How to Use AI for Due Diligence: Streamlining Investigations with Intelligent Tools
Due diligence is a critical part of transactions, compliance reviews, and risk assessment. Whether you are evaluating an acquisition, investment, partnership, or regulatory matter, the goal is the same: verify facts, identify risks, and make informed decisions. The challenge is that modern due diligence often involves large document sets, tight deadlines, and complex data that are difficult to review manually.
This is where AI can make a practical difference. AI-powered tools can help teams process documents faster, extract key terms, flag inconsistencies, and surface potential issues that deserve closer review. Used well, AI does not replace human judgment; it supports it by reducing repetitive work and improving coverage across large volumes of information.
Why AI Matters in Due Diligence
For lawyers, M&A teams, investors, compliance officers, and business analysts, due diligence errors can lead to missed liabilities, delayed deals, reputational harm, or regulatory problems. Manual review becomes especially difficult when teams must examine thousands of contracts, filings, reports, emails, and other records in a short period of time.
AI helps address these pressure points by automating parts of the review process and organizing information into a usable format. Instead of reading every document line by line, teams can use AI to identify where attention is needed first.
AI can help you:
- Speed up document review and extraction
- Improve consistency across large review sets
- Flag unusual terms, missing provisions, and data discrepancies
- Reduce time spent on repetitive manual work
- Support compliance review by systematically checking for required information
- Free legal and business teams to focus on analysis and decision-making
The result is a more efficient due diligence process with better coverage and fewer missed issues.
How AI Is Used in Due Diligence
AI is most useful when applied to specific parts of the workflow. Common use cases include:
- Contract review: extracting clauses, dates, obligations, and risk terms
- Data extraction: pulling structured information from unstructured documents
- Issue spotting: identifying inconsistencies, anomalies, or outlier terms
- Portfolio analysis: reviewing large sets of agreements or records across an organization
- Workflow automation: routing documents, classifying files, and organizing review outputs
The best results usually come from combining AI with human oversight. AI can triage and organize, while professionals validate findings and apply legal or business judgment.
Best AI Tools for Due Diligence
The right tool depends on the type of diligence you are performing. Some platforms are focused on contract analysis, while others are better for broader document understanding and workflow automation.
1. Kira Systems
Kira Systems is an AI-powered contract analysis platform that extracts specific clauses, provisions, and data points from legal documents.
Why it is useful:
Kira is particularly strong in M&A and other contract-heavy diligence projects. It can identify clauses such as change of control, indemnification, termination rights, and other terms that matter when reviewing obligations and liabilities.
Best fit:
- M&A due diligence
- Commercial real estate transactions
- Large-scale contract review
Pros:
- Strong clause identification
- Customizable data points
- User-friendly interface
- Useful reporting and integration options
Cons:
- Focused mainly on contract review
- Requires setup and training for best results
- Can be expensive for some teams
2. Evisort
Evisort is an AI-powered contract management and analysis platform that provides insight into contract portfolios.
Why it is useful:
For due diligence, Evisort helps teams understand the broader contractual landscape. It can identify counterparties, renewal dates, non-standard terms, obligations, and potential compliance issues across many agreements.
Best fit:
- Enterprise due diligence
- Contract portfolio review
- Post-transaction contract tracking
Pros:
- Strong contract lifecycle management
- Good NLP capabilities
- Visual dashboards
- Scales well for larger organizations
Cons:
- Broader than a pure due diligence tool
- May be a larger investment for smaller firms
- Often requires migration and integration work
3. Notable
Notable is an AI platform that extracts data from unstructured documents and helps automate manual workflows.
Why it is useful:
Notable is helpful when due diligence involves more than contracts. It can process documents such as financial statements, HR records, and operational reports, then turn that information into structured data for review and analysis.
Best fit:
- Multi-document due diligence
- Financial and operational review
- Workflow automation across document sets
Pros:
- Handles a wide variety of document types
- Strong data extraction capabilities
- Useful for legacy data digitization
- Can automate more than just extraction
Cons:
- May need customization for specific document types
- More enterprise-oriented
- Less specialized in legal clause analysis than contract-focused tools
4. BlackBoiler
BlackBoiler specializes in AI-powered contract review and risk identification.
Why it is useful:
BlackBoiler helps legal teams quickly flag provisions related to indemnity, liability, termination, exclusivity, and other risk-sensitive terms. This can speed up the review of a target’s contract set and help teams focus on problematic agreements first.
Best fit:
- Transactional law
- M&A diligence
- Financing and risk review
Pros:
- Focused on legal risk
- Efficient at surfacing problematic clauses
- Fits into legal tech workflows
- Improves consistency in review
Cons:
- Primarily contract-focused
- Flagged issues still require legal interpretation
- Pricing may be a factor
5. Nuance, now part of Microsoft
Nuance offers AI solutions for document understanding and automation.
