AI is advancing rapidly across healthcare, but deployment remains far more difficult than the hype suggests. Behind nearly every promising use case lies the same challenge: fragmented data, legacy infrastructure, regulatory complexity, and systems that were never designed for AI at scale. For organizations moving beyond experimentation, the question is no longer what AI can do—but whether the underlying data can support it reliably in real-world environments. What changes when AI becomes embedded into everyday workflows across R&D, operations, manufacturing, and support functions? And how do companies build systems that are secure, interoperable, and robust enough for one of the world’s most regulated industries?
