Reproducibility is a foundational tenet of science. As artificial intelligence (AI) becomes increasingly embedded across science, the need to accurately document the provenance, structure, and behavior of training data, models, and workflows grows correspondingly. Metadata, understood as explicit and structured knowledge about data and related entities, is a critical yet often underexamined component of AI systems that helps address this need. High-quality metadata describing datasets, models, and workflows supports the FAIR (Findable, Accessible, Interoperable, Reusable) principles, strengthens reproducibility, and enables evaluation of AI-readiness by making data and models interpretable, traceable, and structurally consistent. Despite its central importance, sustained discussion of metadata as a core component of AI infrastructure remains limited. This article examines the role of metadata in advancing AI-enabled research, focusing on the design, implementation, and operationalization of metadata standards within an evolving metadata ecosystem. We first present a conceptual view of the metadata ecosystem, framed by data structure, data value, data encoding, and syntax standards, as a foundation for understanding how metadata enables AI and how AI contributes to metadata generation and refinement. We then introduce four case studies that illustrate how metadata can be generated, refined, and leveraged within AI workflows. The discussion synthesizes the cases, highlights limitations, including metadata quality challenges and the role of structured constraints in addressing AI errors, and relates each case to the metadata ecosystem dimensions it engages. Taken together, these cases illustrate that metadata is not a peripheral add-on but an essential component of AI-ready, FAIR-aligned, transparent, and reproducible research systems.
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