SAP systems are deeply embedded, business‑critical, and designed to change slowly. Over time, meaning gets buried in legacy systems, configurations, customization, and undocumented logic. SAP has spent decades perfecting systems of record while AI is building systems of reasoning, and the real opportunity lies in connecting the two. They are complex systems, refined over decades to ensure precision and control. Yet while the data is reliable by design, its business meaning is often implicit and difficult for AI systems to interpret, which prevents AI from reasoning over it reliably, safely, and at scale. We know why AI systems struggle to interpret enterprise data correctly, that many existing semantic layers stop at technical abstraction, and why business meaning must be made explicit to scale trust, reuse, and automation.
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