How to Use AI for Due Diligence: Streamlining Investigations and Reducing Risk
Due diligence is a critical part of business transactions, investments, compliance reviews, and legal investigations. It often requires reviewing large volumes of contracts, emails, financial records, filings, and other unstructured data. That process has traditionally been slow, manual, and expensive.
AI is changing that. Used well, it can help teams review documents faster, surface risks earlier, and organize information more efficiently. For lawyers, M&A teams, compliance professionals, and investors, the question is no longer whether AI can help with due diligence, but how to use AI for due diligence in a way that is practical, secure, and reliable.
Why AI Matters in Due Diligence
Due diligence is about finding relevant facts quickly and accurately before they become problems. Incomplete review can lead to missed risks, deal delays, legal exposure, reputational harm, or poor investment decisions.
AI helps by automating repetitive tasks that consume time during review. It can sort documents, identify likely relevant materials, extract key clauses, and flag patterns that warrant closer inspection. Instead of starting from a blank slate, review teams can focus on higher-value analysis and decision-making.
In large transactions, the document volume alone can be overwhelming. AI is especially useful when teams need to assess thousands of agreements or search across large datasets for specific risks, obligations, or inconsistencies. It does not replace legal judgment, but it can make that judgment more informed and efficient.
Best AI Tools for Due Diligence
Several categories of AI tools are used in due diligence workflows. The best option depends on the type of review, the volume of data, and the level of customization required.
1. RelativityOne
What it does: RelativityOne is a cloud-based eDiscovery and review platform with AI and machine learning capabilities. It is built to process, organize, and analyze large volumes of unstructured data, including documents, emails, and audio files.
Why it is useful: For litigation support, internal investigations, or pre-acquisition reviews with heavy document loads, RelativityOne can help identify relevant materials, detect duplicates, and prioritize files for human review. Its AI features support faster document triage and more efficient review workflows.
Best fit: Large-scale document review for M&A, litigation, regulatory matters, and internal investigations.
Pros:
- Strong eDiscovery and document review functionality
- Scales well for large data volumes
- Useful AI tools for prioritization and categorization
- Secure and enterprise-ready
- Integrates with other legal technology
Cons:
- Can be complex for new users
- Pricing may be better suited to larger organizations
- More focused on document review than broader due diligence tasks
2. Kira Systems, now part of Litera
What it does: Kira is a contract analysis platform that uses machine learning to extract key provisions, clauses, and data points from agreements.
Why it is useful: In M&A due diligence, teams often need to review thousands of contracts for issues such as change of control provisions, termination rights, indemnities, and financial covenants. Kira can automate much of that review and help teams identify risks more quickly and consistently.
Best fit: Contract-heavy due diligence, including M&A and commercial real estate transactions.
Pros:
- Strong contract data extraction
- Customizable for specific clauses and issues
- Speeds up large-scale contract review
- Generally user-friendly for review teams
Cons:
- Best suited to contract analysis, not all types of due diligence
- Requires training and setup for best results
- Can be expensive for smaller firms or occasional users
3. Luminance
What it does: Luminance is an AI-powered legal document review platform that supports due diligence, contract analysis, and transaction review. It can identify key clauses, compare agreements, and highlight anomalies or deviations from standard terms.
Why it is useful: Luminance is designed to help legal teams move faster through large sets of documents while focusing attention on high-risk or unusual provisions. It is useful when the goal is to identify issues across many different document types, not just contracts.
Best fit: M&A, private equity, corporate finance, and other transactional reviews.
Pros:
- Handles large document sets efficiently
- Good at identifying anomalies and risks
- Supports multiple legal document types
- Built for legal workflows
Cons:
- May require configuration to perform optimally
- Typically priced for enterprise users
- May need to be paired with other tools for a fuller diligence process
4. Ex Machina, part of FTI Technology
What it does: Ex Machina is an AI-powered contract analysis and data extraction platform that uses natural language processing to read and categorize information from unstructured documents.
Why it is useful: It can speed up review of contract portfolios by extracting key terms and obligations, such as force majeure clauses, indemnification terms, and change of control provisions. That makes it easier to assess contractual risk and compare documents across a target entity.
Best fit: M&A due diligence, regulatory review, and contract portfolio analysis.
Pros:
- Accurate extraction of specific data points
- Handles complex contract language
- Supports custom review needs
- Integrates with broader FTI services
Cons:
- Can be complex to implement
- Usually priced for enterprise clients
- May require significant setup and training
5. Ada by Everlaw
What it does: Ada is an AI-powered document review tool within the Everlaw platform. It supports conceptual search, clustering, predictive coding, and document prioritization.
