Legal Document Review: AI’s Growing Role in Reducing Costs

Alvin Lang
Jul 08, 2026 15:49

Legal document review remains costly, consuming up to 73% of eDiscovery budgets. AI tools like Harvey may transform efficiency and accuracy.

The legal document review process, a core but costly phase of litigation, is undergoing significant transformation as AI tools like Harvey gain traction. Often consuming up to 73% of eDiscovery budgets, document review is where discovery costs, deadlines, and privilege risks intersect. With hundreds of thousands of electronically stored information (ESI) items to parse, the pressure to deliver accurate, defensible results under tight timelines is immense.

Traditionally, document review follows a structured sequence: collection handoff, processing and culling, first-pass relevance review, issue and privilege coding, quality control, and final production. Each stage narrows the focus from vast collections of ESI to the key documents needed for litigation or investigation. However, inefficiencies—such as over-collection, inconsistent coding, or weak privilege screening—can significantly inflate costs or expose firms to privilege waivers and sanctions.

The Cost Problem in Document Review

According to LegalClarity (April 2026), document review accounts for 60–70% of total eDiscovery costs, with a significant portion of the budget consumed during first-pass review. This phase, where attorneys determine relevance and responsiveness, is the most labor-intensive. Cost overruns are common, with a May 2026 study by Reveal’s Onna reporting that collaboration data alone drains an additional 26 hours per matter.

Privilege screening adds another layer of complexity. A single error—such as producing an email containing attorney-client advice—can lead to privilege waivers and costly disputes. Federal courts permit clawback provisions under Rule 502(d) to protect against inadvertent disclosures, but reasonable efforts to prevent errors remain a baseline expectation, as courts scrutinize review processes for defensibility.

AI Tools Driving Efficiency

This is where AI tools like Harvey enter the equation. AI-integrated platforms are reshaping document review by automating repetitive tasks and enhancing accuracy. Technology-assisted review (TAR) has been in use for over a decade, helping legal teams prioritize relevant documents using predictive coding. Now, generative AI tools like Harvey take this a step further by analyzing documents for meaning, extracting obligations, and identifying privilege risks at scale.

For instance, Harvey organizes results into verified review tables, enabling teams to track decisions and surface inconsistencies earlier in the process. Importantly, these tools don’t replace human reviewers; instead, they speed the first-pass review and provide defensible outputs that attorneys oversee. This blend of AI and attorney judgment ensures compliance with ethical standards and court expectations.

Practical Implications for Legal Teams

Firms adopting AI tools can expect reduced review timelines, lower costs, and improved accuracy. For example, in government investigations, such as DOJ antitrust cases, agencies increasingly expect TAR and AI to meet tight production deadlines. As of May 2026, the DOJ’s reported use of AI raises the bar for private-sector responders, signaling a broader market shift toward advanced tools.

Beyond litigation, AI aids other matter types, including M&A due diligence and internal investigations. In due diligence, where speed is critical, AI helps surface risks like change-of-control clauses or indemnities faster. In investigations, iterative AI-assisted review can uncover facts underprivilege, providing thorough yet confidential insights for boards or regulators.

Best Practices for AI-Driven Workflows

To fully leverage AI, firms must integrate it into a robust review workflow. Key steps include:

• Drafting a detailed review protocol to standardize coding and privilege criteria.
• Using shared calibration samples to align reviewer decisions early.
• Sampling and auditing AI outputs for quality control at regular intervals.
• Maintaining a current privilege log to track sensitive documents in real-time.
• Monitoring throughput and coding trends to identify drift before deadlines.

AI tools like Harvey don’t just speed up document review—they also make it more defensible. With courts increasingly scrutinizing review processes, documented workflows and clear privilege protocols are no longer optional. Integrating AI ensures compliance while reducing costs, making it a must-have in the modern legal toolkit.

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