For buyers evaluating an acquisition, the expensive part of due diligence is not simply collecting documents. It is determining which details could change the value of a business, undermine projected cash flows or make a transaction too risky to complete. Artificial intelligence can help review thousands of records, but faster analysis does not guarantee better investment decisions.
In 2026, dealmakers are increasingly incorporating AI into acquisition screening, contract review and transaction workflows. Deloitte’s 2026 Generative AI in M&A Pulse Study found that 90% of surveyed organizations were using generative AI in M&A, while 37% applied it across multiple stages of the deal lifecycle. These findings indicate adoption, not proof that every buyer is saving money.
For private equity firms, corporate acquirers and other investors, the economic question is more demanding: can AI lower the cost of examining a target without overlooking liabilities that could erase the expected return? The answer depends on the quality of the technology, the underlying records and the professionals responsible for interpreting its findings.
Why M&A Due Diligence Is Ripe for Automation
An acquisition can involve thousands of contracts, financial statements, tax records, employment documents, litigation files and regulatory disclosures. Reviewing this information manually can absorb substantial time from lawyers, accountants, investment professionals and technical specialists.
The pressure becomes greater when several bidders compete for the same target or financing and closing deadlines are tight. Buyers must investigate the business thoroughly without allowing the review process to become disproportionately expensive.
AI can reduce some of the repetitive work. It can classify documents, extract specified contractual terms, compare versions, summarize lengthy reports and direct reviewers toward unusual provisions. These capabilities are particularly useful when the objective is to find relevant evidence across a large document collection.
The advantage is not that every record receives a definitive automated judgment. Rather, deal teams may spend less time locating information and more time deciding what it means for the transaction.
How AI Is Changing the Due Diligence Process
AI-assisted diligence is developing across several areas, although capabilities vary by platform and implementation.
Legal diligence: Systems can identify termination rights, change-of-control clauses, indemnities, exclusivity provisions and restrictions on transferring contracts. Lawyers can then investigate provisions that might affect customer relationships, supplier continuity or the buyer’s ability to operate the acquired business.
Financial diligence: AI can help compare financial periods, organize transaction records and flag unusual movements in revenue, expenses or working capital. These signals can guide further investigation, but an anomaly alone does not establish fraud, misstatement or financial distress.
Tax and compliance: Document-search tools can help identify references to tax disputes, regulatory investigations, compliance failures and other potential exposures. Conclusions require qualified specialists who understand the applicable rules and the circumstances surrounding the records.
Technology and cybersecurity: AI can assist with reviewing security policies, incident documentation, software inventories and third-party dependencies. For technology businesses, these findings may influence integration costs and the investment required to address weaknesses after closing.
AI should not be confused with ordinary workflow automation. A conventional system follows predefined rules; generative AI can interpret and summarize unstructured information, but may also produce convincing answers that are incorrect or incomplete.
Can Automation Actually Reduce Deal Costs?
The most credible savings opportunity lies in reallocating professional time.
If AI can identify relevant clauses or organize financial evidence more quickly, advisers may spend fewer hours on initial searches. Investment teams may also screen potential acquisitions before committing to a full diligence process, allowing them to concentrate resources on opportunities that meet their strategic and financial requirements.
McKinsey’s January 2026 analysis, Gen AI in M&A: From theory to practice to high performance, reported that respondents using generative AI in M&A activities described average cost reductions of roughly 20%. Forty percent of respondents reported deal cycles that were 30% to 50% faster. These are survey-reported results, not a guarantee that another buyer will achieve equivalent savings or that AI alone caused the improvements.
The complete cost calculation must include software licenses, integration, employee training, data preparation, security controls and professional validation. If an automated review misses a significant liability, the resulting rework, delay or contractual dispute could outweigh the initial efficiency gains.
The relevant measure is therefore total transaction cost, not simply the time required to process documents.
Where Automation Can Increase Deal Risk
The most consequential AI failure may be an incomplete answer that appears credible.
Consider a target company whose major customer contracts contain different termination and change-of-control provisions. A summary that captures the general terms but misses an important exception could lead a buyer to overestimate how much revenue will remain after the acquisition.
Financial analysis presents similar challenges. A system might flag declining margins without understanding a one-time expense, or overlook a risk distributed across several records. Related-party transactions, contingent liabilities, disputed tax positions and inconsistent accounting treatments may require context that cannot be established from an isolated document.
