How to Use AI for Due Diligence: A Practical Guide for Legal and Business Teams
Due diligence is a critical part of acquisitions, investments, vendor onboarding, and other high-stakes transactions. The process is designed to surface risk, validate assumptions, and identify anything that could affect valuation, compliance, or deal terms.
Traditionally, due diligence has depended on large manual review teams, long hours, and extensive document-by-document analysis. AI is changing that workflow. Used well, it can help teams review documents faster, spot issues more consistently, and focus human attention on the highest-risk areas.
For lawyers, investors, corporate executives, and compliance professionals, learning how to use AI for due diligence can create a meaningful advantage. The right tools can process large document sets, flag anomalies, extract key terms, and support more informed decisions. This guide explains where AI fits in the due diligence process, which tools are commonly used, and how to choose the right one for your needs.
Why AI Matters in Due Diligence
Modern due diligence often involves reviewing thousands of contracts, financial statements, regulatory filings, correspondence, and other records. That volume makes manual review slow and resource-intensive, and it increases the risk of missed issues.
AI can help in several ways:
- Speed: AI tools can process large volumes of documents far faster than manual review.
- Consistency: AI does not fatigue, which helps reduce missed clauses and uneven review quality.
- Pattern detection: AI can surface trends, anomalies, and relationships that may not be obvious in a first-pass review.
- Cost control: Faster review can reduce outside counsel hours and internal resource strain.
- Risk spotting: AI can help identify red flags earlier, including unusual terms, compliance issues, and litigation-related concerns.
In practical terms, AI shifts due diligence from a purely manual process to a more efficient, data-driven workflow.
Best AI Tools for Due Diligence
The right tool depends on the type of diligence you are running. Some platforms are built for legal document review, while others focus more on risk, compliance, or broader operational analysis.
1. Luminance
What it does: Luminance is an AI-powered legal due diligence platform that reviews large volumes of documents, identifies key clauses, extracts data points, and flags potential risks in contracts, leases, and related legal documents.
Why it is useful: It is designed to reduce the time spent on manual contract review, helping legal teams focus on issue-spotting and analysis rather than document sorting.
Best fit/use case: M&A transactions, contract portfolio reviews, real estate diligence, and other matters involving high volumes of legal documents.
Pros:
- Strong focus on legal documents
- Good at clause identification and extraction
- User-friendly for legal teams
- Built with security in mind
Cons:
- Primarily focused on legal text
- May need to be paired with other tools for financial or operational review
- Can be expensive for smaller firms
2. Kira Systems
What it does: Kira Systems uses machine learning and natural language processing to identify and extract specific clauses and data points from large sets of documents. Its active learning functionality allows the system to improve based on user input.
Why it is useful: It is especially effective when the diligence team needs to locate specific provisions such as change of control clauses, indemnities, termination rights, or payment terms.
Best fit/use case: M&A, corporate finance, and compliance-focused contract review where precision matters.
Pros:
- Strong clause extraction
- Customizable through active learning
- Well suited to detailed contract analysis
- Established reputation in legal tech
Cons:
- Can take time to configure and train
- Focused more on contracts than broader diligence
- Pricing may be out of reach for smaller teams
3. Casetext with CARA AI
What it does: Casetext is best known as a legal research platform, and its CARA AI tool supports document review by analyzing briefs, contracts, and other legal texts. It can help locate relevant passages and connect documents to related legal research.
Why it is useful: For diligence that involves litigation history, regulatory issues, or legal precedent, CARA AI can help teams move faster through large sets of legal materials.
Best fit/use case: Litigation-heavy diligence, regulatory reviews, and matters where legal research and document review overlap.
Pros:
- Combines research and document analysis
- Helpful for finding related authority and similar language
- Useful for reviewing litigation-related materials
Cons:
- Stronger on legal research than on financial or operational diligence
- Requires a Casetext subscription
4. Seal Software, now part of DocuSign
What it does: Seal Software specializes in contract discovery and analysis. It can locate contracts across an organization, then extract obligations, key terms, and risk-related provisions.
Why it is useful: A complete contract inventory is often a prerequisite for meaningful diligence. Seal helps teams find contracts that may otherwise be missed and review them for clauses that affect the transaction.
Best fit/use case: Large organizations, post-merger integration, compliance reviews, and diligence projects with complex contract environments.
Pros:
- Strong contract discovery capabilities
- Useful for large-scale review
- Part of the broader DocuSign ecosystem
Cons:
- Implementation can be substantial
- May be more than smaller teams need for straightforward projects
5. Ana Luisa
What it does: Ana Luisa is an AI platform for legal document review and analysis. It reads and summarizes legal documents, identifies key obligations, and flags deviations from standard clauses.
