Commercial litigation generates more paper than almost any other area of practice. A large interlocutory dispute can produce thousands of pages of affidavit material, expert reports, and discovered documents. A contested trial runs considerably more. The volume is not evenly distributed across the workflow, but instead concentrates at specific pressure points, and those pressure points are where AI has the clearest application.
The most established use case in commercial litigation is document review. AI systems that classify documents for relevance, identify privilege candidates, and surface key provisions across a large corpus have been used in major litigation for several years.
What has changed is accessibility. A litigation team can now run a first-pass relevance review across tens of thousands of documents using tools that don't require specialist vendor engagement, provided the tool is appropriate for the sensitivity of the material and the firm has considered its disclosure obligations under applicable court rules.
The Federal Court's GPN-AI practice note is relevant here. Where AI has been used to prepare submissions or analyse documents, the responsible lawyer must be able to account for that use if asked by the Court. A document review workflow that produces a record of what tool was used and what it found is easier to account for than one that doesn't.
Commercial litigation rarely permits careful research across a long horizon. Directions hearings are listed quickly. Interlocutory applications require submissions on short notice. An urgent injunction application may require identifying and analysing the applicable principles within hours of receiving instructions.
AI research tools like Habeas that retrieve from a verified legal dataset can compress that timeline materially. A practitioner can surface the relevant authorities, identify the applicable principles, and get a first-pass view of how courts have approached analogous fact patterns in the time it previously took to run keyword searches across multiple databases. The value is not in replacing careful legal analysis — it is in reaching the point where that analysis can begin.
One underused application is using AI to identify inconsistencies across large bodies of witness evidence. Where affidavit material runs to hundreds of pages across multiple deponents, finding the inconsistencies that matter, like between what a witness says in their affidavit and what the documents show, or between one deponent's account and another's, is fundamentally a reading volume problem.
AI that works across that material and surfaces specific inconsistencies for the practitioner to evaluate shortens preparation significantly. The judgment call about what to do with each inconsistency remains with the practitioner. The task of finding them in the first place does not.
The Federal Court's GPN-AI practice note, published 16 April 2026, is now binding on practitioners appearing in federal proceedings. Where AI has been used to prepare submissions, the responsible lawyer must personally confirm that cited authorities exist and support the stated proposition, and that evidence referenced is actually in the materials before the Court.
That obligation is manageable for practitioners using tools that retrieve from verified datasets and produce cited outputs. It is considerably harder to satisfy with tools that generate confident-sounding text without grounding it in primary sources: a structural distinction that matters most in a setting where professional liability for AI-assisted errors is now explicit.
The firms extracting consistent value from AI in commercial litigation have done the same thing: they identified specific tasks where AI sits, defined what it produces, and designed the review step that follows. Document review produces a classified set of documents; the review step is practitioner assessment of flagged material against the originals. Research produces a structured summary with citations; the review step is verification of key authorities against the primary source.
The technology is available to almost everyone, but the level of workflow adoption is the difference between practices that see consistent results and those that don't.
Habeas retrieves from a verified dataset of Australian legislation and case law, with citations traceable to the source. Book a demo.
