• A process called retrieval augmented generation (RAG) must be employed to retrieve relevant, up-to-date information from internal and external enterprise data sources and feed it into the language model to generate accurate and context-aware answers. But these data sources are often scattered across the globe, and the standard practice of copying all the data to a single repository for processing is costly, inefficient, and insecure.
• Data is constantly changing, and it’s difficult to keep track of what’s changed, so organizations may simply reprocess the whole data set in batch mode whenever necessary. That can result in answers that are outdated or incorrect and is an inefficient use of compute resources. 
• The key-value (KV) cache used to store context for large language models grows rapidly with each user interaction, making it increasingly difficult to manage at scale. Indeed, as the number of tokens used by an AI system increases with more complex questions and reasoning agents, the size of downstream artifacts like KV caches increases even faster.
• It’s hard to maintain consistent access controls when the number of data sources multiplies, raising concerns about privacy, compliance, and unauthorized data exposure.

If your organization’s AI strategy relies on AI agents and assistants that can provide trustworthy and accurate answers, it’s essential your inferencing systems are top-notch. That’s why it’s worth taking a close look at your storage infrastructure to make sure it has the architecture and capabilities you’ll need to evolve into a truly AI-first enterprise.