How to Use AI for Discovery Review: A Practical Guide for Lawyers
Discovery review is one of the most time-consuming parts of litigation. Teams often have to sort through large volumes of emails, documents, messages, and other data to find relevant evidence, identify privilege issues, and avoid producing the wrong material. That process can be slow, expensive, and difficult to scale.
AI can help. Modern legal AI tools can speed up document review, reduce manual effort, and improve consistency across large datasets. Used well, AI does not replace legal judgment. It supports it by helping lawyers focus on the most important documents sooner.
This guide explains how to use AI for discovery review, what it can do, how to choose the right platform, and what to consider before adopting it in your practice.
Why AI Matters in Discovery Review
Traditional document review depends heavily on manual reading, tagging, and issue spotting. That creates several problems:
- Review teams spend hours on repetitive work
- Important documents can be missed
- Privileged or sensitive material can be overlooked
- Costs increase as document volumes grow
- Large cases become harder to manage efficiently
AI helps address these issues by automating repetitive parts of the workflow. Depending on the platform, it can sort documents, identify likely responsive files, group similar material, surface themes, and help reviewers prioritize the most relevant records.
For law firms and legal departments, the main advantages are:
- Lower review costs
- Faster turnaround times
- More consistent results
- Better risk control
- More time for legal analysis and strategy
How AI Is Used in Discovery Review
AI tools for discovery review typically support one or more of the following tasks:
- Technology-assisted review (TAR): Helps predict which documents are likely responsive based on sample coding
- Predictive coding: Uses reviewer input to rank or classify documents
- Clustering: Groups similar documents together to reveal patterns and themes
- Concept searching: Finds documents related to an idea, even when exact keywords are missing
- Early case assessment: Helps teams understand data volume, key custodians, and likely issues early in the matter
- Privilege and sensitivity detection: Flags material that may need closer review before production
In practice, this means reviewers can work from a more focused set of documents instead of starting with the full dataset in a purely manual way.
Best AI Tools for Discovery Review
The right platform depends on the size of the matter, the complexity of the data, and the needs of your team. Here are some widely used options.
1. RelativityOne
RelativityOne is a cloud-based eDiscovery platform with strong AI functionality, including TAR and Active Learning.
What it does:
- Analyzes large datasets for responsiveness and relevance
- Uses machine learning to improve predictions as reviewers code documents
- Supports full eDiscovery workflows from processing through production
Why it is useful:
- Offers a complete platform for large or complex matters
- Combines AI review tools with broader eDiscovery functionality
- Designed for teams that need scalability and control
Best fit:
- Large law firms
- Corporate legal departments
- Complex litigation with high document volumes
Pros:
- Highly scalable
- Strong AI capabilities
- Mature and widely adopted
- Extensive customization options
Cons:
- Can be complex to learn
- May require more training and setup
- Often a higher-cost option
2. Everlaw
Everlaw is a cloud-native eDiscovery platform known for its intuitive interface and collaboration features.
What it does:
- Uses clustering to group similar documents
- Supports predictive coding and concept searching
- Helps teams explore patterns and themes in review data
Why it is useful:
- Makes advanced AI tools easier to use
- Offers strong visual analytics and collaboration features
- Helps teams work efficiently across matters
Best fit:
- Mid-sized to large firms
- Legal teams that value collaboration and user experience
Pros:
- User-friendly interface
- Strong AI features
- Good collaboration tools
- Generally well suited to team-based review
Cons:
- Cloud-based only
- Some highly specialized needs may require other tools
3. DISCO AI
DISCO AI is a cloud-based discovery platform focused on speed and AI-driven review.
What it does:
- Ranks documents by likely relevance
- Helps identify privileged or sensitive material
- Culls irrelevant documents to reduce review volume
Why it is useful:
- Designed to surface key documents quickly
- Aims to reduce the amount of manual review required
- Useful for teams that want a streamlined workflow
Best fit:
- Law firms and legal departments of different sizes
- Matters where speed and simplicity matter
Pros:
- Fast processing
- Intuitive interface
- Strong focus on usability
- Good for finding key documents quickly
Cons:
- Cloud-based
- May not suit teams that want a desktop-based workflow
4. Logikcull, now part of Everlaw
Logikcull was known for making discovery review more accessible and easier to manage for smaller teams. Its functionality is now part of Everlaw.
What it did:
- Used machine learning to help categorize and cull documents
- Supported early case assessment and faster review
- Reduced the need for broad manual review
Why it was useful:
- Lowered the barrier to using AI in discovery
- Worked well for smaller firms and matters with tighter budgets
Best fit:
- Small to mid-sized firms
- Less complex matters
- Teams looking for a simpler entry point into AI review
Pros:
- Easy to use
- Practical for straightforward matters
- Cost-conscious approach
Cons:
- No longer a standalone product
- Its capabilities now live within a broader platform
5. ZDiscovery, now part of Exterro
ZDiscovery is an eDiscovery platform with AI-driven review and early case assessment features.
