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The Use of AI in Mergers & Acquisitions

Mar 18, 2026

By Richard D. Harroch and David A. Lipkin

The legal landscape, particularly in the area of mergers and acquisitions, is undergoing a significant transformation driven by artificial intelligence (AI). What once often required a large team of analysts, lawyers, and advisors working around the clock can now be accomplished more efficiently and accurately with AI-powered tools. From initial valuation assessments to final contract negotiations, AI is reshaping many phases of the M&A lifecycle, enabling faster transactions, better decision-making, and more favorable outcomes.

Of course the AI tools are available to both buyers and sellers, so it remains to be seen which party will ultimately benefit the most. This article addresses primarily the use of AI tools on the seller side of private transactions, but AI will soon be in pervasive use on all sides of both private and public transactions.

The integration of AI into M&A processes represents more than just incremental improvement—it's a fundamental shift in how deals are sourced, evaluated, negotiated, documented, and closed. Traditional M&A transactions have always been resource-intensive, requiring extensive manual review of financial documents, legal contracts, due diligence materials, and market research; manual development of the purchase agreement and ancillary documents; and a lengthy and laborious process of negotiating, editing and proofreading them over weeks or months.

The complexity of the tasks and the volume of the information involved in modern M&A deals has only increased, making human-only approaches increasingly impractical. AI tools can process vast amounts of data in seconds, identify patterns and risks that humans might miss, and provide insights that dramatically improve deal quality and execution speed.

For M&A professionals, understanding how to leverage AI effectively has become essential to remaining competitive. Whether a deal participant is a business owner preparing to sell, an investment banker structuring deals, a serial acquirer, or legal counsel negotiating agreements, AI tools are now available to enhance the transaction process.

This article explores critical stages of M&A transactions and examines how AI is now available for deployment at each stage, along with specific tools that are transforming the industry. Of course, we used AI for research and editorial assistance in writing this article.

A word of caution: no matter how advanced AI-powered tools become, it will always remain important for humans to ultimately evaluate the output from such tools to ensure that it makes sense and does not have obvious errors.

1. Analyzing Whether the Seller Is Ready for an M&A Transaction

Before embarking on an M&A process, a seller must honestly assess whether its business is truly ready for a transaction. This assessment involves evaluating financial performance, organizational structure, customer concentration, legal compliance, intellectual property protection, and dozens of other business attributes that will be scrutinized during due diligence by the buyer and its legal and financial advisers, using their own AI tools.

AI tools can significantly accelerate and improve this readiness assessment. For example, Claude, Anthropic's AI assistant with advanced analytical capabilities, can review financial statements, organizational charts, customer lists, and contract portfolios to help a seller identify potential red flags that might concern buyers. By uploading key business documents to a secure site that can be evaluated by AI in a secure and confidential setting, sellers can receive comprehensive feedback on areas requiring attention before going to market.

ChatGPT and other large language models can analyze business operations and provide structured readiness checklists tailored to specific industries. These tools can review descriptions of business operations and compare them against typical buyer requirements, highlighting gaps that should be addressed. For legal readiness, tools like Harvey and Legora, and legal information services like Stella Legal, can employ a multitude of AI processes to scan corporate records, board minutes, and governance documents to identify compliance issues, missing documentation, or organizational irregularities that could derail a transaction.

More specialized AI tools can analyze financial data to identify unusual trends or inconsistencies that sophisticated buyers will discover, particularly now that they too will be using similar sophisticated tools. By catching these issues early, sellers can address them proactively before being forced into uncomfortable diligence discussions or demands for price reductions during negotiations, or even risking termination of the deal. The key advantage of using AI tools at this stage is the ability of a seller to see its business through a buyer's eyes before any actual buyer involvement, allowing it to strengthen weak points and maximize value.

2. Determining a Range of Valuation for the Seller

Accurate valuation is fundamental to successful M&A transactions. Overpricing scares away serious buyers, while underpricing leaves money on the table. Traditional valuation methods include analyzing comparable transactions, applying industry multiples, conducting discounted cash flow analyses, and adjusting for company-specific factors.

AI tools have transformed valuation analysis by providing access to vastly larger datasets and more sophisticated modeling capabilities. Platforms like PitchBook and CapIQ, increasingly enhanced with AI features, can identify comparable transactions across multiple dimensions—industry, size, geography, growth rate, and profitability. AI-powered algorithms can weight these comparables based on relevance and generate valuation ranges that reflect current market conditions.

