How to Use AI for Due Diligence: Streamlining Legal and Business Investigations
AI is changing how legal and business teams approach due diligence. Instead of relying only on manual document review and traditional research, professionals can now use AI to process large volumes of information faster, spot patterns earlier, and surface risks that might otherwise be missed.
For lawyers, investors, and deal teams, the value is straightforward: less time spent on repetitive review and more time spent on analysis, strategy, and decision-making.
Why AI Matters in Due Diligence
Due diligence often involves reviewing contracts, financial records, regulatory filings, legal histories, internal reports, and public-source information. That work can be time-consuming and error-prone when handled manually, especially under tight deadlines.
AI helps by automating repetitive tasks, organizing information, and highlighting issues for human review. It does not replace professional judgment, but it can make the process faster and more complete.
Used well, AI can help you:
- Accelerate document review and information gathering
- Improve consistency across large document sets
- Surface risks, anomalies, and missing information
- Reduce the cost of manual review
- Free up teams to focus on higher-value analysis
How to Use AI for Due Diligence
A practical due diligence workflow usually starts with ingestion and ends with human validation.
1. Gather and organize source material
AI tools work best when you feed them a clean, structured set of documents and data. Common inputs include:
- Contracts and amendments
- Corporate governance documents
- Financial statements
- Regulatory filings
- Litigation materials
- Internal reports
- Public records and news coverage
Before analysis begins, make sure the data is organized, deduplicated, and labeled where possible.
2. Use AI to review and extract key terms
Contract analysis tools can identify clauses, extract terms, and flag deviations from standard language. This is especially useful when reviewing large volumes of agreements, such as leases, supplier contracts, loan documents, or customer agreements.
3. Search for risks across multiple sources
Broader AI platforms can help connect legal, financial, and market information. This is useful when a due diligence review requires more than contract analysis, such as evaluating litigation exposure, compliance issues, or reputational risk.
4. Summarize findings for human review
AI can generate summaries, compare documents, and group issues by category. The output should then be reviewed by lawyers or subject matter experts before being used in any final recommendation.
5. Validate before you rely on it
AI output is only as useful as the review process around it. Always confirm key findings against the underlying source documents, especially in high-stakes transactions.
Best AI Tools for Due Diligence
The right tool depends on the type of review you need to perform. Some platforms are built for contract analysis, while others are better suited to legal research or broader document intelligence.
1. Luminance
What it does: Luminance is an AI-powered legal platform focused on document review and analysis. It can read large volumes of legal documents, extract key information, flag anomalies, and identify relevant clauses.
Why it is useful: It is designed to handle high document volumes quickly, which makes it useful for M&A due diligence, litigation support, and contract review.
Best fit: Large-scale reviews involving thousands of documents, especially where clause identification and risk spotting are important.
Pros:
- Strong clause identification and extraction
- Handles large document sets efficiently
- Reduces manual review time
Cons:
- Can be expensive
- Requires some setup and training
- Focused mainly on legal documents
2. Kira Systems
What it does: Kira Systems, now part of Litera, uses machine learning to extract and organize key provisions from contracts and other documents.
Why it is useful: It is especially helpful for standardizing the review of agreements across a portfolio, such as leases, loan agreements, and supplier contracts.
Best fit: Portfolio-level contract review in corporate, finance, and real estate matters.
Pros:
- Excellent at extracting specific data points
- Customizable for different review needs
- Useful for comparing many documents
Cons:
- May require customization for specialized agreements
- Pricing can be a barrier for smaller firms
- Primarily focused on contract review
3. Hyperion by Thomson Reuters
What it does: Hyperion is designed to support M&A due diligence by combining insights from financial data, market intelligence, and risk assessment tools.
Why it is useful: It provides a broader view of a target company by bringing together multiple data sources in one workflow.
Best fit: M&A transactions where financial health, market position, and regulatory risk all need to be assessed.
Pros:
- Combines financial and market data with legal insights
- Useful for broader M&A analysis
- Part of the Thomson Reuters ecosystem
Cons:
- Best suited to M&A
- May need to be paired with other tools
- Enterprise-level pricing
4. Casetext
What it does: Casetext, through CoCounsel, offers AI-assisted legal research, document analysis, and drafting support.
Why it is useful: While it is primarily a research tool, it can help with due diligence by summarizing filings, reviewing legal documents, and identifying relevant case law or statutes.
Best fit: Legal teams that need to assess litigation risk, review legal filings, or research the legal landscape around a target company.