What it does:
- Helps teams assess data early in the case
- Uses predictive coding to identify relevant documents
- Supports analytics for understanding the scope and substance of a matter
Why it is useful:
- Helps legal teams evaluate data sooner
- Supports defensible review workflows
- Can reduce manual review effort in large matters
Best fit:
- Firms and legal departments that need a full eDiscovery platform
- Teams focused on early case assessment and defensible processes
Pros:
- Broad feature set
- Strong early case assessment tools
- Suitable for large datasets
- Supports structured review workflows
Cons:
- Can be complex
- May be more platform than smaller teams need
6. Luminance
Luminance is best known for contract review and due diligence, but it can also support certain discovery-related workflows, especially when the dataset is heavily document-based.
What it does:
- Interprets legal language in contracts and similar documents
- Identifies clauses, anomalies, and key information
- Helps review dense legal text efficiently
Why it is useful:
- Strong for matters that involve transactional documents
- Can accelerate review where contract analysis is central
Best fit:
- Firms or in-house teams handling contract-heavy matters
- Due diligence work that overlaps with discovery needs
Pros:
- Strong at reading legal language
- Good for document-specific review
- Can surface issues missed by keyword searches
Cons:
- More specialized than general eDiscovery platforms
- May not fit every discovery workflow
- Can be expensive
How to Choose the Right AI Tool
The best tool depends on your matter type, team size, and workflow. Before selecting a platform, consider the following:
Case volume and complexity
- Large, complex matters usually require a more robust platform
- Smaller matters may benefit from a simpler, more focused tool
Budget
- Pricing can vary widely depending on the vendor and deployment model
- Compare licensing, processing, storage, and support costs
Ease of use
- Some platforms are intuitive and require less training
- Others offer more power but come with a steeper learning curve
Feature priorities
- Do you need TAR, concept search, analytics, or privilege detection?
- Match the tool to the actual workflow, not just the feature list
Workflow integration
- Consider how the platform fits with your existing review process and tech stack
- Smooth integration can improve adoption and reduce disruption
Deployment model
- Many tools are cloud-based
- If you have security or regulatory requirements, confirm whether that model works for your team
Pricing and Value
AI discovery review tools should be evaluated on total value, not just sticker price.
Common pricing models include:
- Per gigabyte
- Per user
- Per matter
- Monthly or annual subscription
Other cost factors may include:
- Data processing fees
- Storage
- Training
- Support and implementation
The value of AI often comes from:
- Reduced review hours
- Faster matter timelines
- Lower risk of missed documents
- Better consistency across reviewers
- More time for strategic legal work
For many firms, the real benefit is not just lower cost. It is the ability to handle more data with greater control.
How to Use AI for Discovery Review Effectively
AI works best when it is used as part of a disciplined review process. A practical approach usually includes:
1. Define the review objective
Be clear about what the team is trying to find: responsive documents, privilege issues, key custodians, or case themes.
2. Prepare the data
Make sure the dataset is processed, organized, and filtered appropriately before review begins.
3. Train the system
If the platform uses TAR or predictive coding, reviewers should code a representative sample so the model can learn from it.
4. Review the highest-priority documents first
Use AI outputs to focus on likely responsive or likely privileged material before moving into broader review.
5. Validate the results
Check samples, compare outputs, and make sure the system is behaving as expected.
6. Keep human oversight in place
AI should support legal judgment, not replace it. Reviewers still need to handle edge cases, context, and final production decisions.
Frequently Asked Questions
Is AI for discovery review expensive?
It can be, but pricing varies widely. Smaller firms may find accessible options, while enterprise platforms may require a larger investment. The important question is whether the tool reduces enough review time and risk to justify the cost.
Do I need technical expertise to use it?
Usually not. Many legal AI platforms are designed for lawyers and paralegals, not data scientists. Some training may be needed, but the tools are generally built to be usable by legal teams.
How accurate is AI in discovery review?
AI can be highly effective at sorting, ranking, and classifying large volumes of documents. It is not perfect, and human oversight is still necessary, but it often improves consistency in repetitive review tasks.
Can AI replace human reviewers?
No. AI can reduce the amount of manual review, but lawyers still need to make judgment calls, manage privilege issues, and oversee production decisions.
What types of data can AI review?
AI can typically review emails, Word documents, PDFs, spreadsheets, chat messages, and other digitized records. Some platforms also support audio and video when those files are transcribed or otherwise processed.
How do I evaluate data privacy and security?
Ask about the vendor’s security controls, access management, and compliance posture. Confirm that the platform fits your firm’s requirements for confidentiality and data handling.
Conclusion
AI is changing how discovery review works. For law firms and legal departments, it offers a practical way to manage large data volumes, reduce manual effort, and improve review consistency.
The key is choosing the right tool and using it thoughtfully. Whether you need a full eDiscovery platform or a more targeted AI workflow, the best solution is the one that fits your matter size, budget, and internal processes.
If you are evaluating how to use AI for discovery review, focus on real workflow needs, not just vendor promises. With the right approach, AI can make discovery faster, more efficient, and more manageable for your legal team.