The advanced data analysis capabilities of AI tools allow users to upload financial statements and receive detailed valuation assessments using multiple methodologies. But users should be mindful of data privacy and attorney-client privilege issues. By providing historical financials and business descriptions, sellers can generate comprehensive valuation reports that consider revenue multiples, EBITDA multiples, precedent transactions, and discounted cash flow projections. The AI tools can also identify which valuation metrics are most commonly used in specific industries and adjust valuations accordingly.

Machine learning models can also analyze how specific business characteristics impact valuation. For example, AI tools can quantify the valuation premium associated with high recurring revenue percentages, strong customer retention rates, or proprietary technology. These insights can help sellers understand which value drivers matter most to buyers interested in making acquisitions in their industry and focus their preparation accordingly. These tools can also review previous M&A transactions in specific sectors to identify valuation trends and patterns that inform realistic price expectations.

3. Identifying Logical Potential Buyers

Finding the right buyers—those who will see maximum strategic value in an acquisition and pay accordingly—is crucial to achieving optimal M&A outcomes. The universe of potential buyers includes strategic acquirers, private equity firms, family offices, and individual investors, each with different investment criteria and valuation approaches.

AI-powered market intelligence platforms can identify potential buyers by analyzing acquisition histories, stated strategic priorities, portfolio gaps, and geographic expansion plans. These tools scan press releases, SEC filings, earnings calls, and industry publications to build comprehensive profiles of active acquirers in specific industry sectors. Machine learning algorithms can predict which companies are most likely to be interested in a particular acquisition target based on their historical deal behavior and practices, as well as their current strategic positioning.

AI tools can also assist in researching potential buyers by analyzing publicly available information about companies and investors. By describing its business and its key characteristics, a seller can receive curated lists of likely acquirers along with reasoning about why each would find the company attractive. This analysis can include identifying specific synergies, competitive advantages the buyer would gain, and strategic rationales that could justify premium valuations.

LinkedIn and other professional networks, increasingly powered by AI-powered recommendation algorithms, can help identify relevant corporate development executives and private equity professionals who focus on the industry in which a seller operates. AI tools can analyze these contacts' backgrounds, recent activities, acquisition history, and stated current acquisition focus to prioritize outreach. CRM platforms with AI capabilities can even draft personalized initial outreach messages that reference specific reasons why a particular seller would be attractive to each potential buyer, significantly improving response rates compared to generic mass emails.

4. How to Use AI to Create a Pitch Deck for an M&A Seller

When a company prepares to sell, the M&A pitch deck—sometimes called a "teaser"—is one of the most critical documents in the process. It needs to tell a compelling story and give prospective buyers enough confidence to move forward. AI tools have made it much faster to build a document that is more polished than ever. Even if a seller only uses the AI tools to develop a first draft, it will save an immeasurable amount of time and reduce the risk that something critical has been omitted or misstated.

What a Seller's M&A Pitch Deck Typically Includes

A well-structured M&A pitch deck for a seller generally covers the following sections:

Executive Summary: A concise overview of the business, the opportunity, and the traction the company has achieved. This is often the first thing buyers read and must immediately capture attention.

Company Overview: History, mission, business model, products or services, and key competitive advantages.

Market Opportunity: The size and growth trajectory of the addressable market, along with the company's positioning within it.

Financial Performance: Historical revenue, EBITDA, gross margins, and growth trends, typically covering three to five years. Sellers often also include forward projections.

Customer and Revenue Analysis: Customer concentration, retention rates, recurring revenue breakdowns, and key contracts.

Operations and Team: Organizational structure, key management bios, and operational infrastructure that will facilitate the transition and maximize the likelihood of a smooth integration process.

Technology: Description of the company's key technology.

Intellectual Property: Description of key patents, trademarks, copyrights, and other intellectual property

Competitive Landscape: A discussion of the company's principal competitors and the advantages the company has over those competitors.

Growth Opportunities: Strategic levers a buyer could pull post-acquisition, such as geographic expansion, new product lines, or operational efficiencies.

