Legal AI adoption has moved from cautious pilot to something closer to standard infrastructure at the industry's largest firms, faster than most other regulated professions we cover. Harvey, now valued at roughly $3 billion following a December 2025 Series C, is used in some capacity by eighty of the Am Law 100. Up from just fifteen firms eighteen months earlier. A 2026 Wolters Kluwer survey found 92% of legal professionals now using at least one AI tool daily.
The two platforms actually competing
Harvey, founded in 2022, built its platform around firm-side legal workflows (document analysis, clause extraction, and research at scale) and has become close to the default AI platform among elite firms. Thomson Reuters' CoCounsel takes a different position: it's the AI layer built on top of Thomson Reuters' existing Westlaw and legal-research ecosystem, with core use cases centered on first-pass legal research, brief and memo drafting, document review, and deposition prep. These aren't quite direct competitors so much as different entry points into the same underlying shift. One built from a research-database foundation, one built as a standalone platform.
What the volume numbers actually show
In the first quarter of 2026 alone, AI agents built by Harvey and CoCounsel together processed more than 10 million legal documents, a scale that reflects genuine production use, not pilot-stage experimentation. On the independent Vals AI legal benchmark from February 2025, Harvey posted the highest overall score and won five of six tasks it entered, while CoCounsel finished second overall and specifically led on document summarization.
Why document review specifically was the entry point
Document review during discovery remains the clearest fit for current AI capability in law, for the same structural reason it's scaled across every regulated industry we've examined: it's high-volume, the ground truth (relevant or not, privileged or not) is comparatively well-defined, and a human reviewer still checks flagged documents before anything gets produced or withheld, a bounded task with a reviewed output, the same pattern that made ambient clinical documentation the fastest-scaling healthcare AI use case, covered in our separate piece.
Where the caution remains real, despite the adoption numbers
Even with document review and research assistance scaling this fast, fully autonomous contract drafting without close attorney review remains far less common in practice than the adoption headlines might suggest, the liability exposure of an error in a signed legal document keeps most firms treating AI output as a first draft, not a final product. Legal research specifically carries an additional, well-documented caution: AI systems generating plausible but nonexistent case citations was a widely publicized failure mode early in this technology's legal adoption, and it's a large part of why firms remain insistent on independently verifying every citation before filing, the same discipline covered in our guide to fact-checking AI-generated content.
The pattern across the profession
The throughline is consistent with what we've found across regulated industries generally: tools succeed fastest and scale furthest where the task is bounded and a qualified human reviews the output before anything binding happens. Document review and first-pass research check both boxes, which is exactly why they're the tasks behind Harvey and CoCounsel's real, measured adoption numbers, not because the underlying models are uniquely suited to law, but because the workflow around them was designed with the same human-in-the-loop discipline that's worked everywhere else we've looked.