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Generative artificial intelligence is becoming increasingly relevant to pharmaceutical formulation because it can propose compositions, excipient combinations, processing conditions, and optimisation trajectories that may not be obvious through conventional experimental design. These capabilities create the possibility of faster development, broader exploration of formulation space, and more systematic use of prior knowledge. Yet the same models that expand formulation creativity often operate through complex latent representations that are difficult to interpret. This creates a trust problem for both scientific and regulatory decision-making. The central problem is that an AI-generated formulation is not only a predicted technical solution but also a claim about product performance, manufacturability, and quality. If the rationale behind that claim cannot be explained, formulation scientists may struggle to convert model outputs into mechanistic understanding. Regulators may likewise find it difficult to assess whether the proposed formulation is supported by transparent evidence. Opaque formulation design therefore risks becoming a translational bottleneck rather than an innovation accelerator. This perspective develops a conceptual framework for dual-purpose explainability in AI-generated pharmaceutical formulation design. The framework is designed to serve two decision contexts simultaneously. Scientific decision-makers require explanations that clarify formulation logic, reveal influential variables, and support hypothesis generation. Regulatory decision-makers require explanations that are auditable, reproducible, uncertainty-aware, and connected to product quality and safety evidence. The article first defines the conceptual gap between existing AI formulation capabilities and explainability expectations. It then describes the logic of AI-generated formulation, identifies distinct scientific and regulatory explanation requirements, and analyses transparency barriers. The proposed framework integrates global model explanations, local formulation-specific explanations, mechanistic interpretation, uncertainty communication, and regulatory evidence packaging. Three tables summarise the gap analysis, explainability requirements, and framework architecture. The article concludes that explainability must be treated as a design requirement rather than a post hoc add-on to pharmaceutical AI. AI-generated formulation designs will become useful only when their rationale can be interrogated, documented, challenged, and connected to established principles of product and process understanding. A dual-purpose explainability framework can help move the field from black-box prediction toward transparent, accountable, and scientifically meaningful formulation intelligence.