Enterprises are facing growing pressure to prepare unstructured data for use in modern AI systems as organisations struggle to turn prototypes into production tools.
Around forty percent of AI projects advance beyond the pilot phase, largely due to limits in data quality and availability. Most organisational information now comes in unstructured form, ranging from emails to video files, which offers little coherence and places a heavy load on governance systems.
AI agents need secure, recent and reliable data instead of fragmented information scattered across multiple storage silos. Preparing such data demands extensive curation, metadata work, semantic chunking and the creation of vector embeddings.
Enterprises also struggle with the rising speed of data creation and the spread of duplicate copies, which increases both operational cost and security concerns.
An emerging approach by NVIDIA, known as the AI data platform, aims to address these challenges by embedding GPU acceleration directly into the data path. The platform prepares and indexes information in place, allowing enterprises to reduce data drift, strengthen governance and avoid unnecessary replication.
Any change to a source document is immediately reflected in the associated AI representations, improving accuracy and consistency for business applications.
NVIDIA is positioning its own AI Data Platform reference design as a next step for enterprise storage. The design combines RTX PRO 6000 Blackwell Server Edition GPUs, BlueField three DPUs and integrated AI processing pipelines.
Leading technology providers including Cisco, Dell Technologies, IBM, HPE, NetApp, Pure Storage and others have adopted the model as they prepare storage systems for broader use of generative AI in the enterprise sector.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