AI Tools That Can Help Build the M&A Pitch Deck

AI tools can accelerate the creation process. For example, ChatGPT and Claude are excellent for drafting narrative sections, refining executive summaries, and generating compelling language around financial performance. Beautiful.ai, Genspark.ai, and Gamma.app use AI to design slides with professional layouts, saving hours of formatting work. For financial modeling and data visualization, Microsoft Copilot in Excel can help clean up and chart financial data quickly. The capabilities of these and other AI-powered tools are rapidly expanding.

Where to Find Sample M&A Pitch Decks

Before building a pitch deck, reviewing examples is invaluable. Strong resources include DocSend (which hosts real startup and M&A decks), SlideShare (searchable by deal type), Axial.net (focused specifically on middle-market M&A), and Pitchbook's blog, which regularly publishes deal decks.

With the right AI tools and a clear understanding of what buyers expect, a seller can produce a pitch deck that stands out in a competitive process

5. Identifying Investment Bankers or M&A Advisors

Selecting the right M&A advisor can dramatically improve the prospect of a successful transaction outcome. The best advisors bring industry expertise, buyer relationships, negotiation skills, and process management capabilities that justify their fees many times over. However, the M&A advisory landscape is crowded, and identifying advisors with relevant experience and strong track records requires careful research.

AI tools can streamline the advisor selection process by analyzing deal databases to identify which investment banks and advisory firms have completed transactions in the seller’s industry, size range, and geography. Platforms like Refinitiv and Bloomberg, enhanced with AI search capabilities, allow users to filter transactions by multiple criteria and identify which advisors consistently work on relevant deals.

AI tools can help a seller evaluate potential advisors by analyzing their websites, deal announcements, and published thought leadership to assess their industry expertise and transaction experience, and by developing comparative analyses highlighting each firm's strengths, specializations, and potential fit for a specific transaction. Of course these tools are also adept at identifying potential advisors of which a seller was not previously aware.

AI tools can also help prepare questions to ask during advisor interviews, ensuring a seller gathers the information needed to make an informed selection. For example, key questions to ask potential advisors may include:

How many M&A deals has the team that will be involved in this transaction done?
Can you provide us with a list of potential buyers and the contacts you have with those potential buyers?
How would you position our company to attract maximum value?
What is the likely range of valuation for the company? Why?
How long do you anticipate the process taking?
How do you calculate your fees?
Would you target a narrow list of buyers or do a broad outreach?
What particular expertise do you have in our market sector?
What suggestions would you have to make our M&A process faster and smoother?

Harvey, Legora, and similar legal AI tools can also review engagement letters from multiple advisors, comparing fee structures, expense provisions, indemnification obligations, tail periods, and other terms, and potentially suggesting clauses (such as a key person provision) that might protect a seller if its key advisor switches firms in the middle of a process. This analysis helps a seller ensure that it understands exactly what it is agreeing to and can negotiate more effectively.

Online reviews and reputation analysis tools powered by AI can aggregate feedback about various M&A advisors from multiple sources, providing insights into their responsiveness, effectiveness, and client satisfaction. While personal references remain important, AI-powered reputation analysis can supplement direct feedback and help identify advisors worth pursuing further.

6. The Use of AI in Drafting and Negotiating NDAs for Mergers and Acquisitions

The non-disclosure agreement (NDA) is an important document in M&A transactions. Before a seller shares financials, customer lists, or proprietary technology with a prospective buyer, the parties should agree on the scope of confidentiality, permitted uses of disclosed information, employee non-solicitation restrictions, and more.

What was once a straightforward preliminary step has grown increasingly complex, with sophisticated counterparties negotiating aggressively over definitions, carve-outs, and remedies. AI tools are now changing how NDAs are drafted, reviewed, and negotiated in M&A practice.

These tools can generate a first-draft NDA within seconds by drawing on vast training libraries of precedent agreements and current market standards. This first draft can be pro-buyer oriented or pro-seller oriented, or “middle of the road,” if that is called for, and one-way or two-way with respect to the scope of the covenants.

Rather than starting from a stale form, counsel can receive a jurisdiction-specific, deal-specific draft calibrated to the nature of the transaction. The AI tools can factor in the sensitivity of the information to be shared and applicable law to recommend appropriate definitions of confidential information, exclusions for publicly available information, and disclosure permissions for advisors, accountants, lenders, and regulators.

