How to Use AI for Due Diligence: A Practical Guide for Legal and Financial Teams
Due diligence is a core part of business transactions, but it is also one of the most time-consuming. Whether you are reviewing a target company in an M&A deal, assessing a major partnership, or preparing for an investment, you need to examine contracts, financials, compliance records, policies, and operational risks with care.
That process has traditionally required large amounts of manual review. AI is changing that. Today, legal teams, financial analysts, M&A professionals, and business leaders can use AI to speed up document review, surface risks earlier, and make the process more efficient without losing the need for human judgment.
If you are researching how to use AI for due diligence, this guide explains where AI fits, which tools are commonly used, how to choose the right platform, and what to consider on cost and implementation.
Why AI Matters in Due Diligence
Traditional due diligence often involves reviewing mountains of documents under tight deadlines. That creates predictable problems:
- Missed risks, such as unfavorable clauses, compliance gaps, or financial irregularities
- Higher costs from extensive manual review
- Slower timelines that can delay deals
- Inconsistent quality across reviewers and workstreams
- Difficulty scaling for larger or more complex transactions
AI helps address these issues by automating repetitive tasks, processing large document sets quickly, and highlighting information that deserves closer review. In practice, that can mean:
- Faster document review and extraction
- More consistent analysis across large datasets
- Lower manual workload for legal and finance teams
- Better detection of anomalies, outliers, and patterns
- Earlier identification of risks that affect negotiation or deal strategy
How to Use AI for Due Diligence
AI can support due diligence in several practical ways:
- Contract review: Extract key clauses, identify deviations from standard language, and flag missing terms
- Document classification: Sort large sets of files into relevant categories
- Risk spotting: Surface unusual provisions, gaps, or inconsistencies for manual review
- eDiscovery and investigations: Prioritize relevant emails, files, and internal records
- Compliance review: Check documents against internal policies or regulatory requirements
- Financial and operational analysis: Help identify anomalies, control issues, or process weaknesses
The best use of AI is usually not full automation. It is using AI to reduce the volume of manual work so professionals can focus on interpretation, negotiation, and decision-making.
Best AI Tools for Due Diligence
Different tools serve different parts of the due diligence workflow. Some are built for contract analysis, while others are better for eDiscovery, risk management, or broader document review.
1. Kira Systems
What it does: Kira Systems is a contract analysis and due diligence platform that uses machine learning to review documents and extract key provisions, clauses, and data points. It can identify standard clauses such as governing law, term, and termination, and flag deviations from templates.
Why it is useful: Kira is especially valuable for automating contract review in M&A, real estate, and compliance work. It reduces the manual effort involved in identifying risks and pulling deal-critical information from large document sets.
Best fit: M&A transactions, commercial real estate due diligence, and any review involving large volumes of contracts, leases, or legal agreements.
Pros:
- Accurate contract review
- Strong clause library
- User-friendly interface
- Well established in the legal market
- Good for specific data extraction
Cons:
- Focused mainly on contract analysis
- May need other tools for broader due diligence workflows
- Can be expensive for smaller firms
2. Luminance
What it does: Luminance uses AI and natural language processing to analyze legal documents quickly and identify areas of risk or abnormality. It goes beyond simple keyword search by considering context and meaning. It also includes tools for eDiscovery and legal research.
Why it is useful: Luminance is strong at identifying anomalies and risks in large legal document sets. Its contextual analysis can flag clauses or issues that simpler review methods may miss.
Best fit: Complex litigation, large-scale M&A due diligence, and regulatory compliance reviews where nuance matters.
Pros:
- Strong contextual analysis
- Fast document review
- Broader functionality beyond contract analysis
- Good for finding outliers and unusual language
Cons:
- Can have a steeper learning curve
- Pricing may be a barrier for smaller organizations
3. Everlaw
What it does: Everlaw is primarily an eDiscovery platform, but its AI capabilities are also useful in due diligence. It helps identify relevant documents, privileged information, and potential issues across large datasets. Machine learning features can cluster documents, identify themes, and reduce manual review.
Why it is useful: Everlaw is especially helpful when due diligence includes emails, internal records, litigation materials, or other unstructured data. It can help teams focus on the most relevant material first.
Best fit: Legal due diligence involving litigation history, internal investigations, or high-volume document review.
Pros:
- Strong document review and analysis tools
- Good for large unstructured datasets
- Intuitive interface
- Strong collaboration features
- Robust security
Cons:
- Designed primarily for eDiscovery
- Less specialized for contract clause extraction than dedicated contract tools
4. BlackBoiler
What it does: BlackBoiler automates review of transactional documents by identifying deviations from standard forms or approved language. It compares documents against playbooks or preferred terms and flags inconsistencies.
Why it is useful: For teams handling repetitive agreements, BlackBoiler helps maintain consistency and reduce errors. It is useful for quality control and for enforcing internal legal standards.
