Hospitals are adopting new AI tools without ensuring their outdated electronic health record (EHR) systems can handle the demands. These decades-old systems, not designed for AI's real-time queries, are akin to an old runner being asked to sprint, risking breakdown. In healthcare, such system failures put patients at risk, potentially leading to doctors forgetting how to access information manually. While AI promises massive benefits like increased efficiency and faster documentation, few leaders are questioning the underlying infrastructure's capacity. If these systems collapse under the pressure, the consequences for large health systems and insurers could be severe and sudden. The problem is not unique to healthcare, but here, it impacts patient safety directly. AI chatbots querying data constantly will overload systems designed for intermittent, single-user access, causing widespread slowdowns. Rebuilding data architecture is expensive and risky, often deprioritized because it doesn't directly generate revenue. However, the rapid, unsupervised access by AI makes this an urgent issue that was not present even six months ago. Ignoring the capacity of these underlying systems could lead to a self-inflicted crisis across entire health systems. Stakeholders, including health system technologists, vendors, and AI builders, must take shared responsibility to confirm infrastructure readiness. It is crucial to focus on risk mitigation and understand what AI is being bolted onto and the potential for failure. Healthcare organizations must audit their infrastructure's capacity, proactively invest in upgrades, and commit to long-term modernization roadmaps. The true value of AI cannot be realized until the industry addresses the hidden risks of its rapid adoption on aging systems.