On the review side, AI tools can accelerate the redline process. Machine learning algorithms can compare a buyer’s proposed NDA against market standards and the seller’s preferred positions, flagging deviations in key provisions such as the definition of confidential information, the duration of confidentiality obligations, the scope of any standstill, and remedies for breach.

Rather than spending hours analyzing a buyer’s markup, counsel can receive a prioritized issue list identifying high-risk departures from standard terms alongside AI-generated suggested language to resolve each point. This enables attorneys to focus their expertise on genuinely contested issues rather than routine analysis of gaps between the two forms.

AI tools also enhance negotiation strategy by providing data-driven market intelligence. By analyzing many executed NDAs across comparable transactions, AI tools can suggest provisions (such as employee nonsolicitation provisions) that may be appropriate in certain contexts but not others, tell counsel what percentage and type of deals include such provisions, make intelligent recommendations with respect to how disputes are to be resolved, and guide the analysis of what residuals clauses are standard in technology sector deals.

Perhaps most valuably, AI reduces the risk of overlooking critical provisions in NDAs, the absence of which could create long-term risks. NDA breaches in M&A—particularly unauthorized disclosure of a seller’s proprietary technology or premature announcement of a deal—can result in significant damages and reputational harm. AI quality-control tools cross-check every draft against a checklist of essential provisions, ensuring that no clause is inadvertently omitted and that definitions are internally consistent.

For serial acquirers managing multiple simultaneous processes, AI makes it possible to maintain rigorous standards across every NDA without proportionally scaling legal costs.

Streamline AI, Legora, Luminance, and Harvey are particularly helpful in drafting and negotiating NDAs. M&A deal consultants such as Stella Legal deploy a number of these tools, rather than leaving it up to the client to navigate among individual tools themselves.

7. How AI Tools Can Be Used to Develop Disclosure Schedules for M&A Transactions

Disclosure schedules are an integral part of any M&A transaction. The disclosure schedules contain information required by the acquisition agreement—typically including lists of important contracts, intellectual property, employee information, and other material matters, as well as exceptions or qualifications to the detailed representations and warranties of the seller contained in the acquisition agreement.

An incorrect or incomplete disclosure schedule could result in a breach of the acquisition agreement and potentially significant liability to the seller or its stockholders. In contrast, a well-drafted disclosure schedule will provide substantial protection against post-closing allegations that the seller breached its representations and warranties.

Because poorly prepared disclosure schedules increase the risk of significant post-closing liability, it is important that they be compiled carefully and thoroughly. Disclosure schedules prepared at the last minute are likely to be incomplete or inadequate, creating problems to closing a deal or injecting unnecessary risk into the transaction.

Typically, the disclosure schedule process is undertaken by employees of the seller together with inside and outside M&A legal counsel. But the disclosure schedules can require a significant amount of time to assemble, and the initial drafting should be undertaken early on. It is not uncommon for disclosure schedules to go through a dozen or more drafts and negotiations with the buyer’s counsel.

The traditional process demands hundreds of attorney and employee hours and carries substantial risk—both from inadvertent omissions that trigger indemnification claims and from over-disclosure that provides buyers with renegotiation leverage. AI tools are changing this process by automating document review, ensuring consistency, and reducing both cost and liability exposure.

In contrast, AI-powered document review platforms can analyze thousands of contracts and corporate records in a fraction of the time required for manual review. Natural language processing algorithms can identify key provisions, extract material terms, flag unusual clauses, and automatically categorize documents by type and subject matter.

AI tools can also maintain consistency between the disclosure schedule and the underlying purchase agreement to which it relates, which will itself be undergoing multiple rounds of negotiations and revisions.

When preparing material contracts schedules, AI tools can scan entire contract repositories to identify agreements meeting specific materiality thresholds—such as annual payments exceeding defined amounts. The system then can extract critical metadata including party names, effective dates, payment terms, and material obligations, automatically populating structured schedules that would otherwise require days of manual compilation.

One of AI's most valuable capabilities is intelligent exception mapping. A single contract might contain provisions requiring disclosure across multiple schedules—for instance, customer agreements with indemnification provisions, liability limitations, and intellectual property warranties might need disclosure on litigation, obligations, and IP schedules respectively. AI systems can map documents to appropriate disclosure sections by analyzing both purchase agreement language and the substance of disclosed items, reducing the risk of incorrect placement or missing cross-disclosure.

