AI has been one of the biggest trends in the enterprise market, and it is consistently one of the top priorities for IT spend. However, CIOs and IT executives are just starting to catch on that enterprise storage plays a major role in the success of AI in enterprise environments.

As enterprises deploy generative AI (GenAI), they need storage solutions that can handle immense growth in unstructured data, while ensuring low latency for optimal performance. To support Agentic AI and other complex GenAI workflows, an enterprise’s storage infrastructure must be built for high speed, scalability, cyber resiliency, and 100% availability.

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A major key to success with AI in enterprise environments is using a generative AI-centric Retrieval-Augmented Generation (RAG) workflow deployment architecture. RAG significantly improves the accuracy and relevancy of AI models with up-to-date, private data from multiple internal data sources, including unstructured data and structured data. You should select a RAG solution that does not require any specialized equipment.

It’s not just about AI servers, which clearly are important, too, but it is also about the right vector databases, large language models (LLMs) and small language models (SLMs), and understanding what datasets are needed to have the AI workloads and workflows be accurate and constantly up to date. It’s about having the right enterprise storage that can handle AI applications and workloads.

Enterprises use next-generation enterprise storage platforms as the basis to optimize the output of AI models. It’s also important to look for the flexibility of using RAG in a hybrid multi-cloud environment. You’ll want to work with an IT solutions partner who understands how AI pipelines operate and defines the storage stack that delivers on performance, availability, and scale.

Enterprise Storage Must Evolve

Experts in the IT channel are saying that legacy business models centered on IT relationships and standard server builds are rapidly becoming obsolete. The best position to be in is to relook at your storage infrastructure and consider AI-optimized enterprise storage solutions that also deliver both business value and technical value. And, in today’s enterprise, storage teams not only need to have at least a basic understanding of AI, but they also need to understand the business value of data infrastructure.

Infinidat’s partner Stan Wysocki, President, Mark III Systems, a recognized expert in enterprise data storage, recently shared a fascinating insight. He said that “claiming to have ‘AI in the platform’ is no longer a differentiator. What matters now is: can your storage keep up with rapidly advancing GPUs and next-gen AI models? As NVIDIA accelerates compute performance, literally every year, storage must evolve just as rapidly to avoid becoming a bottleneck. The vendor that leans into this challenge with genuine innovation will lead. Those that don’t will fall behind.”

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Augmenting AI Models and Reducing AI Hallucinations

Representing a new phase of innovation, enterprise storage-centric RAG augments AI models using relevant and private data retrieved from any NFS dataset – file or database. RAG enables enterprises to auto-generate more accurate, more informed and more reliable responses to user queries. It enables AI learning models (i.e. LLMs or SLMs) to reference information and knowledge that is beyond the data on which it was trained, continuously refining a RAG pipeline with new data and keeping the AI source data up to date with the most current information.

Without iterative updating and fine-tuning of these models, the return of a query will often deliver incorrect or misleading results referred to as “AI hallucinations.”

AI hallucinations appear as factually inaccurate content, false attributions, or citations of nonexistent information. The RAG workflow has emerged as a key tool to bridge this gap and provide continued refinement of data queries. RAG combines the power of generative AI models with enterprises’ active private data to produce continuously updated, correctly informed responses to live queries.

To address this challenge in GenAI, Infinidat is making its architecture available for enterprises to continuously refine a RAG pipeline with new data, thereby reducing the risk of AI hallucinations. By enhancing the accuracy of AI model-driven insights, Infinidat is helping to advance the fulfillment of the promise of GenAI for enterprises.

Enterprise customers can take advantage of the InfiniBox® system’s high performance, industry-leading low latency, and unique Neural Cache technology, enabling delivery of rapid and highly accurate responses for GenAI workloads. Infinidat’s solution can encompass any number of InfiniBox platforms and enables extensibility to third-party storage solutions via the file-based NFS protocol.

Implications for CIOs

As they navigate the dynamic nature of AI deployments, CIOs and their IT teams need to remember three key points:

  • Enterprise storage must be part of your enterprise AI strategy.
  • Use a RAG AI architecture that runs on existing infrastructure – without requiring specialized equipment.
  • Choose an enterprise storage platform that is optimized for AI workloads and applications and delivers the highest performance, lowest latency, cyber resiliency, and 100% availability − all at multi-petabyte scale.

The IT decision-makers who embrace these key takeaways will be the leaders who are catching on faster than others – and, as a result, increase the competitive advantage for their enterprise.

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