Defines product, value, and portfolio management practices for AI-driven analytics; measures clinical and business outcomes, adoption, model performance, total cost of ownership, vendor performance, and return on investment, and retires solutions that do not meet established thresholds. Directs the development, acquisition, validation, deployment, and monitoring of machine learning, generative AI, natural language processing, predictive and prescriptive models, intelligent agents, and automation, with controls for performance, drift, bias, explainability, privacy, security, and patient safety.