At the same time, workloads are becoming more complex, spanning clinical inference, imaging, large language models, and emerging agent-based systems. So, as AI becomes more widespread in hospitals and labs, priorities are shifting toward greater control over data, performance, cost and governance. Success under these circumstances requires more than adding capacity. It demands aligning infrastructure with workload and regulatory requirements. Organizations that do this effectively are better positioned to scale AI, accelerate insights and deliver more personalized, efficient care