Litigation has always rewarded the side with better preparation. AI doesn't change that — it raises the floor of what "better" means.
Discovery is the part of litigation most obviously transformed by AI. What used to require a battalion of contract attorneys reviewing documents at fifty an hour now runs through a model that classifies, summarizes, and surfaces the small fraction of documents that matter.
The Discovery Inflection
The cost change is dramatic. The strategic change is bigger: cases that were too expensive to litigate are now economically viable, and cases that were over-litigated are getting the proportional attention they deserve.
Strategy and Argumentation
Beyond discovery, AI is increasingly useful in strategy: identifying which arguments have succeeded in front of a particular judge, surfacing the procedural moves that have changed comparable cases, and stress-testing a theory of the case by generating the strongest counter-argument.
None of this replaces the litigator's instinct. It informs it — by giving the litigator more, and better, information than they could gather on their own in the time available.
Outcome Prediction — With Caveats
Outcome prediction tools have improved meaningfully in the past few years. They can now estimate likely ranges for damages, settlement probabilities, and motion outcomes with surprising accuracy in some practice areas.
We use these tools internally to inform strategy. We don't share them with clients as predictions, because they're not. They're priors. They have to be combined with judgment about the specific facts, the specific judge, and the specific opposing counsel.
What Doesn't Change
Cross-examination, opening statements, the read on a witness, the call about whether to settle — these remain human. The technology that's strongest in pattern matching is weakest in moments of genuine novelty, and trial is full of those.
Our litigators use AI heavily in preparation. They don't lean on it in the courtroom. That distinction matters.
