How to Use AI for Due Diligence: Streamlining M&A, Investments, and Beyond
Due diligence is essential, but it is often slow, costly, and document-heavy. Whether you are working on a merger or acquisition, a private equity investment, a real estate deal, or a new business partnership, the core challenge is the same: review large volumes of information, spot risks, and verify key facts quickly and accurately.
AI is changing how that work gets done. For legal teams, investors, and business leaders, knowing how to use AI for due diligence is increasingly important for staying efficient, reducing risk, and making better-informed decisions.
Why AI Matters in Due Diligence
The stakes in due diligence are high. A missed clause, an overlooked liability, or an incomplete risk assessment can lead to financial losses, legal exposure, reputational damage, or a failed transaction.
The problem is usually scale. A typical due diligence review may involve:
- Contracts and amendments
- Financial statements and loan documents
- Regulatory filings and public records
- Internal emails and reports
- News coverage and other external sources
Reviewing all of that manually takes time and increases the chance of human error. AI helps by speeding up the process, improving consistency, and surfacing issues that may be difficult to spot in a manual review.
AI can help due diligence teams:
- Process large document sets faster
- Reduce manual review costs
- Flag unusual language, gaps, and inconsistencies
- Surface contractual and operational risks
- Support compliance and better audit trails
- Free up lawyers and analysts for higher-value work
For legal professionals, that means less time spent on repetitive review and more time on judgment, strategy, and client advice. For investors and operators, it means faster deal execution and better visibility into what they are buying or partnering with.
Best AI Tools for Due Diligence
The right tool depends on the type of due diligence you are doing. Some platforms are built for contract analysis, while others are better suited for broader document review or communication analysis.
1. Kira Systems, now part of Litera
What it does:
Kira uses machine learning to extract and analyze data from contracts and other legal documents. It can identify key clauses, terms, dates, obligations, and other provisions relevant to due diligence.
Why it is useful:
Kira is especially valuable in M&A due diligence, where teams need to review many agreements quickly and identify risks such as change of control clauses, indemnification provisions, and termination rights.
Best fit:
- M&A transactions
- Large-scale contract review
- Legal agreement analysis
Pros:
- Strong accuracy for legal documents
- Customizable for specific review needs
- Well established in legal due diligence
Cons:
- Requires setup and training
- More focused on contracts than broader financial or operational diligence
2. Luminance
What it does:
Luminance is an AI legal platform for rapid contract review and analysis. It can process large volumes of documents, flag discrepancies, highlight important information, and identify risks.
Why it is useful:
Its anomaly detection features help review teams find unusual language, deviations from standard terms, and potential hidden liabilities more efficiently.
Best fit:
- M&A due diligence
- Regulatory review
- Large agreement portfolios
Pros:
- Fast processing
- User-friendly interface
- Strong anomaly detection
Cons:
- Primarily focused on legal documents
- May need to be paired with other tools for broader diligence
3. Relativity
What it does:
Relativity is best known as an e-discovery platform, but its AI features are also useful in due diligence. It can cluster similar documents, identify key themes, predict relevance, and analyze communications.
Why it is useful:
When due diligence includes emails, internal messages, reports, or other unstructured data, Relativity can help teams find key facts buried in large datasets.
Best fit:
- Electronic communication review
- Internal investigations
- High-volume document review
Pros:
- Handles large, complex datasets
- Strong analytical capabilities
- Useful for unstructured data
Cons:
- Can be complex
- Often best for litigation-adjacent or high-volume reviews
4. Everlaw
What it does:
Everlaw is another e-discovery platform with AI tools for document review, including predictive coding, clustering, and conceptual search.
Why it is useful:
Everlaw helps teams narrow large document sets to the most relevant materials, reducing manual review time and helping identify risks faster.
Best fit:
- Due diligence requiring extensive document review
- Transactional work with regulatory or litigation concerns
- Collaborative legal teams
Pros:
- Intuitive interface
- Strong AI-assisted review
- Good collaboration features
Cons:
- More focused on document review than financial analysis
- May need to be supplemented for broader diligence workflows
5. DiligenceEngine, part of Intapp
What it does:
DiligenceEngine is designed specifically for due diligence, particularly in financial contexts. It automates the review of financial statements, contracts, and related materials to identify risks and red flags.
Why it is useful:
It can help teams extract and analyze financial data, review covenants, and spot deviations that may affect valuation or deal viability.
Best fit:
- Financial due diligence
- Private equity
- Investment and corporate finance teams
Pros:
- Purpose-built for due diligence
- Strong financial data extraction
- Useful for identifying financial risks
Cons:
- Less focused on legal contract review than some other platforms
- May require additional tools for a complete legal diligence workflow
6. IBM Watson
What it does:
IBM Watson is a broad AI platform that can be applied to due diligence through natural language processing and machine learning. It can analyze unstructured text from news, reports, social media, and internal documents.