For litigation and regulatory compliance, AI tools can conduct systematic searches of public records, court databases, and regulatory filings to identify matters requiring disclosure.

Intellectual property schedules can benefit significantly from AI's ability to interface with patent and trademark databases. The technology can extract patent numbers, filing dates, and legal status while analyzing claim language to assess scope and identify potential prior art affecting validity. For trademarks, AI tools can conduct comprehensive conflict searches and verify registration status across jurisdictions. AI tools can also identify gaps in IP protection by comparing product offerings against registered rights, and can review codebases for open-source licenses that impose restrictions requiring disclosure.

Beyond initial drafting, AI tools can provide crucial quality control by cross-checking schedules for completeness and consistency. Algorithms verify that disclosed information matches underlying records and identify inconsistencies across schedules—for example, ensuring contracts on material contracts schedules have corresponding related party disclosures when applicable.

Cost Savings. The financial impact is substantial. Traditional disclosure schedule preparation can consume large amounts of legal fees in middle-market transactions. AI tools can reduce these costs significantly while improving quality and comprehensiveness.

See this article on why disclosure schedules are so important: The Importance of Disclosure Schedules in Mergers and Acquisitions.

8. Preparing and Populating an Online Data Room

Virtual data rooms have become standard in M&A transactions, serving as secure repositories for a seller’s due diligence documents. However, organizing and populating data rooms—traditionally involving hundreds of hours of document collection, review, and indexing—remains one of the most time-consuming aspects of deal preparation and execution.

AI-powered document management systems can dramatically accelerate data room preparation. These tools can automatically classify documents by category, extract key information, identify missing items, and flag potential issues requiring attention. Platforms like Datasite, Intralinks, and DealVDR now incorporate AI capabilities that suggest appropriate folder structures based on industry and transaction type, then automatically organize uploaded documents into the correct locations.

AI tools can help create comprehensive data room indices and checklists tailored to a specific transaction. By describing its business and transaction type, a seller can receive detailed lists of documents typically requested during due diligence, organized by category with explanations of why each document is important.

The article The Importance of Virtual Data Rooms in Mergers and Acquisitions provides a comprehensive checklist of documents that should be in an online data room.

AI tools can review documents before they have been uploaded to data rooms, identifying privileged information that should be redacted, spotting inconsistencies between related documents, and flagging potential problems that might concern buyers. This pre-screening can prevent embarrassing discoveries during due diligence and allows sellers to prepare explanations for potentially problematic information before buyers raise concerns.

AI-powered optical character recognition (OCR) and document processing tools can convert paper documents and image files into searchable PDFs, extract data from scanned contracts and financial records, and create searchable databases of key terms across thousands of documents. This technology makes historical records accessible and useful rather than merely archived, significantly improving due diligence efficiency for both sellers and buyers.

9. Drafting and Negotiating a Letter of Intent

Letters of intent (LOIs) establish the basic framework for M&A transactions, including purchase price, deal structure, key terms, exclusivity periods, and conditions to closing. While not traditionally fully legally binding, LOIs set expectations and momentum that can strongly influence final outcomes.

AI tools can assist in drafting LOIs by providing relevant templates and suggesting terms based on market standards for similar transactions. They can generate initial LOI drafts based on deal parameters provided, incorporating provisions appropriate to the seller’s industry and transaction type. These tools can also explain each provision's purpose and implications.

These tools can review proposed LOIs from potential buyers, identifying unusual or unfavorable terms, and suggesting alternative language. Business advisors such as Stella Legal can also provide coordinated review across multiple AI tools. These services and tools can compare proposed terms against market standards, highlighting provisions that fall outside typical ranges. For example, if a buyer proposes an unusually long exclusivity period or unfavorable working capital adjustment, AI tools can flag these as negotiation points and suggest more balanced alternatives.

AI tools that are used more generally can now be customized for use in the M&A process. For example, that legal plugin for Claude enhances its ability to analyze complex legal provisions in LOIs, identifying potential ambiguities, conflicts between provisions, or missing terms that could cause problems later. By uploading buyer-proposed LOIs, sellers can receive detailed analyses of strengths, weaknesses, and recommended negotiation positions before responding.

10. Drafting and Negotiating M&A Purchase Agreements

The definitive purchase agreement represents the culmination of M&A negotiations, documenting all transaction terms, representations and warranties, indemnification provisions, closing conditions, and post-closing obligations. These complex documents, often exceeding 100 pages in length, including extensive exhibits and schedules, require sophisticated legal drafting and careful negotiation.

