The Best AI Tools for Legal Teams: Streamlining Practice and Improving Efficiency
Legal work has always been detail-heavy. Research, document review, contract analysis, and case management can consume enormous amounts of time. AI is changing that. For legal teams, the right tools can automate repetitive tasks, surface key information faster, and help lawyers focus on higher-value work.
If you’re evaluating the best AI tools for legal teams, the challenge is not whether AI is useful. It’s which tools fit your practice, workflow, and budget. This guide breaks down the leading options, what they do, and how to choose the right one.
Why AI Matters for Legal Teams
Legal departments and law firms are under constant pressure to deliver faster, control costs, and maintain quality. At the same time, the volume of legal data keeps growing across contracts, discovery materials, filings, and research.
AI tools help legal teams respond to that pressure by:
- Increasing efficiency by automating repetitive work such as document review and research
- Improving accuracy by processing large volumes of information quickly and consistently
- Surfacing deeper insights from documents, contracts, and case materials
- Reducing costs by limiting time spent on routine tasks
- Supporting risk management by identifying compliance issues and contractual concerns earlier
In short, the best AI tools for legal teams help legal professionals work faster and more effectively without replacing human judgment.
The Best AI Tools for Legal Teams
1. Kira Systems (now part of Litera)
What it does:
Kira Systems is an AI-powered contract analysis and review platform. It uses machine learning to identify, extract, and analyze clauses and provisions in legal documents. It can be trained to recognize specific information such as termination clauses, indemnity terms, or governing law.
Why it is useful:
Kira is especially valuable for legal teams handling large contract volumes. It speeds up due diligence, helps identify key risks and obligations, and reduces the amount of manual review required.
Best fit/use case:
- M&A due diligence
- Large-scale contract review
- Compliance checks
- Clause extraction across document sets
- Contract lifecycle management support
Pros:
- Strong clause identification and extraction
- Can be customized for specific needs
- Reduces manual review time
- Clear reporting and organized results
Cons:
- Can require training and setup
- Ongoing customization may be needed
- May be expensive for very small firms
2. Casetext (with CoCounsel)
What it does:
Casetext is a legal research platform with AI capabilities, including CoCounsel, an AI assistant that can draft documents, summarize depositions, analyze briefs, and support legal research.
Why it is useful:
CoCounsel helps lawyers move faster through early-stage work. It can draft a first version of a memo, summarize a court opinion, or help identify relevant case law more quickly than traditional manual workflows.
Best fit/use case:
- Drafting initial legal documents
- Summarizing complex materials
- Rapid legal research
- Early case assessment
- Identifying analogous cases
Pros:
- Combines drafting and research in one platform
- User-friendly interface
- Helps speed up routine legal work
- Continues to expand AI capabilities
Cons:
- AI outputs still require human review
- Some features may require higher-tier access
- Most useful for teams that draft and research frequently
3. Relativity Trace
What it does:
Relativity Trace is an AI-powered tool focused on identifying potentially privileged or confidential information in large e-discovery datasets. It uses natural language processing and machine learning to detect patterns and communication styles associated with sensitive content.
Why it is useful:
Privilege review is one of the most sensitive parts of litigation. Trace helps teams spot potentially privileged documents before they are produced, reducing the risk of waiver and streamlining review.
Best fit/use case:
- E-discovery
- Large-scale litigation
- Privilege review
- Internal investigations
- Confidential document screening
Pros:
- Focused on privilege and confidentiality detection
- Helps reduce legal risk
- Integrates with the broader Relativity platform
- Strong fit for complex discovery matters
Cons:
- Primarily built for e-discovery workflows
- Value is most apparent with large data volumes
- Part of a broader platform that may be complex to manage
4. Logikcull (now part of Relativity)
What it does:
Logikcull is an AI-enhanced e-discovery platform designed to simplify processing, review, and production of electronically stored information (ESI). Its AI features help reduce irrelevant data and automate parts of document review.
Why it is useful:
E-discovery is often one of the most expensive and time-consuming phases of litigation. Logikcull helps cut down the volume of material that needs human review, which can save both time and cost.
Best fit/use case:
- Large e-discovery projects
- Early data reduction
- Teams looking for a simpler discovery workflow
- Firms seeking a more intuitive e-discovery option
Pros:
- Streamlines discovery workflows
- Helps reduce review volume early
- User-friendly interface
- Can lower discovery costs
Cons:
- More focused on e-discovery than broader legal work
- May need to be paired with other tools
- Best suited to teams with recurring discovery needs
5. LawGeex
What it does:
LawGeex is an AI-powered contract review platform that automates review and approval of routine legal contracts. It compares agreements against a company’s playbooks and policies, flagging risks or deviations from standard terms.
