How to Use AI for Due Diligence: Streamlining Legal and Business Investigations
Due diligence is a core part of major business and legal transactions. Whether you are acquiring a company, entering a strategic partnership, or reviewing an investment opportunity, you need a reliable way to assess risk, verify facts, and identify hidden issues.
Traditionally, due diligence has meant manual review of large document sets, which is slow, expensive, and prone to oversight. AI is changing that process by helping teams review documents faster, extract key information, and surface issues that deserve closer attention.
For lawyers, investors, and business teams, knowing how to use AI for due diligence is now a practical advantage. The right tools can improve speed, reduce repetitive work, and support better decision-making without replacing human judgment.
Why AI Matters in Due Diligence
Due diligence is only as strong as the quality and completeness of the review behind it. In complex transactions, teams often need to work through contracts, corporate records, financial statements, emails, litigation materials, and compliance documents. Manual review can create several problems:
- Time delays: Large document sets can take weeks or months to review.
- Human error: Fatigue and oversight can cause important issues to be missed.
- Limited coverage: Teams may not be able to review every document in depth.
- Higher costs: Manual review requires significant legal and business hours.
AI helps address these issues by automating repetitive tasks and highlighting patterns that may not be obvious during manual review. Used well, it can:
- Speed up document review and analysis
- Flag potentially problematic clauses or inconsistencies
- Process larger volumes of data more consistently
- Free lawyers and analysts to focus on strategic judgment
- Improve cost efficiency over the course of a transaction
AI is not a substitute for legal analysis, but it can make the review process more focused and manageable.
Best AI Tools for Due Diligence
Different AI tools serve different parts of the due diligence workflow. Some are built for contract analysis, while others are better suited to broad document review, financial data extraction, or risk screening.
1. Kira Systems
Kira Systems is a legal document review platform built for identifying and extracting clauses and key data from contracts and related documents.
What it does:
- Detects specific clauses and provisions
- Extracts data points such as dates, parties, and monetary values
- Organizes findings across large document sets
- Supports risk scoring and comparison workflows
Why it is useful:
Kira is especially helpful in M&A due diligence, where teams need to review large volumes of contracts, leases, and corporate agreements. It can quickly flag obligations, restrictions, revenue terms, and other issues that may affect the transaction.
Best fit:
- M&A due diligence
- Real estate transactions
- Compliance reviews
- Large-scale contract analysis
Pros:
- Strong focus on legal documents
- High-value clause detection
- Customizable for specific review needs
- Designed for legal teams
Cons:
- Can be expensive
- Requires setup and training
- Less flexible for non-legal document types
2. RelativityOne
RelativityOne is a broader e-discovery and legal data review platform with AI features that are useful in due diligence workflows.
What it does:
- Supports technology-assisted review
- Groups similar documents by concept
- Enables conceptual search
- Helps teams prioritize documents for review
Why it is useful:
RelativityOne works well when due diligence involves large, unstructured data sets, including emails, communications, and mixed document collections. It helps teams find relevant information more efficiently and prioritize what needs human attention.
Best fit:
- Complex M&A transactions
- High-volume investigations
- Cross-border reviews
- Litigation-adjacent due diligence
Pros:
- Scalable for large datasets
- Strong analytical features
- Security and compliance capabilities
- Integrates with legal workflows
Cons:
- Steeper learning curve
- Broader than contract review alone
- Can be costly for smaller teams
3. LegalSifter
LegalSifter focuses on contract review and helps teams identify risks, obligations, and non-standard terms more quickly.
What it does:
- Analyzes contracts for key risks
- Extracts important terms
- Summarizes obligations
- Compares language against playbooks or standards
Why it is useful:
For due diligence on a target’s contract portfolio, LegalSifter helps teams quickly identify unfavorable language, missing terms, and potential dispute areas. It is useful when you need a practical way to review contracts at scale without starting from scratch each time.
Best fit:
- Pre-acquisition contract review
- Vendor contract analysis
- Contract standardization
- Compliance issue spotting
Pros:
- Focused on contract risk
- Practical outputs
- Relatively easy to implement
- Can work with contract management systems
Cons:
- Less suited to broad document review
- May have limits in highly specialized legal frameworks
4. Eigen Technologies
Eigen is designed for extracting information from complex, unstructured documents, especially in financial and legal use cases.
What it does:
- Extracts data from legal and financial documents
- Handles unstructured and semi-structured formats
- Learns from user input to improve output quality
Why it is useful:
Eigen can support financial due diligence by automating the extraction of information from loan agreements, financial statements, covenants, and other dense documents. It is also useful where legal documents contain detailed financial terms that need to be reviewed consistently.
Best fit:
- Financial due diligence
- Loan portfolio analysis
- Regulatory review
- Complex document extraction tasks
Pros:
- Works well with complex documents
- Adaptable to different data types
- Strong extraction capabilities
- Can be trained for industry needs
Cons:
- May require more technical support
- Often positioned for enterprise use
- Not a general-purpose document management system
5. Seal Software, now part of DocuSign
Seal Software provides AI-powered contract analytics and clause management, now integrated into the broader DocuSign ecosystem.
What it does:
- Reviews and analyzes contracts
- Extracts key information
- Identifies risks, obligations, and opportunities across contract portfolios
Why it is useful:
In M&A and large contract reviews, Seal can help teams quickly identify clauses tied to liability, revenue, intellectual property, and compliance. That makes it useful for spotting issues that may require follow-up before closing.
