How to Use AI for Due Diligence: A Practical Guide for Legal and Risk Teams
Due diligence requires fast, careful review of large volumes of information. Whether you are evaluating an acquisition, onboarding a vendor, screening a counterparty, or assessing litigation risk, the work often involves contracts, financial records, communications, public filings, and other documents that are difficult to review manually at scale.
AI can help streamline that process. The right tools can extract key terms, surface anomalies, organize large document sets, and reduce the time spent on repetitive review. Used well, AI does not replace human judgment, but it can make due diligence faster, more consistent, and more efficient.
Why AI Matters in Due Diligence
The stakes in due diligence are high. Missed clauses, incomplete reviews, or overlooked red flags can lead to financial loss, reputational harm, compliance issues, or deal failure. Traditional manual review is often slow, expensive, and vulnerable to fatigue-related errors.
AI changes the workflow by handling repetitive tasks and highlighting areas that need closer attention. It can help legal and business teams:
- identify non-standard contract terms
- flag inconsistencies across large document sets
- surface potentially privileged or sensitive materials
- screen third parties for adverse media or risk indicators
- summarize large volumes of source material for faster review
For mergers and acquisitions, that can mean faster deal assessment and better visibility into liabilities. For compliance teams, it can support stronger screening and monitoring. For litigation and investigations, it can help teams prioritize the most relevant documents sooner.
Best AI Tools for Due Diligence
The best tool depends on the type of due diligence you are conducting. Some platforms are built for contract analysis, while others focus on e-discovery, risk screening, or broader legal research.
1. Kira Systems (now part of Litera)
Kira specializes in machine learning-powered contract review and analysis. It extracts specific data points, clauses, and provisions from large sets of legal documents, including contracts, leases, and loan agreements.
Why it is useful: Kira is strong for M&A due diligence when teams need to identify clauses such as change of control, assignment, termination, or other deal-critical terms across many agreements.
Best fit: M&A contract review, real estate portfolios, intellectual property audits, and other high-volume document analysis.
Pros:
- strong structured data extraction
- intuitive for legal users
- established market presence
- useful reporting features
Cons:
- focused mainly on contract data
- may require other tools for broader due diligence workflows
- can be a significant investment
2. Relativity
Relativity is a comprehensive e-discovery and legal analytics platform with AI features such as Active Learning. It uses machine learning to categorize documents based on reviewer input and helps teams identify relevant and privileged material more quickly.
Why it is useful: In large investigations or regulatory matters, Relativity helps legal teams manage massive datasets and prioritize review more efficiently.
Best fit: Large-scale investigations, litigation support, regulatory reviews, and document-heavy due diligence.
Pros:
- scalable for large data volumes
- strong AI-driven document categorization
- integrates well with e-discovery workflows
- advanced analytics capabilities
Cons:
- broader platform can take time to learn
- not as specialized for contract clause extraction as dedicated contract tools
3. Diligent Insights
Diligent Insights provides AI-driven tools for risk and compliance, including background checks, adverse media screening, and third-party risk assessment. It analyzes news sources, legal databases, and public records to identify potential issues.
Why it is useful: It is especially helpful when vetting vendors, partners, or clients and when screening for AML and KYC purposes.
Best fit: Third-party risk management, supplier vetting, compliance screening, KYC/AML workflows, and reputational monitoring.
Pros:
- broad screening coverage
- real-time risk monitoring
- useful audit trails and reporting
- designed for compliance teams
Cons:
- less focused on deep contract review
- ongoing monitoring costs can add up
4. Eigen Technologies
Eigen uses AI and natural language processing to extract structured data from unstructured documents. It is built to read complex documents such as leases, loan agreements, and financial reports.
Why it is useful: For financial institutions and legal teams handling complex agreements, Eigen can automate extraction of critical information for review and analysis.
Best fit: Financial services due diligence, loan portfolios, regulatory reporting, and large real estate document sets.
Pros:
- strong NLP capabilities
- accurate data extraction
- customizable for specific document types
- handles varied document formats
Cons:
- needs setup and training for best results
- often requires enterprise-level integration
5. Casetext (CoCounsel)
CoCounsel is an AI legal assistant that supports research, document review, and drafting. In due diligence, it can help summarize documents, review large sets of materials, and identify relevant legal issues or authorities.
Why it is useful: CoCounsel can speed up the early stages of due diligence by helping legal teams organize findings and draft initial summaries.
Best fit: Research and initial document review for M&A, compliance, and litigation-related due diligence, especially for smaller firms or in-house teams.
