How to Use AI for Due Diligence: Streamline Your Investigations
Due diligence is a core part of transactions, compliance reviews, and risk assessment. Whether you are working on a merger, acquisition, investment, or regulatory review, the process usually requires analyzing large volumes of contracts, financial statements, filings, emails, and other records to identify risks and confirm key facts.
Traditionally, due diligence has relied on manual review, which can be slow, expensive, and prone to oversight. AI is changing that. By helping teams process documents faster, surface relevant issues, and organize information more efficiently, AI is becoming a practical tool for legal, financial, and business professionals.
This guide explains how to use AI for due diligence, which tools are commonly used, how to choose the right solution, and what to consider before adopting one.
Why AI Is Changing Due Diligence
AI is useful in due diligence because it can handle large document sets at a speed and scale that manual review cannot match. It can also help teams spot patterns, extract key terms, and flag potential issues more consistently.
Key benefits include:
- Speed and efficiency: AI can review thousands of documents far faster than manual workflows.
- Improved consistency: Automated review can reduce missed issues and repeated work.
- Better issue spotting: AI can help identify unusual clauses, missing terms, inconsistencies, and potential red flags.
- Lower review burden: Teams can focus on analysis and decision-making instead of basic document sorting.
- Stronger workflow support: AI can organize materials and make it easier to prioritize what needs human attention.
AI does not replace professional judgment, but it can make due diligence work more manageable and more effective.
How to Use AI for Due Diligence
A practical AI due diligence workflow usually follows these steps:
1. Define the scope
Start by identifying what you need to review. The scope may include contracts, financial records, regulatory filings, emails, litigation history, or operational documents. A narrow scope calls for different tools than a broad, multi-source review.
2. Organize and upload documents
AI tools work best when documents are cleanly organized. Group files by category, date, entity, or topic where possible. This makes it easier for the system to classify content and extract relevant information.
3. Run document analysis
Use AI to extract clauses, identify obligations, detect anomalies, or sort documents by relevance. In contract-heavy reviews, this can significantly reduce the time spent on first-pass review.
4. Review flagged items
AI output should be treated as a review layer, not a final answer. Human reviewers should verify extracted data, assess context, and confirm whether flagged items are material.
5. Summarize findings
Use AI-assisted summaries to help consolidate findings across documents. This can be useful for issue lists, deal notes, and reports for internal stakeholders or clients.
6. Finalize with human judgment
The final due diligence decision should always reflect professional analysis. AI can support the process, but legal and business teams must interpret the results.
Leading AI Tools for Due Diligence
The best tool depends on the type of due diligence you are performing. Some platforms are built for contract analysis, while others are stronger in e-discovery, legal research, or financial review.
Kira Systems, now part of Litera
Kira is a contract analysis platform that uses machine learning to identify and extract data from legal documents. It is commonly used in M&A due diligence to review large sets of agreements for important provisions.
Best for:
- Contract review
- M&A due diligence
- Portfolio analysis
- Compliance-related document review
Strengths:
- Strong at clause extraction
- Useful for complex legal language
- Customizable for specific data points
Limitations:
- Focused mainly on contract analysis
- May need to be paired with other tools for broader reviews
Anticipate, formerly Hyperion, now part of HighRadius
Anticipate is focused on financial due diligence and helps analyze financial statements, identify anomalies, and assess risk patterns.
Best for:
- Financial review
- Credit risk assessment
- Investment analysis
Strengths:
- Useful for financial data processing
- Helps detect irregularities
- Can support faster review of financial records
Limitations:
- Less suited for contract-heavy work
- More focused on financial than legal analysis
Leverton, now part of Exterro
Leverton is an AI platform for extracting and analyzing information from legal and transactional documents. It is often used in due diligence workflows where teams need to review large numbers of contracts and related records quickly.
Best for:
- Transaction due diligence
- Portfolio management
- Regulatory compliance
Strengths:
- Good at extracting structured data from documents
- Scales well across large document sets
- Useful for high-volume review
Limitations:
- Primarily a document analysis platform
- May require integration for broader workflows
Disco AI
Disco AI uses natural language processing and machine learning to analyze large document collections. While often associated with litigation, it can also support due diligence by helping teams identify relevant materials and organize large data sets.
Best for:
- Broad document review
- Internal communications
- Identifying relationships across documents
Strengths:
- Strong contextual document analysis
- Useful for large, unstructured collections
- Helpful for issue spotting across many sources
Limitations:
- Less specialized for contract clause extraction
- May require more setup for targeted due diligence tasks
Casetext, with CARA AI
Casetext is a legal research platform that uses AI to improve search and case-law analysis. In due diligence, it can help teams research legal issues, regulatory frameworks, and litigation risk.