Best fit:
- M&A due diligence
- Large contract portfolio review
- Contract compliance analysis
- Risk management
Pros:
- Strong contract risk detection
- Good for large-scale review
- Works within broader contract workflows
Cons:
- Less flexible outside contract analysis
- More focused on contract lifecycle use cases
6. Risk and compliance platforms with AI capabilities
Some due diligence workflows rely on broader risk and compliance platforms rather than a single contract review tool. These solutions use AI to analyze public records, litigation data, sanctions lists, adverse media, and ownership information.
What they do:
- Screen entities and individuals for risk
- Flag sanctions or adverse media
- Support KYC and AML checks
- Surface background information for transaction review
Why they are useful:
These tools are valuable for reputational due diligence, compliance screening, and preliminary background checks. They help teams identify risks tied to counterparties, owners, or related entities before a deal progresses.
Best fit:
- Reputational due diligence
- KYC and AML workflows
- Counterparty screening
- Preliminary risk checks
Pros:
- Broad risk coverage
- Useful for compliance-heavy work
- Access to large public and proprietary datasets
Cons:
- Often enterprise-focused
- Can be expensive
- Less suited to deep internal document review
How to Choose the Right AI Tool for Due Diligence
The best tool depends on the type of review you are running and the documents involved.
Consider the following factors:
- Scope of review: Are you focused on contracts, financial records, litigation files, or a mix of all three?
- Data volume: Large datasets may require a scalable platform like RelativityOne.
- Document complexity: Dense financial or legal documents may be better suited to tools like Eigen.
- Core capability needed: Decide whether you need clause extraction, risk scoring, clustering, search, or background screening.
- Workflow integration: Check how well the tool fits with your document management and legal tech stack.
- User skill level: Some platforms are designed for legal teams with limited technical support, while others require more setup.
- Budget and ROI: Compare cost against expected time savings, risk reduction, and efficiency gains.
- Customization: Make sure the platform can adapt to your review standards and transaction type.
In many cases, the most effective approach is to combine tools. For example, a team might use one platform for initial document triage, another for contract analysis, and human reviewers for final assessment and decision-making.
Pricing and Value Considerations
AI due diligence tools are priced in different ways, including:
- Subscription pricing: Common for ongoing use, often based on users, documents, or data volume
- Per-project pricing: Based on a specific review or transaction
- Tiered pricing: Different feature sets at different price points
- Enterprise pricing: Custom pricing for larger teams or organizations
When evaluating cost, do not focus only on the subscription fee. Consider the broader value:
- Reduced manual review time
- Lower risk of missed issues
- Faster transaction timelines
- Better allocation of legal and business resources
If possible, request demos or trial periods from multiple vendors. That gives you a better view of usability, workflow fit, and the likely return on investment.
How to Use AI for Due Diligence in Practice
A practical due diligence workflow usually follows a few core steps:
1. Define the review scope
Identify what you need to examine and what risk areas matter most. For example, you may focus on contracts, employment issues, litigation, or regulatory exposure.
2. Organize the document set
Gather documents into a clean, searchable repository. Good input organization improves the quality of AI output.
3. Choose the right AI tool
Match the platform to the task. Contract-heavy reviews may need clause extraction, while broader investigations may benefit from document clustering or search.
4. Set review criteria
Use playbooks, checklists, or issue lists to guide the AI and human reviewers toward the questions that matter.
5. Run the initial AI review
Use the tool to extract clauses, flag anomalies, group similar documents, or surface likely risk areas.
6. Validate the results
Have lawyers or experienced reviewers check the AI output. Human review remains essential for context, interpretation, and final judgment.
7. Document findings
Organize the issues into a clear summary that can support negotiation, diligence memos, or decision-making.
Common Use Cases for AI in Due Diligence
AI is especially useful in situations where the review volume is high or the issues are repetitive. Common use cases include:
- M&A contract review
- Lease and real estate diligence
- Financial covenant analysis
- Compliance screening
- Litigation and dispute review
- Vendor and procurement contract review
- Reputational and counterparty checks
Frequently Asked Questions About AI for Due Diligence
Can AI completely replace human lawyers in due diligence?
No. AI is best used as an assistive tool. It can speed up review and surface issues, but human lawyers are still needed for legal judgment, strategy, and interpretation.
How do you make sure AI results are accurate?
Use reputable tools, provide clean input data, and always validate key findings with a human reviewer. AI should support the review process, not replace verification.
What types of documents can AI analyze?
AI can review contracts, financial statements, emails, corporate records, legal filings, regulatory documents, and other unstructured or semi-structured files, depending on the platform.
How long does it take to implement an AI due diligence tool?
It depends on the platform. Some contract review tools can be implemented quickly, while larger systems with integrations and custom training may take longer.
Is AI due diligence cost-effective for smaller firms?
It can be, especially for focused use cases or smaller projects. Many vendors offer scalable pricing, but the value depends on your workflow, deal volume, and review needs.
What about data security and privacy?
Security should be a major factor when choosing a vendor. Review the provider’s controls, storage practices, certifications, and compliance with applicable privacy requirements before uploading sensitive data.
Conclusion
AI is becoming a practical part of modern due diligence. It helps teams review documents faster, identify risks more consistently, and reduce the burden of manual analysis.
The best results come from using AI as part of a structured process: define the scope, choose the right tool, validate the output, and keep human expertise at the center of the final decision.
For legal teams, investors, and business leaders, learning how to use AI for due diligence is less about replacing traditional review and more about making that review faster, more focused, and more effective.