AI Tools for Lawyers

Best Ai Tools For Legal Teams

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Best AI Tools for Legal Teams in 2024

Legal teams are using artificial intelligence to handle research, document review, contract analysis, and other time-intensive tasks more efficiently. These tools can process large volumes of information, identify patterns, flag potential risks, and provide useful starting points for legal work.

For law firms and in-house legal departments facing growing workloads, tighter budgets, and increasing expectations for speed, AI can improve productivity without replacing professional judgment. The right platform can reduce repetitive work, support better decision-making, and give lawyers more time for strategy, client service, and complex legal analysis.

Why AI Tools Matter for Modern Legal Teams

Legal work involves large volumes of documents and information, often under strict deadlines. Discovery files, contracts, regulations, case law, and internal records can take significant time to review manually.

AI tools help by:

  • Automating repetitive review and classification tasks
  • Finding relevant documents and legal authorities more quickly
  • Identifying key clauses and potential contract risks
  • Summarizing lengthy cases and documents
  • Supporting initial drafts of legal materials
  • Improving consistency across high-volume workflows
  • Reducing the time and cost associated with manual work

AI does not eliminate the need for lawyers. Instead, it helps legal professionals focus on tasks that require judgment, context, negotiation, and client communication.

The Best AI Tools for Legal Teams

The best tool depends on a team’s practice areas, workflow, budget, and technology environment. The following platforms cover major legal use cases, including eDiscovery, legal research, contract review, and contract lifecycle management.

1. RelativityOne: eDiscovery and Litigation Support

#### What it does

RelativityOne is a cloud-based eDiscovery platform designed to help legal teams identify, collect, review, and produce electronically stored information (ESI) for litigation and investigations.

Its AI-supported capabilities include technology-assisted review (TAR), document clustering, entity identification, and near-duplicate analysis. TAR uses machine learning to help predict document relevance and prioritize review.

#### Why it is useful

Discovery can consume a significant portion of a case’s budget and timeline. RelativityOne helps teams analyze large datasets more efficiently than manual review alone. By identifying potentially relevant documents earlier, it can support faster case preparation and provide useful insight during the early stages of a matter.

#### Best for

  • Large-scale litigation
  • Internal investigations
  • Regulatory and compliance reviews
  • Matters involving substantial volumes of ESI

#### Pros

  • Established TAR capabilities
  • Scalable cloud infrastructure
  • Extensive eDiscovery functionality
  • Security and compliance features
  • Integration options for broader legal workflows

#### Cons

  • Can require a significant learning curve
  • Pricing may be difficult for smaller firms
  • May require training and implementation support

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2. Casetext and CoCounsel: Legal Research and Drafting

#### What it does

Casetext is a legal research platform that uses AI to help lawyers locate relevant case law, statutes, and secondary sources. Its CoCounsel feature supports tasks such as legal research, document summarization, drafting, and legal analysis.

The platform is designed to help lawyers move from an initial question to a more structured research or drafting workflow.

#### Why it is useful

Legal research can be slow and difficult when a query involves complex facts or terminology. AI-supported search can help users locate relevant authorities and summarize lengthy materials more quickly.

Generative AI features can also produce initial drafts of briefs, motions, and other legal documents. These outputs should be treated as starting points that require careful review, editing, and verification by a qualified legal professional.

#### Best for

  • Legal research
  • Drafting pleadings and motions
  • Summarizing cases and legal documents
  • Generating initial legal analysis
  • Firms and departments that need research support across multiple practice areas

#### Pros

  • AI-supported legal search
  • Generative features for drafting and analysis
  • User-friendly workflow
  • Useful case and document summarization capabilities

#### Cons

  • AI-generated content requires thorough human verification
  • Advanced features may require additional training
  • Product features and workflows may continue to evolve

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3. LegalZoom AI: Contract Review and Management

#### What it does

LegalZoom has incorporated AI into parts of its legal services, including contract analysis and management. These tools can help identify key clauses, flag potential risks, compare terms, and support the creation of contract drafts based on user inputs.

#### Why it is useful

Contract review is a regular responsibility for many legal departments and transactional practices. AI-powered analysis can reduce the time required to review standard agreements, improve consistency, and identify terms that may require closer attention.

It can also support compliance with internal contract policies and help legal teams move routine agreements through the workflow more efficiently.

