By John Tredennick

A year ago, skeptics questioned whether AI could reliably review documents. Today, those skeptics are watching others finish reviews in weeks instead of months at a fraction of the cost. Contract reviewers who were fully booked for years are now scrambling for work. One major review provider recently told me that their traditional human review volumes dropped by half between 2023 and 2024.

The cause isn’t a slowdown in litigation. It’s the rapid rise of large language models that have become remarkably effective at exactly what document review requires: reading text, understanding meaning in context, and applying defined criteria consistently across millions of documents. And once you have one AI reviewer with these capabilities, you can deploy ten more, or a hundred, or a thousand—all equally competent, all working in parallel, all applying identical judgment to every document.

The question is no longer whether AI can handle document review. It’s whether you can afford not to use it.

Why Use AI for Document Review?

For readers who remain skeptical, consider what the evidence now shows. Today’s large language models deliver five compelling advantages that traditional human review cannot match:

1. Smarter: AI reviewers deliver elite-level intelligence and the ability to master complex review protocols that rival even the most sophisticated senior attorneys.

2. Tireless: AI reviewers never get tired, distracted, or just have an off day. Document 100,000 gets the same attention as document one.

3. Replicable: You can add hundreds of new reviewers in minutes, not days. Each immediately brings the same intelligence, attention, and training as the first.

4. Multilingual: AI reviewers read every language natively. No translation, no document segregation, no special language teams.

5. More Secure: AI reviewers can’t leak, share, or remember confidential documents—dramatically reducing the human security risk.

Let’s examine each advantage in detail:

1. Smarter: Elite Intelligence Applied to Every Document

Modern large language models can analyze documents with comprehension that matches or exceeds skilled legal reviewers. These systems understand context, recognize subtle distinctions between legal advice and business discussion, identify privilege indicators across complex communication chains, and apply nuanced criteria consistently.

This is specialized intelligence optimized for exactly what document review requires: reading text, understanding meaning in context, and applying defined criteria to make determinations. Within this domain, properly implemented AI operates at the level of an elite reviewer—the caliber who would command $2,000 per hour in traditional billing.

The comparison to traditional review teams deserves careful consideration. Most document review relies on contract attorneys who may have limited litigation experience, minimal training in privilege law, and varying levels of attention to detail. Even experienced reviewers make mistakes, miss nuances, and apply inconsistent judgment when faced with ambiguous documents.

Consider a complex privilege review involving communications among in-house counsel, outside counsel, business executives, and third-party consultants across multiple jurisdictions. Human reviewers must identify which participants have attorney roles, determine whether communications primarily seek or provide legal advice, and handle code-switching between legal and business discussions. Even trained attorneys struggle with this complexity.

AI review, when properly implemented, applies comprehensive analysis to every determination, evaluating all privilege indicators systematically and documenting reasoning for each decision. The system doesn’t work harder on complex documents and coast on simple ones—it applies the same rigorous analytical process to every document it encounters.

The sophistication advantage becomes particularly valuable in specialized reviews. For technical patent litigation, AI can comprehend complex specifications and identify relevant prior art. For financial fraud investigations, it can analyze accounting records and identify suspicious patterns across thousands of transactions. For regulatory compliance, it can apply intricate regulatory definitions consistently across massive document populations.

This elite-level intelligence doesn’t mean AI is infallible or should operate without oversight. Rather, it means the baseline analytical capability is exceptionally high. When combined with proper protocol development and quality control, AI review consistently produces results that meet or exceed human review standards.

2. Tireless: Unwavering Consistency Across Millions of Documents

Human reviewers inevitably experience attention drift and fatigue. A reviewer begins the day fresh and focused, carefully reading each document and thoughtfully applying review criteria. By afternoon, fatigue sets in. Attention wavers. By the end of an eight-hour session, documents receive less thorough analysis than those reviewed in the morning. Complex determinations get simplified. Edge cases get forced into clearer categories to maintain pace. Multiply this effect across days and weeks of review, and then again across teams of dozens or hundreds of reviewers. The result is systematic inconsistency that undermines review quality no matter how carefully the team is trained.

