Artificial intelligence (AI) has taken over legal technology conversations. Vendors promise faster reviews, deeper insights, and dramatic cost reductions, often positioning AI as a complete transformation of the eDiscovery process. While there is genuine innovation happening in the field, the reality is more nuanced.
For law firms and corporate legal teams, understanding what AI actually does in eDiscovery –and what it does not do – is critical to making informed decisions about discovery strategy, vendor partnerships, and technology investments.
The truth is that AI has been part of eDiscovery for more than a decade. What has changed recently is the sophistication of these tools and the increased attention around them. Today’s legal teams are navigating a market where genuine capability and marketing hype often exist side by side.
The Evolution of AI in eDiscovery
Long before generative AI became a headline topic, eDiscovery platforms were already using machine learning to improve document review and data analysis. Technologies such as technology-assisted review (TAR) and predictive coding have been accepted in courts for years and are widely used to accelerate large-scale document reviews.
These tools analyze reviewer decisions and learn to identify documents that are likely to be relevant to a case. Over time, the system becomes better at prioritizing important documents and filtering out irrelevant material, allowing legal teams to focus their time where it matters most.
Today’s AI-powered eDiscovery tools build on this foundation with additional capabilities such as:
- Recognizing types of privileged communications and understanding the parties that maintain or break privilege
- Relevance analysis with generative AI content supporting reasons for relevance
- Concept clustering that groups documents by topic
- Near-duplicate detection to eliminate repetitive review
- Email threading to organize communications into conversations
- Advanced analytics that identify patterns and relationships in data
When implemented correctly, these technologies can significantly improve efficiency without compromising the defensibility of the discovery process.
What AI Can Do in eDiscovery
Despite the hype surrounding artificial intelligence, most successful AI applications in eDiscovery are focused on improving how legal teams analyze and prioritize large volumes of information. Because at its core, eDiscovery is a data problem. Litigation and investigations frequently involve millions of documents, emails, and other electronically stored information (ESI). AI helps legal teams manage that scale by identifying patterns that would be impossible to detect manually.
Some of the most practical applications include:
- Faster document prioritization – AI-driven review workflows can identify documents that are most likely to be responsive or relevant early in the review process, providing generative content that far exceeds the work product of normal human attorney review. This allows attorneys to start building their case strategy sooner rather than waiting for a full dataset review.
- Improved review efficiency – By clustering related documents and detecting duplicates, AI reduces redundant review work. Review teams spend less time evaluating similar documents repeatedly and more time analyzing crucial content.
- Early case insight – Analytics tools can highlight communication patterns, emerging themes, and key custodians in the data. These insights are particularly valuable during early case assessment (ECA) when legal teams are evaluating risk and determining litigation strategy.
- More effective quality control – AI models can help identify inconsistencies in review decisions and flag documents that may require additional review, strengthening overall defensibility.
- Summarization of longer documents and transcripts – AI can generate effective summaries of longer documents and lengthy transcripted conversations, helping get to the root information quickly
What AI Cannot Replace
One of the most persistent myths about AI in eDiscovery is that it can replace legal expertise. In reality, effective discovery workflows still depend heavily on experienced attorneys and discovery professionals. AI systems do not understand legal strategy, privilege considerations, or the nuances of a particular case. Instead, they rely on human input to train models, validate results, and ensure that review decisions align with legal requirements.
Human expertise remains essential for:
- Defining relevance criteria and review protocols
- Training and validating predictive coding models
- Making privilege and responsiveness determinations
- Interpreting data patterns in the context of a case strategy
In other words, AI is most effective when it augments legal judgment, leveraging that judgement and decision-making into larger data sets, rather than attempting to replace it.
The Importance of Defensible AI Workflows
For law firms and corporate legal teams, the primary concern with any discovery technology is defensibility. Courts expect discovery processes to be transparent, repeatable, and well documented.
AI tools must therefore be implemented within a structured workflow that includes:
- Clear training methodologies
- Documented review protocols
- Validation and quality control procedures
- Audit trails showing how decisions were made
A responsible eDiscovery provider should be able to explain exactly how AI tools are used within the review process and how those tools support defensible discovery practices. Without this level of transparency, even the most advanced AI technology can introduce unnecessary risk.
Evaluating AI Claims from eDiscovery Vendors
Because “AI” has become a prominent marketing term, legal teams should approach vendor claims with a healthy level of scrutiny. Not all AI capabilities are equally mature, and some may provide limited value depending on the specific needs of a case.
When evaluating AI-driven eDiscovery services, it is helpful to ask practical questions such as:
- How is the AI model trained and validated?
- What was this particular AI tool developed to do, and is it being used for that purpose? (i.e., If the tool was optimized to understand the relationship between entities, are you using it to try to estimate damages, and will that even work?)
- What role do attorneys and discovery specialists play in the workflow?
- Can the vendor demonstrate defensible methodologies?
- How are results measured and quality-controlled?
- Does the technology integrate with the broader eDiscovery lifecycle?
These questions help separate meaningful innovation from superficial marketing language.
AI as a Tool, Not a Silver Bullet
Artificial intelligence has unquestionably improved the way legal teams approach large-scale discovery. When used appropriately, AI can reduce review time, uncover critical evidence earlier, and help legal teams make more informed decisions about their cases.
However, AI should be viewed as one component of a broader discovery strategy rather than a standalone solution. The most effective outcomes come from combining advanced technology with experienced discovery professionals who understand both the legal and technical complexities of modern litigation.
As the legal industry continues to adopt new AI capabilities, the focus should remain on practical value: improving efficiency, strengthening defensibility, and helping legal teams manage the growing scale of digital evidence.
For organizations navigating complex litigation or investigations, the key is not simply adopting AI – it’s ensuring that the technology is applied thoughtfully, transparently, and with expert oversight throughout the eDiscovery process.
To discuss Avalon’s use of AI in eDiscovery or to learn more about our litigation support services, contact our experts today.
