UK Trends and Developments Contributed by: Amélie Chollet, Hannah Curtis and David Dennis, CMS
leadership with assurances based on outdated risk assessment frameworks. Regulatory Arbitrage Becomes a Regulatory Trap The fragmented global approach to AI regula- tion creates another layer of complexity that challenges traditional compliance strategies. Life sciences companies have long managed regulatory complexity through careful market- by-market compliance planning. AI disrupts this approach by creating scenarios where compli- ance in one jurisdiction can create non-compli- ance in another. For example, an AI system designed to meet FDA requirements for explainability may fail to satisfy EU AI Act transparency obligations. An algorithm that complies with UK innovation- friendly guidance may violate more restrictive EU approaches. These are not simply matters of parallel compliance – they represent funda- mental conflicts in regulatory philosophy that require strategic choices about market access and risk tolerance. This regulatory fragmentation forces in-house counsel to make risk assessments that go beyond legal compliance to strategic business positioning. The question becomes not just “are we compliant?” but “which regulatory framework do we optimise for, and what are the strategic implications of that choice?”. The Liability Time Bomb: When Risk Materialises Years Later Traditional product liability follows predictable patterns – defects are typically discoverable relatively quickly, and liability theories are well established. AI systems can have the tendency to create latent liability that may not surface until years after deployment; AI-related liability can
emerge gradually as algorithms evolve, data- sets change or new use cases reveal unforeseen risks. This creates a particularly acute challenge for legal risk assessment. How do you quantify potential liability for an AI system that may develop new capabilities or exhibit different behaviours years after deployment? How do you advise leadership about risk tolerance when the full scope of potential exposure will not be apparent until long after business decisions have been made? The compounding effect of this latent liability is that organisations may be accumulating AI- related exposure across multiple deployments, creating portfolio risks that are difficult to assess and potentially difficult to manage through tradi- tional risk transfer mechanisms. Rethinking Legal Risk Architecture for the AI Era The strategic imperative for in-house legal teams is clear: the legal risk architecture that served life sciences companies in the pre-AI era requires fundamental reimagining. This is not about add- ing “AI risk” as a new category to existing risk registers – it is about recognising that AI integra- tion changes the nature of legal risk itself. The challenge for senior in-house counsel is not just managing AI risk – it is helping their organisa- tions understand that AI changes the fundamen- tal nature of legal risk in ways that require new approaches to risk assessment, mitigation and communication. AI will require legal departments to develop new frameworks for identifying interconnected risks, assessing evolving exposures, and communi- cating dynamic risk profiles to leadership. Tra- ditional approaches to risk quantification, which
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