Pros:
- Strong legal research capabilities
- Useful for summarizing and analyzing legal materials
- Helpful in early-stage due diligence
Cons:
- Less focused on large-scale contract analysis
- More research-oriented than document-review oriented
- Due diligence use is secondary to its core function
5. Seal Software by DocuSign
What it does: Seal Software specializes in contract discovery and analytics. It can locate, ingest, and analyze contracts across an organization, then extract clauses, obligations, and risks.
Why it is useful: It helps teams get a more complete view of contractual commitments, which is critical in due diligence and compliance work.
Best fit: M&A, compliance audits, and risk assessments where contract discovery is a priority.
Pros:
- Strong contract discovery capabilities
- Useful for identifying hidden obligations
- Supports compliance and risk analysis
Cons:
- Can be complex in fragmented environments
- Typically geared toward enterprise users
- May require integration work
6. Verity by HighQ
What it does: Verity is an AI-powered document review platform that helps legal teams analyze and manage large document sets.
Why it is useful: It can speed up review of corporate records, agreements, and governance documents while flagging issues for closer review.
Best fit: Legal due diligence, litigation support, and compliance workflows within HighQ-based environments.
Pros:
- Useful for repetitive document review
- Integrates with HighQ
- Helps improve speed and consistency
Cons:
- Mainly a document review tool
- May need to be paired with other systems
- Part of a larger platform
7. IBM Watson Discovery
What it does: IBM Watson Discovery analyzes structured and unstructured data using natural language processing and machine learning.
Why it is useful: It can be used to search across internal documents, financial materials, external research, and news sources to identify patterns and risks.
Best fit: Complex due diligence matters that require analysis across multiple data types and sources.
Pros:
- Highly flexible
- Works with many types of data
- Scalable for enterprise use
Cons:
- Requires technical setup and customization
- Can be costly
- May need a front-end workflow layer
How to Choose the Right AI Tool
The best tool depends on the scope of your review and the type of information you need to analyze.
Consider the following:
- Scope of due diligence: Are you focused on contracts, litigation, financial risk, or a broader review?
- Volume of data: Large document sets usually call for tools built for high-volume review.
- Industry requirements: Some tools are better suited to finance, real estate, life sciences, or corporate transactions.
- Integration needs: Check whether the tool fits your current document management and collaboration systems.
- Budget and resources: Enterprise platforms often come with implementation and support costs.
- Ease of use: Make sure the team can adopt the tool without a steep learning curve.
In many cases, the best approach is to layer tools. For example, you might use one platform for contract review, another for legal research, and a broader search and analytics engine for cross-source analysis.
Pricing and Value Considerations
AI due diligence tools vary widely in price. Some use subscription models, while others offer custom enterprise pricing with implementation fees and ongoing support.
When comparing options, focus on value, not just cost. A more expensive tool may still be worthwhile if it saves time, reduces risk, or helps you close deals faster.
Look at:
- Time saved on manual review
- Risk reduction from better issue spotting
- Faster turnaround on deals and investigations
- Increased team capacity without adding headcount
Many vendors offer demos or trials, which can help you assess whether the tool fits your workflow before you commit.
Frequently Asked Questions About AI for Due Diligence
Can AI replace human due diligence professionals?
No. AI is a support tool, not a replacement. Human lawyers and analysts are still needed to interpret findings, assess context, and make final judgments.
How much technical knowledge is required?
That depends on the platform. Many tools are built for legal and business users, while more advanced platforms may require IT support or technical customization.
Is sensitive due diligence data secure in AI tools?
Reputable vendors typically use security and privacy controls designed for enterprise use. Before adopting any tool, review its data handling policies, security certifications, and contractual terms.
What types of data can AI analyze?
AI can analyze contracts, financial statements, legal documents, emails, reports, public records, news articles, and regulatory filings, depending on the platform.
How quickly can AI improve due diligence?
Some gains can appear quickly, especially in document review. More advanced workflows may take longer to configure, ingest, and fine-tune.
Is AI only useful for large firms and corporations?
No. While enterprise buyers often adopt these platforms first, smaller firms and businesses can also use cloud-based AI tools with scalable pricing.
Conclusion
AI is now a practical part of modern due diligence. It can speed up document review, improve consistency, and help teams identify risks and opportunities more efficiently.
The key is to choose tools that match your workflow. Contract-focused platforms like Luminance and Kira are useful for document-heavy reviews, while tools like Casetext, Hyperion, and IBM Watson Discovery can support broader legal and business analysis.
Used thoughtfully, AI can make due diligence faster, more thorough, and more actionable without replacing the judgment that professionals still need.