Why More Accurate (Gen)AI Doesn’t Always Reduce Total Document Review Costs

Comfortably Numb-ers Driven: How to Quantify Savings in Document Review

Most discussions about AI-powered document review focus on accuracy (precision and recall). The assumption is straightforward: if a model is more accurate, review volumes fall, and costs follow. Reality is more complicated. A more accurate model can be more expensive than a less accurate one, and that expense can outweigh savings from decreased review. In some matters, the approach that reviews the fewest documents is not the approach that delivers the greatest savings.

The reason is that document review costs are shaped by more than simply number of documents reviewed. Technology costs, subject-matter expert effort, corpus size, and richness all interact with review volume in ways that can fundamentally change the economics of a matter. This complexity creates a challenge for law departments and law firms evaluating AI-powered workflows. How should savings be measured? Which costs matter most? And how do you determine whether a new approach is genuinely better than the one already in use?

In this whitepaper, Jeremy Pickens presents a practical framework for answering those questions. Using quantitative examples drawn from real-world eDiscovery economics, he demonstrates why precision and recall tell onlya part of the story, and why the most economical approach often depends on the shape of the matter itself.

Download the full whitepaper to learn:

• The three primary cost drivers behind document review spend
• Why accuracy influences only one component of total cost
• How corpus size and richness affect economic outcomes
• When GenAI creates savings, and when it introduces additional cost
• Why baseline selection can dramatically alter savings calculations
• How to evaluate document review economics more accurately

The most effective approach is not determined by accuracy alone. It is determined by how a matter’s underlying cost structure responds to the technology being used.