The rapid advancement of artificial intelligence (AI) has made companies that develop innovative AI technologies and those that leverage AI to create competitive advantages in their products and services highly attractive acquisition targets. These acquisitions present unique challenges since AI systems differ fundamentally from traditional technologies. Unlike static systems, AI often involves self-learning capabilities that can change its behavior over time, leading to novel risks or manifesting risks in unpredictable ways.
The regulatory landscape for AI is also complex and evolving, adding further intricacies that require specialized attention. While thorough due diligence is critical for identifying risks and opportunities, representations and warranties are equally important in mitigating these risks. To address the distinct risks of AI and to ensure the success of these acquisitions, traditional representations and warranties often need to be revisited and adapted.
In M&A transactions, representations and warranties provided by the seller to the acquirer or investor serve as a risk allocation mechanism and form the basis for potential indemnification claims if losses arise due to false or inaccurate representations and warranties. These must account for the unique characteristics of AI and the associated risks with its development, deployment, and use. Clearly define AI assets, including models, training data, algorithms, and system components.
These definitions form the foundation for representations and warranties and must strike a balance between comprehensive coverage and over-inclusiveness, which can lead to friction during negotiations. Ensure comprehensive disclosure of key AI assets, such as models, datasets, and related intellectual property.
Tailoring representations for AI acquisitions
Focus on the use cases, as risks can vary significantly depending on how AI technology is applied. For instance, AI in health applications will have vastly different regulatory and liability concerns compared to AI used in entertainment. Expand traditional ownership representations to include not only software but also AI-related models, datasets, and algorithms.
Ensure the target has clear title to these assets and has secured the necessary rights to use and commercialize them, particularly emphasizing third-party data licensing agreements or datasets created through partnerships. Augment standard data use representations to include assurances regarding the quality, accuracy, and legality of the data used for training AI systems. Address potential issues related to licensing, privacy, regulatory compliance, and provenance of the training data to mitigate downstream risk and liability.
Incorporate representations addressing AI-specific risks, such as transparency, bias, accuracy, and explainability. These representations will need to ensure that the AI systems are compliant with current and anticipated regulations and industry standards to reduce potential liabilities. The complexity of AI transactions continues to grow, exacerbated by advancements that reshape traditional software-as-a-service (SaaS) models and usher in the era of autonomous AI systems, often referred to as “agentic AI.” This new landscape requires a tailored approach to representations and warranties to manage new layers of risk, such as the unpredictability in the evolution of AI systems and the potential for unintended outcomes.
Capturing these novel risks and liabilities through precisely crafted representations and warranties is crucial for the successful acquisition and integration of AI companies. By addressing these issues with a bespoke approach, acquirers and investors can more effectively navigate the complexities associated with AI transactions, thereby aligning both parties’ interests and facilitating smoother and more secure M&A processes.
April Isaacs is a news contributor for DevX.com She is long-term, self-proclaimed nerd. She loves all things tech and computers and still has her first Dreamcast system. It is lovingly named Joni, after Joni Mitchell.
