How to Use AI for Discovery Review: A Practical Guide for Legal Teams
Legal discovery has changed. The volume of emails, documents, chats, and other electronically stored information can overwhelm even well-staffed teams. Manual review is slow, expensive, and difficult to scale. AI offers a more efficient way to review large data sets, identify relevant material, and support defensible legal workflows.
If you are evaluating how to use AI for discovery review, the key is to match the tool and workflow to your matter, your team, and your review objectives. Used well, AI can reduce manual effort, improve consistency, and help legal teams focus on strategy instead of document sorting.
Why AI Matters in Discovery Review
Discovery is the phase where parties exchange information relevant to litigation or investigations. Traditionally, that meant teams of paralegals and junior attorneys manually reviewing large document populations for relevance, privilege, and other issues.
That approach is still common, but it does not scale well in modern matters. AI can help by:
- identifying likely relevant documents
- grouping similar content together
- surfacing concepts instead of relying only on keyword search
- flagging potentially privileged or sensitive material
- prioritizing review based on human feedback
The practical benefit is not just speed. AI can help legal teams work more efficiently while maintaining a review process that is structured, traceable, and easier to defend.
How AI Supports Discovery Review
AI tools used in discovery typically rely on technologies such as natural language processing and machine learning. In practice, that means they can learn from reviewer decisions and use those patterns to sort and prioritize documents.
Common uses include:
- predictive coding or active learning
- concept clustering
- duplicate and near-duplicate detection
- entity and topic identification
- PII detection
- sentiment or issue tagging
- email threading and communication mapping
These capabilities are useful because they reduce the amount of material that needs to be reviewed manually. They also help reviewers understand patterns across large data sets more quickly.
Best AI Tools for Discovery Review
The right platform depends on matter size, budget, and workflow needs. Some tools are built for enterprise-scale litigation, while others are better for teams that want a simpler interface or managed support.
1. RelativityOne
What it does: RelativityOne is a cloud-based eDiscovery platform that combines processing, review, analytics, and production in one environment. It includes AI-powered features such as active learning, conceptual search, and text analytics.
Why it is useful: RelativityOne is built for large and complex matters. Its active learning tools help prioritize likely relevant documents based on reviewer input, which can significantly reduce time spent on linear review. It also offers strong security and compliance features.
Best fit/use case: Large law firms and corporate legal departments handling high-volume litigation, investigations, or multi-matter review work.
Pros:
- Highly scalable and flexible cloud platform
- Strong active learning and predictive coding tools
- Broad eDiscovery feature set
- Strong security and compliance options
- Integrates with other legal technology
Cons:
- Steeper learning curve for new users
- Higher cost than some specialized tools
- Works best with reliable infrastructure and internal support
2. DISCO AI
What it does: DISCO AI is an eDiscovery platform focused on faster, more intuitive review. It uses machine learning to help identify key documents, surface context, and reduce manual effort. Features include clustering, near-duplicate detection, and automated PII detection.
Why it is useful: DISCO AI is designed to be approachable while still offering strong AI support. It can help teams move through review more quickly by finding concepts and relationships, not just keywords.
Best fit/use case: Mid-sized law firms and legal departments that want a user-friendly AI-powered review platform.
Pros:
- Intuitive interface
- Fast processing and review workflows
- Strong AI-assisted document grouping and search
- Automated PII detection
- Emphasis on defensibility
Cons:
- Less customization than some enterprise platforms
- Fewer third-party integrations than some competitors
- Pricing can rise with data volume
3. Logikcull
What it does: Logikcull, now part of Relativity, was known for making cloud-based eDiscovery more accessible. It offered document review, organization, collaboration, and AI-assisted insights in a simpler interface.
Why it is useful: Logikcull was designed to reduce barriers to entry for legal teams that needed a practical eDiscovery solution without a heavy technical lift. Its strengths were ease of use and quick adoption.
Best fit/use case: Teams already using the Relativity ecosystem or looking for a simpler entry point into AI-assisted review within a broader platform.
Pros:
- User-friendly interface
- Historically strong for smaller teams
- Good for organizing large data sets
- Supports collaborative review
Cons:
- Standalone capabilities are now part of a larger platform
- May not offer the depth of advanced analytics found in some enterprise tools
4. Everlaw
What it does: Everlaw is a cloud-native eDiscovery platform focused on speed, collaboration, and intuitive review. It includes AI and machine learning features such as active learning, concept clustering, and natural language processing.
Why it is useful: Everlaw is well known for combining a modern interface with strong review functionality. Its AI tools are built into the workflow, which makes them easier to adopt during active matters.
Best fit/use case: Legal teams that want a modern, collaborative platform with strong AI-assisted review features.
Pros:
- Clean and intuitive user interface
- Strong active learning and clustering tools
- Good collaboration features
- Fast search and processing
- Secure cloud environment
Cons:
- Can be more expensive than entry-level tools
- Fewer integrations than some larger platforms
- Support depth may be less extensive than some enterprise providers
5. Casepoint
What it does: Casepoint is a cloud-based eDiscovery and legal document management platform that uses AI and analytics to improve review efficiency. It supports document processing, case management, review, clustering, and sentiment analysis.
