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Cheat Sheet
- White space. There are opportunities for use of artificial intelligence (AI) and generative AI (GenAI) in mergers and acquisitions (M&A) to reduce costly billable hours and human error.
- M&A efficiency. Use of AI can solve for reliance on subjective analyses, predicting the likelihood of a successful merger and streamlining the integration process with real-time monitoring and corrective action planning.
- Adapt and thrive. The legal industry must adapt and innovate to harness the potential of AI technology, especially as GenAI tools become more prevalent.
- Protect your privilege. It is important to thoroughly vet AI usage, platforms, and vendors to ensure maintenance of the attorney-client privilege.
As AI technology matures — and new GenAI innovations emerge — the legal industry has begun to explore its potential applications in M&A activities, from deal sourcing and due diligence to contract negotiation and post-merger integration.
This article explores the opportunities within the legal marketplace for AI and GenAI in M&A using a white space analysis to identify the gaps between the current state of the industry and its AI-powered potential, while also addressing the pros and cons for in-house counsel and outlining considerations for maintaining attorney-client privilege.
White space opportunities
Enhanced due diligence
Current state: Traditional due diligence is costly because it is labor-intensive (read: billable hours), requiring the manual review of vast amounts of documents, financial statements, and contracts. It is prone to human error and often leads to extended timelines.
AI and GenAI potential: AI can streamline due diligence by automating the review process, identifying patterns, and flagging potential risks or inconsistencies. GenAI can assist by generating summaries of complex documents, drafting preliminary reports, and even predicting outcomes based on historical data.
White space: The white space lies in the development of specialized AI tools that can handle industry-specific due diligence, tailor outputs to specific legal requirements, and integrate seamlessly with existing M&A platforms.
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Automated contract drafting and negotiation
Current state: Drafting and negotiating acquisition agreements are time-consuming processes that involve extensive back-and-forth between parties. The complexity of M&A contracts often requires meticulous attention to detail.
AI and GenAI potential: AI can automate the initial drafting of contracts by using predefined templates and adapting them to the specifics of a deal. GenAI can assist in generating alternative clauses and scenarios, facilitating faster negotiations.
White space: There is an opportunity to develop AI systems that are capable of understanding and incorporating the nuanced language of M&A contracts, ensuring compliance with local and international regulations, and learning from previous deals to optimize future negotiations.
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Predictive analytics for deal success
Current state: Predicting the success of an M&A deal is inherently challenging, often relying on historical data and subjective analysis.
AI and GenAI potential: AI can analyze large datasets, including market trends, financial performance, and company-specific factors, to predict the likelihood of a successful merger or acquisition. GenAI can generate scenarios based on these predictions, offering strategic insights to both buy-side and sell-side clients.
White space: The legal marketplace could benefit from AI tools that not only predict deal success but also offer actionable recommendations on deal structure, financing options, and potential regulatory hurdles.
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Post-merger integration (PMI) optimization
Current state: PMI is often plagued by cultural clashes, operational inefficiencies, and misaligned expectations, leading to value erosion.
AI and GenAI potential: AI can facilitate PMI by monitoring integration progress, identifying potential issues in real-time, and recommending corrective and preventative actions. GenAI can generate communication plans, integration strategies, and even employee engagement initiatives to ensure smoother transitions.
White space: AI solutions tailored to the PMI phase, particularly in the areas of cultural integration and operational alignment, are underdeveloped. There is significant potential for AI tools that can dynamically adjust integration plans based on real-time data and feedback.
Pros and cons of AI in M&A
Pros
- Increased efficiency: AI reduces the time and effort required for due diligence, contract drafting, and negotiation, leading to faster deal closures.
- Improved accuracy: AI’s ability to process and analyze large datasets can result in more accurate risk assessments and predictions.
- Cost reduction: Automation of repetitive tasks can reduce legal costs, making M&A more accessible to smaller firms and companies.
- Enhanced decision-making: AI-driven insights can help attorneys and clients make more informed decisions throughout the M&A process.
Cons
- Loss of human judgment: Over-reliance on AI may lead to the undervaluing of human judgment and experience, which are crucial in complex negotiations.
- Data privacy and security risks: AI systems, especially those processing sensitive M&A data, are vulnerable to cybersecurity threats — and are valuable targets thereof.
- Ethical concerns: The use of AI in decision-making raises ethical questions, particularly regarding transparency, bias, and accountability.
- High initial costs: Implementing AI solutions requires significant upfront investment in technology and training (although these costs may be amortized across multiple client engagements, thus minimizing the large upfront investment law firms may need to make).
Attorney-client privilege considerations
One of the most critical issues in the use of AI in M&A is maintaining attorney-client privilege. The confidentiality of communications between a lawyer and their client is a cornerstone of legal practice, and any use of AI must safeguard this privilege.
Key considerations
- Data handling: AI systems must be designed to handle data in a way that maintains confidentiality. This includes using secure servers, encryption, and access controls.
- AI vendor relationships: When using third-party AI tools, it is crucial to ensure that the vendor's operations do not compromise attorney-client privilege.
- This may require thorough vetting of vendors and contractual agreements that address confidentiality.
- Additionally, it is important to understand and distinguish between closed-source AI tools and open-source AI tools (while open source AI allows for transparency and learns more broadly from public collaboration – it brings risk to maintaining the attorney-client privilege; oppositely, closed source AI are typically proprietary and can provide more security, privacy, and can be programmed to follow strict protocols, including preventing unauthorized access or copying of data).
- All that said, here is something to chew on: Since the third-party AI vendor owns and has access to its platform, (you can ask, but) how can it be proven that the AI vendor does not have access to, and has not accessed attorney-client-privileged data fed into its AI platform (both of which would bust the privilege)?
- Human oversight: To maintain privilege, attorneys should remain actively involved in the AI-driven processes. AI outputs should be reviewed and contextualized by a human lawyer before being shared with clients.
- Legal precedents: Attorneys should stay informed about evolving legal standards related to AI and attorney-client privilege. Courts may develop new rules or interpretations that impact how AI can be used in privileged communications.
Lingering questions
The integration of AI in M&A represents a significant white space in the legal marketplace. While there are numerous opportunities to enhance efficiency, accuracy, and decision-making, there are also challenges, particularly related to ethical considerations and maintaining attorney-client privilege.
As AI technology continues to evolve, the legal industry must adapt and innovate to harness its potential, but thorough vetting of AI vendors must include vendor confirmation of the following questions:
- Will the vendor use the confidential information fed into the AI tool in anonymous or aggregated form to improve their system?
- Who has access to confidential information?
- Who has ownership of ownership of the confidential information?
- How is the confidential information stored?
- What technical and organizational measures and safeguards does the vendor actually have in place to preserve confidentiality and thwart cybersecurity breaches?
- How can the confidential information be encrypted and ported out of the AI system should the AI vendor relationship end?
These questions are not by any means exhaustive and should be revisited as AI technology continues to evolve, but hopefully they provided an initial framework for considering the implementation of AI into the M&A process.
Disclaimer: The information in any resource in this website should not be construed as legal advice or as a legal opinion on specific facts, and should not be considered representing the views of its authors, its sponsors, and/or ACC. These resources are not intended as a definitive statement on the subject addressed. Rather, they are intended to serve as a tool providing practical guidance and references for the busy in-house practitioner and other readers.
