Artificial intelligence is entering pharmaceutical manufacturing through process models, soft sensors, adaptive process analytical technologies, digital twins, and increasingly direct links to advanced process control. These applications create a lifecycle problem that is insufficiently described when manufacturing-process change and algorithmic change are governed as separate maintenance events. A process may move beyond the conditions under which a model was calibrated or validated even when the algorithm itself is unchanged, while a model update may alter state estimation, predicted quality attributes, diversion decisions, or control actions despite unchanged equipment. This article develops a lifecycle account of pharmaceutical quality under such coupled change. It synthesizes evidence from continuous manufacturing, process analytical technology, digital twins, model-predictive control, machine-learning process models, and quality-system digitalization to examine how model applicability, measurement validity, control meaning, validation status, and process knowledge evolve together. The central argument is that the relevant object of lifecycle governance is the joint process–model state: the combination of the physical manufacturing condition, analytical interface, data lineage, model version, applicability domain, control linkage, and evidence supporting quality-relevant decisions.  Quality is threatened when one component changes while the others are treated as stationary. The framework therefore treats revalidation and comparability as impact-based questions concerning whether a transition changes a quality-critical inference, action, or evidentiary assumption.  This approach also makes knowledge transfer a quality function because process engineers, data scientists, quality units, and regulatory actors must preserve the reasoning that connects process evidence to model behavior and product disposition.