Why it is useful: In due diligence, Ada can help teams organize large sets of electronic documents, surface key themes, and identify materials most likely to matter. It is especially helpful when review teams need to move quickly through a large dataset while maintaining structure and consistency.
Best fit: Litigation review, internal investigations, and due diligence projects involving substantial electronic document collections.
Pros:
- Easy to use within the Everlaw workflow
- Helpful for clustering and thematic analysis
- Can reduce review time and cost
- Strong eDiscovery capabilities
Cons:
- Primarily a document review tool
- Pricing may scale with usage
- Works best when combined with human review
6. AI-powered tools from major legal tech providers
What they do: Legal technology providers such as Thomson Reuters and LexisNexis increasingly offer AI features for contract analysis, research, and due diligence support. These tools often use natural language processing to extract data points, identify risks, and compare clauses across document sets.
Why they are useful: For firms and in-house teams already using these platforms, AI features can fit into existing workflows without introducing a completely new system. That can make adoption easier, especially when teams want to improve document review without rebuilding their process.
Best fit: Law firms and corporate legal departments that already rely on established legal tech ecosystems.
Pros:
- Easier integration with existing tools
- Backed by established vendors
- Often supported by legal research and workflow features
- Regular product updates
Cons:
- May be less specialized than standalone tools
- Pricing can be high
- Feature depth varies by provider
How to Choose the Right AI Tool for Due Diligence
The right tool depends on the type of diligence work you do and how your team operates. Key factors to evaluate include:
1. Scope of review
Do you need to review contracts only, or do you also need to analyze emails, filings, financial records, and other documents? Contract-focused tools may be enough for narrow use cases, while broader platforms are better for multi-source reviews.
2. Data volume and complexity
Large document sets require platforms that can handle scale without slowing down. Consider both the size of the dataset and the complexity of the content.
3. Customization and training
Some tools work well out of the box, while others need training to recognize your preferred clause types, risk categories, or industry terms. The amount of setup required should match your team’s resources and timeline.
4. Integration with existing workflows
The best tool is one your team will actually use. Look for software that works with your current review process, storage system, and legal tech stack.
5. Budget and pricing model
Pricing may be based on users, projects, data volume, or enterprise licensing. Make sure the model aligns with how often you perform diligence and how large those matters typically are.
6. Ease of use and support
Even strong AI tools are only useful if your team can adopt them efficiently. Clear interfaces, onboarding support, and responsive customer service matter.
Pricing and Value Considerations
AI due diligence tools range from subscription-based products to enterprise platforms with custom pricing. When evaluating cost, look beyond the headline price and consider the full cost of ownership, including onboarding, training, implementation, and support.
Smaller teams may prefer per-project or usage-based pricing. Larger firms and organizations with recurring diligence work may benefit more from enterprise licensing.
The value of AI in due diligence usually comes from:
- Reducing manual review time
- Improving consistency in document analysis
- Helping teams identify risks earlier
- Supporting faster and better-informed decisions
- Allowing lawyers and analysts to focus on higher-level judgment
How to Use AI for Due Diligence Effectively
AI works best when it supports a clear process. A practical workflow usually includes:
- Define the diligence scope before review starts
- Organize and clean the dataset
- Use AI to classify, search, and prioritize documents
- Review flagged items with human judgment
- Validate AI outputs before making decisions
- Document findings in a structured report
The most effective teams use AI to narrow the field, not to make final decisions without review.
Frequently Asked Questions About AI for Due Diligence
Can AI completely replace human reviewers in due diligence?
No. AI is best used to support human review, not replace it. It can process large amounts of data quickly, but legal and business judgment still requires human oversight.
How much data is needed to train AI for due diligence?
It depends on the tool and the task. Some contract analysis systems perform best with a large set of representative documents and annotations. Others improve as they process more data.
What types of due diligence can AI assist with?
AI can support M&A due diligence, financial due diligence, legal due diligence, regulatory compliance reviews, vendor and customer diligence, cybersecurity reviews, and internal investigations.
Is AI reliable for due diligence findings?
AI-generated results should be treated as preliminary outputs that need human validation. They are useful for spotting issues, but they are not a substitute for legal or professional advice.
How does AI handle confidential information?
Reputable tools should include security features such as encryption, access controls, and data privacy protections. Vendor review is important, especially when handling sensitive or regulated information.
How long does implementation take?
Implementation can take anywhere from a few weeks to several months, depending on the platform, data volume, and level of customization required.
Conclusion
AI is becoming a practical part of modern due diligence. Used correctly, it can help teams review documents faster, identify risks more efficiently, and work through large datasets with greater consistency.
The best results come from choosing the right tool for the task, integrating it into a defined review process, and keeping human oversight at the center of the workflow. For legal teams, investors, and compliance professionals, that combination can improve both speed and quality in due diligence work.