How to Use AI for Due Diligence: Streamlining Your Investigations
Due diligence is a critical part of business decision-making. Whether you are acquiring a company, entering a partnership, investing in a startup, or onboarding a vendor, the goal is the same: confirm facts, identify risks, and verify that the information provided is complete and accurate.
Traditionally, due diligence has been slow, manual, and resource-intensive. Lawyers, accountants, and other specialists often have to review large volumes of contracts, financial records, and compliance documents by hand. AI is changing that process by helping teams review documents faster, spot patterns more efficiently, and focus on the issues that matter most.
AI is not a replacement for human judgment. It is a practical tool that can streamline repetitive work, surface red flags, and support more informed decisions. For legal teams and business leaders, that means faster reviews, stronger analysis, and better use of expert time.
Why AI Matters in Due Diligence
For organizations that handle frequent transactions or operate in regulated industries, the stakes are high. Weak due diligence can lead to legal exposure, reputational harm, missed risks, and avoidable costs. AI can improve the process in several important ways:
- Faster reviews: AI can process documents far more quickly than manual review, helping teams complete contract, financial, and regulatory analysis in days or weeks instead of months.
- Better consistency: AI can scan large document sets for clauses, anomalies, and deviations from standard language without fatigue or distraction.
- Lower costs: Automating document review and data extraction can reduce the amount of time spent on manual work, which may lower overall transaction costs.
- Stronger risk detection: AI can uncover inconsistencies, unusual terms, compliance issues, and other patterns that may not be obvious during a manual review.
- More time for strategy: When AI handles the first pass, professionals can spend more time on negotiation, interpretation, and deal strategy.
- Competitive advantage: Teams that use AI effectively can move faster and make more confident decisions in competitive transactions.
Best AI Tools for Due Diligence
The right tool depends on your workflow, document types, and the level of analysis you need. Below are several widely used AI platforms that can support due diligence work.
1. Luminance
What it does: Luminance is an AI-powered legal transaction platform built for document review and due diligence. It can read and analyze large volumes of legal documents, categorize clauses, extract key information, and flag risks or anomalies. It is commonly used for M&A, real estate, and broader legal review.
Why it is useful: Luminance speeds up document review by highlighting relevant clauses, deviations from standard language, and potential risk areas. It produces structured outputs that help teams focus on the most important issues.
Best fit/use case: Well suited for law firms and in-house legal teams handling large-scale M&A, contract review, and buyer-side due diligence.
Pros: Strong legal text analysis, intuitive interface, detailed reporting, well suited to M&A workflows, and continuous learning capabilities.
Cons: Can be expensive, and advanced customization may require a learning curve.
2. Kira Systems
What it does: Kira Systems, now part of Litera, is a contract analysis and due diligence platform that extracts specific clauses and data points from legal documents. Users can train it to identify custom data points relevant to their review.
Why it is useful: Kira helps teams quickly extract key information from large document sets and compare terms across contracts. Its flexibility makes it useful across many due diligence scenarios.
Best fit/use case: Useful for law firms and corporate legal departments that need detailed clause extraction and comparative analysis across large contract volumes.
Pros: Strong clause extraction, highly customizable, user-friendly, robust reporting, and good integration options.
Cons: Training for highly specific clauses may require user input, and pricing may increase with usage and features.
3. ThoughtRiver
What it does: ThoughtRiver focuses on contract risk assessment. It analyzes contracts to identify obligations, key provisions, and potentially problematic language, often comparing terms against a company’s playbook or risk standards.
Why it is useful: It works well as a first-pass reviewer, helping legal teams prioritize contracts that need deeper review. It can also support more consistent contract review processes.
Best fit/use case: Ideal for legal teams that want to manage contract risk, speed up compliance review, and identify high-risk agreements early in the due diligence process.
Pros: Strong risk flagging, clear risk visualization, useful for pre-deal and ongoing contract review, and helpful for standardizing review criteria.
Cons: More focused on risk assessment than broad due diligence, so it may need to be paired with other tools for full document review.
4. Casetext (CoCounsel)
What it does: Casetext’s CoCounsel is an AI legal assistant that supports legal research, summarization, and drafting. It can review documents, answer questions about legal text, and identify relevant authorities or background information.
Why it is useful: CoCounsel is helpful when due diligence requires legal research, regulatory context, or quick summaries of dense materials. It can reduce the time spent getting oriented in a new matter or industry.
Best fit/use case: Best for research-heavy due diligence, compliance review, and background analysis.
Pros: Strong legal research capabilities, natural language interaction, useful for summaries and first-pass analysis, and continuously expanding features.
Cons: Its core strength is research and drafting, so it may not provide the same structured document analysis as dedicated due diligence platforms.
5. Eigen Technologies
What it does: Eigen provides an AI platform for extracting and analyzing data from unstructured documents. It can work with legal agreements, financial instruments, operational reports, and other complex files, regardless of format.
Why it is useful: Eigen is built for document sets that are varied, messy, or highly unstructured. It can help teams surface key financial, compliance, and operational data more efficiently.
