Why AI Document Review Matters for eDiscovery

Author : Gemean Corporation | Published On : 20 Oct 2026

AI document review uses software, trained or guided by lawyers, to find responsive, privileged and key documents faster than reading every file by hand. It matters because it cuts review time and cost while still allowing a defensible result, provided the process is validated by sampling, supervised by people and documented. Document review is usually the largest cost and the longest phase of a discovery effort. Data volumes keep growing, and deadlines do not move. Legal teams need a way to review more material without lowering the standard of care.

This is where AI document review earns its place. It does not replace attorney judgment. It changes where attorney time goes. Instead of reading every document in sequence, reviewers train, check and correct a system that ranks or classifies documents at scale.

The legal footing is not new. In a March 2015 opinion in Rio Tinto PLC v. Vale S.A., a federal magistrate judge in the Southern District of New York wrote that "it is now black letter law that where the producing party wants to utilize TAR for document review, courts will permit it." The open question today is how to run the process so it holds up if challenged.

What is AI document review in eDiscovery?

AI document review is the use of machine learning or generative AI to help decide which documents are relevant, privileged or important to an issue. Humans set the criteria and verify the output.

The EDRM, the group that publishes the standard eDiscovery reference model, defines technology-assisted review as "a review process in which humans work with software to train it to identify relevant documents." That phrase captures the core idea. The software learns from human decisions, and humans check what the software does.