AI-powered tools are transforming the process of drafting and analyzing M&A purchase agreements. They can generate initial agreement drafts based on transaction parameters, incorporate specific deal terms, and adapt standard provisions to unique circumstances. More importantly, they can review draft agreements from opposing counsel, identifying unusual provisions, comparing terms against market standards, and suggesting specific language changes to better protect clients' interests.

M&A consultants such as Stella Legal can provide contract analysis capabilities through their partnerships with AI platforms (such as Sirion and Luminance). As an integration layer across AI tools, Stella Legal and other consultants can extract key terms from lengthy agreements, create summary charts comparing different draft versions, and highlight where negotiated changes have been accepted or rejected. This tracking capability is invaluable during multi-round negotiations involving complex agreements with numerous disputed provisions.

AI tools such as Claude's legal plugin enhance the contract review capabilities of a seller or buyer, allowing detailed analysis of representations and warranties, indemnification baskets and caps, material adverse change definitions, and closing conditions. By uploading agreement drafts, parties can receive explanations of complex provisions in plain language, analysis of how specific terms allocate risk between buyer and seller, and identification of potentially problematic language that could cause disputes later.

AI-powered redlining tools can automatically identify changes between agreement versions, generate comparison documents, and even suggest compromise language when parties are deadlocked on specific provisions. These tools accelerate the negotiation process by eliminating confusion about what has changed and focusing discussions on substantive issues rather than tracking edits.

11. Protecting and Rewarding Management and Employees in an M&A Transaction

AI tools can be helpful in suggesting steps to reward and protect the CEO, management team, and employees in an M&A transaction. Such suggestions could include:

Success bonuses and “carveouts” for the management team
Enhanced severance protection in the event of termination of employment without cause
Accelerated stock option vesting on close of the deal or on a “double-trigger” basis for a period following closing
Continuation of Indemnification agreements and charter protections for officers, and the procurement of the proper D&O tail policies
Employee hiring terms with the buyer
Analysis of proposed employment agreements for the management team by the buyer (including with respect to retention bonuses, non-competes, non-solicits, etc.)

See this comprehensive article for a description of these and other key compensation and employment considerations: How CEOs and Management Teams Can be Rewarded and Protected in an M&A Transaction.

12. Corporate and Stockholder Documents

AI tools can be useful in preparing the many corporate and shareholder documents necessary in an M&A deal, including:

Board of Director written consents or meeting minutes
Stockholder written consents or meeting minutes
Stockholder Proxy or Information Statements
Letters of transmittal
Secretary of State filings
Certificates of Merger
Officer certificates
Director resignations
Stockholder voting or support agreements

13. Closing the M&A Deal

The closing process involves satisfying all conditions precedent, obtaining required approvals, exchanging final documents, and transferring consideration. While conceptually straightforward, closings involve intense coordination among multiple parties and careful attention to detail to ensure nothing is missed at the finish line.

AI-powered closing management platforms can create comprehensive closing checklists based on transaction agreements, track completion status for each item, send automated reminders about approaching deadlines, and flag potential delays before they become critical problems. These systems can help avoid something falling through the cracks during the hectic final weeks of a transaction.

AI tools can assist in preparing closing documents by generating initial drafts of closing deliverables. By providing relevant information about the company and the transaction, a seller can quickly produce properly formatted documents that require review but eliminate the task of drafting from scratch. This capability is particularly valuable for smaller transactions where parties may not have extensive in-house resources.

These tools can review closing documents to ensure consistency with the definitive purchase agreement, verify that required deliverables have been prepared, and check that conditions precedent have been satisfied. This verification can prevent embarrassing last-minute discoveries that conditions weren't actually met or required documents are missing.

Document execution platforms like DocuSign and Adobe Sign, enhanced with AI capabilities, can automatically route signature pages to appropriate signatories, track signing status, send reminders about pending signatures, and compile fully executed documents. These platforms eliminate the logistical challenges of coordinating signatures across multiple parties, time zones, and jurisdictions, ensuring closings aren't delayed by administrative issues.