Why it is useful:
Its document understanding tools can be applied to extract information from complex records, parse legal text, and automate classification tasks. This makes it useful for building custom due diligence workflows or supporting large-scale document processing.
Best fit:
- Custom due diligence workflows
- High-volume document ingestion
- Teams with technical implementation support
Pros:
- Mature AI technology
- Scalable and customizable
- Backed by Microsoft resources
Cons:
- Often requires technical expertise
- Not always an off-the-shelf diligence product
- Custom deployments can be costly
6. Eigen Technologies
Eigen Technologies is an AI platform built to extract and analyze data from unstructured documents, with strong use cases in legal and financial contexts.
Why it is useful:
Eigen can extract key data from contracts, financial statements, and regulatory filings, then structure it for downstream analysis. It is especially valuable when due diligence requires cross-referencing information across multiple document types.
Best fit:
- Financial due diligence
- Regulatory review
- M&A transactions involving complex document sets
Pros:
- Strong extraction from complex documents
- Handles multiple document types
- Produces structured data for analysis
- Well suited to legal and financial work
Cons:
- Premium pricing may apply
- Works best with clear data goals
- Requires workflow integration
How to Choose the Right AI Tool for Due Diligence
The best tool depends on your review scope, document volume, and internal workflow.
Consider the following:
- Scope of diligence: Are you reviewing contracts, financial records, compliance documents, or a mix of sources?
- Data volume and complexity: Large volumes of standardized contracts may be best handled by contract-focused tools. Highly varied records may require a more flexible platform.
- Integration needs: Check whether the tool works with your data room, CRM, deal platform, or internal systems.
- Technical expertise: Some tools are easy to deploy. Others require implementation support or technical resources.
- Budget: Pricing can vary widely, from SaaS subscriptions to enterprise contracts.
- Required features: Prioritize capabilities such as clause extraction, anomaly detection, risk flagging, or workflow automation.
In many cases, the best approach is to combine tools. For example, a team might use one platform for contract analysis and another for broader data extraction or reporting.
Pricing and Value Considerations
AI due diligence tools typically use one of three pricing models:
- Subscription-based SaaS: Monthly or annual pricing, often based on users, document volume, or features
- Usage-based: Fees based on the number of documents or amount of data processed
- Enterprise or custom pricing: Tailored agreements for larger organizations or specialized implementations
When comparing tools, do not focus only on price. Consider the total value, including:
- Time saved on review
- Reduced risk of missed issues
- Faster transaction timelines
- Improved consistency across reviewers
- Better visibility into risks and obligations
A tool with a higher upfront cost may still be worthwhile if it saves significant manual effort or helps avoid a costly oversight. Demos and pilot programs are often the best way to evaluate fit before making a larger commitment.
Frequently Asked Questions About AI for Due Diligence
Can AI completely replace human due diligence professionals?
No. AI is best used to support, not replace, human professionals. It is useful for data collection, document review, pattern recognition, and initial analysis. Human judgment is still needed for interpretation, negotiation, strategy, and final decisions.
How much data do I need for AI to be effective?
It depends on the tool and the use case. Contract analysis tools often work best with a substantial document set, while financial extraction tools need the relevant reports or statements. In general, more complete and representative data leads to better results.
Is data secure when using AI due diligence tools?
Reputable providers should have strong security controls, including encryption, access controls, and privacy compliance measures. Before using any tool, review its security practices, data handling terms, and compliance posture.
How long does implementation take?
Implementation can take anywhere from a few days to several months. Simple cloud-based tools are often faster to deploy, while enterprise platforms may require configuration, integration, and user training.
Can AI help identify fraud or anomalies?
Yes. Many AI tools are useful for spotting unusual patterns, inconsistencies, and deviations that may point to fraud, errors, or compliance concerns. These findings still need human review, but they can help teams focus their attention.
What does AI due diligence software typically cost?
Costs vary widely. Smaller contract analysis tools may start at a few hundred dollars per month, while enterprise platforms can cost tens or even hundreds of thousands of dollars per year depending on scope, usage, and customization.
Conclusion
AI is becoming an important part of the due diligence process for legal, financial, and compliance teams. It helps reduce manual workload, improve consistency, and surface risks faster, especially when dealing with large and complex document sets.
Tools like Kira Systems, Evisort, BlackBoiler, Notable, Nuance, and Eigen Technologies each serve different due diligence needs. The right choice depends on your review scope, document types, technical requirements, and budget.
For teams that want faster, more reliable investigations without sacrificing review quality, AI offers a practical way to improve due diligence workflows. The most effective approach is to use AI where it adds leverage and keep human expertise at the center of the final decision-making process.