Best for:
- Legal research
- Regulatory analysis
- Litigation risk assessment
Strengths:
- Helps with legal context and precedent research
- Improves search efficiency
- Useful for issue-driven due diligence
Limitations:
- Not a primary contract review tool
- Better suited to research than document extraction
Everlaw
Everlaw is an e-discovery platform with AI features for document review and organization. It can help teams work through large sets of emails, internal communications, and operational records during due diligence.
Best for:
- Large-scale document review
- Email and communication analysis
- Reviewing unstructured data
Strengths:
- Good for broad review workflows
- User-friendly for review teams
- Strong sorting and coding capabilities
Limitations:
- Less specialized for contract clause extraction
- Better for review and organization than deep contract analytics
Seal Software, now part of DocuSign
Seal Software focuses on contract analytics and contract management. It can review agreements to extract obligations, identify risks, and surface key terms that may affect a transaction.
Best for:
- Contract review
- Pre- and post-closing analysis
- Contract compliance
Strengths:
- Strong contract analytics
- Helps identify obligations and risks
- Works well with contract lifecycle workflows
Limitations:
- Primarily focused on contract documents
- May need support from other tools for broader diligence work
How to Choose the Right AI Tool
The right tool depends on your workflow, document types, and review priorities. Consider the following before choosing a platform:
Scope of review
Determine whether the project is centered on contracts, financials, legal research, or a broader mix of documents. Contract-focused tools and research tools serve different purposes.
Document volume
If the deal involves a large number of documents, scalability matters. Make sure the platform can handle the expected volume without slowing down the workflow.
Type of data extraction
Some tools are designed to extract specific clauses or fields, while others are better at categorizing documents or identifying risks. Choose a platform that matches your review requirements.
Integration needs
Consider whether the tool needs to work with your existing document management, CRM, ERP, or e-discovery systems. Integration can reduce friction and duplicate work.
Ease of use
If multiple team members will use the platform, usability and training requirements matter. A powerful tool is less helpful if it is difficult to adopt.
Budget and pricing
Pricing may be based on subscriptions, users, document volume, or project scope. Make sure the cost fits the expected value of the tool.
In some cases, the best approach is to use more than one tool. For example, a contract analysis platform may handle agreements, while a separate tool manages financial review or unstructured communications.
Pricing and ROI Considerations
AI due diligence tools can range from relatively modest annual subscriptions to enterprise-level deployments with higher costs. Pricing usually depends on the vendor, feature set, number of users, and volume of documents processed.
When evaluating cost, focus on value rather than price alone. Important factors include:
- Time savings: Less manual review means faster turnaround.
- Risk reduction: Better issue spotting can help avoid missed problems.
- Deal speed: Faster diligence can support quicker decisions and closings.
- Team capacity: AI can help existing teams handle more work without proportional headcount growth.
Many vendors offer demos or trial access. Testing the tool with real documents is one of the best ways to evaluate fit before committing.
Frequently Asked Questions
Can AI completely replace human due diligence teams?
No. AI is best used to support human review, not replace it. It can process data quickly, but human judgment is still needed to assess context, negotiate outcomes, and make final decisions.
How accurate is AI for due diligence?
Accuracy can be strong, especially for structured tasks like clause extraction or document classification. However, results depend on document quality, tool design, and the complexity of the material being reviewed. Human verification is still important.
What types of data can AI analyze?
AI can be used on contracts, financial statements, emails, internal communications, filings, public records, news articles, and other document types. The exact scope depends on the platform.
Is AI due diligence expensive?
There is usually an upfront investment, but the savings from reduced manual work, fewer errors, and faster deal cycles can make it worthwhile. Pricing varies widely by vendor and use case.
How quickly can AI deliver results?
Some tools can surface useful findings within hours or days. Full value depends on the size of the project, the quality of the documents, and how well the tool fits the workflow.
Conclusion
AI is becoming a valuable part of modern due diligence workflows. It helps teams review documents faster, identify risks more efficiently, and focus attention on the issues that matter most.
The best results come from pairing AI with experienced human reviewers. When used well, AI can reduce manual burden, improve consistency, and support better decisions across transactions, compliance reviews, and investigations.
If you are evaluating how to use AI for due diligence, start with the type of documents you review most often, then choose a tool that matches your workflow, volume, and review goals.