Best fit: Legal departments and law firms working on high volumes of standard agreements such as NDAs, loan documents, and purchase agreements.
Pros:
- Good for standardizing transactional documents
- Flags deviations efficiently
- Can help speed up deal workflows
- Useful for quality control
Cons:
- More specialized than broader due diligence platforms
- May require customization for unique document types
5. AuditBoard
What it does: AuditBoard is a platform for risk, compliance, and audit management with AI-enabled features. It centralizes data, automates workflows, and helps identify risks across operational and financial areas. Its AI can support anomaly detection, risk assessment, and compliance checks.
Why it is useful: AuditBoard is useful when due diligence needs to go beyond document review and include operational controls, financial processes, and compliance posture.
Best fit: Operational due diligence, financial due diligence, and compliance audits for larger organizations.
Pros:
- Broad risk and compliance functionality
- Useful for financial data analysis and control assessment
- Supports a more holistic view of the business
- Integrated workflow management
Cons:
- More complex than single-purpose tools
- May be more expensive than specialized solutions
6. CogniCor
What it does: CogniCor offers AI-powered legal operations tools, including contract review and due diligence support. It is designed to extract information from legal documents, identify risks, and automate repetitive tasks.
Why it is useful: CogniCor can help legal teams surface key information faster and improve efficiency in transactional and compliance-related reviews.
Best fit: Legal departments and firms looking to improve document understanding and risk identification with AI.
Pros:
- Strong focus on legal document intelligence
- Can extract nuanced information
- Supports risk assessment
- Designed to improve legal operations
Cons:
- Less widely known than some larger vendors
- Feature depth and integrations should be evaluated carefully
How to Choose the Right AI Tool
The best AI tool for due diligence depends on the type of work you do and the documents you need to review. Key factors to consider include:
- Scope of due diligence: Contract review, litigation review, financial analysis, compliance, or operational risk
- Type of data: Structured financial data, unstructured documents, emails, or a mix
- Complexity of tasks: Simple extraction versus contextual analysis, anomaly detection, or predictive features
- Integration needs: Compatibility with your document management system, data room, or legal tech stack
- Ease of use: The tool should fit the skill level of your team and not create unnecessary friction
- Scalability: It should handle larger or more complex matters as your needs grow
- Vendor support: Training, onboarding, and ongoing support matter during implementation
In many cases, one tool will not cover every part of due diligence. A contract review platform and an eDiscovery tool may work better together than a single system alone.
Pricing and Value Considerations
AI due diligence tools vary widely in pricing. Some offer subscription plans for smaller teams, while others are priced at the enterprise level.
When evaluating cost, consider more than the list price:
- Return on investment: Time saved, lower review costs, and fewer missed issues
- Subscription structure: Per user, per matter, usage-based, or tiered plans
- Implementation costs: Setup, configuration, integration, and training
- Additional fees: Storage, advanced features, or dedicated support
- Customization: If the platform needs tailoring, ask about both cost and timing
A demo or trial is often the best way to evaluate whether a tool fits your workflow. It can help you assess usability, output quality, and whether the platform actually supports your due diligence process.
Frequently Asked Questions
Can AI completely replace human due diligence professionals?
No. AI can automate many tasks and improve efficiency, but human oversight remains essential. Professionals still need to interpret findings, assess context, and make final decisions.
How accurate is AI at identifying risks?
Accuracy depends on the tool, the training data, and the task. AI is often strong at spotting predefined clauses, inconsistencies, and anomalies, but human review is still needed to understand legal and business implications.
What types of due diligence can AI support?
AI can be used for:
- Legal due diligence: Contract review, compliance checks, litigation review, IP review
- Financial due diligence: Financial statement analysis, fraud detection, risk assessment
- Operational due diligence: Policies, procedures, supply chain data, control reviews
- Commercial due diligence: Market analysis, customer contracts, competitive research
Is AI mature enough for critical due diligence tasks?
Yes, many AI tools are now mature enough for serious due diligence work, especially for document review and risk identification. However, the tool should be selected carefully and used with human oversight.
What are the main benefits of AI in M&A due diligence?
AI can speed up document review, identify key risks and obligations, reduce manual costs, and free deal teams to focus on negotiation and strategy.
How can I start with AI for due diligence on a limited budget?
Start with a narrow use case, such as contract review. Look for trial versions, entry-level plans, or pay-as-you-go options. Proving value in one area first can make it easier to justify broader adoption later.
Conclusion
AI is changing due diligence by reducing manual work, improving consistency, and helping teams surface risks faster. For legal and financial professionals, the value is not in replacing judgment but in making that judgment more efficient and better informed.
If you are exploring how to use AI for due diligence, start by defining the part of the workflow that consumes the most time or creates the most risk. Then choose a tool that matches your document types, review needs, and budget. With the right setup, AI can become a practical part of your due diligence process and a useful advantage in competitive transactions.