How to Use AI for Due Diligence: Streamlining Investigations and Reducing Risk
AI is changing how due diligence gets done. What used to require long hours of manual review can now be accelerated with tools that sort documents, extract key terms, flag anomalies, and surface potential risks faster than traditional workflows.
For lawyers, investors, in-house legal teams, and business leaders, learning how to use AI for due diligence is becoming an important part of efficient, informed decision-making. The key is not replacing human judgment, but using AI to make reviews faster, more consistent, and more complete.
Why AI Matters in Due Diligence
Due diligence is often central to mergers and acquisitions, vendor onboarding, partnership reviews, compliance checks, and internal risk assessments. These processes usually involve large volumes of contracts, financial records, filings, and other business documents.
Manual review can be slow, expensive, and vulnerable to oversight. AI helps address those challenges by automating repetitive work and making it easier to focus on the issues that matter most.
The main benefits include:
- Faster review timelines: AI can process large document sets far more quickly than manual review alone.
- Better consistency: Automated analysis helps reduce the risk of missed clauses or inconsistent review standards.
- Improved risk identification: AI can flag unusual terms, missing information, and patterns that may warrant closer review.
- Lower manual burden: Legal and deal teams can spend less time on document sorting and more time on analysis and strategy.
- Stronger decision support: AI can help uncover trends and connections that may not be obvious in a first-pass review.
Used well, AI makes due diligence more efficient without removing the need for human oversight.
Best AI Tools for Due Diligence
The right tool depends on the type of diligence you are doing. Some platforms focus on contract review, while others support identity verification or broader transaction workflows.
1. Kira Systems (now part of Litera)
What it does: Kira is a contract analysis and due diligence platform that uses machine learning to review legal documents and extract key provisions, terms, and data points.
Why it is useful: It is especially helpful in transactions that involve large numbers of contracts, leases, or corporate records. Legal teams can use it to quickly identify change-of-control clauses, indemnities, termination rights, and other important terms.
Best fit: M&A, commercial real estate, and regulatory review involving high-volume contract analysis.
Pros:
- Strong accuracy for contract review
- Intuitive interface
- Useful reporting features
- Integrates with other legal tech tools
Cons:
- Focused mainly on contract analysis
- May need to be paired with other tools for broader diligence
- Can require a meaningful upfront investment
2. Luminance
What it does: Luminance uses natural language processing and machine learning to review legal documents at scale, identify key clauses, and highlight anomalies.
Why it is useful: It can speed up first-pass review by flagging unusual provisions and deviations from expected terms, helping teams prioritize what needs closer attention.
Best fit: Large transactional diligence, litigation support, and compliance reviews with heavy document volumes.
Pros:
- Strong document analysis capabilities
- Useful for spotting irregularities
- Handles many document types
- Visual tools support faster review
Cons:
- Best at document review rather than full end-to-end diligence
- Pricing may be a barrier for smaller teams
3. DiligenceEngine (part of Reiterate)
What it does: DiligenceEngine is designed for M&A due diligence and automates review of financial, legal, and operational materials to identify risks and key metrics.
Why it is useful: It helps deal teams work through large data rooms more efficiently and spot issues that could affect valuation, timing, or deal structure.
Best fit: Investment banks, private equity firms, and corporate development teams handling mergers, acquisitions, and divestitures.
Pros:
- Built specifically for M&A diligence
- Supports financial, legal, and operational review
- Helps flag risks early
Cons:
- Broad functionality may mean a steeper learning curve
- Best suited to teams that handle transactions regularly
4. Evisort
What it does: Evisort uses AI and NLP to extract structured data from unstructured documents, especially contracts.
Why it is useful: It gives teams a centralized way to search, organize, and analyze contract portfolios, making it easier to understand obligations, risk exposure, and key terms across many agreements.
Best fit: Contract portfolio management, compliance review, vendor due diligence, and M&A support.
Pros:
- Strong data extraction from unstructured text
- Scales well for large contract volumes
- Useful search and reporting functions
- Helpful beyond initial diligence
Cons:
- Broader diligence work may require additional tools
- Most valuable when reviewing a substantial contract set
5. Onfido
What it does: Onfido provides AI-powered identity verification and document authentication. It checks identity documents and compares selfies against government-issued IDs.
Why it is useful: It helps verify that individuals, partners, or key personnel are legitimate, which is important for KYC, AML, and fraud prevention workflows.
