How to Use AI for Due Diligence: Streamlining Investigations and Reducing Risk
In fast-moving transactions, due diligence can determine whether a deal closes smoothly or becomes a costly problem. Traditional due diligence often requires teams to review large volumes of contracts, financial records, filings, and background materials by hand. That process is time-consuming, expensive, and vulnerable to missed details.
AI is changing that workflow. Used well, it can help lawyers, investors, compliance teams, and corporate development professionals review more material in less time, spot potential issues earlier, and focus human attention on the highest-risk areas. This article explains how to use AI for due diligence, what types of tools are commonly used, and how to choose the right solution for your needs.
Why AI Matters in Due Diligence
Due diligence is about understanding the real risks and obligations connected to a business, asset, or transaction. Without a careful review, buyers and investors may miss hidden liabilities, contract issues, litigation exposure, regulatory concerns, or weak internal controls.
AI helps address these challenges by automating parts of the review process and improving consistency across large data sets. It can analyze documents faster than manual review, highlight unusual terms or patterns, and organize information so professionals can focus on judgment, negotiation, and issue-spotting.
For legal teams in particular, AI is most useful when it reduces repetitive review work without replacing human oversight. It supports faster triage, better document organization, and more efficient analysis across legal, financial, and operational materials.
How AI Is Used in Due Diligence
AI can support due diligence in several practical ways:
- Contract review: identify key clauses, missing provisions, and unusual terms
- Legal research: surface relevant case law, statutes, and regulatory materials
- Financial analysis: flag anomalies, benchmark performance, and organize data
- Workflow management: track diligence requests, findings, and deal progress
- Risk and compliance review: assess control gaps and regulatory exposure
The best results usually come from combining AI with experienced legal and business review. AI handles speed and scale; humans handle context, strategy, and final conclusions.
Best AI Tools for Due Diligence
The AI due diligence market includes tools for contract analysis, legal research, financial intelligence, deal management, and risk review. The right choice depends on the type of diligence you perform most often.
1. Kira Systems
What it does:
Kira Systems is a contract review and analysis platform that uses machine learning to extract and identify key provisions from legal documents. It can classify documents and flag relevant clauses such as change of control, indemnification, termination rights, and assignment restrictions.
Why it is useful:
Kira is well suited to high-volume contract review during M&A, financing, real estate, and other transaction work. It helps teams identify key terms quickly and reduces the risk of overlooking important issues in large document sets.
Best fit/use case:
Legal due diligence, especially where the review involves many complex contracts.
Pros:
- Strong contract analysis capabilities
- Customizable for specific data points
- Good reporting and review workflows
- Improves over time with user input
Cons:
- Focused mainly on contract review
- May need to be paired with other tools for broader diligence needs
- Setup and training can take time
2. Casetext with CoCounsel
What it does:
Casetext’s CoCounsel is an AI legal assistant that can summarize legal documents, assist with legal research, identify relevant authority, and support early-stage analysis for due diligence matters.
Why it is useful:
CoCounsel can speed up the research phase by helping teams review lengthy legal materials and identify issues that may affect a transaction or target company.
Best fit/use case:
Legal teams conducting transactional or litigation-related due diligence, especially when legal research is an important part of the review.
Pros:
- Useful for research and document analysis
- Flexible across multiple legal tasks
- Fits into broader legal workflows
- Designed to support, not replace, legal professionals
Cons:
- Broader in scope than a dedicated contract review platform
- May not match specialized tools for deep clause-level analysis
3. FactSet
What it does:
FactSet is a financial data and analytics platform that provides information on public and private companies, markets, and economic conditions. Its AI-supported features help users identify trends, perform modeling, and analyze risk more efficiently.
Why it is useful:
For financial due diligence, FactSet provides broad access to company filings, market data, historical financial information, and benchmarking tools. It can help teams review performance trends, compare peers, and identify anomalies in financial data.
Best fit/use case:
Investment bankers, financial analysts, and corporate development teams conducting financial diligence.
Pros:
- Extensive financial data coverage
- Strong analytics and reporting tools
- Useful for benchmarking and quantitative review
- Good fit for market and performance analysis
Cons:
- Can be expensive
- More focused on financial data than legal review
- May require training to use effectively
4. BlackBoiler
What it does:
BlackBoiler is an AI-powered contract review platform that uses natural language processing to extract data points and identify risks in legal agreements. It is designed to automate much of the document review process and fit into existing workflows.
Why it is useful:
BlackBoiler helps teams review contracts more consistently and quickly. It can highlight key terms, detect deviations from standard language, and flag potential issues that may require legal attention.
Best fit/use case:
Corporate legal departments, law firms, and transaction teams reviewing large contract portfolios.
Pros:
- Strong automation for contract review
- Customizable data extraction
- Integrates with document workflows
- Reduces manual review effort
Cons:
- Primarily focused on contracts
- May need to be combined with other tools for full due diligence coverage
5. Intapp DealCloud
What it does:
DealCloud is a deal and relationship management platform that uses AI to support deal sourcing, execution, and post-closing processes. It helps teams manage due diligence workflows and track deal progress in one place.
Why it is useful:
DealCloud is helpful when the main challenge is managing the diligence process across multiple stakeholders, documents, and deadlines. Its AI features can surface patterns from past deals, support workflow prioritization, and help identify bottlenecks.