AI review eliminates this variability completely. The system applies exactly the same analytical process to document one million that it applied to document one. There is no fatigue, no distraction, no ‘off days’ where judgment suffers. The AI doesn’t get tired after reviewing technical specifications for eight hours. It doesn’t lose focus while reading the five-hundredth email chain about routine business operations.

This unwavering consistency has profound implications for review quality. Documents reviewed late in a project receive the same careful analysis as those reviewed early. Complex privilege determinations made at the end of review apply the same rigorous evaluation as those made at the beginning. Responsive documents buried deep in low-priority custodian files receive the same attention as those from key players.

The consistency advantage also manifests in how edge cases are handled. In traditional review, marginal documents—those in the gray zone between clearly responsive and clearly non-responsive—often get inconsistent treatment. Different reviewers make different judgment calls on similar facts. The same reviewer might reach different conclusions on similar documents reviewed days apart.

AI review applies identical standards to all edge cases. Documents presenting similar fact patterns receive identical analysis and determination. This consistency reduces both under-production (missing responsive documents) and over-production (incorrectly tagging non-responsive documents) while creating a clear, defensible record of how determinations were made.

The practical impact becomes most visible in large reviews where traditional teams might include hundreds of reviewers working across multiple shifts and locations. Maintaining consistency across such teams requires extensive training, ongoing calibration exercises, and constant supervision—yet inconsistency persists. With AI review, consistency is inherent in the technology. Once the protocol is validated, every instance applies it identically.

3. Replicable: A Thousand Identical Elite Reviewers

With AI review, you don’t deploy one elite reviewer. You deploy one hundred, one thousand, or however many instances are needed—and every single one is identical to the first.

Imagine attempting to replicate this with human reviewers. You would need to find one thousand attorneys who are all equally skilled, equally trained, equally focused, and who all apply judgment identically. This is impossible. Human reviewers bring individual backgrounds, experiences, and judgment that create variability even with extensive training and calibration.

Even if you could somehow find one thousand identically capable reviewers, you could not make them all available simultaneously. Recruiting takes weeks. Training takes more weeks. As the team scales, quality control becomes exponentially more difficult. Communication gaps emerge. Different team members develop different interpretations of protocol nuances. Consistency degrades as team size increases.

AI review operates without these constraints. Once a protocol is validated, deploying additional AI reviewers requires no recruiting, no training, no calibration, and introduces zero additional variability. The thousandth reviewer is identical to the first in every respect—same analytical capability, same application of criteria, same attention to detail, same reasoning process.

This perfect replication enables review strategies impossible with human teams. For urgent matters requiring rapid completion, platforms can deploy hundreds of AI reviewers working in parallel around the clock. These reviewers process documents continuously without shift changes, handoffs, or communication gaps. They maintain perfect consistency across time zones and work schedules. They scale instantly when timelines compress and scale down instantly when work completes, with no staffing logistics or personnel management.

The replication advantage extends beyond raw speed. It enables sophisticated quality control strategies that would be impractical with human teams. Because every AI reviewer applies criteria identically, spot-checking one reviewer’s work validates all reviewers’ work. Statistical sampling can occur at lower rates while maintaining high confidence because there is no reviewer-to-reviewer variability to account for. Quality control focuses on validating the protocol rather than monitoring individual reviewer performance.

This consistency also simplifies project management dramatically. Traditional large reviews require extensive infrastructure: reviewer coordinators, quality control teams, calibration specialists, and multiple layers of management to maintain quality as teams scale. AI review requires a small team of expert review managers who develop protocols, validate accuracy, and monitor results—but who don’t need to supervise hundreds of individual reviewers because the AI instances are self-consistent.

The economic implications are substantial. Traditional review costs scale linearly with document volume because each additional document requires additional human review time. AI review costs scale sub-linearly because the same validated protocol can analyze ten million documents as easily as one million, with only incremental increases in computational cost. The marginal cost of reviewing additional documents drops dramatically as volume increases.

4. Multilingual: Every Language, One Review Workflow

Cross-border investigations and international litigation typically involve documents in German, French, Mandarin, Portuguese, Japanese, Spanish, and dozens of other languages. Traditional multilingual review creates a logistical nightmare requiring either specialized multilingual reviewers (scarce and expensive) or translation of entire populations (costly, time-consuming, and prone to errors).

