How to Use AI for Due Diligence: A Practical Guide for Legal and Business Teams
Due diligence is often one of the most time-consuming parts of a transaction, compliance review, or risk assessment. Whether you are working on an M&A deal, an investment round, a contract review, or a regulatory investigation, the process usually involves reviewing large document sets, identifying issues, and confirming that nothing material has been missed.
AI is changing how this work gets done. Used well, it can speed up document review, improve consistency, and help teams focus on the highest-risk issues first. For lawyers, investors, and business leaders, knowing how to use AI for due diligence is becoming an important operational advantage.
Why AI Matters in Due Diligence
Traditional due diligence is thorough, but it is also labor-intensive. Teams often face:
- Tight timelines
- Large volumes of contracts and supporting documents
- A high risk of human oversight
- Inconsistent review standards across reviewers
- Rising costs tied to manual review hours
AI helps address these challenges by automating repetitive tasks, extracting key data, surfacing anomalies, and organizing information for faster analysis. It does not replace legal judgment, but it can make the review process faster, more consistent, and more manageable.
The main value of AI in due diligence is simple: it helps teams find what matters sooner.
Best AI Tools for Due Diligence
The right tool depends on the type of review you are running. Some platforms are built for contract analysis, while others are better suited for large-scale document review and e-discovery.
1. Kira Systems
Kira Systems is an AI-powered contract analysis platform designed to extract data points and clauses from large volumes of legal documents.
What it does:
- Reads and analyzes contracts
- Extracts key provisions and data points
- Identifies clauses such as termination, force majeure, and change of control
Why it is useful:
Kira is especially effective in M&A due diligence, where teams need to quickly review large sets of agreements and identify material terms.
Best fit:
- Transaction lawyers
- Corporate counsel
- Private equity firms
- Lease abstraction and regulatory compliance review
Pros:
- Strong contract review capabilities
- Customizable for specific data points
- Useful reporting and visualization features
Cons:
- May require setup and training
- Best suited to contract analysis rather than broader unstructured data review
2. Luminance
Luminance is an AI-powered legal review platform that uses natural language processing and machine learning to analyze documents and flag potential issues.
What it does:
- Reviews legal documents at scale
- Identifies clauses, discrepancies, and unusual provisions
- Flags potential risks for further review
Why it is useful:
Luminance is helpful when teams need to move quickly through large document sets and prioritize documents that deserve closer human attention.
Best fit:
- Large-scale M&A due diligence
- Corporate restructuring
- Complex litigation document review
Pros:
- User-friendly interface
- Strong visualization of findings
- Handles a wide variety of document types
Cons:
- Can be a premium-priced option
- Very specialized or highly unstructured data may still require manual work
3. ContractPodAi
ContractPodAi is an AI-powered contract lifecycle management platform with strong due diligence capabilities.
What it does:
- Automates contract review
- Extracts key data points
- Identifies risk and compliance issues
- Supports ongoing contract lifecycle management
Why it is useful:
It is a good option for teams that want due diligence tools as part of a broader contract management workflow, rather than as a standalone project tool.
Best fit:
- Legal departments
- Businesses with ongoing compliance needs
- Transactional due diligence and post-deal management
Pros:
- Broad CLM functionality
- Strong for risk identification
- Useful for ongoing compliance and scalability
Cons:
- May be more feature-rich than needed for one-off diligence projects
- Implementation can be more involved
4. LegalSifter
LegalSifter focuses on AI-assisted contract review and comparison against playbooks or internal standards.
What it does:
- Reviews contracts against predefined legal criteria
- Flags deviations from company policy
- Identifies problematic clauses
Why it is useful:
LegalSifter is well suited to teams that want to check whether target contracts align with internal standards, investor expectations, or legal requirements.
Best fit:
- In-house legal teams
- Law firms
- Policy-based contract review
Pros:
- Strong for playbook-driven review
- Fast at flagging deviations
- Useful for identifying standard contract risks
Cons:
- More focused on contract review than broad data analysis
- Less suited to highly unstructured datasets
5. RelativityOne
RelativityOne is a leading e-discovery platform with AI features that can also support due diligence review.
What it does:
- Organizes and categorizes large document collections
- Uses machine learning and NLP to identify relevant documents
- Supports review of emails, chats, and other electronic data
Why it is useful:
RelativityOne is especially useful when due diligence includes litigation history, regulatory investigations, or large volumes of unstructured data.
