How to Use AI for Due Diligence: Streamlining the Investigation Process
Due diligence is a critical part of any major business transaction, whether you are acquiring a company, investing in a startup, or entering into a partnership. Traditionally, it has been a time-consuming process involving the review of large volumes of contracts, financial statements, regulatory filings, emails, and other records.
AI is changing that process. Used well, it can speed up review, surface risks earlier, and help teams focus their time on analysis instead of manual document sorting. For lawyers, investors, financial analysts, and business leaders, understanding how to use AI for due diligence can make the process more efficient and more reliable.
Why AI Matters in Due Diligence
The challenge in due diligence is not just the amount of information. It is also the need to identify what matters quickly and accurately. A missed clause, overlooked filing, or hidden inconsistency can create deal risk, delay closing, or affect valuation.
AI helps by automating repetitive work and highlighting information that deserves closer review. In practice, that can mean:
- Reducing manual review time
- Accelerating document analysis
- Flagging inconsistencies, anomalies, and missing information
- Identifying key clauses and obligations across large document sets
- Helping legal and business teams focus on higher-value judgment calls
AI does not replace human review. It supports it by handling the first pass more efficiently and making the review process more manageable.
Common Ways to Use AI for Due Diligence
AI can support due diligence in several practical ways:
- Contract review: Extracting key terms, renewal dates, termination rights, and unusual provisions
- Document classification: Grouping documents by type, topic, or relevance
- Risk spotting: Highlighting clauses, patterns, or data points that may indicate exposure
- Search and retrieval: Finding relevant material faster than manual keyword searching
- Summarization: Turning long documents into shorter, usable summaries
- Cross-document comparison: Checking for inconsistencies across contracts, policies, or filings
- Financial and operational review: Flagging unusual transactions or control issues in selected workflows
The best use case depends on the type of transaction, the volume of data, and the level of detail needed.
Top AI Tools for Due Diligence
The right tool depends on what you are reviewing and how your team works. Here are several widely used AI-powered platforms that can support due diligence workflows.
1. Kira Systems
What it does: Kira Systems is a contract analysis platform that uses AI and machine learning to extract and analyze key provisions from legal documents. It can identify items such as parties, effective dates, renewal terms, termination clauses, and force majeure provisions across large volumes of contracts.
Why it is useful: Contracts are often the core of due diligence. Kira helps teams review large sets of agreements faster by flagging important clauses, inconsistencies, and potential liabilities that may be buried in the text.
Best fit: M&A transactions, real estate due diligence, and other contract-heavy reviews.
Pros:
- Strong contract review capabilities
- Useful reporting features
- Pre-built models for common clauses
- Custom model building for specialized needs
Cons:
- Focused mainly on contract analysis
- May need to be paired with other tools for broader diligence
- Can be costly for smaller firms
2. Clause AI (part of Luminance)
What it does: Luminance, through its Clause AI component, uses deep learning to review legal documents, identify key clauses, flag deviations from standard templates, and highlight risks.
Why it is useful: Clause AI helps teams move through large document sets quickly while surfacing potential legal exposure and non-standard language.
Best fit: M&A due diligence, large-scale contract review, and document sets that require fast risk identification.
Pros:
- Advanced AI capabilities
- Strong legal risk focus
- Broad document support
- Integrates into legal workflows
Cons:
- Can require a learning curve
- Pricing may be better suited to larger organizations
3. Everlaw
What it does: Everlaw is a cloud-based eDiscovery platform with AI features for document review and analysis, including predictive coding, clustering, and concept searching.
Why it is useful: In diligence projects with large electronic data collections, Everlaw can help teams find relevant documents faster and organize large volumes of digital information more efficiently.
Best fit: Due diligence involving email archives, shared drives, or other large electronic repositories.
Pros:
- Strong for data categorization and relevance ranking
- Handles large datasets well
- User-friendly interface
- Secure cloud-based platform
Cons:
- More focused on eDiscovery than clause-level legal analysis
- Can take time to master fully
4. Casetext with CoCounsel
What it does: Casetext’s CoCounsel is an AI legal assistant that can review documents, summarize information, draft memos, and answer legal questions. In due diligence, it can help extract facts, summarize materials, and flag possible issues.
Why it is useful: CoCounsel is a flexible tool for initial review across different document types. It can help teams quickly orient themselves in a large diligence set and identify what needs deeper analysis.