Both approaches carry substantial costs and delays. Specialized multilingual reviewers command premium rates and require weeks to recruit. Translation of large document sets costs tens or hundreds of thousands of dollars and consumes weeks of timeline. Even after translation, subtle meanings and context often get lost, leading to errors in responsiveness and privilege determinations.

Perhaps most problematic, traditional multilingual review creates separate workflows for each language. English documents go to one team, German documents to another, Mandarin documents to a third. Different teams apply different judgment to similar situations, creating inconsistency across the collection. Coordination becomes complex. Quality control requires language-specific oversight. Timeline extends as each language-specific workflow proceeds sequentially rather than simultaneously.

AI review eliminates these barriers entirely. Modern large language models can read documents in their original languages, assess responsiveness and privilege, and provide analysis in English—all without separate translation. The AI reads a German technical specification, applies English-language review criteria, and returns a clear English summary of why the document is or isn’t responsive. The same system reads Mandarin emails about pricing strategy and flags potential antitrust concerns—in a single workflow alongside English documents.

Consider a cross-border antitrust investigation with 500,000 documents in five languages. Traditional review would require substantial investment in translation (often $100,000 or more), specialized multilingual reviewers at premium rates, separate workflows creating coordination overhead, and an 8-12 week timeline.

AI multilingual review handles the same matter with no translation, a single workflow across all languages, simultaneous processing, and completion in 2-3 weeks—at roughly half the cost of traditional approaches.

This capability transforms multilingual review economics and logistics. What once required separate language teams, extensive translation, and sequential processing now operates as a unified workflow. Documents stay in their original languages. Analysis occurs simultaneously across all languages. Consistency improves because the same criteria apply uniformly rather than varying based on which language-specific team handles each document.

The capability also handles code-switching seamlessly—international communications that mix languages within single emails require multilingual human reviewers but pose no challenge for AI. For less common languages like Norwegian, Thai, or Polish, the advantage becomes even more pronounced: AI review treats all languages equally, without premium costs or extended timelines.

5. More Secure: Reducing the Human Risk

Traditional document review creates a security vulnerability that most organizations underestimate. Every review project sends confidential materials—trade secrets, privileged communications, financial records, personal information—to dozens or hundreds of contract attorneys, often working remotely through staffing agencies. Each reviewer represents a potential security breach.

The risk is inherent in the human element. Reviewers face financial pressures, may have connections to competitors or journalists, and work with thousands of confidential documents daily. Large review providers implement security measures—background checks, non-disclosure agreements, monitoring software—but these controls have limits. You cannot monitor what someone photographs with a personal phone or prevent them from memorizing key information.

The problem compounds with scale. A million-document review might involve three hundred reviewers across multiple locations over months. That’s three hundred individuals with access to your most sensitive materials, with vetting and monitoring becoming increasingly difficult as teams expand and turn over.

AI review dramatically reduces this human security exposure through three fundamental protections.

First, AI reviewers cannot retain information. Large language models process documents through a context window—the AI analyzes the document, generates its determination, and then that context is cleared before the next document. There is no memory bank storing what was reviewed. Each analysis is an isolated event, leaving no trace after completion.

Second, the AI has no motivations that could lead to disclosure. It cannot be bribed or coerced. It has no financial incentive to leak trade secrets, no connections to competitors, no personal grievances. The technology simply executes its analytical task with no capacity for unauthorized sharing.

Third, major AI providers operate under commercial contracts with explicit zero data retention and no-training commitments. Leading providers contractually guarantee that documents processed through enterprise APIs are not retained, not used for model training, and not accessible after processing completes. These are contractual obligations backed by billion-dollar commercial relationships.

Platform security still matters—documents require secure transmission, encrypted storage, and appropriate access controls. And human review managers still have access to documents during quality control. But AI review dramatically reduces the number of individuals who handle confidential materials, eliminating the large-scale human reviewer access that creates the most significant vulnerability.

