How to Use AI for Discovery Review: A Practical Guide for Lawyers
The legal landscape is changing quickly, and discovery review is one of the areas where AI can make the biggest difference. Emails, documents, chat logs, and other electronically stored information can pile up fast, turning review into a costly, time-consuming process.
If you’re evaluating how to use AI for discovery review, the goal is simple: reduce manual effort, surface relevant information faster, and support more consistent review decisions without losing human oversight.
Why AI Matters in Discovery Review
Discovery review is more than a document-sorting exercise. It affects case strategy, client costs, and deadlines. When AI is used well, legal teams can:
- Reduce manual review time
- Identify relevant documents faster
- Improve consistency across large review teams
- Spot patterns, themes, and relationships that keyword search may miss
- Free attorneys to focus on legal analysis and case strategy
AI is most effective as a support tool. It helps legal teams work more efficiently, but human judgment still matters for privilege calls, responsiveness decisions, and final legal review.
Best AI Tools for Discovery Review
Several eDiscovery platforms now include AI-powered features that can help streamline discovery review. The right choice depends on case size, budget, workflow, and the level of analytics your team needs.
1. RelativityOne
RelativityOne is a cloud-based eDiscovery platform with AI features built into the review workflow.
What it does:
- Uses Active Learning, also known as Technology Assisted Review (TAR)
- Supports conceptual search
- Groups similar documents through clustering
- Learns from reviewer decisions over time
Why it’s useful:
RelativityOne is designed to help reviewers prioritize the most relevant documents first, which can reduce the total volume of material that needs manual attention. Its search and clustering tools can also help teams uncover themes and connections across large datasets.
Best fit:
Law firms and legal departments handling complex litigation or large-scale discovery.
Pros:
- Mature, full-featured platform
- Strong AI and review capabilities
- Good scalability and security
- Broad integration ecosystem
Cons:
- Steeper learning curve
- Can be a significant investment
2. Everlaw
Everlaw is a cloud-native eDiscovery platform built with collaboration in mind.
What it does:
- Offers auto-categorization
- Includes predictive coding/TAR
- Supports conceptual search
- Provides analytics to identify patterns and trends
Why it’s useful:
Everlaw combines AI review tools with a user-friendly interface, making it easier for legal teams to collaborate while managing large document sets. It is especially useful when speed, usability, and team coordination matter.
Best fit:
Boutique firms, large firms, and in-house teams that want a collaborative and intuitive platform.
Pros:
- Easy to use
- Strong collaboration features
- Effective AI and analytics
- Transparent pricing
Cons:
- May not offer the same depth as some enterprise-focused platforms for highly specialized use cases
3. DISCO AI
DISCO offers a cloud-based eDiscovery platform with a strong focus on speed and AI-assisted review.
What it does:
- Uses AI for auto-categorization
- Supports legal hold automation
- Includes advanced search and TAR capabilities
- Helps identify key documents, concepts, and relationships
Why it’s useful:
DISCO is designed to reduce review volume and accelerate investigation workflows. Its AI tools are built to help teams find important information quickly in large, unstructured datasets.
Best fit:
Law firms and corporate legal teams that want a streamlined, AI-driven discovery platform.
Pros:
- Fast and efficient
- Strong AI capabilities
- Good for identifying themes and concepts
- User-friendly interface
Cons:
- Premium pricing
- Some advanced features may require training
4. Logikcull by Relativity
Logikcull is a cloud-based platform focused on making eDiscovery faster and easier, especially for smaller teams.
What it does:
- Supports fast data processing
- Uses AI for auto-categorization, clustering, and intelligent search
- Helps organize data for review quickly
Why it’s useful:
Logikcull is a practical option when you need to process and review data quickly without a heavy setup. It can be especially helpful for early case assessment and urgent matters.
Best fit:
Smaller firms, solos, or legal teams that need a simple and efficient discovery tool.
Pros:
- Fast processing
- Easy to use
- Cost-effective
- Useful for early case assessment
Cons:
- Less advanced than some enterprise-grade platforms
- Limited customization for complex matters
5. ZDiscovery by FTI Technology
ZDiscovery is an AI-powered platform built for large and complex discovery matters.
