AI in the Law: Top Use Cases for Litigators This Year
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Litigation teams are using AI less like a novelty and more like a force multiplier. The biggest wins right now are practical: turning messy records into structured summaries, drafting first-pass litigation documents, and building issue-focused outlines faster, without losing attorney control.
Below are the top AI in the law use cases for litigators this year, organized by where they show up in a real case timeline.
1) Intake triage and early case assessment
When a new matter arrives, speed matters, but so does consistency. AI can help you triage large volumes of intake material and surface what an attorney actually needs to decide next steps.
Common outputs:
- A timeline of key events (treatment, incident, notice, communications)
- Parties, venues, key documents, and gaps (what you do not have yet)
- Red flags (limitations issues, causation breaks, missing records)
Why it matters: earlier clarity helps you choose the right track, value the case sooner, and request the right documents before time is lost.
2) Medical record summarization for injury cases
Medical records are where cases go to slow down. AI can convert hundreds (or thousands) of pages into a chronological medical summary that is easier to verify, cite, and update.
High-value outcomes litigators use:
- Date-stamped treatment summaries and diagnoses
- Procedure and medication snapshots
- Causation-relevant notes and prior history callouts
Tip: treat AI summaries like an associate’s first draft. Require pinpoint cites back to source pages so attorneys can validate quickly.
3) Demand letters and settlement packages
AI is especially useful for producing a strong first draft of a demand letter, then letting counsel refine theory, tone, and negotiation posture.
What AI can accelerate:
- Case narrative from the record
- Damages sections anchored to treatment chronology
- Exhibit references (with attorney verification)
This is one of the most immediate ROI areas because it compresses the time between “we have records” and “we are ready to make a persuasive demand.”
4) Discovery planning and document review support
In discovery, AI can help teams get organized and stay organized.
Practical ways litigators apply it:
- Summarizing production sets into themes and issue tags
- Spotting inconsistent statements across documents
- Building case timelines from email chains, notes, and reports
Important: AI can help you navigate your own corpus, but privilege, protective orders, and confidentiality still require rigorous human governance.
5) Deposition outlines that track your case theory
A deposition outline is more than topics. It is sequence, purpose, and exhibits tied to claims and defenses.
AI can help by:
- Generating a structured outline by element or theme
- Proposing follow-ups based on known factual conflicts
- Suggesting exhibit clusters (for you to select and confirm)
Best practice: keep your outline “court-ready” by forcing specificity. Every section should map to a claim element, impeachment point, or damages proof.
6) Trial prep materials (and the unglamorous work that wins trials)
Trial work includes a lot of repeatable drafting and organization. AI helps most where there is structure.
Examples:
- Summaries of key witnesses and what they support
- Issue-based chronologies and exhibit lists
- Drafting building blocks for trial binders (for attorney finalization)
The goal is not to outsource judgment, it is to shorten the time from raw record to usable trial material.
7) Team collaboration and workflow standardization
Even strong teams lose time to version chaos and handoffs. AI becomes more valuable when it is embedded in a consistent workflow that the whole team uses.
Look for workflows that:
- Centralize case inputs and outputs
- Make drafts easy to review and revise
- Preserve a clear audit trail of what was generated and what was edited
Quick guide: matching use cases to litigation work
| Use case | Typical input | AI output | Best time to use it |
|---|---|---|---|
| Intake triage | Intake packet, early records | Timeline, gaps, key facts | Day 1 to Day 7 |
| Medical summary | PDFs of medical records | Chronological med summary | Pre-demand through mediation |
| Demand drafting | Records, liability facts, damages | Demand letter draft | After records stabilize |
| Discovery support | Productions, emails, reports | Theme summaries, issue tags | Rolling throughout discovery |
| Deposition prep | Pleadings, record highlights | Depo outline draft | 1 to 2 weeks pre-depo |
| Trial materials | Key exhibits, depo excerpts | Witness and exhibit summaries | 30 to 90 days pretrial |
Risk, ethics, and the “don’t get sanctioned” checklist
AI in the law is now mainstream, and so are the consequences for careless use.
- Competence includes understanding legal tech. ABA Model Rule 1.1, Comment 8 is widely cited for the idea that lawyers should keep up with technology relevant to their practice (ABA Model Rules).
- Hallucinations are not a theory of the case. Courts have sanctioned lawyers for filing AI-generated citations that were not real, including the widely discussed Mata v. Avianca matter (opinion).
- Govern AI like any other risk system. If you want a structured framework for assessing and mitigating AI risks, the NIST AI Risk Management Framework is a useful reference.
Operational checklist you can adopt immediately:
- Require source citations back to your documents for factual summaries.
- Prohibit AI from inventing case citations, always verify in your research tools.
- Keep humans responsible for final wording in filings and attorney communications.
- Confirm confidentiality controls before uploading client materials.
Where TrialBase AI fits for litigators
TrialBase AI is built for intelligent litigation support from intake to verdict. If your day-to-day includes converting documents into work product, such as demand letters, medical summaries, deposition outlines, and trial materials, it is designed to help you get litigation-ready outputs in minutes, then refine with attorney judgment.
Explore the platform here: TrialBase AI.
Frequently Asked Questions
Is AI in the law mainly for big firms? No. Many of the highest-ROI use cases, like medical summaries and demand letter drafting, help small teams the most because they reduce bottlenecks.
Can AI replace document review or deposition prep? It can accelerate both, but it does not replace attorney responsibility. Use AI for first-pass structure and synthesis, then verify, refine, and decide strategy.
How do litigators use AI without risking hallucinations? Require citations to your source documents, verify any legal authorities in trusted research tools, and keep a clear human review step before anything is filed or sent.
Call to action
If you want to spend less time converting PDFs into drafts and more time on case strategy, try a litigation-focused workflow. Visit TrialBase AI to see how AI-driven document analysis can produce demand letters, medical summaries, deposition outlines, and more, in minutes.