Best fit:
- Large transactions
- Investigations
- Due diligence involving broad digital evidence sets
Pros:
- Highly scalable
- Strong workflow and collaboration features
- Handles many data types beyond contracts
Cons:
- More complex to implement
- Less specialized for contract analysis than dedicated contract AI tools
6. Everlaw
Everlaw is another e-discovery platform that uses AI and machine learning to speed up document review.
What it does:
- Supports predictive coding
- Uses conceptual search and clustering
- Helps teams prioritize relevant documents
Why it is useful:
Everlaw works well when teams need to review large datasets quickly and collaboratively.
Best fit:
- M&A
- Investigations
- Review of extensive digital records
Pros:
- Intuitive interface
- Strong collaboration features
- Efficient for large-scale review workflows
Cons:
- More e-discovery focused than contract-specific tools
- Less specialized for deep contract analysis
How to Choose the Right AI Tool for Due Diligence
Choosing the right platform starts with the scope of the review.
Consider the following:
- Scope of review: Are you reviewing contracts only, or also emails, financial documents, and other unstructured data?
- Deal size and complexity: Larger transactions usually require more scalable platforms.
- Risk focus: Do you need clause extraction, compliance review, litigation screening, or all three?
- Workflow integration: Will the tool work with your existing data room, legal tech stack, and team processes?
- Ease of use: A powerful tool is only useful if your team can actually use it efficiently.
- Budget and ROI: Consider not just license cost, but the time saved and risk reduced.
If your work is mainly contract review, tools like Kira Systems or LegalSifter may be the best fit. If you need to process broader datasets, platforms like RelativityOne or Everlaw may be more appropriate. For teams looking for an end-to-end CLM workflow, ContractPodAi may offer the right balance.
Pricing and Value Considerations
AI due diligence tools can be priced in different ways, including:
- Per-user licenses
- Per-document fees
- Project-based pricing
- Tiered subscriptions
When comparing options, do not focus only on the software price. A better question is whether the tool reduces review time, improves consistency, and helps your team avoid costly misses. In many cases, the value comes from faster turnaround and better risk detection, not just lower labor costs.
Before committing, request a demo and test the tool against a real use case if possible.
How to Use AI for Due Diligence in Practice
A practical AI-enabled due diligence workflow usually looks like this:
1. Define the review scope
Identify what you are looking for before you start. This may include key clauses, compliance issues, litigation risks, or unusual contract terms.
2. Prepare and organize the document set
Clean up files, remove duplicates where appropriate, and organize documents so the AI tool can process them effectively.
3. Configure the tool
Set up the relevant playbook, clause list, or review criteria. The better the setup, the more useful the output.
4. Run the AI review
Use the platform to extract data, flag risks, group documents, and prioritize what needs human attention.
5. Validate the results
AI output should always be checked by a qualified reviewer. Use human review to confirm context, nuance, and legal significance.
6. Summarize findings for decision-makers
Turn the review results into a clear issue list, risk summary, or diligence report that supports transaction or business decisions.
Frequently Asked Questions About AI for Due Diligence
Can AI completely replace human reviewers in due diligence?
No. AI is best used to support human reviewers, not replace them. It is strong at extraction, pattern recognition, and prioritization, but legal judgment still requires human expertise.
How accurate is AI at identifying legal risks?
Accuracy depends on the tool, the quality of the data, and the type of task. AI is generally strong for structured review tasks like clause extraction, but nuanced issues still need human review.
What kind of data can AI analyze for due diligence?
AI can analyze contracts, financial records, emails, internal memos, filings, and other text-based documents, depending on the platform.
Is AI useful for smaller due diligence projects?
Yes. Even smaller projects can benefit from faster review and more consistent analysis. Some tools also offer flexible pricing for limited-scope work.
How do I protect data privacy and security?
Choose a provider with strong encryption, access controls, and clear security practices. Make sure the platform meets your organization’s internal requirements and any applicable legal obligations.
How long does implementation usually take?
Some cloud-based tools can be deployed quickly, while more complex platforms may require more time for setup, training, and integration.
Conclusion
AI is becoming a practical part of modern due diligence. It can help legal and business teams move faster, review more consistently, and focus their attention on the issues that matter most.
The best results come from using AI for what it does well: document processing, clause extraction, issue spotting, and review prioritization. Human experts still need to interpret results, assess risk, and make final decisions.
If you are evaluating how to use AI for due diligence, start by matching the tool to the scope of your review, the type of data involved, and the workflow your team already uses. With the right setup, AI can make due diligence more efficient, more defensible, and more valuable.