What it does:
- Uses predictive coding/TAR
- Supports conceptual search
- Includes topic modeling and intelligent clustering
- Helps identify relevant, privileged, and high-priority documents
Why it’s useful:
ZDiscovery is designed for high-volume review where accuracy, reporting, and analytics matter. It can help teams prioritize documents and gain a clearer view of the dataset.
Best fit:
Large law firms, corporate legal departments, and government agencies handling complex discovery.
Pros:
- Advanced AI and analytics
- Strong performance on large datasets
- Comprehensive reporting and audit trails
- Well suited to high-volume matters
Cons:
- Higher cost
- May be more than smaller teams need
- Can require more training
6. Luminance
Luminance is best known for contract review and due diligence, but its AI capabilities can also support certain discovery review tasks.
What it does:
- Reviews documents for key clauses and deviations
- Flags unusual language and potential risks
- Can be trained to identify specific document types or information
Why it’s useful:
Luminance is most effective when discovery review involves identifying specific language, terms, or document patterns across a large set of files.
Best fit:
Primarily due diligence and contract-heavy workflows, but also useful in some corporate discovery scenarios.
Pros:
- Strong for contract analysis
- Good at spotting anomalies
- Can be trained for targeted review tasks
Cons:
- Not a traditional litigation eDiscovery platform
- May need to be paired with other tools for full discovery workflows
How to Choose the Right AI Tool for Discovery Review
Choosing the right platform starts with the practical needs of your matter and your team.
1. Case complexity and data volume
For large datasets and complex litigation, platforms like RelativityOne and ZDiscovery are built for scale. For smaller matters or faster turnaround, Everlaw or Logikcull may be a better fit.
2. Budget and pricing model
AI tools vary widely in cost. Some use subscriptions, while others charge based on data volume, users, or processing. Look at total cost, including support, storage, and training.
3. Ease of use
If your team needs a short learning curve, a more intuitive platform may be the better choice. Ease of use matters when multiple reviewers need to work quickly and consistently.
4. AI features you actually need
Not every matter needs the same tools. Consider whether you need:
- TAR / Active Learning
- Conceptual search
- Clustering
- Topic modeling
- Auto-categorization
5. Workflow integration
The platform should fit into your existing process, including document management, case management, and review workflows.
6. Security and compliance
Discovery often involves sensitive information. Make sure the platform meets your security, confidentiality, and compliance requirements.
7. Vendor support
Strong onboarding, training, and support can make a major difference in adoption and long-term value.
Pricing and Value Considerations
AI can change the economics of discovery review by reducing manual work and helping teams move faster. When evaluating value, consider:
- Reduced attorney hours
- Faster case turnaround
- Better consistency in review decisions
- More flexible scaling for large or small matters
When comparing vendors, ask about:
- Subscription pricing
- Pay-as-you-go pricing
- Tiered pricing based on features or volume
- Storage, processing, and support fees
A demo or trial can be especially useful before committing.
Frequently Asked Questions
Can AI replace human reviewers in discovery?
No. AI supports review, but it does not replace legal judgment. Human reviewers are still needed for privilege decisions, strategy, and final validation.
How does Technology Assisted Review work?
TAR, also called Active Learning, uses reviewer feedback to train the system. As reviewers label documents, the model learns which materials are likely to be responsive and helps prioritize the rest.
Is AI for discovery review only useful for large firms?
No. While large firms adopted it early, many tools now support smaller firms, solos, and in-house teams.
How quickly can AI improve discovery review?
Results depend on the size and complexity of the matter, but many teams see meaningful time savings soon after implementation.
What kinds of data can AI review?
AI can review many forms of electronically stored information, including emails, documents, spreadsheets, presentations, chat logs, and some audio or video files if they are machine-readable or transcribed.
How do I know whether an AI tool is accurate?
Accuracy depends on the platform, the training process, and the quality of human oversight. Validation testing and review protocols are important.
Conclusion
AI is now a practical part of modern discovery review. Used correctly, it can reduce manual work, improve consistency, and help legal teams find relevant information faster.
If you are evaluating how to use AI for discovery review, focus on the needs of your matter, the strength of the platform’s AI features, and how well the tool fits your existing workflow. The right choice can make discovery faster, more manageable, and more cost-effective without sacrificing legal judgment.