Why it is useful:
For in-house teams handling many standard agreements, LawGeex helps move contracts through review faster and with more consistency. It is particularly useful for NDAs, SOWs, vendor agreements, and other routine documents.
Best fit/use case:
- High-volume contract review
- Corporate legal departments
- Sales contract approvals
- Compliance-driven contract workflows
Pros:
- Fast review of routine contracts
- Consistent policy-based analysis
- Reduces reliance on outside counsel for standard work
- Clear flagging of deviations
Cons:
- Best for standardized contracts
- Less suited to highly bespoke agreements
- Requires upfront setup of playbooks and risk rules
- Not designed as a general-purpose legal AI tool
6. Handle.ai
What it does:
Handle.ai is an AI platform designed to automate legal tasks such as document generation, legal text summarization, and research assistance. It aims to serve as a broad AI assistant for legal professionals.
Why it is useful:
Handle.ai offers multiple capabilities in one platform, which can make it useful for firms that want a flexible tool for drafting, summarizing, and research support.
Best fit/use case:
- Solo practitioners
- Small and mid-sized firms
- Legal teams looking for a general-purpose AI assistant
- Drafting and summarization workflows
Pros:
- Broad set of AI features
- User-friendly interface
- Can support many common legal tasks
- Suitable for teams wanting one flexible tool
Cons:
- Less specialized than dedicated tools
- May not match niche platforms in depth
- Outputs still need careful human review
How to Choose the Right AI Tool for Your Legal Team
The best AI tools for legal teams depend on your practice area, matter volume, budget, and workflow needs. A tool that works well for corporate contracts may not be the best fit for litigation or research-heavy work.
Here’s a practical way to narrow the options:
- For contract-heavy practices: Consider Kira Systems or LawGeex. Kira is strong for detailed analysis across large document sets, while LawGeex is better for standardized contract review.
- For litigation and e-discovery: Relativity Trace and Logikcull are strong choices for discovery workflows, privilege review, and data reduction.
- For research and drafting: Casetext with CoCounsel and Handle.ai can help speed up legal research, summaries, and first drafts.
- For solo practitioners and smaller firms: A flexible platform like Handle.ai or the AI features within Casetext may offer the best balance of cost and capability.
Before choosing a tool, evaluate:
1. Core pain points
What takes the most time today: research, drafting, review, or discovery?
2. Integration
Will the tool work with your document management system, practice management software, or e-discovery stack?
3. Ease of use
How much training will your team need to use it effectively?
4. Accuracy
Does the tool produce reliable results, and how easy is it to verify outputs?
5. Scalability
Can it support more users, larger matter volumes, or more complex use cases as your team grows?
6. Vendor support
Does the vendor provide onboarding, training, and ongoing product updates?
Pricing and Value Considerations
AI tools for legal teams can vary widely in cost. Some research tools may be available at relatively modest monthly prices, while enterprise contract analysis or e-discovery platforms may require much larger annual commitments.
When evaluating pricing, look at total value, not just subscription cost.
Consider:
- Time saved on repetitive work
- Lower risk of missing a clause, privilege issue, or compliance concern
- Higher throughput on contracts, research, or discovery
- Better staff satisfaction by reducing routine manual work
Many vendors offer pricing based on user count, feature tier, or data volume. A pilot or trial is often the best way to test whether a tool fits your team before making a longer commitment.
Frequently Asked Questions
Will AI replace lawyers?
No. AI is meant to support legal professionals, not replace them. It is best at automating repetitive tasks and processing data, while lawyers remain essential for judgment, strategy, negotiation, and client relationships.
How accurate are AI legal tools?
Accuracy varies by tool, data quality, and use case. Strong tools can be highly effective for tasks like clause identification and summarization, but human review is still necessary.
Are there data security concerns with legal AI tools?
Yes. Legal teams should review vendor security practices carefully, including encryption, storage, access controls, and privacy policies. Any tool must align with client confidentiality and ethical obligations.
How should a legal team train on AI tools?
Training should cover the tool’s strengths, best practices for prompts or inputs, and how to verify outputs. Vendor training resources, webinars, and internal workflows can help adoption.
Can AI support predictive analytics in law?
Some tools are beginning to do this. Predictive analytics may help with litigation forecasting, trend analysis, or regulatory monitoring, but this area is still evolving.
Conclusion
AI is becoming a practical part of modern legal work. The best AI tools for legal teams can reduce manual effort, improve consistency, and help lawyers spend more time on strategic work.
The right choice depends on your specific needs. Contract-heavy teams may benefit most from Kira Systems or LawGeex. Litigation teams may prefer Relativity Trace or Logikcull. Research and drafting teams may find value in Casetext with CoCounsel or Handle.ai.
Start with your biggest workflow bottlenecks, test the tools that fit those needs, and evaluate both performance and value. Used well, AI can make legal teams faster, more efficient, and better equipped to serve clients and stakeholders.