This research brief explains why enterprises need a unified approach to data and cloud architecture to fully realise the value of AI. With data now scattered across on-prem, multiple clouds and edge environments, organisations face fragmentation, bottlenecks and complexity that slow insight and innovation. Oracle’s native multicloud model offers a different path: one where data, applications and AI services work together seamlessly across clouds, without duplication, latency or lock-in.
Key takeaways
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Fragmented data is the biggest barrier to AI
Enterprises are struggling with scattered, inconsistent and hard-to-unify data that limits the effectiveness of analytics and generative AI.
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Traditional ETL-heavy integration cannot keep up
Constantly rebuilding pipelines across clouds introduces delays, duplication and high operational overhead.
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A single data platform simplifies AI adoption
Oracle Database 23ai and Autonomous Database support all data types and integrate cleanly with lakes, warehouses and cloud storage.
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Exadata X11M delivers high-performance AI workloads
Co-engineered hardware and software provide major gains for transactions, analytics and AI vector search.
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Native multicloud removes friction
Oracle Database@Azure, @AWS and @Google Cloud reunite applications and data, reduce latency and streamline management across providers.
Download the guide
See how a unified, native multicloud model can simplify your data landscape and accelerate AI across your organisation.
