

Marketing agencies serving mass tort firms now advertise algorithmic, platform-driven targeting as standard practice. In essence, they let social platforms’ own AI identify receptive audiences based on user behavior and demographics, then use generative AI tools — rather than a traditional copywriter — to produce tailored ad creative. Several agencies also offer “self-service intake” products that apply a firm’s screening criteria automatically, filtering inbound leads before a human ever reviews the file. There is also a growing market of vendors offering AI-powered intake chatbots that capture and pre-qualify leads around the clock. For a claimant researching mesothelioma symptoms at 11 p.m., the first “conversation” about their case may now be with software, not a person.

Non-lawyer claims generation operations also have emerged as significant players in the mass tort ecosystem. These outfits identify potential claimants, gather preliminary information and sell qualified leads to firms that handle the litigation. A parallel trend at the opposite end of the litigation lifecycle is the rise of non-lawyer settlement services that market directly to consumers, offering step-by-step instructions for filing claims without the intervention of counsel — effectively commoditizing the claims process at the expense of traditional attorney oversight.

The Volume Game

Mass tort litigation has always been a volume business. The typical plaintiff-side model is to amass a large inventory of claims, work up a handful of the strongest cases and leverage those results to drive global settlements. Many individual claims would not survive rigorous scrutiny on their own and only persist to maximize aggregate settlement pressure. Faster and cheaper client acquisition translates directly into greater leverage and bigger settlements. AI does not change the strategy; it accelerates and amplifies it. Critically, much of the technology described above is available at scale at least in part because of third-party litigation funding, which has injected over $15 billion into commercial litigation in the U.S. alone. Funders seeking returns have invested heavily in the infrastructure that powers AI-driven claim origination, making sophisticated targeting, automated intake and high-volume processing accessible to a broader range of plaintiffs’ operations.

Three Dimensions to Watch

1. Claim volume and composition. AI-optimized targeting and automated qualification make it faster and cheaper to originate claims, affecting the volume and mix of cases reaching defendants — including the completeness of the intake record behind each one.
2. A discoverable record. Chatbot transcripts, ad targeting parameters and automated qualification logic are all data. Where a case’s origin story becomes relevant (to exposure history, timing, or how a claimant’s account was first captured) these systems may leave a more detailed and more discoverable record than a traditional intake call.
3. New exposure for the plaintiffs’ bar. A 2026 putative TCPA and state telemarketing law class action, Sutton v. DV Injury Law PLLC (W.D. Tex.), alleges a mass tort firm used AI-generated voice calls to solicit clients from purchased lead lists without verifying consent, masked its caller ID and continued calling after recipients declined. The case signals that AI tools accelerating claim origination can also generate consumer-protection liability for the firms using them.

Emerging Risks: Narrative Priming and Defendant Identification

Jury pool priming. The plaintiffs’ bar has long used “reptile theory” — a trial strategy that appeals to jurors’ primal instincts around safety and danger. Generative AI tools are highly trainable; given examples of effective reptile-style messaging, they can produce emotionally resonant content at scale. Deployed through social media advertising, where algorithmic targeting already optimizes for engagement, such tools could prime potential jury pools before any lawsuit is filed. Some plaintiffs’ lawyers have been candid about this strategy. At the Spring 2025 Mass Torts Made Perfect conference, Mike Papantonio of Levin Papantonio reportedly told attendees that mass torts “are manufactured by plaintiffs’ lawyers” and advised colleagues to “[d]on’t wait for the next big tort. Build it.” He emphasized that controlling the narrative outside the courtroom is essential: “If you don’t control the narrative outside the courtroom, you’ve already lost inside the courtroom.” To that end, his firm has promoted documentaries and legal thrillers designed to shape public perception of corporate defendants. As Papantonio put it, “the litigation doesn’t wag the media tail — the media tail wags the litigation.”

Defendant identification. As mass tort litigations mature, plaintiffs turn to secondary and tertiary defendants: component suppliers, retailers, pharmacies and online marketplaces. Identifying these parties traditionally required time-intensive investigation. Web-scraping tools combined with large language models can now parse product listings, ingredients, supply chain disclosures and archived marketing materials in seconds. Defendants further down the supply chain should expect their exposure to expand as these tools proliferate.

What to Do

For defendants, claims facilities and insurers already dealing with asbestos or other mass tort exposure:

• Build AI into discovery. Consider whether intake records, chatbot logs, lead-source data and ad targeting criteria are worth pursuing, particularly where a claimant’s account of exposure or timing is contested. In MDL or other consolidated/class proceedings, think about broader discovery into plaintiffs’ firms’ use of AI tools.
• Prepare for motion practice. Track Sutton-style consumer-protection litigation for arguments that may translate to the mass tort context, including challenges to the reliability of AI-generated records, the admissibility of chatbot transcripts and the adequacy of disclosures about how claims were sourced.
• Think about jury selection. If plaintiffs’ counsel or their marketing vendors are using AI to shape public narratives around your product or industry, that messaging may reach your jury pool before voir dire. Consider how to identify exposed jurors and address it in questioning.

The infrastructure is evolving faster than the rules. AI is not just changing how plaintiffs’ firms find clients: it may be changing how they build leverage, shape narratives and identify who to sue. Orrick’s mass tort, product liability and technology litigation teams are available to help you navigate these issues. Please reach out to the author or your usual Orrick contact.