At this point I think we can all agree that generative AI (GenAI) has moved beyond being a potential buzzword or a trend, but is actually shaping industries. And eDiscovery is no exception. In our industry, it is shaping how we approach review, analysis and production of electronically stored information (ESI).
But with the promise of speed and scale comes a fundamental question for review directors: how do we test, trust and thrive with this technology in service of defensible results? Below, I explore three dimensions of this shift: why trust is the core challenge, how to validate GenAI workflows both qualitatively and quantitatively, and what it takes to move beyond experimentation so teams truly thrive.
My hope is that this will serve as a practical roadmap for any legal operations team or law-firm review group navigating these tools.
At its heart, the adoption of GenAI in eDiscovery is about automation and speed, sure, but it also has to be about building trust. The technology may be impressive, but as front-line review professionals, we know our obligations remain: defensibility, transparency, and litigation readiness. That means not simply deploying an AI model and hoping for optimal results, but establishing frameworks to validate and document how the model is utilized.
For example, the International Legal Technology Association (ILTA) recently published a Generative AI Best Practice Guide that explicitly recognizes this gap in guidance for discovery workflows and the need for transparent, auditable AI usage.
The Guide underscores that while GenAI presents “significant opportunities,” it also introduces “unique challenges” for legal review and disclosure. Similarly, commentary from industry analysts emphasizes that GenAI may offer more “contextual understanding” than traditional TAR (technology-assisted review) models. However, it still lacks the decades-old precedent and reliability that those models enjoy.
When eDiscovery vendors adopt GenAI tools in managed review workflows, they should do so with the same discipline we apply to any review methodology, with meaningful documentation, audit readiness, and human judgment front and center. The technology supports the review; it does not replace it.
To operationalize trust, review workstreams must integrate both qualitative and quantitative validation techniques. Here’s how I am applying that in our managed review operations.
Qualitative validation: This is about human expertise meeting machine reasoning. In practice, it means that when a GenAI model classifies documents or provides a rationale (for example, summarizing or flagging a complex issue), our reviewers don’t just take the output at face value—they inspect it. They ask: “Why did the model classify this as responsive?” “What rationale did it provide?” “Which document citations support that classification?” “What considerations did the model suggest that might impact the document’s disposition?” The ILTA Guide, for instance, highlights the importance of audit trails, prompt logs, and transparency around how prompts, instructions, and models evolve.
In our managed review protocols, we build the review team’s workflow around that inspection: SMEs define the criteria, prompts are tracked, model outputs are annotated, and human reviewers validate the rationale, considerations, and citations. This creates a defensible path for review decisions that involve GenAI.
Quantitative validation: We apply the familiar metrics of recall, precision and elusion—but adapted for GenAI workflows. For instance, we may test a sample set of documents, track how many truly responsive documents the model catches (recall), how many of the flagged documents are actually responsive (precision), and how many responsive documents were missed entirely (elusion). Reports such as the 2025 eDiscovery Innovation Report show that GenAI adoption is accelerating (about 37% of eDiscovery professionals say they’re actively using GenAI) and that many legal teams are starting to measure time-savings and efficiency gains.
But measurement alone isn’t sufficient. The workflow needs to show not just good numbers, but consistent outcomes and defensible documentation of how the model behaves, how prompts evolve, and how human reviewers intervene.
By combining both qualitative and quantitative validation, we ensure review workflows that integrate GenAI meet the same defensibility standards we apply to any managed review. The technology becomes an amplifier of our human litigator-led oversight rather than a black box.
Testing and trust are foundational, but thriving means adapting how we work, how we staff, and how we deliver services.
For this:
Generative AI is a transformative force in the eDiscovery world. For a managed review director like me, the key question is how do we harness the technology to elevate what we do?
If you are part of any legal team, my invitation is this- don’t wait for GenAI to be “perfect.” Start building your validation frameworks now, invest in prompt-craft and human-AI teaming, and you’ll be well-positioned to thrive in this new era of eDiscovery.