Why AI document review works best with humans side by side

AI-assisted review enhances discovery by surfacing insights, improving workflow efficiency and supporting legal teams’ final decisions.

In brief

• Generative AI (GenAI) tools have the potential to mitigate discovery costs and accelerate insights but can increase risk and costs when used without oversight.
• Discovery teams can streamline review processes using workflows that include human interaction with AI outputs and validation checkpoints.
• Combining AI and human review produces the best of both worlds: AI accelerates insight. Humans apply judgment, experience and context.

Legal teams are increasingly turning to AI-enhanced document review to support complex and expanding discovery obligations. AI tools can analyze large quantities of data within tight timelines and assist in identifying relevant and privileged information. However, organizations will struggle to achieve efficiency, accuracy and cost savings by relying on AI alone.

A big misconception in the market is that AI usage will automatically drive down data volumes, removing the need for human review and minimizing cost. In practice, the insights derived from AI are maximized with an integrated approach that makes human analysis part of the equation. To prevent gaps that could expose the organization to more risk, legal teams need to strategically create a methodology that integrates AI and human judgment.

What AI does well in document review

Discovery teams apply AI across a broad range of use cases, from workflow optimization to more advanced analytical applications. At one end of the spectrum, AI supports organizational efficiency by helping review teams identify, organize and analyze relevant information more quickly, while surfacing key issues, potentially privileged communications and sensitive content that may require redaction. These applications allow legal teams to focus time and attention where it is most impactful. At the other end of the spectrum, AI can be applied in more predictive ways to support substantive assessments that would traditionally be performed by human reviewers. Additional use cases include data set summarization, chronologies and privilege log creation. Together, these applications underscore that AI is not a single solution but a flexible set of capabilities that can be tailored to support different discovery objectives and risk tolerances.

Technologies such as technology assisted review (TAR) and advanced analytics platforms are well-established and accepted by courts. The evolution to large language models (LLMs) significantly improved analysis beyond prior keyword-focused methods. GenAI expands these capabilities by providing reasoning about why data received a given relevancy ranking or other substantive designation. However, there are limits. GenAI may hallucinate or fabricate fact patterns by creating plausible connections out of unrelated facts, creating a risk that critical matter details may be misunderstood or inaccurately described. When AI outputs are acted on without human review, these errors can have widespread impact on relevance and privilege decisions, increasing the potential likelihood of inconsistent coding, over-production or inadvertent disclosure.

Legal teams must thoughtfully assess risk tolerance and design the appropriate level of oversight in the process. The more robust and potentially riskier the AI use case, the more critical it becomes to employ human validation and quality control.

Why human review teams are essential in AI assisted review 

As organizations decide how to enable AI within discovery workflows, a critical strategic consideration is how best to maximize human insight and intentionally integrate human oversight throughout the process. AI delivers its greatest value when it is not deployed in isolation but instead embedded within a review framework designed to incorporate professional judgment, validation and iterative learning.

Review professionals play a central role in shaping effective AI outcomes. Their experience and subject matter knowledge inform how AI prompts are engineered and refined, helping verify that AI applications are aligned with case strategy, risk tolerance and discovery objectives. By reviewing and validating AI outputs, review teams provide essential quality control, identify gaps or inconsistencies and surface nuanced issues that automated analysis alone may miss. These insights enable continuous prompt refinement and improve the accuracy and reliability of AI-driven results.

Human review teams are also well positioned to collaborate closely with counsel, escalate substantive issues and identify emerging actors or themes that were not apparent at the outset of discovery. Through this iterative feedback loop, in which human judgment informs AI design and AI accelerates human insight, legal teams can respond more effectively to legal obligations, minimize unnecessary disclosures and maintain control over privileged information. Rather than treating AI as a static solution, mature discovery programs evolve AI workflows over time, validating that advancing technology is guided and amplified by experienced professionals rather than used as a substitute for defensible decision-making. As AI-based discovery tools continue to evolve, it increasingly benefits legal professionals to deepen their understanding of these technologies on an ongoing basis so they can leverage them to their full potential while maintaining a clear, well-tuned appraisal of associated risks and limitations.