Oracle AI Database drives 87 percent faster data refresh

AI workloads are extending across enterprise infrastructure, and the data primitives that power them, vectors, embeddings, and knowledge graphs, now sit alongside the relational and document data organizations already manage. Organizations running these workloads across fragmented or single-purpose database systems face compounding costs in data latency, operational overhead, and redundant infrastructure that erode both financial agility and competitive positioning. To investigate the value of addressing this challenge, Nucleus interviewed Oracle AI Database customers and found that consolidating transactional, analytical, and integration workloads onto Oracle’s converged data platform reduced data refresh times by 87.5 percent by eliminating inter-tool latency, compressed database migration timelines from over a year to two months, and replaced fragmented toolsets with a single engine that natively supports vector search and generative SQL. As vector search, agent memory, and generative capabilities become standard database primitives, organizations that have already consolidated their data platforms are positioned to absorb AI workloads without a second integration cycle.