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Enterprise AI projects frequently fail not due to model limitations, but because underlying data architectures weren't designed for context-driven reasoning. Traditional analytics stacks that separate graph data, vector embeddings, documents, and transactional records across multiple systems create bottlenecks through excessive pipelines, duplication, and latency. Forrester research identifies multimodel data platforms as the solution, unifying multiple data models in a single engine to enable AI systems to retrieve and reason with full context. Companies like NVIDIA, Siemens, and Synopsys are already adopting this architectural approach to achieve accurate, explainable, and scalable AI in production environments.

Why AI Fails Without Context — And Why Multimodel Data Platforms Are Becoming the New Enterprise Standard • Arango

The Wrong Assumption Most Teams Still Hold About AI

Why the Old “Analytics Stack” Can’t Support AI in Production

Named Examples of the Shift Already Underway

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