Artificial intelligence has quickly become part of the legal review conversation and is often positioned as a cure-all for the growing volume, cost, and complexity of document review. Yet for many legal teams, that enthusiasm is tempered by an equally important concern: legal risk.
The reality is that AI can be a powerful ally in eDiscovery—but only when its strengths and its AI limits are clearly understood. Knowing where AI adds value and where human review remains essential allows legal teams to adopt technology with confidence rather than caution.
AI excels at tasks that involve scale, repetition, and pattern recognition. In modern discovery, these capabilities are increasingly indispensable.
1. Managing volume and prioritization
Today’s matters often involve millions of documents across emails, chat platforms, and shared drives. AI can rapidly analyze large datasets to identify patterns, cluster similar documents, and prioritize likely relevant materials. This helps legal teams get to the most important documents faster, especially during early case assessment.
2. Improving efficiency without sacrificing oversight
Used correctly, AI reduces the number of documents requiring full manual review. By filtering out duplicative, low-value, or clearly non-responsive materials, AI allows attorneys and reviewers to focus their time where it matters most—on substantive analysis and strategy.
3. Enhancing consistency across review teams
Human reviewers vary in experience, speed, and interpretation. AI applies learned patterns consistently across a dataset, helping reduce variability and increasing overall review quality. This is particularly valuable in large, multi-reviewer matters where consistency is critical to defensibility.
4. Supporting smarter workflows
AI-driven analytics can reveal trends that might otherwise go unnoticed, such as communication patterns, issue hot spots, or time-based activity spikes. These insights support more informed decision-making and better alignment between review teams and case strategy.
Despite these advantages, AI has clear boundaries—and ignoring them introduces risk.
1. Understanding nuance and intent
AI can identify patterns, but it does not truly understand context, tone, or legal significance. Sarcasm, coded language, or subtle shifts in meaning often require human interpretation. Relying solely on AI in these situations increases the risk of missed or mischaracterized evidence.
2. Making legal judgments
AI does not practice law. Decisions about relevance, privilege, responsiveness, and risk tolerance ultimately require legal judgment. While AI can assist by surfacing likely candidates, final determinations must be made by experienced attorneys.
3. Adapting to evolving case strategy
Legal strategies change as facts emerge. AI models rely on historical inputs and training decisions. Without continuous oversight and recalibration, AI may lag behind strategic shifts—creating gaps or inconsistencies in review.
4. Eliminating the need for quality control
One of the most dangerous misconceptions is that AI reduces or eliminates the need for validation. In reality, rigorous sampling, testing, and human review are essential to ensure accuracy and defensibility. AI without quality control increases—not decreases—legal risk.
The most effective legal review strategies are not “AI-first” or “human-only.” They are balanced.
Human review provides legal reasoning, contextual understanding, and ethical judgment. AI provides speed, scale, and consistency. When combined thoughtfully, the two complement each other.
For example, AI can prioritize documents likely to be relevant, while attorneys focus on interpreting meaning, assessing risk, and making final calls. This hybrid approach allows legal teams to benefit from technology without surrendering control.
Understanding this balance also helps address skepticism among stakeholders. AI is not being asked to replace attorneys—it is being used to remove friction from the review process so legal professionals can apply their expertise more effectively.
Legal risk increases when AI is treated as a shortcut rather than a tool. Responsible adoption requires:
When these elements are in place, AI becomes a risk-reduction tool rather than a risk multiplier.
Technology alone does not determine outcomes—implementation does.
At Array, AI is embedded throughout the litigation support process to help legal teams work smarter, not just faster. By combining predictive coding, Continuous Active Learning (CAL), and generative AI with attorney oversight, we create review workflows that adapt to each matter’s priorities—whether that’s speed, cost control, or minimizing legal risk. Teams retain full control over how much AI is applied, ensuring that human judgment guides key decisions while technology handles repetitive, high-volume tasks.
With deep industry expertise and proven workflows, Array helps legal teams streamline document review, maintain defensibility, and gain clarity across complex data sets. The result is a review process that accelerates outcomes without sacrificing quality or confidence.
AI is neither a magic solution nor a threat to the legal profession. It is a tool—with strengths, limitations, and responsibilities.
Understanding where AI helps—and where it doesn’t—allows legal leaders to move beyond hype and hesitation. By respecting AI limits, prioritizing human review, and keeping legal risk front and center, legal teams can adopt AI in a way that is efficient, defensible, and aligned with the realities of modern eDiscovery.