Using Artificial Intelligence in Corporate and M&A Legal Practice

The following article is a summary of the full practice note, available to Practical Guidance subscribers by following this link. Not yet a Practical Guidance subscriber? Sign up for a free trial here.

The article explores how artificial intelligence (AI) is reshaping the corporate and mergers & acquisitions (M&A) legal landscape. It outlines the fundamental concepts behind AI, discusses its practical applications in legal work, and addresses the challenges and ethical considerations that come with its adoption. Below is a comprehensive summary of the key points discussed.

Defining Artificial Intelligence

AI is described as computer software capable of executing specific algorithms designed to recognize patterns in large datasets. By analyzing these patterns, AI can make predictions, draw conclusions, and generate outputs that mimic human reasoning. The article explains that AI is not “intelligent” in the human sense but works through statistical and probabilistic methods driven by extensive training data.

Core Elements of AI

The article breaks down AI into four main components:

• Machine Learning: This involves using algorithms and statistical models that allow AI systems to learn from data. Large language models (LLMs), such as those powering chatbots, are highlighted as examples that recognize, summarize, translate, and generate text.
• Natural Language Processing (NLP): NLP enables AI to comprehend and process human language. Tools like Siri and Alexa demonstrate how AI can interpret spoken or written language.
• Machine Perception: Often referred to as computer vision, this element teaches AI systems to interpret visual data, allowing them to identify patterns, objects, and even people.
• Machine Control: Although this generally refers to AI’s ability to operate physical systems (such as autonomous vehicles), in legal practice, it underscores the broader idea of systems controlling processes without explicit programming.

Types of AI in Legal Practice

The article differentiates between two primary types of AI relevant to legal work:

• Extractive AI: This type of AI focuses on extracting relevant data from a given set of documents. For example, it can scan contract databases to locate specific provisions, such as those related to assignments.
• Generative AI (GenAI): Unlike extractive systems that pull preexisting data, generative AI creates new content—be it text, images, or audio—based on the data it has been trained on. Tools such as Lexis+AI exemplify this capability, especially when drafting or refining legal documents.

In both cases, data is the essential fuel powering these AI systems. The richer the dataset, the more effective AI becomes at identifying patterns and generating useful outputs.

Integrating AI into Corporate and M&A Practice

Meeting Client Expectations

Clients, particularly large corporate entities and in-house counsel, increasingly expect law firms to employ AI technologies to enhance efficiency and reduce costs. While some attorneys remain cautious or unfamiliar with the technology, the article stresses that failing to adopt AI could lead to competitive disadvantages.

Enhancing Legal Research

AI accelerates legal research by:

• Quickly providing initial answers to basic legal questions.
• Aggregating and summarizing relevant sources such as statutes, case laws, or legal articles.
• Allowing iterative refinement through prompt engineering, a technique that involves crafting clear, contextual, and specific queries to optimize AI responses. For example, instead of asking a general question about due diligence issues, a well-designed prompt will include detailed context and specific objectives, leading to a more useful and precise answer.

Streamlining Drafting Processes

One of the most potent applications of AI is generating and analyzing legal documents. In corporate and M&A work, AI can be used to:

• Draft comprehensive legal agreements, from board resolutions to confidentiality agreements.
• Generate initial drafts that capture key terms and structures, which can then be refined by attorneys.
• Assist in revising and improving existing documents by suggesting modifications to clauses or highlighting areas needing clarification.
• Produce specific clauses quickly, such as anti-sandbagging provisions or material adverse effect definitions. When provided with detailed prompts, AI can produce nuanced language that meets specific transactional needs.

Supporting Due Diligence

Extractive AI plays a vital role in due diligence by:

• Scanning large volumes of documents in data rooms.
• Extracting key contractual provisions such as change of control or assignment clauses.
• Reducing the time and expense associated with the initial document review, though the final output still requires thorough human review for accuracy.

Additional Applications

Beyond core legal tasks, AI is also useful for everyday business operations. It can:

• Draft basic correspondence and emails.
• Create client pitches, PowerPoint presentations, and manage time entry narratives.
• Assist with data analysis in spreadsheets, generate meeting transcripts, and summarize large documents, thereby streamlining routine administrative tasks.

Addressing Key Challenges and Risks

AI Hallucinations

A significant concern is the phenomenon of hallucinations, where AI outputs may include incorrect or misleading information, such as fabricated case law or erroneous statistical associations. This occurs when the AI reaches the limits of its training data and resorts to generating statistically probable—but not necessarily accurate—outputs. The article emphasizes that:

• AI-generated content should be used as a starting point.
• Every output must be meticulously reviewed by a qualified attorney to verify its accuracy.

Ethical and Professional Responsibilities

Lawyers must adhere to professional standards and ethical guidelines, such as those outlined in the Model Rules of Professional Conduct. Key considerations include:

• Maintaining Competence: Attorneys have a duty to remain informed about technological advancements, including AI, to ensure that their work products are both accurate and defensible.
• Supervision: Attorneys must review and validate work produced by junior associates or AI systems, ensuring that it complies with professional standards and ethical obligations.
• Client Disclosure: There may be a requirement to inform clients about the use of AI tools, especially if sensitive or client-specific data is being processed.

Data Privacy and Confidentiality

Given the risk of exposing client information when using AI tools—especially those that send data to external vendors—it is critical to:

• Understand the data retention policies of AI service providers.
• Ensure that robust cybersecurity measures are in place.
• Obtain informed consent from clients where necessary to protect sensitive information.

Conclusion

The article concludes that while AI presents transformative opportunities for efficiency in legal research, drafting, and due diligence, its deployment must be balanced with careful oversight and ethical considerations. AI should be viewed as an invaluable assistant that enhances, rather than replaces, the human judgment central to legal practice. As AI technology continues to evolve and integrate into corporate and M&A practices, proactive adoption—coupled with rigorous validation and adherence to ethical standards—will be essential for legal professionals seeking to maintain their competitive edge.

This article is a summary of the full practice note, written by the Practical Guidance Corporate and M&A team, available to Practical Guidance subscribers by following this link. Not yet a Practical Guidance subscriber? Sign up for a free trial here.