14. Post-Closing Integration and Compliance

While often overlooked in discussions of the use of AI in M&A, post-closing activities including integration planning, earnout tracking, purchase price adjustment provisions, indemnification claim management, and compliance with transaction covenants represent critical areas where AI tools can add significant value.

AI-powered integration management tools can help acquirers plan and execute post-closing integration by identifying synergies, tracking integration milestones, monitoring combined financial performance, and flagging integration risks requiring attention. These tools can analyze data from both legacy organizations to identify operational inefficiencies, redundant systems, and quick-win opportunities for cost reduction or revenue enhancement.

For transactions with milestones or other earnout provisions, AI tools can monitor financial performance against earnout targets, calculate earnout payments based on agreement formulas, and identify potential disputes before they escalate. Machine learning algorithms can even predict whether earnout targets are likely to be achieved based on current performance trends, allowing parties to proactively address problems.

Harvey, Legora, and similar tools can monitor compliance with post-closing covenants, track survival periods for representations and warranties, manage indemnification claims, and organize documentation supporting or defending against claims. This capability is particularly valuable for sellers who need to track multiple obligations across extended time periods.

These tools can also assist in preparing regular reports required under transaction agreements, analyzing whether specific events trigger notification obligations, and drafting required communications to transaction parties. By maintaining a clear record of post-closing compliance, parties can avoid disputes and demonstrate good faith performance of their obligations.

15. How AI Tools Can Be Improved for Mergers and Acquisitions

Despite the progress AI tools have made in transforming M&A processes, significant opportunities remain for improvement. Current AI tools, while powerful, still have limitations that prevent them from reaching their full potential in facilitating transactions. Understanding these limitations and the pathways to improvement can help shape the development of next-generation M&A AI solutions. Opportunities for improvement include the following:

Most current AI tools are generalists trained on broad datasets that span multiple industries and transaction types, and do not have industry-specific training and specialization in all areas. While this provides versatility, it often means the AI tools lack the deep industry expertise that human M&A advisors develop over decades of focused work.

Integration between different AI tools represents another significant opportunity for improvement. Currently, M&A professionals often use separate AI tools for legal review, financial analysis, buyer identification, document management, virtual data rooms, and other functions. These disconnected systems require manual data transfer, create inefficiencies, and prevent holistic analysis that considers all transaction aspects simultaneously. Future AI platforms should offer seamless integration across all M&A functions, allowing data to flow automatically between modules and enabling comprehensive analysis that considers legal, financial, strategic, and operational factors together.

It can be advantageous to use a service such as Stella Legal that has access and subscriptions to all the important AI legal tools, and can act as the implementor/manager of those tools for a specific deal.

Real-time market intelligence and predictive capabilities need substantial enhancement. While current AI tools can analyze historical transactions and identify patterns, they struggle to predict future market conditions, buyer appetite, or optimal timing for transactions. Advanced machine learning models should incorporate real-time data feeds from financial markets, M&A announcements, regulatory changes, economic indicators, and industry trends to provide dynamic recommendations about when to launch sale processes, which buyers are most active, and how market conditions might affect achievable valuations.

The ability to handle complex, multi-jurisdictional transactions requires improvement. Current AI tools generally work well for straightforward domestic transactions but struggle with cross-border deals involving multiple regulatory regimes, tax jurisdictions, currency considerations, and cultural factors.

M&A lawyers have built up expertise by having done hundreds of deals. The authors of this article alone have participated in over 500 M&A transactions and have acquired expertise that incorporates judgment, knowledge of the legal risks, and understanding of deal dynamics. Today’s AI tools do not fully reflect this type of expertise and the judgment it brings. By infusing this type of expertise into the capabilities of AI tools, these tools will be continuously improved over time.

The explanation and transparency of AI-powered recommendations need improvement to build user trust and facilitate adoption. Many current AI systems operate as "black boxes" that provide conclusions without adequate explanation of their reasoning. M&A professionals, particularly lawyers and advisors with fiduciary duties to clients, are understandably reluctant to rely on recommendations they cannot explain or validate. Enhanced AI systems should provide clear, detailed explanations of how they reached conclusions, cite specific data sources or precedents supporting their recommendations, and allow users to interrogate the reasoning behind suggestions. This transparency would enable professionals to trust AI insights while maintaining the ability to exercise independent judgment and explain recommendations to clients.

