Can AI Reduce Discovery Costs?

The win that already happened

Long before generative AI, litigation had a data problem: matters routinely turn on millions of documents, and reviewing each by hand costs a fortune. Technology-assisted review — predictive coding — solved much of it. A reviewer tags a training set; the model then ranks the rest, culling collections from millions to thousands. NIST-linked research puts the productivity gain at roughly 80%, and the landmark Grossman–Cormack study found TAR could be more effective and more efficient than exhaustive manual review.^{Source 1,2}

The uncomfortable finding underneath: manual review was never the gold standard. In a classic study, skilled reviewers were convinced they had found 75% of relevant documents when they had actually found 20%.^{Source 3} Against that baseline, a well-run TAR process is cheaper and more thorough — and courts have accepted it for years where the process is properly documented.

Two very different AIs

The critical distinction, endlessly blurred by the single word "AI": predictive coding classifies documents that exist, while generative models produce text that may not. The first is mature and defensible. The second is drawing a wave of penalties — at least US$145,000 in sanctions for fabricated citations in the first quarter of 2026 alone, and in one Nebraska appeal, 57 of 63 citations were defective, ending in the first indefinite suspension over AI filings.^{Source 4}

Where governance decides the outcome

Discovery's specific governance burdens are privilege and defensibility: an AI process that inadvertently surfaces or leaks privileged material, or that cannot be explained to a court, forfeits the cost saving many times over. The signing lawyer remains accountable for every citation and production choice — as one appellate panel put it, a citation the lawyer has not personally read does not go in, whatever produced it.^{Source 5} The ABA's 2024 guidance is equally blunt: competence, candour and confidentiality do not transfer to a tool.^{Source 6}

How to think about adoption

Use mature, documented TAR for review with confidence — it is the clearest cost win AI offers any profession. Keep generative drafting on a much shorter leash: verify every citation, wall off privileged and client data, and record the process so it is defensible. Treat "AI" not as one capability but as two with opposite risk profiles, and govern each on its own terms.