The dominant narrative in enterprise data strategy has been that more data equals more value. However, as AI systems take center stage, this playbook is showing cracks due to the context gap between raw data and knowledge. The problem lies in the lack of understanding and context, which leads to misleading sales spikes, outdated database queries, false revenue alarms, and misguided customer outreach. Additionally, the architecture issue with centralized systems stripping away nuance and breaking easily when upstream changes happen is also a significant contributor to these problems. To address this, teams are experimenting with keeping data where it already lives and finding ways to query it directly in context, which requires a shift towards decentralized access patterns and data orchestration.