Due diligence is a critical part of mergers and acquisitions, investment review, vendor onboarding, and compliance assessments. It requires careful analysis of contracts, financial records, filings, emails, and other documents to identify risk and confirm key facts.
That process has traditionally been slow, manual, and expensive. AI is changing that by helping legal and business teams review large document sets faster, extract relevant information more consistently, and focus attention on the issues that matter most.
If you are evaluating how to use AI for due diligence, the practical answer is simple: use AI to streamline document review, organize information, flag risk, and support faster decision-making. Human judgment still matters, but AI can significantly reduce the time spent on repetitive analysis.
Why AI Matters in Due Diligence
Traditional due diligence often involves reviewing thousands of documents under tight deadlines. That creates three common problems: limited time, high review costs, and the risk of missing important issues buried in large volumes of information.
AI helps address those challenges by:
- Speeding up document review
- Extracting clauses, dates, entities, and obligations
- Flagging anomalies and non-standard terms
- Improving consistency across large document sets
- Reducing manual review workload
- Helping teams focus on higher-value analysis
For legal teams, that means more efficient contract review. For investors and business leaders, it means clearer visibility into risk before a deal closes.
How to Use AI for Due Diligence in Practice
AI can support due diligence in several ways depending on the deal type and the documents involved.
1. Start with document classification
Before review begins, AI can sort incoming files into categories such as contracts, financial statements, leases, regulatory filings, or correspondence. This helps teams organize large data rooms more efficiently and reduces time spent manually sorting documents.
2. Extract key data points
AI tools can pull out important details such as:
- Parties to a contract
- Renewal and termination dates
- Change of control provisions
- Indemnities and limitations of liability
- Payment terms
- Compliance obligations
- Key financial figures
This makes it easier to build summaries, compare documents, and identify issues that need closer review.
3. Flag deviations and risks
One of AI’s most useful roles in due diligence is spotting language that differs from a standard template or expected position. This can help identify unusual clauses, missing terms, or provisions that create business or legal risk.
4. Prioritize manual review
AI does not replace human review, but it can help teams triage. Documents or clauses that appear high-risk can be reviewed first, while lower-risk materials can be processed more efficiently.
5. Support reporting and analysis
Many AI due diligence platforms generate structured outputs that can feed into issue lists, red flag reports, or internal summaries. This is useful when working under tight deadlines or coordinating across legal, finance, and deal teams.
Best AI Tools for Due Diligence
Several AI-powered tools are widely used for legal and business due diligence. Each has different strengths, so the right choice depends on your workflow and document profile.
Kira Systems
Kira Systems is a well-known contract analysis platform used for due diligence and document review.
What it does:
- Identifies and extracts key provisions from contracts and related documents
- Recognizes clauses such as change of control, indemnification, and force majeure
- Pulls out key dates, parties, and financial terms
Why it is useful:
- Strong for M&A due diligence
- Helps review large contract sets quickly
- Useful for finding standard and non-standard terms
Best fit:
- M&A transactions
- Real estate portfolio review
- Contract-heavy compliance checks
Considerations:
- Can be costly
- Best suited to contract-focused work rather than broader financial analysis
Leverton
Leverton focuses on extracting structured data from legal and financial documents.
What it does:
- Processes unstructured and semi-structured documents
- Extracts entities, key figures, and contractual data
- Organizes information for downstream analysis
Why it is useful:
- Speeds up abstraction work in large transactions
- Helpful when data comes from many different document types or legacy systems
Best fit:
- Large-scale M&A
- Private equity due diligence
- Complex corporate structures
Considerations:
- May require more setup and specialized expertise
- Typically a stronger fit for enterprise users
ThoughtRiver
ThoughtRiver uses AI to assess contract risk and highlight potential issues against predefined policies.
What it does:
- Reviews contracts for deviations from standard terms
- Flags risk based on configured playbooks
- Helps identify clauses that may need negotiation
Why it is useful:
- Supports rapid triage of contracts
- Helps legal teams focus on high-risk agreements first
Best fit:
- Pre-deal risk assessment
- Vendor contract review
- Post-acquisition contract analysis
Considerations:
- Best for contract risk analysis, not full-spectrum due diligence
- Depends on the quality of the policy framework configured by the user
BlackBoiler
BlackBoiler is designed to review contracts and suggest changes based on standard language.