Such findings can influence valuation, financing terms, warranties, indemnities and closing conditions. The risk is especially significant when deal teams treat an AI-generated summary as a substitute for examining the original evidence.
This matters in transactions where the purchase price already assumes a particular financial position. ALT Finance’s article on working capital in M&A explains how differences between expected and actual operating working capital can affect the final economics of a deal. AI may help identify those differences, but the contractual definitions and calculations still need proper verification.
Confidentiality and Data Governance Matter
Due diligence involves sensitive information about customers, employees, intellectual property, financial performance and pending disputes. Uploading it to an unapproved AI system can create confidentiality and security risks.
Before using AI, deal teams should establish which tools are permitted, how information is stored, who can access it and whether providers retain or reuse submitted data. They should also consider privacy requirements, contractual restrictions and professional confidentiality obligations.
Material findings should be traceable to their source documents. Teams need records showing what was reviewed, which issues were escalated and who approved the final conclusions.
The voluntary NIST AI Risk Management Framework offers a useful foundation for organizing AI risk management around governance, context, measurement and ongoing management. It is not a substitute for transaction-specific legal or security requirements, but it reinforces the importance of evaluating risks throughout a system’s use.
The Human Oversight Model
The most defensible approach assigns repetitive tasks to technology while preserving professional responsibility for material decisions.
A practical process should follow four principles:
- Define the assignment. Specify the documents, questions and limitations relevant to the review.
- Require source-linked findings. Reviewers should be able to trace important conclusions to original records.
- Escalate material exceptions. Potential tax exposures, litigation, unusual financial movements and restrictive contract terms require specialist attention.
- Verify before deciding. Lawyers, accountants, tax advisers and investment professionals should validate significant findings against underlying evidence and independent records.
The depth of review should reflect the potential financial consequences. A minor formatting inconsistency does not require the same attention as a provision that could jeopardize a major customer contract.
For investors in alternative assets, disciplined underwriting remains central. The expansion of private credit illustrates the importance of assessing borrower quality, repayment capacity and contractual protections rather than relying on headline returns alone. Acquisition diligence follows the same principle: better tools are useful only when they support sound judgment.
What AI Means for Private Equity and Corporate Buyers
AI could help private equity firms evaluate more opportunities, identify issues earlier and allocate specialist resources more efficiently. Corporate buyers may use it to compare acquisition targets, examine integration requirements and identify operational weaknesses before committing capital.
However, a larger acquisition pipeline is not automatically more valuable. Additional opportunities matter only when screening identifies businesses with credible economics and risks that the buyer can understand and manage.
AI capabilities may also become part of a target company’s operational assessment. Buyers could examine the quality of its data, technology controls and governance, particularly when future returns depend on digital transformation or AI adoption.
Ultimately, a faster diligence process is valuable when it improves decisions or reduces unnecessary work without weakening scrutiny. It should not become a reason to lower standards or rush a transaction.
Conclusion
AI in M&A due diligence can reduce repetitive work and help buyers focus on material risks. But lower processing costs do not necessarily mean lower total transaction costs, and faster reviews do not guarantee better acquisitions.
Risk-adjusted savings depend on secure systems, reliable records, traceable findings and qualified human oversight. For investors, the objective should not be to automate the greatest possible share of diligence. It should be to reach better-informed decisions at a defensible cost.
Frequently Asked Questions
What is AI in M&A due diligence?
It is the use of AI to help review and organize acquisition-related information, including contracts, financial records, compliance documents and operational data.
Can AI reduce acquisition costs?
It may reduce repetitive work and improve the allocation of professional resources. Actual savings depend on implementation costs, transaction complexity and the reliability of the results.
Which tasks can AI assist with?
Common applications include document classification, contract comparison, clause extraction, summarization, targeted searches and preliminary financial anomaly detection.
What are the main risks?
Key risks include missed exceptions, inaccurate summaries, fabricated outputs, poor-quality source data, confidentiality breaches and excessive reliance on automated conclusions.
Can AI replace M&A advisers?
No. AI can support their work, but qualified professionals remain necessary to interpret significant findings, assess transaction consequences and advise on decisions.
Disclaimer: This article is for informational purposes only and does not constitute individualized legal, financial, tax or investment advice.
David Seidman is the Principal of Seidman Law Group LLC, where he serves as outside general counsel for small to mid-sized companies. A seasoned legal advisor and entrepreneur, he specializes in contract negotiation, commercial litigation, and strategic risk management across the hospitality and finance sectors.