Best fit/use case: A strong option for due diligence involving complex financial institutions, regulatory-heavy organizations, or large and diverse document sets.
Pros: Handles varied document types, strong unstructured data extraction, scalable, and highly customizable.
Cons: Implementation can be complex and may require upfront data preparation and custom training.
6. ContractExpress
What it does: ContractExpress, now part of DocuSign, is primarily a contract drafting and automation tool. It can also support due diligence by helping teams analyze existing contracts for specific clauses, terms, and deviations.
Why it is useful: For contract-focused due diligence, it can help identify standard language, exceptions, and inconsistencies across a portfolio of agreements.
Best fit/use case: Useful where the main goal is understanding a company’s contractual obligations and risk profile, especially if its agreements were created or managed in ContractExpress.
Pros: Strong contract lifecycle management features, useful for identifying clause variations, and integrates well with document systems.
Cons: It is more of a complementary tool than a specialized due diligence platform.
How to Use AI for Due Diligence in Practice
If you want to use AI effectively, start with a clear workflow. The most successful teams use AI to support specific stages of due diligence rather than trying to automate everything at once.
A practical approach looks like this:
- Define the review scope: Identify whether you are reviewing contracts, financial statements, regulatory materials, or a mix of documents.
- Organize your data: Gather documents into a clean, searchable set before running them through an AI tool.
- Set review criteria: Decide what matters most, such as change-of-control clauses, indemnities, termination rights, compliance gaps, or unusual financial terms.
- Run an initial AI review: Use the tool to extract key terms, flag exceptions, and summarize important provisions.
- Review the outputs manually: Have lawyers or subject matter experts validate the findings and assess the context.
- Prioritize issues: Focus human review on the highest-risk or highest-value items.
- Document conclusions: Keep a clear record of what the AI flagged, what was confirmed, and what requires follow-up.
The key is to use AI as a first pass, not a final decision-maker. That approach saves time while preserving professional oversight.
How to Choose the Right AI Tool
The right tool depends on your budget, document types, and workflow requirements. Consider the following factors before choosing a platform:
- Scope of due diligence: Are you mainly reviewing contracts, financials, regulatory filings, or a broader mix?
- Document type and volume: Does the tool handle structured legal text, unstructured files, or both?
- Level of analysis required: Do you need clause extraction, risk flagging, summaries, or comparative review?
- Integration needs: Will it need to work with your document management system, CRM, or e-discovery tools?
- Team expertise: How much training can your team realistically absorb?
- Budget: Are you looking for a subscription model, project-based pricing, or an enterprise deployment?
- Customization: Do you need the tool to learn your risk tolerance, terminology, or review standards?
A useful starting point is to focus on your biggest bottleneck. If contract review is slowing you down, a dedicated legal document analysis platform may be the best fit. If your documents are highly varied and unstructured, a more flexible extraction tool may be a better choice.
Pricing and Value Considerations
AI due diligence tools vary widely in price. Some offer modest monthly subscriptions, while enterprise platforms with advanced customization and support can cost significantly more.
Common pricing models include:
- Subscription pricing: Based on users, document volume, storage, or features.
- Project-based pricing: Based on the number of matters or documents reviewed.
- Enterprise licensing: Includes custom deployment, support, integrations, and advanced configuration.
When evaluating cost, look beyond the purchase price. Consider the time saved, the reduction in manual review, the speed of deal execution, and the potential cost of missing a key risk. Many vendors offer demos or trials, which can help you assess fit before committing.
Frequently Asked Questions About AI in Due Diligence
Can AI completely replace human due diligence experts?
No. AI is best used to support human experts, not replace them. It can speed up repetitive work and surface patterns, but human judgment is still needed to interpret findings, assess context, and make final decisions.
How accurate is AI for legal document review?
Accuracy varies by tool, document quality, and use case. Modern AI can be very effective at clause identification and data extraction, but important findings should still be reviewed by a human.
What types of due diligence can AI be used for?
AI can support many types of due diligence, including:
- Mergers and acquisitions
- Investment due diligence
- Vendor and third-party due diligence
- Regulatory compliance review
- Real estate due diligence
How do I protect data privacy and security when using AI tools?
Choose vendors with strong security controls, clear data handling policies, and appropriate compliance standards. Look for encryption, access controls, and certifications such as SOC 2 or ISO 27001 where relevant. You should also confirm where data is stored and who can access it.
What is the learning curve for these tools?
It depends on the platform. Some tools are designed for quick adoption, while others require more setup and training, especially if you want custom workflows or advanced analysis.
Can AI help identify fraud or hidden risks?
Yes. AI can help identify anomalies, unusual patterns, inconsistent reporting, and red flags in contracts or transaction data that may warrant closer human review.
Conclusion
AI is becoming an important part of the due diligence process. Used well, it can make reviews faster, more consistent, and more insightful without replacing human expertise.
The best approach is to use AI where it adds the most value: document review, clause extraction, risk flagging, summarization, and first-pass analysis. From there, your team can focus on judgment, strategy, and final decision-making.
For legal teams and business leaders, the result is a more efficient due diligence process that supports better decisions and reduces avoidable risk.