What it does:
- Identifies key provisions
- Flags deviations from preferred terms
- Suggests alternative language for negotiation
Why it is useful:
- Helps spot unusual or seller-favorable clauses quickly
- Reduces time spent on first-pass review
Best fit:
- Transactional due diligence
- Buy-side contract review
- Sales-side contract assessment
Considerations:
- Works best as part of a broader review process
- Effectiveness depends on document quality and contract complexity
Luminance
Luminance is an AI document review platform used across legal workflows, including due diligence and litigation review.
What it does:
- Reviews large volumes of legal documents
- Extracts clauses and data points
- Flags anomalies, risks, and compliance issues
Why it is useful:
- Handles large document repositories well
- Offers visual analytics and reporting features
Best fit:
- M&A due diligence
- Compliance audits
- Litigation file review
Considerations:
- Setup can be more involved
- May be a bigger investment for smaller teams
Eigen Technologies
Eigen Technologies focuses on extracting and analyzing data from complex documents.
What it does:
- Works with unstructured legal and financial text
- Identifies relationships, obligations, and risks
- Produces structured outputs for analysis
Why it is useful:
- Helpful for difficult or highly nuanced documents
- Useful when information is spread across multiple sources
Best fit:
- Complex M&A
- Financial services due diligence
- Regulatory and legacy document review
Considerations:
- More enterprise-oriented
- May require greater implementation effort
How to Choose the Right AI Tool
Choosing the best AI tool for due diligence depends on the type of work you do and the documents you review most often.
Consider these factors:
Scope of work
- If your focus is contract review, tools like Kira, ThoughtRiver, or BlackBoiler may be a strong fit.
- If you need broader extraction across many document types, Leverton or Eigen may be better suited.
Volume and complexity
- Large, complex document sets may require more scalable platforms such as Luminance or Eigen.
- More standardized reviews may be handled well by contract-focused tools.
Team workflow
- Consider whether your team needs a simple interface or can support a more technical setup.
- Check whether the platform integrates with your document management system or other internal tools.
Budget and ROI
- Compare subscription, per-document, and enterprise pricing models.
- Balance cost against expected time savings, reduced review hours, and faster deal execution.
Required features
- Look for capabilities such as clause extraction, risk scoring, template comparison, reporting, and audit trails.
In many cases, the best approach is to start with the most urgent due diligence pain point and test tools against that workflow before expanding.
Pricing and Value Considerations
AI due diligence tools are priced in different ways depending on the vendor and deployment model.
Common pricing structures include:
- Per-document pricing: Useful for smaller, project-based reviews
- Subscription plans: Better for recurring use and predictable budgeting
- Enterprise pricing: Designed for large organizations with ongoing, high-volume needs
When evaluating value, look beyond the sticker price. Consider:
- How many review hours can be reduced
- Whether deal timelines can be shortened
- How much risk can be identified earlier
- Whether the team can work more consistently and accurately
For many firms, the value of AI comes from faster turnaround, fewer manual bottlenecks, and better issue spotting, not just lower labor costs.
Frequently Asked Questions
Can AI completely replace human due diligence professionals?
No. AI is best used to support human review, not replace it. It is effective at processing large volumes of material and surfacing issues, but legal and business judgment is still needed to interpret findings and make decisions.
How accurate is AI in due diligence?
Accuracy depends on the tool, the quality of the source documents, and the use case. AI can be highly effective for repetitive review tasks, but critical findings should still be validated by a human reviewer.
What types of documents can AI analyze for due diligence?
AI can analyze contracts, leases, financial statements, loan agreements, regulatory filings, court records, emails, and internal memos, depending on the platform.
Is AI due diligence suitable for small businesses or solo practitioners?
Yes, in some cases. While many tools are built for enterprise users, some vendors offer pricing and product tiers that make AI useful for smaller firms and lower-volume reviews.
How do I protect sensitive data when using AI due diligence tools?
Review the vendor’s security and privacy practices carefully. Look for data encryption, access controls, retention policies, and compliance with relevant standards before uploading sensitive information.
Conclusion
AI is becoming an important part of modern due diligence because it helps legal and business teams review documents faster, extract key information more reliably, and identify risk earlier in the process.
The best results come from using AI to handle repetitive review tasks while humans focus on interpretation, judgment, and negotiation strategy. Tools like Kira Systems, Leverton, ThoughtRiver, BlackBoiler, Luminance, and Eigen Technologies each offer different strengths, so the right choice depends on your document volume, workflow, and budget.
If you are evaluating how to use AI for due diligence, start with the part of your process that is most time-consuming or error-prone. A focused use case is often the fastest way to see value.