Why it is useful: Casepoint is built for scale. It is well suited to matters involving large volumes of data and complex review requirements, where AI can help surface themes and connections across large collections.
Best fit/use case: Large corporations, government agencies, and law firms managing high-volume or sensitive matters.
Pros:
- Strong scalability for large data sets
- Broad eDiscovery functionality
- Useful AI analytics for theme identification
- Security and compliance features
- Good for complex, multi-matter environments
Cons:
- Interface may feel less intuitive than some competitors
- Implementation may require more planning
- Can be a larger investment for smaller organizations
6. Logiksolve
What it does: Logiksolve is a specialized eDiscovery and document review service provider that combines AI with human review support. It offers managed review services designed to help legal teams handle large review projects more efficiently.
Why it is useful: Not every team wants to build its own AI review workflow. For firms with limited internal bandwidth, a managed service can provide a practical way to use AI without investing in a full in-house platform.
Best fit/use case: Law firms and legal departments that need help with high-volume review, tight deadlines, or limited internal resources.
Pros:
- Combines AI with human oversight
- Useful for large-scale projects
- Reduces strain on internal teams
- Can speed up turnaround
- Practical for outsourced review needs
Cons:
- Less direct control than in-house review
- Requires trust in a third-party provider
- May offer less customization than owning the platform
How to Choose the Right AI Tool for Discovery Review
Choosing the right tool depends on your matter profile and internal capabilities. The best platform for one team may be a poor fit for another.
Consider these factors:
Firm size and resources
Large firms with dedicated support teams may prefer enterprise platforms like RelativityOne. Smaller firms may prioritize simplicity and cost efficiency. If your team does not have the capacity to manage technology internally, a managed service may be a better fit.
Volume and complexity of data
High-volume matters require tools that can scale efficiently. If your cases involve millions of documents or multiple data sources, platforms like Casepoint or RelativityOne may be more appropriate. For matters with nuanced subject matter, look for tools that go beyond keyword search and support concept-based review.
Technical skill level
Some tools are easy to adopt quickly. Others offer more power but require more training. Choose a platform that matches your team’s comfort level and available support.
Specific review needs
Think beyond general responsiveness review. Do you need PII detection, sentiment analysis, issue tagging, or collaboration features? The right tool should support the tasks that matter most in your workflow.
Integration with existing systems
Check whether the platform works well with your document management systems, case management tools, and other legal tech. Poor integration can create unnecessary friction.
Defensibility and transparency
A discovery workflow needs to be explainable. Look for tools with audit trails, reporting features, and human oversight options. The process should be documented and consistent enough to defend if challenged.
Budget and pricing model
Pricing structures vary. Some platforms charge by data volume, user seat, or license. Managed review services may use hourly or project-based pricing. Make sure you understand the total cost, including onboarding, support, and training.
Pricing and Value Considerations
AI tools for discovery review can range from affordable matter-based solutions to large enterprise platforms with significant annual costs. The right choice is not always the cheapest one.
When evaluating value, look at:
- Cost reduction: Less manual review can reduce overall eDiscovery spend.
- Time savings: Faster review can accelerate case preparation and response times.
- Accuracy and consistency: AI can help standardize review decisions and reduce missed documents.
- Team efficiency: Lawyers can spend more time on strategy and less time on repetitive review work.
Ask for a clear pricing proposal and confirm what is included. Storage, support, onboarding, and training may add to the total cost. In many matters, the time saved and the reduction in manual effort can justify the investment.
Best Practices for Using AI in Discovery Review
To get the most value from AI, use it as part of a structured review process rather than as a standalone shortcut.
A few practical best practices:
- Define the review objective before selecting the tool
- Start with a representative sample of documents
- Use human reviewers to train and validate the model
- Document your workflow and review criteria
- Monitor results and adjust as needed
- Keep privilege and confidentiality checks in place
- Make sure final review decisions remain human-led
AI works best when it supports legal judgment, not when it replaces it.
Frequently Asked Questions
Is AI capable of replacing human reviewers entirely in discovery?
No. AI is best used to support human review, not replace it. It can prioritize, organize, and classify documents, but legal judgment is still needed for privilege, nuance, and strategy.
How does AI help with defensibility?
Defensible review depends on transparency, documentation, and human oversight. Reputable tools provide audit logs, reporting, and workflows that show how review decisions were made.
What kinds of data can AI analyze?
AI can review emails, PDFs, Word documents, spreadsheets, presentations, chat logs, cloud files, social media content, and other unstructured digital data.
How is AI trained for discovery review?
Many tools use active learning or predictive coding. Reviewers label sample documents, and the system learns from those decisions to improve future classifications.
What are the most common implementation challenges?
Typical challenges include staff training, workflow changes, data migration, integration with existing systems, and concerns about security or accuracy.
Conclusion
AI is now a practical part of modern discovery review. For legal teams managing large data sets, it can improve speed, reduce manual work, and support more consistent review decisions.
The best approach is to choose a tool that fits your matter size, review goals, and internal workflow. Whether you need enterprise-scale analytics, a simpler user experience, or managed review support, the right AI solution can make discovery more efficient and more defensible.
For firms and legal departments evaluating how to use AI for discovery review, the opportunity is clear: better review workflows, lower friction, and more time focused on legal strategy.