The Impact of Artificial Intelligence on M&A Deals

By Vivian Breier

Target Identification and Early Risk Assessment

Outside counsel rarely selects an acquisition target. That decision is usually made by the client, often with the assistance of investment bankers or internal corporate teams. Once a target is identified, however, legal counsel is asked to evaluate risk quickly, including whether the transaction is likely to clear regulatory review, whether the target is subject to pending litigation that could affect valuation or deal structure, and whether material contracts contain change-of-control provisions that could interfere with the transaction prior to signing. AI tools are beginning to accelerate that preliminary assessment process. Platforms that review public litigation records, regulatory filings, intellectual property databases, and financial disclosures can identify potential issues earlier and more systematically than traditional manual review.

Current tools can compile a target’s litigation history within minutes. Regulatory screening software can flag government contracting exposure or industry-specific compliance concerns before the client has fully committed to the transaction.

The primary advantage is speed and broader coverage. Faster identification of relevant issues allows counsel to focus more quickly on the questions that matter most. That does not eliminate the need for experienced legal judgment. Technology cannot replace an attorney who understands the current regulatory environment for a specific buyer or who knows from experience which change-of-control provisions present genuine transaction risk and which can be managed through waivers or negotiated solutions.

Due Diligence: Where the Practice Is Changing Most

Due diligence is where AI is having the most visible impact on M&A practice and where the implications for law firm staffing and transaction management may be most significant.

A typical diligence process involves reviewing a virtual data room containing thousands of documents. A mid-market transaction may involve 1,500 to 3,000 documents, while a large transaction may involve substantially more.

Traditionally, deal teams organize the diligence process by preparing diligence request lists, assigning document review responsibilities to associates and specialists, tracking outstanding materials, and ultimately preparing a legal due diligence memorandum for the client.

AI-assisted document review platforms are changing what is possible during that process. These systems can review large volumes of documents within hours, categorize agreements by type, identify provisions that differ from market norms, and flag inconsistencies or anomalies that might otherwise be overlooked.

Work that previously required teams of associates reviewing documents over several days can now often be completed much more quickly. That changes both how transactions are staffed and how rapidly counsel can identify key legal issues.

The consistency benefit is also significant. Unlike human reviewers, AI systems do not become fatigued late at night or apply inconsistent analysis from one document to another.

AI tools are also influencing the representations and warranties insurance (RWI) process. Diligence findings directly affect insurance underwriting, including both coverage and pricing. More systematic diligence review can improve the quality of the underwriting record and help identify issues earlier in the transaction timeline.

Even so, the core limitation remains unchanged. AI can identify that a key customer agreement lacks a material adverse change provision. It cannot determine whether that issue materially threatens the transaction or can be addressed through negotiated protections in the purchase agreement. Experienced M&A lawyers evaluate those findings in context. They consider customer concentration, revenue dependency, the likelihood of obtaining third-party consent, and the client’s broader business objectives and risk tolerance.

That analysis remains central to the practice of law. AI may identify the issue more quickly, but attorneys still must determine what the issue means and how to address it.

Post-Closing Work and Integration

From a legal perspective, many transactions generate their most difficult work after closing.

Post-closing matters can include satisfaction of outstanding closing conditions, obtaining third-party consents, securing clearance under the Hart-Scott-Rodino (HSR) Act, assigning key contracts, negotiating transition services agreements, advising on Worker Adjustment and Retraining Notification (WARN) Act obligations, addressing employee compensation and equity treatment, and managing indemnification escrows.

Studies from consulting firms have long suggested that a substantial percentage of large mergers fail to achieve the value anticipated at signing. Lawyers involved in post-closing disputes frequently see the consequences firsthand. Integration problems often emerge later as indemnification claims, earnout disputes, or working capital adjustment disputes arise. The legal work frequently continues well after closing.

AI tools are beginning to assist with portions of the post-closing process as well. Contract management platforms can track assignment obligations, monitor consent requirements, and organize customer, vendor, and licensing agreements throughout the integration period.

When integration problems develop into legal disputes, attorneys with access to organized transaction and contract data may be better positioned to advise clients efficiently and in real time.

Still, technology does not change the fundamental challenge of integration. Combining two organizations’ legal, compliance, human resources, and operational systems is as much a management exercise as a legal one.

That work requires lawyers who understand the client’s business, the acquired company’s risk profile, and the negotiated transaction terms well enough to advise on issues that do not fit neatly within the four corners of the purchase agreement.

AI can identify that a consent requirement was overlooked in a material contract. It cannot manage the resulting business relationship or advise the client on the practical consequences of that issue.

What AI Tools Cannot Do

At a basic level, AI systems are trained to recognize patterns in documents and language. They do not understand the transaction, the client relationship, or the broader commercial dynamics at stake.

M&A practice requires transaction-specific judgment. Lawyers must assess the particular target, the counterparty, the regulatory environment, the client’s business objectives, and the practical realities of integration and execution.

AI cannot determine whether a client has the operational capacity to integrate an acquisition successfully. It cannot reliably assess whether opposing counsel is negotiating strategically or merely delaying the process. It cannot independently evaluate how a changing enforcement environment may affect regulatory risk for a specific transaction. Those are not simply document review questions. They are judgment calls developed through experience.

There is also a significant interpretive limitation. AI systems answer the questions they are asked. They do not necessarily recognize when the underlying question is incomplete or misguided.

Knowing which outputs to trust, which require further testing, and which should be disregarded altogether requires practical transaction experience, including experience with failed deals, repriced transactions, and post-closing disputes arising from issues that initially appeared immaterial. That judgment cannot be automated.

Professional responsibility considerations also remain critical. Attorneys who advise clients, negotiate transaction documents, and approve diligence findings remain accountable for that advice. AI tools do not bear legal or ethical responsibility for transaction outcomes.

Reliance on unverified AI analysis in a live transaction could expose a client to undisclosed liabilities, delay regulatory approval, or materially affect transaction economics. AI may help identify issues, attorneys remain responsible for analyzing them and advising the client accordingly.

Conclusion

AI is already changing how M&A legal work is performed. Firms using AI-assisted diligence tools are completing certain workstreams substantially faster than was previously possible, and that trend is likely to continue.

The more important point, however, is that the core responsibilities of M&A lawyers remain unchanged. Clients still need counsel to advise them whether to proceed with a transaction, negotiate against sophisticated counterparties, assess risk under conditions of uncertainty, and solve problems that emerge during and after the deal process. Those skills are developed through experience, particularly through transactions that encountered unexpected complications rather than transactions that closed smoothly.

AI can improve efficiency and accelerate information gathering. It cannot replace the judgment required when significant legal and business decisions must ultimately be made.