Best fit: Legal teams that want a general-purpose AI assistant for research, document review, and preliminary issue spotting.
Pros:
- Strong natural language processing
- Useful beyond contract review
- Good for research integration
- Conversational interface
Cons:
- May be less specialized than dedicated diligence tools in some narrow areas
- Results depend on careful prompting and review
5. AuditBoard
What it does: AuditBoard is a cloud-based platform for audit, risk, and compliance management. While it is not exclusively a due diligence tool, it can support financial and operational review through workflow automation, risk tracking, and data analysis.
Why it is useful: In financial due diligence, AuditBoard can help identify unusual transactions, review internal controls, and organize findings in a structured way.
Best fit: Financial due diligence, operational audits, and compliance reviews in transaction-related work.
Pros:
- Broad audit and risk management capabilities
- Useful for financial analysis workflows
- Good for tracking findings and follow-up
Cons:
- Less focused on legal document analysis
- Requires configuration for due diligence-specific use
6. Cellebrite Physical & Digital Forensics
What it does: Cellebrite specializes in digital forensics and data extraction from mobile devices, computers, and cloud sources.
Why it is useful: In higher-risk matters, such as suspected fraud, IP theft, or regulatory non-compliance, digital forensics may be necessary to uncover communications or data that standard review would miss.
Best fit: Complex investigations, sensitive M&A matters, and situations where forensic evidence is part of the diligence process.
Pros:
- Strong forensic extraction and analysis capabilities
- Useful for defensible investigative workflows
- Can uncover hidden digital evidence
Cons:
- Highly specialized
- Requires technical expertise
- More appropriate for targeted investigations than routine document review
How to Choose the Right AI Tool
The best AI tool for due diligence depends on the nature of the matter and the kind of data involved. Before choosing a platform, consider the following:
- Scope of review: Are you focused on contracts, financials, emails, or a broader document set?
- Data type: Can the tool handle your file formats and sources?
- Depth of analysis: Do you need basic extraction, issue spotting, or deeper review workflows?
- Team experience: Will your team need a simple interface or more advanced setup?
- Budget: Is the pricing sustainable for one-off matters or ongoing use?
- Integration: Does it work with your document management or legal tech stack?
If possible, test the tool on a sample of your actual data before making a commitment. A demo or pilot can reveal how well it fits your workflow and whether it produces useful results in practice.
Pricing and Value Considerations
AI due diligence tools range widely in cost. Some cloud-based platforms may be priced for smaller teams, while enterprise-grade solutions can require a much larger investment.
When evaluating cost, focus on value, not just price. Ask:
- How much manual review time will the tool reduce?
- Will it help identify risks earlier?
- Can it speed up closing timelines?
- Will it scale as your matters grow?
- Does it reduce the chance of missing important issues?
Also factor in onboarding, training, support, and implementation. These can affect the real cost of adoption.
Frequently Asked Questions
Can AI completely replace human review in due diligence?
No. AI should support human judgment, not replace it. It can handle repetitive tasks and surface potential issues, but humans are still needed for interpretation, strategy, and final decisions.
Is AI accurate enough for critical due diligence tasks?
AI can be highly effective for specific tasks such as clause extraction, document classification, and anomaly detection. Accuracy depends on the quality of the model, the data, and the complexity of the review.
What are the biggest risks of using AI in due diligence?
The main risks are overreliance on AI, privacy and security concerns, misinterpretation of context, and poor implementation without proper review controls.
Do I need technical expertise to use these tools?
Not necessarily. Many modern tools are designed for legal and business users, though there may still be a learning curve and some setup required.
What types of data can AI analyze?
Depending on the platform, AI can analyze contracts, financial statements, filings, emails, internal reports, news, public records, and other structured or unstructured data.
Conclusion
AI is becoming an important part of modern due diligence. When used effectively, it can reduce manual workload, improve review speed, and help teams identify risks more efficiently.
The best approach is to match the tool to the task. Kira Systems and Clause AI are strong options for contract-focused reviews. Everlaw is useful for large electronic data sets. Casetext with CoCounsel offers broader AI-assisted legal support. AuditBoard and Cellebrite serve more specialized financial, operational, and forensic use cases.
If you are evaluating how to use AI for due diligence, start with your workflow, your data, and your risk priorities. The right tool should make your review process faster, more organized, and more useful without replacing the judgment that due diligence ultimately requires.