Why it is useful:
Its flexibility makes it useful for broader diligence tasks such as reputational review, market research, competitor analysis, and trend analysis.
Best fit:
- Custom due diligence solutions
- Reputational risk assessment
- Market and competitor analysis
Pros:
- Versatile
- Works across many data types
- Can be customized
Cons:
- Requires technical expertise
- Not a ready-made due diligence solution on its own
7. Abnormal Security
What it does:
Abnormal Security is primarily an email security platform, but its AI can also support due diligence by analyzing internal communications and flagging unusual behavior or potential fraud indicators.
Why it is useful:
In M&A or internal risk reviews, unusual email activity can point to data leakage, fraud, or other operational issues. Abnormal Security can help identify suspicious patterns in communication data.
Best fit:
- Email and communication review
- Fraud and anomaly detection
- Operational risk screening
Pros:
- Strong behavioral anomaly detection
- Useful for communication-based risk analysis
Cons:
- Limited to email and communication data
- Best used as a supplement, not a standalone diligence tool
How to Choose the Right AI Tool
The best AI tool for due diligence depends on your goals, your data, and your workflow.
Start by asking:
- What type of due diligence are you conducting?
- Legal contract review
- Financial review
- Operational review
- Reputational or communications analysis
- How much data do you need to review?
- Smaller deals may need simpler tools
- Large transactions may require platforms that can handle high volume and complexity
- What systems do you already use?
- Integration with your existing legal tech stack can save time and reduce friction
- How technical is your team?
- Some tools are easy for legal teams to adopt
- Others may require more training or support
- What risks matter most?
- Contractual risks
- Financial anomalies
- Fraud indicators
- Compliance issues
- Reputational concerns
In many cases, a multi-tool approach works best. For example, a team might use Kira for contracts, DiligenceEngine for financial review, and Relativity for emails and internal communications.
Pricing and Value Considerations
AI due diligence tools vary widely in price. Some basic platforms may cost a few thousand dollars per year, while enterprise solutions with advanced features and support can cost tens or even hundreds of thousands annually.
When evaluating cost, look beyond the sticker price and focus on value:
- ROI: Can the tool reduce review time, lower labor costs, or help avoid expensive mistakes?
- Scalability: Will pricing remain workable as deal volume grows?
- Implementation: Will you need training, onboarding, or IT support?
- Customization: Can the tool be adapted to your specific industry or risk profile?
- Support: Does the vendor offer reliable onboarding and ongoing assistance?
The best tool is not always the cheapest. It is the one that improves speed, accuracy, and confidence in the review process.
Frequently Asked Questions About AI for Due Diligence
Is AI going to replace human lawyers in due diligence?
No. AI is best viewed as a support tool, not a replacement. It is useful for repetitive review, pattern detection, and document processing, but human lawyers are still needed for judgment, negotiation, strategy, and legal analysis.
How accurate is AI for due diligence?
Accuracy depends on the quality of the data, the strength of the model, and the task being performed. AI can be highly effective for tasks like clause extraction and anomaly detection, but human review is still important to validate results and catch mistakes.
What types of data can AI analyze for due diligence?
AI can review a wide range of materials, including:
- Contracts and leases
- Corporate and litigation documents
- Financial statements and loan records
- Emails and internal messages
- Public filings and court records
- News coverage and social media
- Internal reports and compliance materials
How can I protect data when using AI due diligence tools?
Security should be a priority. Review the vendor’s encryption, access controls, audit practices, and data handling policies. Make sure the tool aligns with your organization’s privacy and compliance requirements. Certifications such as SOC 2 or ISO 27001 may also be relevant.
Can AI help predict future risks or outcomes?
Yes, to a point. AI can analyze historical patterns, market data, and communications to surface possible future risks, but those outputs should be treated as decision support, not certainty.
How long does implementation usually take?
It depends on the tool and the use case. Some contract review platforms can be deployed in days or weeks. Larger systems that require integration, data migration, or custom training may take longer.
Conclusion
AI is becoming a practical part of modern due diligence. It helps teams review documents faster, identify risks more consistently, and work through complex transactions with greater confidence.
If you are evaluating how to use AI for due diligence, the best starting point is to match the tool to the task. Contract-heavy M&A may call for Kira or Luminance. Broader document review may point to Relativity or Everlaw. Financial diligence may be better supported by DiligenceEngine. For broader risk analysis, IBM Watson or Abnormal Security may help fill in specific gaps.
Used well, AI does not replace diligence. It makes it faster, more focused, and more effective.