#### Best for

  • In-house legal teams managing standard contracts
  • Transactional law firms
  • Small and midsize businesses
  • Common commercial agreements and routine contract reviews

#### Pros

  • Accessible interface
  • Focus on common contract workflows
  • Integration with other LegalZoom services
  • Suitable for organizations seeking a more approachable entry point into legal technology

#### Cons

  • May provide less customization than specialized enterprise platforms
  • Best suited to more standardized contract work
  • May not meet the needs of highly complex or heavily negotiated agreements

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4. LexisNexis: Legal Research, Analytics, and Generative AI

#### What it does

LexisNexis offers a broad range of AI-supported legal tools. Lexis+ AI includes capabilities for legal research, document summarization, and drafting. The wider LexisNexis ecosystem also supports litigation analytics, legal workflow automation, and the analysis of legal trends.

#### Why it is useful

LexisNexis combines AI functionality with a large legal information database. This can help lawyers find relevant authorities, summarize legal materials, assess litigation information, and create initial document drafts more efficiently.

Its broader range of tools may also help legal teams use research, analytics, and drafting capabilities within a connected platform.

#### Best for

  • Law firms of different sizes
  • Corporate legal departments
  • Government legal teams
  • Advanced legal research and analytics
  • Teams seeking an integrated legal information platform

#### Pros

  • Extensive legal content
  • Research, analytics, and generative AI capabilities
  • Established platform and support resources
  • Broad range of legal workflow applications

#### Cons

  • Can require a substantial investment
  • The range of features may feel overwhelming
  • Teams may need time to integrate the platform into existing workflows

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5. Ironclad: AI-Powered Contract Lifecycle Management

#### What it does

Ironclad is a contract lifecycle management (CLM) platform that uses AI to support contract review and analysis. It can extract important data, categorize clauses, identify potential risks, and compare contract language with an organization’s approved playbooks.

The platform supports contracts from negotiation and approval through execution and ongoing management.

#### Why it is useful

Contract processes often involve multiple departments, manual approvals, and repeated reviews. Ironclad helps automate these steps so legal teams can identify important terms earlier, route agreements to the right stakeholders, and improve visibility into contract obligations.

This can support faster deal cycles, more consistent reviews, and better contract compliance.

#### Best for

  • Companies managing large volumes of commercial contracts
  • In-house legal departments
  • Sales and procurement teams
  • Organizations focused on contract risk management

#### Pros

  • Comprehensive CLM capabilities
  • AI-supported clause analysis and data extraction
  • Workflow and approval automation
  • Strong focus on compliance and risk management
  • Collaboration features for legal and business teams

#### Cons

  • Primarily focused on contract management
  • May require integrations for broader legal practice management
  • Can be expensive for very small organizations

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6. Everlaw: eDiscovery and Litigation

#### What it does

Everlaw is a cloud-based eDiscovery and litigation platform. It combines document review, case analysis, collaboration, advanced search, and AI-supported analytics to help legal teams work through large collections of electronic evidence.

Its AI-related capabilities include clustering, document analysis, and tools for identifying themes and connections within a dataset.

#### Why it is useful

Everlaw is designed to make eDiscovery more manageable for litigation teams. Its search and analytics features can help users locate important documents, understand case themes, and organize evidence more efficiently.

The platform’s collaboration features also support coordination among attorneys, litigation support staff, and other case participants.

#### Best for

  • Litigation teams
  • Complex cases involving large volumes of electronic evidence
  • Firms seeking cloud-based eDiscovery
  • Teams that prioritize collaboration and ease of use

#### Pros

  • Intuitive interface
  • Strong search and analytics capabilities
  • Collaboration tools
  • Cloud-native platform
  • Useful document review functionality

#### Cons

  • More focused on eDiscovery than general legal practice management
  • AI capabilities emphasize review and analytics rather than generative drafting
  • May require other tools for research, drafting, or contract management

How to Choose the Right AI Tool for Your Legal Team

There is no single best AI platform for every legal department or law firm. Use the following criteria to compare tools and identify the best fit.

1. Identify Your Main Workflow Problems

Start with the tasks that consume the most time or create the greatest risk. Common pain points include:

  • Manual document review
  • Slow legal research
  • Contract bottlenecks
  • Repetitive drafting
  • Inconsistent clause review
  • Difficulty managing large datasets

A clearly defined problem makes it easier to determine which AI capabilities matter most.

2. Review Your Existing Technology Stack

Consider how a new platform will work with your current systems, such as:

  • Document management software
  • Case management platforms
  • Contract repositories
  • Billing and financial systems
  • Collaboration tools
  • Knowledge management databases

Strong integrations can improve adoption and prevent lawyers from having to duplicate work across multiple systems.