Best fit/use case:
Private equity firms, investment banks, and corporate development teams managing a high volume of transactions.
Pros:
- Strong deal management capabilities
- Useful CRM and pipeline features
- Helps organize diligence workflows
- Supports broader deal lifecycle management
Cons:
- More focused on process management than deep document analysis
- Less specialized for legal or financial review than dedicated tools
6. AuditBoard
What it does:
AuditBoard is a cloud-based platform for audit, risk, and compliance management. Its AI features support risk assessments, control testing, and compliance gap identification.
Why it is useful:
In due diligence, understanding a target’s internal controls and compliance posture can be just as important as reviewing contracts or financials. AuditBoard can help teams assess operational risk and spot weaknesses in control environments.
Best fit/use case:
Operational due diligence, compliance reviews, and assessments in regulated industries.
Pros:
- Strong focus on risk and compliance
- Useful for internal control review
- Supports structured operational diligence
- Helps identify regulatory and control issues
Cons:
- Less suited to contract or financial due diligence
- Best used as part of a broader diligence toolkit
How to Choose the Right AI Tool for Due Diligence
Choosing the right AI solution depends on the scope of your review, the kind of data you handle, and how your team works.
Define the scope of diligence
Start by identifying the main focus of the work. Are you reviewing legal contracts, financial performance, operational controls, or all three? A contract-heavy matter may call for Kira Systems or BlackBoiler. A research-heavy legal workflow may benefit from Casetext with CoCounsel. Financial diligence may require FactSet. Deal execution and workflow oversight may be better handled through DealCloud.
Assess the data type and volume
Large sets of unstructured documents usually require strong natural language processing capabilities. Structured financial data calls for analytics and reporting tools that can organize, compare, and model numbers efficiently.
Check integration options
The best tool is often the one that fits into your existing systems. Look for compatibility with document management platforms, CRM tools, and other legal or financial software your team already uses.
Evaluate usability and support
Even strong AI tools have a learning curve. Consider how easy the platform is to adopt, what training is available, and whether your team can use it efficiently without slowing down the workflow.
Look for scalability and customization
Your diligence needs may grow over time. Choose tools that can handle larger data volumes and be configured to extract the specific information your team cares about.
Compare pricing against expected value
AI tools may be priced by user count, usage, or enterprise subscription. The right comparison is not just cost, but return on investment through saved time, faster deal cycles, and reduced risk.
Pricing and Value Considerations
AI due diligence tools can vary widely in cost. Some offer tiered subscriptions, while others use custom enterprise pricing.
When evaluating value, consider the following:
- Reduced manual review time
- Faster deal execution
- Better identification of legal, financial, and compliance risks
- Improved consistency in analysis and reporting
- More efficient use of senior attorney and analyst time
In many cases, a hybrid approach works best. A team may use one platform for contract review, another for legal research, and a third for financial or workflow management. That combination can be more effective than relying on a single general-purpose tool.
How to Use AI for Due Diligence in Practice
A practical AI-enabled diligence process usually looks like this:
1. Collect and organize the source materials
Gather contracts, filings, financial statements, compliance records, and other relevant documents.
2. Use AI to triage the review
Let the tool sort documents, extract key terms, and highlight likely risk areas.
3. Review flagged issues manually
Have lawyers, analysts, or compliance professionals validate the results and assess the real significance of each issue.
4. Track findings in a structured format
Organize exceptions, questions, and follow-up items in a way that supports the deal team.
5. Use the outputs to support decisions
Combine AI-generated insights with human analysis to inform negotiation, pricing, risk allocation, and closing decisions.
Frequently Asked Questions About AI for Due Diligence
Can AI completely replace human due diligence professionals?
No. AI is best used to support human review, not replace it. It can process large volumes of information and flag issues quickly, but it cannot fully replace legal judgment, strategic thinking, or context-specific decision-making.
How accurate is AI in due diligence work?
Accuracy depends on the tool, the quality of the data, and the complexity of the review. Some tools perform very well at specific tasks like clause extraction or summarization, but human oversight is still necessary to confirm findings.
What types of AI are commonly used in due diligence?
The most common technologies are machine learning and natural language processing. Machine learning helps systems improve from data, while NLP helps them understand and analyze text-heavy materials.
Can smaller law firms or businesses use AI for due diligence?
Yes. Many tools are available through subscription or modular pricing, which can make them accessible to smaller teams. Even one focused tool can improve efficiency in contract review or research.
Is sensitive data secure on AI due diligence platforms?
Reputable providers typically offer security controls, encryption, and compliance features. Before using any platform, review its security policies, data handling practices, and certifications to make sure they meet your standards.
Conclusion
AI is now a practical part of modern due diligence. It helps teams review documents faster, organize large amounts of information, and identify risks earlier in the process. For legal professionals, the biggest value often comes from combining AI-driven speed with human judgment and oversight.
Whether you need better contract review, stronger legal research, deeper financial analysis, or more efficient workflow management, there are AI tools that can support the process. The key is choosing the right solution for your diligence scope, data type, and team structure.
Used well, AI can make due diligence faster, more consistent, and more effective without sacrificing the careful review that complex transactions require.