The role of artificial intelligence in legal document review is changing the way legal teams think about review strategy. AI-assisted review is becoming part of standard eDiscovery and managed review workflows, particularly when large data volumes, tight deadlines, and pressure to control costs make traditional approaches more difficult to manage.
This does not mean attorneys are handing discovery decisions over to a machine. The most effective approach combines AI-enabled review technology with experienced attorneys, subject-matter expertise, thoughtful workflows, and measurable quality controls.
Anyone who has managed a large document review knows that communicating instructions to multiple reviewers is only the beginning. The bigger challenge can be making sure everyone applies those instructions consistently and identifying when something has been missed. AI-assisted review helps create greater consistency across the process, while giving legal teams more visibility and control without removing human judgment from the equation.
What Is AI-Assisted Document Review?
AI-assisted document review uses artificial intelligence to help identify, classify, prioritize, and analyze documents within an eDiscovery population. Rather than requiring attorneys to manually examine every document, AI evaluates documents against criteria established for a particular matter. This includes determining whether a document is relevant to litigation, surfacing potentially important evidence, identifying potentially privileged or work-product-protected material, or helping prioritize documents for attorney review.
Legal professionals still establish the objectives of the review, define the criteria, evaluate results, address ambiguous documents, and make decisions about privilege, responsiveness, production, and case strategy. AI simply becomes a more powerful tool in that process to leverage the legal professional’s understanding of the matter into the documents to find answers. Platforms such as Relativity aiR for Review are designed around this human-in-the-loop approach, providing document-level rationales and citations, while legal professionals develop instructions, evaluate predictions, and validate performance.
Why AI-Assisted Review is Becoming an Operational Decision
The economics of document review have always been influenced by volume. Reviewing thousands or millions of documents manually requires substantial attorney time, project management, quality control, and coordination.
AI changes the economics by allowing legal teams to focus human attention where it’s most valuable. An AI-assisted workflow helps narrow the population requiring extensive human review, identify potentially important documents earlier, and create a more consistent first-pass assessment. The result can be a review process that is more predictable from both a cost and scheduling perspective.
This is particularly relevant for small and midsize law firms and corporate legal departments. Large matters do not necessarily come with large budgets or unlimited attorney resources. A review strategy that can scale without simply adding more reviewers gives legal teams another way to manage demanding discovery obligations. By making sophisticated analytical capabilities more accessible, AI-assisted review also brings greater efficiency to smaller matters, helping teams manage timelines and costs while giving them greater flexibility when negotiating discovery issues with opposing counsel or addressing them with the court.
AI Doesn’t Replace Review Strategy
Effective AI review requires legal judgment before, during, and after the technology is applied. The first step is understanding the matter. What constitutes a responsive document? Which issues are relevant? Are there contextual distinctions that a simple keyword search might miss? What types of documents could create risk if overlooked? These questions inform the criteria provided to the AI system.
From there, the workflow should include testing and validation. For example, Relativity’s validation process allows teams to compare AI predictions with human coding on a sample of documents and calculate recall, precision, and elusion.
- Recall answers – Of the documents that truly matter, how many did the review process find?
- Precision answers – Of the documents identified as relevant, how many actually were relevant?
- Elusion looks at what was potentially missed – Particularly important in discovery where efficiency means little if significant responsive documents are overlooked.
Validation transforms AI-assisted review from a technology claim into a measurable process, giving legal teams a way to assess performance, identify potential gaps, and make informed decisions about whether the results are reliable enough for the matter.
Defensibility Matters
Efficiency is valuable, but legal teams cannot evaluate an eDiscovery workflow solely on how quickly it produces results. Discovery decisions need to be explained to opposing counsel (without disclosing legal strategy), to clients, courts, or other stakeholders. That makes documentation, quality control, and consistency important parts of an AI-assisted review strategy.
A defensible workflow should be able to explain how the review criteria were developed, how the technology was tested, what human oversight was involved, and how the results were evaluated. This is one reason experienced managed review teams remain important even as AI becomes more capable. Technology can perform large-scale analysis, but experienced professionals provide the context and judgment needed to determine whether the workflow is appropriate for the matter. In other words, AI doesn’t eliminate the need for review expertise. It increases the importance of using that expertise strategically.
The Numbers Are Starting to Tell the Story
Recent research illustrates why legal teams are taking AI-assisted review seriously. In a 2026 independent study conducted by Redgrave LLP, researchers compared Relativity aiR for Review with a traditional managed review workflow using active learning. The study involved approximately 45,000 documents and deliberately used a challenging responsiveness standard requiring nuanced legal judgment.
The aiR workflow required approximately 18 hours of attorney time, compared with approximately 1,123 hours for the active-learning (AI-enhanced prioritized review) managed review workflow. In the study, aiR achieved 88% recall, compared with 64% for active learning. (Meaning, aiR for Review found more of the documents the experts ultimately determined to be responsive.)
These results should not be interpreted as a universal promise that every AI-assisted review will produce the same savings or accuracy. Results depend on the data, review criteria, technology, workflow, and validation methodology. But what the study does demonstrate is much more important: AI-assisted review can be evaluated empirically rather than theoretically.
What an Effective AI-Assisted Review Workflow Looks Like
A successful workflow generally begins well before AI analyzes the full document population.
First, the legal team and review professionals establish the scope and objectives of the review. Next, AI criteria are developed and refined using representative documents. The results are then tested against human decisions (i.e., subject matter experts, or SMEs) to identify potential gaps or areas requiring adjustment.
Once the criteria are sufficiently reliable, the workflow can be applied at scale. Human reviewers and subject-matter experts remain intimately involved in quality control, reviewing borderline or important documents, and evaluating areas where AI and human decisions diverge. Finally, the results should be validated and documented. This requires a heavy lift by a SME to provide the “gold standard” coding against which the AI tool is measured.
This process may sound more involved than simply sending documents through an AI tool, but that’s precisely the point. Defensible AI-assisted review is a process, not an “easy” button. The technology dramatically reduces repetitive work, but the quality of the outcome depends on how the technology is incorporated into the broader eDiscovery strategy.
What This Means for Legal Teams
For organizations evaluating managed document review, AI should be considered alongside traditional review approaches rather than as an entirely separate category. Some matters may still call for substantial attorney review. Others may be well suited to an AI-assisted workflow. Even purely AI-based review workflows will involve documents not suitable for AI tools (no text or too much text), which may need to be manually reviewed in parallel with the AI review process. Many will benefit from a combination of approaches. The right approach depends on the nature of the data, the legal issues, the required level of review, deadlines, budget, and risk tolerance.
The larger shift is that AI-assisted review is no longer simply a conversation about emerging technology. It’s part of the operational discussion around eDiscovery costs, document review efficiency, quality, scalability, and defensibility. For legal teams, that creates an opportunity to rethink review economics without treating quality as something that has to be sacrificed for efficiency.
Is Your Review Strategy Ready for the Next Matter?
AI-assisted review is changing the economics of eDiscovery, but the technology is only part of the equation. The right combination of AI, experienced legal professionals, and defensible workflows can help organizations manage growing data volumes without sacrificing quality.
If you have an upcoming matter where review efficiency could make a meaningful difference, get in touch with our experts to discuss.