Cybersecurity and data privacy protections can be enhanced as AI systems handle increasingly sensitive M&A information. Current data room and AI analysis platforms maintain strong security protocols, but the integration of AI across multiple platforms and the use of cloud-based AI services can create new vulnerabilities. Future systems should incorporate advanced encryption, architectures that allow AI analysis without exposing underlying data, and robust audit trails that track every access to sensitive information. As regulatory scrutiny of AI data practices increases, particularly in jurisdictions with strict privacy laws like the European Union.

Parties should also be mindful that materials created with the use of AI tools may not be protected by attorney-client or work-product privileges. In February 2026, the U.S. District Court for the Southern District of New York in United States vs. Heppner ruled that materials an executive created using Anthropic's Claude and later shared with his lawyers were not protected by attorney-client or work-product privileges. See the discussion here on lessons learned from that case.

The development of industry standards and best practices for the use of AI tools in M&A could significantly accelerate improvement and adoption. Currently, each AI provider operates independently with its own methodologies, data sources, and quality standards. The M&A industry would benefit from collaborative efforts to establish standards for AI accuracy, transparency, security, and ethical use. Professional organizations, regulatory bodies, and leading AI providers should work together to create frameworks that ensure AI tools meet minimum quality thresholds, protect sensitive information, and serve the best interests of transaction parties. Such standards would give M&A professionals confidence in AI-powered recommendations and facilitate the responsible expansion of AI capabilities.

Conclusion on Use of AI in M&A

AI tools have already transformed how M&A transactions are conducted, bringing unprecedented efficiency, accuracy, and insight to every phase of the deal process, and this transformation will only accelerate as such tools improve rapidly over time. Tools like Harvey, Legora, Claude's legal plugin, and numerous other AI platforms are no longer experimental—they are becoming essential components of modern M&A practice. By their very nature, they automatically “learn” from each successive implementation, enabling exponential growth of their capabilities.

As these technologies continue to evolve and improve, M&A professionals who embrace AI capabilities will deliver superior results for their clients, while those who resist will find themselves increasingly disadvantaged in an AI-enhanced competitive landscape. The future of M&A is here, and it is critical that participants in M&A transactions not only be aware of these tools, but learn to use them effectively.

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About the Authors:

Richard D. Harroch is a Senior Advisor to CEOs, management teams, and Boards of Directors. He is an expert on M&A, venture capital, startups, and business contracts. He was the Managing Director and Global Head of M&A at VantagePoint Capital Partners, a large venture capital fund in the San Francisco area. His focus is on internet, AI, legaltech, and software companies, and he was the founder of several internet companies. His articles have appeared online in Forbes, Fortune, MSN, Yahoo, FoxBusiness, and AllBusiness.com. Richard is the author of several books on startups and entrepreneurship as well as the co-author of Poker for Dummies and a Wall Street Journal-bestselling book on small business. He is the co-author of the 1,500-page book “Mergers and Acquisitions of Privately Held Companies: Analysis, Forms and Agreements,” published by Bloomberg Law. He was also a corporate and M&A partner at the law firm of Orrick, Herrington & Sutcliffe, with experience in startups, mergers and acquisitions, and venture capital. He has been involved in over 200 M&A transactions and 250 corporate financings. He has acted as an M&A advisor to a number of Boards, companies, and CEOs. He is an advisor to Stella Legal and a number of legal and tech companies. He can be reached through LinkedIn.

David A. Lipkin is Senior Counsel in the Silicon Valley and San Francisco offices of the law firm of McDermott Will & Schulte LLP. He represents public and private acquirers, target companies, and company founders in large, complex, and sophisticated M&A transactions, primarily in the technology and life sciences spaces, as well as working with startups and other emerging growth companies. David has been a leading M&A practitioner in the Bay Area for over 25 years, prior to that having served as Associate General Counsel (and Chief Information Officer) of a subsidiary of Xerox, and practiced general corporate law in San Francisco. He has been recognized for his M&A work in the publication “The Best Lawyers in America” for a number of years, and is the co-author of the 1,500-page book “Mergers and Acquisitions of Privately Held Companies: Analysis, Forms and Agreements,” published by Bloomberg Law. David has also been a member of the Board of Directors of the Giffords Law Center to Prevent Gun Violence for over 20 years, and has served on additional educational and charitable boards. He has been involved in over 250 M&A transactions. He can be reached through LinkedIn.