The security advantage extends beyond intentional disclosure. Human reviewers make mistakes—accidentally producing privileged documents, forwarding materials to wrong recipients, discussing cases in public spaces. AI review reduces these errors because the system has no mechanism for disclosure beyond its programmed analytical function.

For matters involving trade secrets, competitive intelligence, or highly sensitive personal information, this security advantage can be determinative. The question isn’t whether AI provides adequate security. The question is whether traditional human review provides acceptable security in comparison.

What About Hallucinations?

Any discussion of AI in legal work must address hallucinations—the tendency of large language models to generate plausible-sounding content that is simply wrong. The lawyers sanctioned for citing non-existent cases generated by ChatGPT provide a cautionary tale that every legal professional should understand.

But document review and legal research are fundamentally different tasks, and the hallucination risks differ accordingly.

Hallucinations occur when LLMs generate information from training memory rather than from provided source material. When a lawyer asks ChatGPT to find cases supporting a legal argument, the system isn’t searching a database—it’s predicting what a helpful response should look like based on patterns learned during training. The result can be perfectly formatted citations to cases that don’t exist, complete with plausible party names, court identifiers, and holding descriptions.

Document review operates differently. The AI isn’t asked to recall information or generate citations from memory. It’s asked to read a specific document, apply defined criteria, and make a determination based solely on what’s in front of it. This is extraction and classification, not generation from training memory.

Well-designed review systems reinforce this distinction through explicit instructions. The AI is told—both in system-level prompts and in the review protocol itself—to base every determination exclusively on the document provided. The system cannot supplement with training knowledge. If the document doesn’t contain information needed to make a determination, the system must acknowledge that rather than inventing an answer.

Every AI determination includes documented reasoning tied directly to document content. When the system marks a document as responsive, it explains why based on specific content it identified. When it flags potential privilege, it points to the communications and participants that triggered that assessment. This reasoning creates an audit trail that makes fabrication immediately apparent—you can verify that the document actually contains what the AI says it contains.

The result is a workflow where hallucination risk approaches zero for the task at hand. We’re not asking the AI what the law says or requesting case citations. We’re asking it to read documents and apply consistent criteria—exactly the task where LLMs excel without the risks that plagued lawyers who misused them for legal research.

The Complete Package: Faster, Better, and Less Expensive

The five advantages examined in this article work together to transform document review economics and capabilities. Elite intelligence ensures sophisticated analysis on every document. Unwavering consistency eliminates the variability that plagues human review teams. Perfect replication enables instant scaling without quality degradation. Universal multilingual capability removes translation barriers and costs. And superior security reduces the risk of human disclosure.

Together, these advantages deliver results that traditional review simply cannot match. AI review completes million-document projects in weeks rather than months. It maintains accuracy that meets or exceeds human review standards. It handles documents in dozens of languages without separate workflows or translation delays. It reduces the security vulnerability of trusting confidential documents to large teams of unknown individuals. And it achieves all of this at a fraction of traditional review costs.

Traditional document review forced impossible choices. Deploy senior attorneys and watch costs spiral beyond budget. Use contract reviewers and accept inconsistent quality. Build large teams and struggle with coordination. Handle multilingual matters and face translation delays. Move fast and sacrifice thoroughness. Maintain security by trusting unknown reviewers with confidential materials.

AI review eliminates these compromises. Every document receives elite-level analysis. Perfect consistency applies across millions of documents. Hundreds of identical reviewers work in parallel around the clock. Documents in any language receive native analysis without translation. And confidential materials pass through far fewer human hands.

The transformation is already underway. What early adopters proved in 2024, hundreds of organizations are validating in 2025. The operational advantages align too perfectly with what clients demand: faster completion, comprehensive analysis, perfect consistency, and dramatically lower costs—all delivered more securely than traditional approaches.

The transition is inevitable not because the technology is perfect, but because it’s demonstrably superior across every dimension that matters in document review. The only question is timing—how quickly your organization begins developing the expertise and methodology needed to implement AI review responsibly and effectively.

The technology exists. The methodology is proven. The results are measurable and substantial. The question is no longer whether AI can handle document review. It’s whether you can afford not to use it.