3. Compare the Relevant AI Capabilities

Different legal tasks require different AI functions.

For eDiscovery, look for:

  • Technology-assisted review
  • Clustering
  • Concept search
  • Near-duplicate analysis
  • Early case assessment tools

For legal research, evaluate:

  • Natural-language search
  • Case and document summarization
  • Citation and authority tools
  • Legal analytics
  • Research workflow support

For contract review, consider:

  • Clause identification
  • Risk flagging
  • Data extraction
  • Playbook comparison
  • Approval and workflow automation

For drafting, review:

  • First-draft generation
  • Summarization
  • Document comparison
  • Identification of missing issues or arguments
  • Editing and revision support

4. Evaluate Usability and Training

A technically capable tool will have limited value if lawyers find it difficult to use. Evaluate the interface, onboarding process, available training, and quality of vendor support.

Ask whether the platform can be tested through a pilot program or demonstration using representative workflows.

5. Calculate the Potential ROI

Compare the expected cost with measurable improvements, such as:

  • Hours saved on manual review
  • Faster contract turnaround
  • Reduced outside counsel costs
  • More efficient discovery
  • Improved consistency
  • Increased matter capacity

Include implementation, training, integration, and ongoing support costs in the calculation.

6. Prioritize Security and Compliance

Legal teams handle confidential client information, privileged communications, personal data, and commercially sensitive documents. Before adopting an AI tool, review the provider’s:

  • Data storage and retention policies
  • Encryption practices
  • Access controls
  • Data-use policies
  • Compliance certifications
  • Subprocessor arrangements
  • Data residency options
  • Policies regarding the use of customer data to train models

Organizations should also consider relevant requirements such as GDPR, CCPA, professional conduct rules, and internal information security policies.

Pricing and Value Considerations

Legal AI platforms use a variety of pricing models. Some charge by user, feature tier, data volume, or usage. Others may bill based on the number of documents reviewed or the amount of AI processing required.

The lowest subscription price is not always the best value. A more expensive platform may deliver a stronger return if it substantially reduces manual work, accelerates case timelines, or lowers the risk of missed information.

When comparing vendors, consider:

  • User and matter limits
  • Data or document volume
  • Implementation fees
  • Integration costs
  • Training requirements
  • Support and service levels
  • Contract length and renewal terms
  • Costs for additional features or storage

Free trials, product demonstrations, and limited pilot programs can help a legal team test functionality before making a larger commitment.

Frequently Asked Questions

Can AI replace lawyers?

No. AI is intended to support legal professionals rather than replace them. It can automate repetitive tasks, analyze large datasets, and provide useful summaries or drafts. Lawyers remain responsible for applying legal judgment, verifying information, counseling clients, and making strategic decisions.

How can AI help with legal research?

AI legal research tools use natural-language processing and machine learning to interpret queries, identify relevant authorities, and summarize legal materials. They can help lawyers begin research more quickly and identify connections that may be difficult to find through basic keyword searches.

All research results should still be reviewed and verified by a lawyer, particularly when citations, quotations, or legal conclusions are involved.

What are the main data security concerns with legal AI?

The main concerns include unauthorized access, improper data retention, unclear data-use policies, and the exposure of confidential or privileged information. Legal teams should carefully review vendor security controls, encryption, access permissions, data retention terms, and whether customer data may be used to train AI models.

Is AI too complex for a small law firm?

Not necessarily. Many legal AI platforms are designed for different organization sizes and offer scalable features. Smaller firms may start with a focused tool for legal research, contract review, or document management rather than adopting a broad enterprise platform.

How can AI improve contract review?

AI can scan agreements for key clauses, identify deviations from standard language, extract important data points, and flag provisions that may require additional review. This can reduce manual effort, improve consistency, and support faster contract negotiations. AI-generated findings should always be reviewed by an appropriately qualified legal professional.

Conclusion

The best AI tools for legal teams can improve efficiency across eDiscovery, legal research, drafting, contract review, and contract lifecycle management. RelativityOne and Everlaw support litigation and document review, Casetext and LexisNexis assist with research and drafting, LegalZoom AI supports more accessible contract workflows, and Ironclad focuses on end-to-end contract management.

The right choice depends on a team’s specific workflow, budget, technology environment, and security requirements. By defining clear objectives, testing tools with realistic use cases, and maintaining human oversight, legal teams can adopt AI responsibly while improving productivity, service quality, and operational efficiency.

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