Pros:
- broader legal capabilities beyond document review
- user-friendly interface
- can reduce research and drafting time
- generally accessible compared with some enterprise tools
Cons:
- less specialized for deep contract extraction
- capabilities continue to evolve
6. ThoughtTrace
ThoughtTrace uses AI, including NLP and machine learning, to analyze contracts and other business documents. It focuses on extracting key terms, identifying risks, and surfacing actionable insights.
Why it is useful: It can quickly identify non-standard or risky language across portfolios of agreements and help teams focus on the most important issues.
Best fit: M&A due diligence, real estate portfolio review, supply chain contracts, and intellectual property agreements.
Pros:
- strong contractual language analysis
- useful for finding risks and opportunities
- generates actionable outputs
- scalable for large volumes
Cons:
- mainly focused on text-based documents
- may need to be paired with other tools for broader workflows
7. Everlaw
Everlaw is a cloud-based e-discovery platform with AI features such as clustering, concept search, and predictive coding. These tools help teams find relevant documents and themes more efficiently.
Why it is useful: Everlaw is valuable when due diligence overlaps with litigation, internal investigations, or regulatory review and large amounts of electronic evidence need to be analyzed.
Best fit: Investigations, litigation support, compliance reviews, and other high-volume document review projects.
Pros:
- intuitive interface
- strong collaboration features
- secure, scalable cloud platform
- useful AI for relevance and theme identification
Cons:
- not as specialized for contract data extraction
- may need to be combined with other due diligence tools
How to Choose the Right AI Tool
The right tool depends on your workflow, data, and budget. Before selecting a platform, consider the following:
- Scope of due diligence: Are you focused on contracts, financial documents, screening, or e-discovery?
- Data types: Can the tool handle PDFs, Word files, scanned images, spreadsheets, and emails?
- Integration: Will it fit into your current document management, e-discovery, or compliance systems?
- Ease of use: Is the interface practical for lawyers, analysts, and reviewers?
- Accuracy and customization: Can the tool be trained on your terminology, risk criteria, or industry-specific language?
- Scalability: Can it handle the volume of data you expect?
- Vendor support: Does the vendor provide implementation help, training, and responsive support?
For example, if you need to identify problematic clauses across thousands of contracts, a dedicated contract analysis platform may be the best fit. If you need to review large email archives or investigation data, a broader e-discovery platform may be more appropriate.
Pricing and Value Considerations
AI tools for due diligence are typically sold through subscription or enterprise pricing models. Common structures include:
- Per-user or per-seat pricing
- Per-document or volume-based pricing
- Tiered subscriptions
- Project-based or enterprise licenses
When comparing tools, do not focus only on price. Look at the total value the tool can create:
- Time savings from faster review
- Lower manual review costs
- Reduced reliance on outside counsel for repetitive tasks
- Better risk detection
- Faster decision-making and deal execution
The right tool should fit your workflow and deliver measurable efficiency gains, not just new technology.
Practical Tips for Using AI in Due Diligence
To get the best results, AI should be used as part of a structured review process:
- Define the review objectives before uploading documents
- Clean and organize data where possible
- Use AI to prioritize issues, not to make final decisions alone
- Validate high-risk findings through human review
- Keep clear records of how outputs were reviewed and acted on
- Train teams on the tool’s strengths and limitations
AI is most effective when it supports legal judgment rather than replacing it.
Frequently Asked Questions
Can AI completely replace human lawyers in due diligence?
No. AI is best used to support lawyers and reviewers by automating repetitive work and surfacing issues faster. Human judgment is still needed for legal analysis, strategy, negotiation, and final decisions.
How accurate are AI tools for due diligence?
Accuracy depends on the tool, the quality of the data, and how well the system is configured. Many tools are highly effective for specific tasks, such as contract extraction or document prioritization, but human review is still important, especially in high-stakes matters.
What are the biggest challenges in using AI for due diligence?
Common challenges include data quality, system integration, implementation cost, training requirements, and privacy or security concerns.
Which industries benefit most from AI in due diligence?
Industries that handle large volumes of contracts and documents tend to benefit most, including finance, real estate, technology, pharmaceuticals, and legal services.
How do I protect data privacy and security?
Choose vendors with strong security controls, clear data handling policies, and relevant compliance practices. Confirm where data is stored and processed, and make sure the tool fits your privacy and regulatory obligations.
Conclusion
AI is now a practical part of due diligence for legal, compliance, and risk teams. It can reduce manual work, improve review speed, and help teams identify issues earlier in the process. The key is to choose the right tool for the task, integrate it into your existing workflow, and keep human oversight in place.
For organizations handling complex transactions, investigations, or third-party screening, learning how to use AI for due diligence can improve efficiency and strengthen risk management without sacrificing review quality.