Exploring generative AI in cross-border M&A: Deals, data, and deep learning

Generative artificial intelligence (AI) is revolutionizing business operations and reshaping the landscape of mergers and acquisitions (M&A) by driving deal flow and playing a crucial role in deal execution. In the context of Canada-US cross-border transactions, where legal frameworks and regulations vary, AI is both a challenge and an opportunity for strategic advantage.

When it comes to cross-border M&A, generative AI is playing a pivotal role in influencing key legal considerations, diligence concerns, regulatory dynamics, and value creation opportunities. This technology is not only transforming how companies operate but also how M&A deals are structured and executed in the modern business world.

Generative AI systems, which have the ability to create content, code, and insights based on massive datasets, are becoming integral to acquisition strategies. The 2024 Laws of AI Traction report by Dentons revealed that 70% of business leaders are looking to leverage M&A to enhance their AI capabilities in the next few years. Acquiring businesses that are AI-driven is seen as a faster and more scalable approach compared to building those capabilities from scratch.

The primary categories of AI target businesses in M&A transactions typically include AI-powered Software as a Service (SaaS) tools, sector-specific AI platforms like legal tech or health data, and proprietary data assets that are valued as core intellectual property for training models. However, acquiring such targets comes with unique legal and technical diligence challenges, particularly in areas such as copyright issues, fair use, and privacy regulations.

Buyers in cross-border M&A deals involving AI-driven targets must pay special attention to aspects such as clear ownership of training data and outputs, algorithm auditability, and compliance with evolving AI frameworks like Canada’s proposed Artificial Intelligence and Data Act (AIDA) or state-level regimes in the US. Due to the complexity of AI assets, deal terms and transaction documents need to be tailored, forward-looking, and jurisdiction-specific to effectively manage risks and protect the value of the deal.

In dealing with AI-driven businesses, contractual protections play a crucial role in addressing key risks, ensuring compliance with laws and regulations, and allocating liabilities appropriately. Representations and warranties must cover areas such as ownership and provenance of training data, ownership or licensing of AI models and outputs, compliance with applicable laws, and national security concerns related to critical technologies or infrastructure.

Indemnification provisions should also be expanded in deals involving AI, covering risks such as copyright infringement, privacy violations, and biases or safety concerns in AI outputs. Negotiating indemnity caps, baskets, and survival periods becomes essential in deals where AI tools have significant impact or are consumer-facing. Moreover, covenants and post-closing obligations need to be carefully negotiated to address ongoing compliance and operational issues related to AI technologies.