Why it is useful: It can shorten review time for transactional lawyers and corporate counsel working through large document sets.
Best fit/use case: M&A, real estate transactions, and contract portfolio review where fast legal analysis is needed.
Pros:
- Designed for legal document analysis
- Can help identify key risk areas
- Can be trained to client-specific needs
- Straightforward interface
Cons:
- Focuses mainly on legal documents
- Not a full financial or operational diligence platform
- Cost may be a concern for smaller teams
6. AuditBoard
What it does: AuditBoard is a cloud-based platform for audit, risk, and compliance management. While it is not built solely for AI due diligence, its capabilities can support operational and compliance review, including anomaly detection, control assessment, and reporting.
Why it is useful: It gives buyers and investors a structured way to evaluate internal controls, compliance posture, and operational risk beyond the legal document set.
Best fit/use case: Diligence involving regulated businesses, complex operations, or a strong need to assess controls and compliance frameworks.
Pros:
- Broad risk and compliance coverage
- Useful for internal control assessment
- Can support operational diligence workflows
- Strong reporting features
Cons:
- Less specialized for legal contract review
- Requires familiarity with audit and compliance concepts
How to Use AI for Due Diligence Effectively
AI is most useful when it is applied to the right part of the workflow. In practice, that usually means using it to organize, review, and prioritize, while humans handle judgment and final conclusions.
A practical approach looks like this:
1. Define the scope clearly
Decide what you are reviewing: contracts, litigation history, compliance issues, financial controls, or a combination. The tool should match the scope of the diligence work.
2. Organize the document set
AI performs best when documents are well collected, labeled, and deduplicated. A clean data room or document set improves results and reduces noise.
3. Focus on the highest-value tasks
Use AI for repetitive, high-volume work such as clause extraction, issue spotting, document classification, and initial summarization.
4. Train or configure the tool where needed
Some platforms work best after users teach them which provisions or risk categories matter most. This can improve precision over time.
5. Review flagged items manually
AI should not be treated as the final decision-maker. Human review is still necessary for context, judgment, and deal-specific interpretation.
6. Document findings clearly
Use the output to build a review trail, summarize risks, and support internal or client reporting.
How to Choose the Right AI Tool
The best AI tool depends on the type of diligence, the volume of material, and the resources available to your team.
Consider the following:
- Primary use case: Legal review, compliance assessment, financial analysis, or a mix of all three
- Data volume: Some tools are better for thousands of documents, while others suit smaller, targeted reviews
- Ease of use: Consider how much setup, training, and user adoption the platform requires
- Integration: Check whether the tool fits your existing legal tech stack, document management system, or enterprise workflow
- Security: Due diligence materials are sensitive, so privacy, encryption, and access controls matter
- Budget: Compare pricing against the time saved, the risks avoided, and the speed gained
Pricing and Value Considerations
AI due diligence tools are commonly priced in a few ways:
- Subscription-based: Monthly or annual access fees
- Per-project or per-document: Pricing tied to volume
- Enterprise licensing: Custom agreements for larger organizations
When evaluating cost, focus on value rather than list price alone. The most important return-on-investment factors are:
- Time saved on manual review
- Reduced outside counsel or internal labor costs
- Lower risk of missed liabilities or compliance issues
- Faster transaction timelines
- Better-informed negotiation and decision-making
Frequently Asked Questions
Can AI replace human due diligence experts entirely?
No. AI is best used as an assistant, not a replacement. Human reviewers are still needed for context, strategy, negotiation, and final judgment.
How much data do I need for AI to be useful?
That depends on the tool and use case. Some platforms can add value with a few hundred documents, while others improve as they are trained on larger datasets.
Is AI always accurate for due diligence?
No tool is perfect. Accuracy depends on the quality of the input data, the strength of the model, and how the platform is configured. Human review remains essential.
How do I make sure the tool is secure and privacy-compliant?
Ask about data handling, encryption, access controls, retention policies, and compliance certifications. This is especially important when handling sensitive legal or cross-border data.
How long does implementation usually take?
Implementation can take anywhere from a few days to several months, depending on the tool, the document volume, and the level of customization or integration required.
Conclusion
AI is changing how due diligence is done. For legal and business teams, it offers a faster and more structured way to review large amounts of information, identify risks, and support better decisions.
The key is to match the tool to the task. Legal document review, compliance assessment, and operational due diligence each call for different capabilities. AI works best when it is integrated into a disciplined review process, with clear oversight and human judgment at the end.
If you are evaluating how to use AI for due diligence, start with your most repetitive and time-consuming review tasks. From there, choose a platform that fits your workflow, protects sensitive data, and gives your team a practical advantage.