Most M&A due diligence teams do not suffer from a lack of information.

They suffer from too much information, stored in too many places, reviewed by too many people, under intense time pressure.

The virtual data room may contain thousands of files. Functional teams are reviewing contracts, financial reports, customer data, employee information, technology documentation, compliance records, and operating procedures.

At the same time, management meetings, diligence requests, risk discussions, and follow-up questions are generating even more information.

This creates a familiar problem:

The deal team spends a significant amount of time searching, organizing, summarizing, comparing, and reconciling information before it can begin making decisions.

This is where AI can create meaningful value.

The best use of AI in due diligence is not to replace experienced functional experts, attorneys, accountants, or deal professionals.

It is to help those experts review information faster, identify inconsistencies earlier, and focus their attention on the issues that could affect valuation, deal structure, integration, separation, or closing readiness.

Here is a practical approach.

1. Start with a controlled and secure AI environment

Before uploading any transaction information, confirm that the AI tool is approved for confidential company data.

Public AI tools should not be used for sensitive transaction materials unless the organization has specifically approved their use and established appropriate data protections.

The deal team should define:

• Which documents may be analyzed
• Who may access the AI workspace
• How outputs will be reviewed
• How confidential information will be protected
• Whether prompts and files are retained
• How the tool complies with the NDA and data-room restrictions

Governance is not an administrative detail. It is the foundation for using AI responsibly in a transaction.

2. Organize the diligence materials before analyzing them

AI will not fix a poorly structured diligence process.

Before analysis begins, organize documents by functional workstream, such as:

• Finance
• Tax
• Legal
• Human Resources
• Commercial and customers
• Operations
• Information technology
• Cybersecurity
• Regulatory and compliance
• Real estate
• Integration or separation planning

Use consistent file names, document dates, versions, and categories.

This makes it easier for the AI tool to understand the context and reduces the risk that outdated or duplicate documents will distort the analysis.

3. Use AI to create an initial document inventory

One of the first high-value applications is document classification.

An AI tool can review a large set of files and create an inventory showing:

• Document name
• Document type
• Functional owner
• Date
• Relevant legal entity
• Period covered
• Key topics
• Potential duplicates
• Missing information
• Suggested diligence category

This can save hours of administrative effort and provide the deal team with a clearer picture of what has actually been provided.

It also helps identify an important diligence issue early: documents that were requested but never delivered.

4. Summarize documents using transaction-specific questions

Generic summaries are rarely enough.

The AI tool should be directed to analyze each document from the perspective of the transaction.

For example, instead of asking:

“Summarize this customer agreement.”

Ask:

“Identify the contract term, renewal date, termination rights, change-of-control provisions, assignment restrictions, pricing obligations, customer concentration risks, service-level commitments, and any provisions that could affect the acquisition or integration.”

For an employee plan, ask the tool to identify:

• Change-in-control payments
• Retention obligations
• Severance provisions
• Unfunded liabilities
• Benefit continuation requirements
• Collective bargaining obligations
• Key-person dependencies

The quality of the AI output depends heavily on the quality of the question.

Good prompts should reflect the actual decisions the deal team needs to make.

5. Compare information across multiple documents

This is where AI can move beyond basic summarization.

During diligence, critical facts are often inconsistent across the data room.

A customer list may not agree with reported revenue. An organization chart may not match the employee census. A technology inventory may conflict with the separation plan. A contract schedule may not include all agreements referenced in the financial records.

AI can help compare information across documents and flag inconsistencies, such as:

• Different customer revenue figures
• Conflicting employee counts
• Missing legal entities
• Unexplained financial variances
• Inconsistent contract dates
• Duplicate assets
• Unsupported management assumptions
• Gaps between the operating model and the separation plan

These inconsistencies do not automatically mean there is a problem.

They indicate where human review is required.

6. Use AI to strengthen the diligence request list

A traditional diligence request list is often static. It is prepared at the beginning of the process and updated manually.

AI can help turn it into a more dynamic tool.

Based on the documents received, the AI tool can suggest follow-up questions such as:

• What information is incomplete?
• Which assumptions lack supporting evidence?
• Which contracts require additional review?
• Which liabilities are not quantified?
• Which operational dependencies remain unclear?
• Which documents appear outdated?
• What information is required to validate management’s claims?

The deal team should review and approve every follow-up request, but AI can help identify gaps that may otherwise be missed.

7. Create a first draft of the diligence findings

Each functional team typically prepares a diligence summary covering findings, risks, recommendations, and required actions.

AI can help prepare the initial draft using a standard format:

• Finding: What was identified?
• Evidence: Which documents or management statements support it?
• Risk: Why does it matter?
• Potential impact: Could it affect valuation, the purchase agreement, integration cost, separation complexity, timing, or regulatory approval?
• Recommendation: What additional action is required?
• Owner: Who is responsible for resolving the issue?
• Deadline: When must it be resolved?

This gives functional experts a structured starting point.

The expert must still validate the finding, assess materiality, and determine the appropriate recommendation.

8. Connect diligence findings to integration or separation planning

A common mistake is treating due diligence as a stand-alone phase.

The most valuable diligence findings should flow directly into the execution plan.

For an acquisition, AI can help translate findings into:

• Day One requirements
• Integration workstreams
• Synergy assumptions
• Retention plans
• Technology migration activities
• Customer communication needs
• Regulatory actions
• Transition service requirements
• Integration risks and dependencies

For a divestiture, findings may become:

• Separation requirements
• Stranded-cost assumptions
• Shared-service dependencies
• Data migration activities
• Transitional service agreements
• Legal entity actions
• Contract assignments
• Employee transfer requirements
• Buyer readiness items

This connection prevents the deal team from rediscovering important issues after signing.

9. Maintain human review and accountability

AI can produce confident answers that are incomplete, inaccurate, or based on weak evidence.

Every important output should be reviewed by a qualified professional.

The team should require the AI tool to cite the source document, page, section, or data field supporting each conclusion.

No material finding should be accepted simply because the AI tool produced it.

The correct operating model is:

• AI performs the initial review.
• Experts validate the evidence.
• Deal leaders assess the business impact.
• Decision-makers determine the action.

10. Measure the return on investment

The value of AI should be measured, not assumed.

Useful metrics may include:

• Reduction in document review hours
• Faster identification of missing information
• Shorter turnaround time for diligence reports
• Number of inconsistencies identified
• Reduction in manual status reporting
• Earlier escalation of material risks
• Percentage of findings transferred into the integration or separation plan

The objective is not simply to use AI.

The objective is to improve the quality and speed of transaction decisions.

The Bottom Line

AI can materially improve M&A due diligence when it is used as a disciplined review and decision-support tool.

Its highest value comes from helping the deal team:

• Process large volumes of information
• Identify gaps and inconsistencies
• Draft structured findings
• Prioritize expert review
• Connect diligence results to execution planning

AI will not replace transaction judgment.

But it can reduce the time experienced professionals spend on low-value administrative work and increase the time they spend evaluating risk, challenging assumptions, and protecting deal value.

That is where the real return on investment begins.

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Need help in your next M&A due diligence phase?

Contact Peregoff & Company
b.peregoff@mergerintegrators.com