What Mass Torts Made Perfect 2026 Was Really About
Every year, Mass Torts Made Perfect draws the people who are serious about where plaintiff litigation is heading. Not just the verdicts and the dockets, but the infrastructure behind the work. This year, three themes kept surfacing across sessions and hallway conversations. None of them are new. All of them are accelerating.
The Labor Model Is Being Replaced
For years, mass tort firms scaled by adding people. More paralegals, more intake staff, more bodies to move cases through the pipeline. That model is under pressure.
Automation is collapsing the cost and time assumptions that model depended on. What used to require a team is now being handled by software, faster and with fewer errors. The shift is not from people to technology in a superficial sense. It is a structural change in how throughput gets produced.
Firms that are not actively building toward automated workflows are not just behind. They are becoming structurally uncompetitive on two dimensions at once: cost and speed. In mass torts especially, where case volume and operational efficiency determine margin, that gap compounds quickly.
The question worth asking is not whether to automate. It is which parts of your operation still depend on manual labor that software could handle, and what it is costing you to leave that unchanged.
AI Is Moving From Tool to Operating System
There is a meaningful difference between adding AI features to your existing stack and building toward an agentic AI ecosystem. Most firms are still doing the former while the leading firms are quietly building the latter.
Agentic AI systems do not just assist with individual tasks. They operate across the full litigation lifecycle: intake, medical records, demand generation, settlement. They make decisions across connected workflows, not just within isolated ones. The competitive advantage in this environment does not come from which point solutions you have licensed. It comes from who controls the decision layer that connects them.
This is early. But the firms that are thinking about their tech stack as an orchestrated ecosystem rather than a collection of tools are positioning themselves for a different order of efficiency. The ones treating AI as a feature add-on are building a floor, not a foundation.
Social Media Litigation Is Expanding the Map
Cases emerging from jurisdictions like San Francisco and Arizona are signaling something worth paying attention to. Platforms themselves are becoming mass tort defendants, and the implications are significant.
This is not just a new category of defendant. It is a new theory of harm. The shift is from product liability grounded in physical injury to digital harm at scale, with entirely new categories of plaintiffs, new forms of evidence, and litigation strategies that are still being developed in real time.
For firms already working in mass torts, this is worth watching closely. The case volume potential is substantial. The legal infrastructure around these claims is still forming, which means the firms that develop early expertise will have an advantage that is difficult to replicate later.
What This Means Operationally
These three themes point in the same direction. The firms that perform over the next several years will not necessarily be the ones with the best cases. They will be the ones with the most efficient, most adaptable operations behind those cases.
That means taking automation seriously before it becomes a necessity. It means thinking about your technology strategy as an ecosystem question, not a vendor question. And it means watching emerging litigation categories with enough lead time to build real expertise.
If any of these themes are shaping decisions inside your firm right now, we are happy to get into the specifics. That is exactly the kind of work we do.
Mirena and Company works with plaintiff law firms on the financial and operational side of litigation, including law firm consulting, litigation funding, settlement planning, and QSF services.
Contact us to start a conversation.