Best fit: Client onboarding, regulated industries, and any process where identity verification is a critical part of diligence.
Pros:
- Fast identity checks
- Strong support for compliance workflows
- Useful for fraud prevention
- Robust security features
Cons:
- Focused on identity verification rather than broader diligence
- Often needs to be integrated into a larger workflow
6. BlackBoiler
What it does: BlackBoiler automates contract review and analysis, with a focus on identifying key clauses, risks, and deviations from preferred language.
Why it is useful: It helps legal teams review contracts faster and maintain consistency during diligence, especially when time is limited and document volume is high.
Best fit: In-house legal teams, law firms, and corporate counsel reviewing M&A, real estate, or vendor agreements.
Pros:
- Efficient for contract review
- Helps standardize analysis
- Supports existing legal workflows
Cons:
- Primarily focused on contracts
- May need to be paired with other tools for full diligence coverage
How to Choose the Right AI Tool for Due Diligence
Choosing the right tool depends on the kind of diligence work you need to do and the workflow you want to improve.
Consider the following factors:
- Your diligence focus: Are you reviewing contracts, financials, identities, or a combination of all three?
- Document volume and complexity: Some tools are better for large-scale reviews, while others work best on targeted sets of documents.
- Integration needs: Check whether the tool works with your document management system, CRM, or other legal tech.
- Ease of use: The best tool is one your team can adopt without a heavy training burden.
- Accuracy and oversight: AI should support human review, not replace it. Look for tools that make it easy to validate results.
- Scalability: Make sure the platform can grow with your workload and transaction volume.
A practical way to start is by identifying your biggest bottleneck. If contract review is the slowest part of your process, tools like Kira, Luminance, or BlackBoiler may be the best fit. If identity verification is the priority, Onfido is more relevant. For broader M&A workflows, DiligenceEngine may be the better starting point. In some cases, a combination of tools will be necessary.
Pricing and Value Considerations
AI due diligence tools come with different pricing models, including subscriptions, per-user licensing, and project-based fees. When comparing vendors, cost should be evaluated alongside functionality and expected value.
Common cost factors include:
- Subscription fees: Often based on users, document volume, or feature access
- Implementation and training: Setup, onboarding, and integration costs may apply
- Scaling costs: Pricing may increase as usage grows
- Extra charges: API access, storage, or premium support may add to the total
When assessing value, consider how the tool affects:
- Time savings: Less manual review means more efficient use of lawyer and analyst time
- Risk reduction: Earlier identification of problems can prevent costly mistakes
- Deal speed: Faster diligence can support quicker decisions and closings
- Decision quality: Better analysis can lead to more informed and defensible outcomes
The best choice is not always the cheapest one. It is the tool that delivers the best balance of accuracy, usability, and workflow improvement for your specific needs.
Frequently Asked Questions About AI for Due Diligence
Can AI completely replace human lawyers in due diligence?
No. AI is best used to support human lawyers, not replace them. It can handle repetitive review tasks, but legal judgment, strategy, negotiation, and final decision-making still require human expertise.
How accurate are AI tools for due diligence?
Accuracy varies by tool, training data, and use case. Many platforms are highly effective at repetitive review tasks, but human validation remains essential.
What types of data can AI analyze for due diligence?
AI can review contracts, agreements, court filings, financial statements, internal reports, emails, news content, social media, and structured databases, depending on the platform.
Is AI for due diligence expensive?
It can be, especially for enterprise-grade tools. But the value often comes from time saved, improved accuracy, and reduced risk. Many vendors offer flexible pricing models.
How do I protect sensitive information when using AI tools?
Choose vendors with strong security controls, including encryption, access restrictions, and clear data handling policies. Review compliance with applicable privacy requirements before uploading sensitive materials.
Can AI help identify fraud during due diligence?
Yes. AI can flag anomalies, unusual patterns, and inconsistencies that may point to fraud, financial irregularities, or other suspicious activity.
Conclusion
AI is making due diligence faster, more consistent, and more scalable. By automating document review, extracting key information, and flagging potential risks, it helps legal and business teams focus on higher-value analysis.
The most effective approach is to use AI as a support layer for human judgment. Start with the part of the process that creates the most friction, choose a tool that fits your workflow, and build from there. For teams that want to improve speed, reduce risk, and handle larger document sets with more confidence, AI is becoming an important due diligence advantage.