Extract from Reveal’s article, “From Pilot to Production: What It Takes to Operationalize AI Agents in eDiscovery.”
Most legal AI initiatives never make it past the pilot stage, and eDiscovery is not immune to that pattern. MIT’s NANDA initiative, in its 2025 report on the state of AI in business, found that despite tens of billions of dollars invested since 2023, roughly 95% of enterprise generative AI pilots fail to deliver measurable business impact, based on interviews with 150 leaders and analysis of 300 public AI deployments. McKinsey’s November 2025 survey of nearly 2,000 organizations found a similar pattern: 88% of respondents report regular AI use, but only 39% report any impact on enterprise earnings. The gap is not a technology problem. It is an operationalization problem.
Operationalizing AI in eDiscovery means moving an AI agent from an isolated pilot, tested on a single matter or by a small team, into a standard, repeatable part of the review and investigation workflow, with defined governance, training, and defensibility built in from the start. Legal teams that treat this as a deployment decision rather than an ongoing operational shift tend to end up back at pilot stage.
Why Legal AI Pilots Stall Before Production
The same structural issues that stall AI pilots elsewhere in the enterprise show up in legal teams specifically. A pilot is often run by a small group of enthusiastic early adopters, tested on a narrow use case, and never connected to the systems or workflows the rest of the team relies on daily. When the pilot ends, there is no clear path to rolling the tool out more broadly, no budget line for ongoing training, and no established way to measure whether it actually improved outcomes.
