Good. Fast. Cost-Effective. AI Makes All 3 Achievable.

How AI-Powered Review Is Redefining What’s Possible in eDiscovery

For decades, legal teams have lived by a simple truth: every document review project requires tradeoffs.  As the old saying goes, “Quality, speed, cost—pick two.” If you wanted a review completed quickly and inexpensively, quality often suffered. If quality and speed were the priority, costs escalated rapidly. If you wanted high-quality results at a lower cost, timelines inevitably stretched.

In the world of eDiscovery, this wasn’t merely conventional wisdom, it was reality. Today, however, advances in AI-assisted review are fundamentally changing that equation. While technology does not eliminate the need for legal judgment, modern AI-powered review workflows, combined with experienced legal teams, are enabling organizations to achieve something that once seemed impossible: higher quality outcomes, delivered faster, at a lower overall cost.

The result is not simply a better review process. It is a completely different economic model for document review.

Why the Tradeoff Existed

Traditional document review has always been constrained by a relatively straightforward formula: more documents require more reviewers, more reviewer hours result in more cost.

When deadlines were compressed, organizations typically added reviewers to accelerate throughput. While this increased speed, it also increased costs and often introduced additional quality-control challenges. Larger review teams naturally create more variability in coding decisions, privilege determinations, and issue identification.

At the same time, reviewer fatigue has long been one of the industry’s most persistent challenges. Human reviewers performing repetitive tasks across hundreds of thousands—or even millions—of documents inevitably encounter consistency issues over time.

The industry learned to manage these realities through quality-control processes, sampling, workflows, and experienced project management. Yet the underlying tradeoff remained. Quality, speed, and cost were interconnected variables. Improving one often came at the expense of another.

AI Changes the Underlying Economics

What makes today’s generation of AI-assisted review tools different is that they do not simply help reviewers work faster. They fundamentally change how review work is performed.

Rather than requiring every potentially relevant document to be reviewed in a linear fashion, AI can rapidly analyze large document populations, identify likely responsive content, surface potentially privileged information, summarize documents, categorize issues, and prioritize materials for human review.

This allows legal teams to focus attorney time where it delivers the greatest value.

Instead of reviewing everything equally, teams can review intelligently. Instead of allocating resources broadly, they can allocate them strategically. The result is a significant reduction in unnecessary human review effort while simultaneously increasing visibility into the data set earlier in the process. That is not merely an efficiency gain. It is an economic transformation.

Consider what this looks like in practice. Cimplifi was engaged by a telecommunications company to provide technology-enhanced review on a matter involving a contract dispute.  We deployed aiR for Review against 80,000 documents under a tight deadline. The result: 75% of the documents were bypassed entirely, the team achieved 94% recall[1] with a 1.13% elusion rate[2], and the client saved approximately $70,000 and three weeks of review time. In a separate privilege review engagement for a global law firm involving 852,000 documents, aiR for Privilege reduced attorney review to just over 100,000 documents, bypassing roughly 90% of the collection and delivering nearly $1 million in cost savings with more than 21,000 hours of review eliminated.

Why Human Expertise Matters More Than Ever

As AI adoption accelerates, one misconception continues to persist: that AI will replace legal reviewers. In reality, the most successful implementations are not replacing human expertise, they are amplifying it.

The legal industry increasingly recognizes that defensible outcomes require both advanced technology and experienced professionals. AI can identify patterns, classify content, and accelerate analysis at extraordinary scale. Human reviewers provide context, judgment, strategic insight, and accountability. The strongest workflows combine both.

AI performs the repetitive work that consumes time and budget. Experienced legal professionals validate results, make nuanced decisions, and ensure that review outcomes remain accurate and defensible. This human-in-the-loop approach is becoming increasingly important as clients shift their focus away from specific technologies and toward measurable outcomes.

The question is no longer, “What review workflow are you using?” The question is, “Can you deliver accurate, defensible results efficiently?”

The Shift from Process to Outcomes

Historically, legal teams evaluated document review projects by focusing on process metrics.

• How many reviewers are assigned?
• What review rate are they achieving?
• What technology-assisted review methodology is being used?
• How many documents remain?

Today, organizations are increasingly focused on outcomes.

• Can we identify key evidence faster?
• Can we reduce review costs?
• Can we make better strategic decisions earlier in the matter lifecycle?
• Can we achieve defensible results with greater consistency?

AI-assisted review helps answer each of these questions. By reducing the volume of documents requiring traditional review and accelerating insight into large data sets, organizations gain earlier visibility into risk, stronger budget predictability, and more efficient use of legal resources.

Independent validation is reinforcing this shift. Findings from the Redgrave Accuracy Study highlight two important trends:

• AI-assisted workflows can achieve high levels of accuracy and consistency when paired with human validation.
• Statistical validation techniques such as recall and elusion testing are critical to demonstrating defensibility and building stakeholder confidence.

These insights align with what we see in practice: earlier access to key documents, better-informed strategy, and more predictable outcomes.

The New Reality: Good, Fast, and Cost-Effective

Technology does not completely eliminate these tradeoffs. Legal matters vary in complexity, risk, and strategic importance. Yet organizations no longer need to accept the historic and rigid limitations that defined document review for decades. When AI-assisted review is combined with experienced project management, proven workflows, and appropriate human oversight, legal teams can achieve higher levels of quality, faster turnaround times, and lower overall review costs than previously thought possible.

In other words, the old rule no longer applies in the same way. For years, the eDiscovery industry operated under the assumption that you could only choose two: good, fast, or cost-effective. AI-assisted review is proving that, with the right technology and the right expertise, organizations can increasingly achieve all three.

The numbers tell the story. Across a class action QC engagement for a Fortune 500 client, Cimplifi narrowed a second-level review from 36,000 to 3,000 documents using aiR for Review. In that engagement, only 4% of prior manual coding decisions were changed after the initial AI review and critical “hot documents” were surfaced weeks ahead of schedule. In a smaller insurance litigation matter of just 14,000 documents, AI eliminated 70% of review hours and flagged fewer than 4% of documents as borderline, demonstrating that the economics in favor of using AI apply at every scale.

The future of document review is not about choosing between quality, speed, and cost. It is about redefining what is possible.

[1] Recall: The percentage of all truly responsive documents in a collection that were correctly identified as responsive. For example, a 94% recall rate means that 94 out of every 100 genuinely relevant documents were successfully retrieved. Recall is a primary metric for demonstrating the defensibility of AI-powered review workflows.

[2] Elusion Rate: The percentage of documents classified as non-responsive—and therefore bypassed or excluded from full human review—that are, upon sampling, found to actually be responsive. A low elusion rate (e.g., 1.13%) confirms that very few relevant documents “eluded” the review process and were incorrectly set aside, supporting the statistical defensibility of the review.

About the Author
Sashi Valavala leads the development of defensible AI and analytics solutions across the legal data lifecycle. With over 18 years of experience in eDiscovery analytics and consulting, he designs sound workflows for matters, including HSR Second Requests and complex litigation. Sashi brings a collaborative, practical approach to AI adoption and results.