Five Ways Generative AI Is Reinventing Modern Litigation Workflows
For decades, the discovery phase of litigation has involved a rigorous, manual search process. Legal teams often found themselves in a high-stakes game of digital hide-and-seek, leveraging armies of associates and contract review attorneys to search through millions of documents. While these methods were long considered the norm, the sheer volume of modern data has pushed these legacy processes to their natural limits, often delaying strategic insights until months into a case.
With the introduction of generative AI into the litigation process, it’s not just accelerating these old tasks, but fundamentally reimagining the lifecycle. By allowing legal professionals to focus on a strategy-first approach, AI tools allow them to interrogate their data in plain language, automate the heavy lifting of document coding, and bridge the gap between evidence and advocacy with live-linked drafting.
Initial Fact-Finding: From Keyword Guessing to Direct Interrogation
Historically, the early phase of discovery involved attorneys spending days crafting complex search strings, a process that essentially forced a high-stakes guessing game where one missed term or piece of internal jargon could mean missing the evidence entirely.
With Everlaw’s generative AI tool Deep Dive, however, you can now use natural language questions to interrogate an entire database. The AI understands the context of the inquiry, not just the literal characters in a search string.
For example, in a lawsuit against a chemical manufacturer, you might ask whether any internal discussions mention bypasses of the filtration system during the heavy rain events of 2022.
Based on that single question, Deep Dive can identify a chain of technical Slack messages where a supervisor tells a technician to open the secondary gate to prevent overflow. It’ll then synthesize an answer that connects this action to the specific dates of the rain, providing direct links to the logs.
Instead of spending weeks refining keywords and missing hidden jargon, the lead attorney now has a clear narrative of events within the first hour of opening the database. That frees up time to immediately begin building their case narrative, getting a head start on the other side.
Document Review: From Doc Review Marathons to Strategic Validation
Document review has long been a time-consuming process in which teams of associates and contract review attorneys would click through thousands of emails, often leading to reviewer fatigue and inconsistent coding.
Through the use of EverlawAI Coding Suggestions, you can now accelerate the review process by classifying documents on a scale based on a specific set of criteria that you provide, and then applying that criteria across entire document sets.
Coding Suggestions allows users to evaluate documents in accordance with their coding sheet, which contains all the coding categories a user has created. For every code configured for use, Coding Suggestions will offer a suggestion of whether it should be applied to the document or not, and provide rationale for its suggestion based on analysis of the document text. Users can easily apply, remove, or replace a code directly in the review assistant based on the suggestion.
For example, imagine that a former engineer at a tech company is accused of stealing proprietary code. You set a protocol for the tool to analyze, review, and code unauthorized data transfers to personal repositories.
The AI flags a "soft yes" on an email that doesn't mention "code" but discusses "backing up my personal settings" on the day the employee resigned. It notes that the file size attached is suspiciously large for "settings," alerting human reviewers to a potential smoking gun.
By maintaining your criteria across millions of files, the AI filters out the noise, allowing the high-priced legal team to focus only on the 2% of documents that actually matter and cutting review time significantly.
Evidence Synthesis: From Siloed Drafting to Integrated Storytelling
In traditional litigation, the finding of evidence and the writing of advocacy were often siloed. This created a constant, inefficient loop of exporting documents, copy-pasting quotes into separate Word files, and manually re-searching for those same files weeks later to verify a footnote. With large, multi-office cases, marrying up tranches of documents, deposition highlights, and expert reports was a full-time administrative job—a literal search for where the yellow Post-it matched the yellow highlighter.
Everlaw bridges this gap by integrating Storybuilder and EverlawAI Writing Assistant into the drafting process directly into the evidence environment. Instead of manual synthesis, attorneys can use AI to build connections across a shared digital brain, allowing the team to shift from administrative coordination to high-level strategy.
Say for example you’re representing a plaintiff against a pharmaceutical company that has been accused of ignoring early safety warnings for one of its medications. Your team has already identified a massive tranche of documents—ranging from 50-page clinical trial reports to informal internal Slack threads—that suggest the company ignored early safety warnings. Instead of an associate spending days manually cross-referencing these files to build a timeline, you prompt Writing Assistant to draft a statement of facts regarding internal knowledge of adverse cardiovascular events prior to 2018.
The AI generates a structured narrative based on the documents you’ve already surfaced, and links every sentence to the specific documents where it appears. If you click on a paragraph about a 2016 internal study, the original PDF opens instantly on your screen.
This helps your team move past the manual labor of citation. It ensures the narrative is always anchored in the record, making the final brief much more resilient to challenges.
Complex Data Extraction: Taming the Document Behemoth
Some documents are too dense for a quick human scan—think 100-page reports or 20-page meeting agendas. Reviewers often skim these, potentially missing a single line that changes the case.
As a feature within EverlawAI Review Assistant, Everlaw’s custom extractions tool can be used to pull structured information out of long, unstructured documents in seconds. You can extract out specific values from large document sets, which can be particularly helpful when keyword or regular expression searches are too ineffectual or cumbersome.
For example, consider you’re investigating a global construction firm for potential bribery. You have a 150-page unstructured ledger that details years of project expenses. Using Everlaw’s custom extractions, you configure the tool to extract three specific data points: Any individual or entity receiving funds, the specific dollar value of the transaction, and the internal justification or project code associated with the spend.
The tool then generates a clean, sortable table with the results. By looking at these specific fields in tandem, you quickly spot three entries listed as "Consulting Fee - Government Liaison." Because the tool has organized the dates and amounts for you, it becomes immediately obvious that these specific payments coincided exactly with a major contract win.
Now, what was once a three-day manual data entry task is a 10-second automated summary. The tool catches small details that a human eye would almost certainly skim over in a 150-page PDF.
Post-Deposition Analysis: Finding Inconsistencies at Scale
After a deposition, attorneys typically spend days highlighting transcripts and manually cross-referencing them against previous testimony or exhibits. Identifying where a witness changed their story across eight hours of testimony is a manual, error-prone task.
With generative AI, you can interrogate transcripts at scale, comparing testimony across different witnesses and against the documentary evidence.
For example, in a case against a technology company, you can ask the AI to identify any instances where the CEO's testimony about the “blind” hiring process contradicts internal emails or other witness statements.
The AI uncovers that on page 82, the CEO claimed he never saw candidate names, but then it cites a specific internal email from three months prior where he explicitly requested the names of candidates for the final round.
This provides instant material you can use in your case. You aren't just summarizing what was said, but are actively hunting for the truth across the entire record.
The New Bottom Line
By automating the most labor-intensive parts of the discovery lifecycle—from initial fact-finding to post-deposition analysis—generative AI allows legal teams to focus on what they do best: high-level analysis, client counseling, and building a narrative that helps win their case.
Attorneys need a tool that works alongside them and adapts to their processes. Discovery is rarely linear. It’s an iterative process of asking a factual question, following a hunch, and often pivoting strategy the moment a key document comes to light. Generative AI adapts to this fluid reality, keeping pace with an attorney's intuition and allowing them to interrogate the evidence as quickly as new theories emerge.
Ultimately, these tools provide a significant competitive advantage. Instead of being buried by the volume of modern data, you can use generative AI as the foundation for a winning strategy. In the high-stakes world of litigation, the smoking gun is no longer found through luck or manual labor—it’s found through intelligence.
