AI in Life Sciences isn’t struggling because of weak models—the technology can already classify safety cases, predict trial risk, identify patient cohorts, and generate narratives. The real barrier I uncovered through research in Pharmacovigilance, Clinical Operations, and RWE is the absence of a Decision Accountability Layer. While AI systems present outputs, they rarely capture who made the final decision, how AI influenced it, what evidence justified it, or how uncertainty was handled. In regulated environments, these elements matter far more than predictions themselves, because decisions—not algorithms—are audited. By studying XAI literature, PV research, regulatory guidance, and real workflow patterns, I found that explainability alone is insufficient. What’s missing is a UX framework that makes decision ownership explicit, preserves AI-to-human lineage, requires justification, and embeds audit-readiness into the workflow. My ongoing work focuses on designing this decision accountability layer for AI-assisted safety systems and beyond.