The article is dedicated to examining structural managerial risks arising from the integration of artificial intelligence across the pharmaceutical value chain, from drug discovery to downstream market access decision processes. Relevance is grounded in the rapid expansion of AI-driven analytical, generative, and optimization systems that increasingly shape both research activities and commercial governance mechanisms. Novelty lies in the demonstration that risk exposure originates not from isolated misuse but from the underlying computational and organizational logic of AI architectures developed for discovery, clinical trials, supply-chain management, and quality-control operations. The work describes the structural mechanisms through which these systems influence downstream prioritization and allocation decisions and examines how cross-domain model transfer, data-driven inference, explainability gaps, and optimization pressure reshape market-facing decision-support practices. Special attention is paid to the ways in which predictive precision, dataset heterogeneity, and generative pipelines alter the boundaries of accountable decision-making in pharmaceutical market access. The article sets a goal to identify systemic sources of managerial risk and to outline conditions required for responsible AI deployment across organizational domains. Analytical methods are used to achieve this objective, supported by a broad review of current scientific literature. The conclusion discusses implications for governance, model design, and organizational oversight. The article will be useful for researchers, regulatory professionals, and managers seeking to align AI-enabled decision processes with principles of accountable pharmaceutical governance.