There is no shortage of advice right now telling mass tort firms to adopt AI. The harder question nobody is answering clearly is which AI, built for what, and what it actually does when a paralegal sits down to work a case or a partner needs to understand where the inventory stands.
Generic AI tools can read a document and summarize it. That is useful in some contexts. In mass tort, it is a starting point at best. The work is not reading one record. It is understanding what thousands of records mean together, against specific litigation criteria, in a way that holds up as those criteria change and a settlement deadline moves closer.
That distinction is where purpose-built technology earns its place.
Pattern Data founder Matt Francis explained it plainly in a recent conversation on The LegalTech Funds InStudio podcast with Gordon Crenshaw. General AI is a technology, he noted. Pattern is an operational system built on years of mass tort-specific expertise. The difference is not what the technology can do in isolation. It is what it was designed to accomplish inside a specific, complex workflow.
This is also why mass tort sits in its own category. As of the January 2026 report from the federal Judicial Panel on Multidistrict Litigation, nearly 198,000 actions were pending across active MDL dockets, with the large majority concentrated in litigations carrying more than 1,000 cases each. Tools built for a single matter were never designed for inventories at that scale.
Here is what that looks like across the lifecycle of a litigation.
When a firm takes on a large inventory, the first challenge is understanding it. Not case by case, but across the whole docket at once. Which cases have strong exposure evidence? Which are missing key records? Which meet the current intake criteria and which do not?
Without a structured evaluation framework, answering those questions means pulling files manually, tracking findings in spreadsheets, and hoping the picture that emerges is accurate enough to act on. By the time it is assembled, the docket has grown and the analysis is already dated.
A purpose-built platform ingests that inventory and evaluates each case against litigation-specific criteria in real time. As records come in, they are processed and analyzed. Paralegals see exactly where to focus. Partners get an immediate read on inventory composition without waiting for someone to compile a report. The intake stage produces structured data rather than a pile of PDFs with notes attached.
That structure is what everything downstream depends on.
Once a firm has screened its inventory, the work shifts to developing the cases that cleared intake. This is where most of the operational complexity lives. Treatment patterns need to be documented. Risk factors identified. Gaps in the record flagged and tracked. All of it across hundreds or thousands of claimants simultaneously.
Managing that work without docket-level visibility is reactive by definition. Teams focus on whatever file is in front of them rather than the cases where attention will have the most impact.
With structured data already in place from intake, development looks different. The platform surfaces which cases need attention and why. Workflow is organized around priority rather than proximity. Progress is trackable across the entire inventory, not just within individual files. And when litigation criteria shift, as they do in nearly every large mass tort, the evaluation logic is reapplied across the docket automatically. Work that has already been done does not have to be redone.
Matt described this as one of the core reasons firms historically avoided re-evaluating cases mid-litigation. The cost and time of rework made it impossible to justify. Structured data eliminates that calculation.
Settlement preparation in mass tort is not a single task. It is the culmination of everything that happened before it. Eligibility needs to be determined across the full inventory. Cases need to be tiered and valued. Work product needs to be generated at scale and in a format that meets the requirements of the settlement program.
Firms that arrive at this stage without structured data spend it catching up. Re-reviewing files, resolving inconsistencies, and assembling documentation that should have been organized months earlier.
Firms with structured data in place move differently. Eligibility logic gets applied across the inventory automatically. Tier distribution and projected values are visible before a single submission goes out. Work product is generated from data that has already been validated rather than compiled from scratch. The settlement stage becomes execution rather than triage.
For paralegals, purpose-built technology means a guided workflow that surfaces the right cases, identifies what is missing, and makes the work faster without requiring them to hold the full picture in their heads.
For litigation managers and operations leads, it means visibility across the entire docket without manual reporting. Progress is trackable. Gaps are visible. Development priorities are clear.
For partners, it means knowing what the inventory is worth, where it stands, and what needs to happen before the next major decision point, without waiting for someone to pull it together.
The software is not doing the legal work. It deploys human judgment efficiently, handling high-confidence extraction automatically and pulling reviewers into the edge cases where their judgment matters most. That is the practical difference between generic AI and a platform built for this work, a difference we explore further in why medical record review is so hard for LLMs and humans.
General AI tools are built to process one document or one case at a time. Mass tort firms are managing inventories that can run into the thousands of claimants across a single MDL, and federal dockets regularly carry cases at that scale. Pattern is built with inventory-level logic, so it evaluates a docket as a whole instead of one file after another.
No. Pattern handles high-confidence extraction automatically and routes cases needing clinical or legal interpretation to a reviewer. Reviewers spend their time on the edge cases where judgment matters, not on manual extraction across the full inventory.
Yes, and this is where firms gain the most leverage with Pattern. When evaluation is structured at intake, the same data carries through development and settlement without re-reviewing records. As criteria evolve, Pattern's logic reapplies across the docket automatically, so a firm reaches settlement with eligibility already mapped instead of reconstructing it under deadline.