Outstanding Enterprise Storage Drives AI Adoption

AI, and especially the generative AI systems that form the basis for LLMs (Large Language Models), are attracting huge levels of interest from enterprise executives. At a time when many aspects of IT expenditure are being scrutinised, there seems to be an ever-expanding appetite for AI. In particular, executives are interested in developing internal LLMs to create the ultimate strategic asset – a highly customised, secure and continually evolving ‘enterprise data store’. 

Key motivators to LLM adoption

A wide variety of motivations are driving enterprises to develop their own LLMs, including a need for increased data privacy, data security concerns, legal issues, and a desire to implement their own internal knowledge management systems. A survey by Tata Consultancy Services (TCS) across 1,300 CEOs found that 51% of respondents had plans to build their own generative AI implementations. 

Given that we are still in the early adoption stage of the AI lifecycle, that’s a lot of interest. According to a recent Gartner report, it’s also a major reason why enterprise storage has risen up the strategic IT agenda. As evidenced in Gartner’s Top Trends in Enterprise Data Storage for 2025, enterprises are now prioritising storage performance, scalability and integration to support AI workloads. This article explains why enterprise storage technology has evolved to take centre stage in the AI revolution, and how investing in the right storage technology allows enterprises to embrace the development of their LLM systems with greater confidence.

Protection from AI hallucinations

Perhaps it is surprising to read that at the centre of successful enterprise adoption of AI is an advanced enterprise storage infrastructure. Having spent four decades working in the storage industry, I have observed that although it is enterprise critical, storage technology has traditionally been perceived as the routine and unglamorous part of IT infrastructure, rather than a driver of innovation.  This attitude is changing rapidly and the right sort of storage dramatically reduces the risk of AI hallucinations – those increasingly common situations when the answers delivered by AI are, quite simply, wrong! They might sound convincing, but the answers are factually incorrect. When this occurs, it can clearly leave an enterprise extremely vulnerable to all manner of problems, ranging from the obvious legal and compliance issues to credibility and reputational damage. 

AI hallucinations occur because the system basically makes up an answer - by piecing bits of disconnected data together that are out of context. Most commonly it happens because the LLM system lacked access to the exact information that the user was seeking and had insufficient datasets to learn from. Instead, it went on a virtual scavenger hunt and did the best it could. 

The key to making AI more accurate and relevant lies with the proprietary, up-to-date information that an enterprise has in its multitude of datasets sitting on a storage system. This information allows the AI system to refine and validate its response to a query, reliably delivering the correct answers each time. But it needs the right type of enterprise storage to reliably access this level of data volume at speed.

Benefits of RAG approach

This capability is made possible by a Retrieval-Augmented Generation (RAG) workflow deployment architecture, as part of an enterprise storage infrastructure. When an IT team deploys a storage infrastructure-led RAG architecture, this will significantly improve the accuracy and speed of its LLM systems. RAG enables enterprises to ensure that the answers from AI models remain relevant, up to date, within the right context, and it will look for the data sources across the enterprise that may have relevant information. By reducing the prevalence of “AI hallucinations”, RAG storage systems also eliminate the need to continually re-train AI models.

CIOs will be happy to learn that an enterprise can utilise its existing storage systems as the basis to optimise the output of new AI models, without the need to purchase any specialised equipment. This means that every LLM project within an organisation can adopt RAG as a standard part of its IT strategy. It is ultimately widening access to AI across every industry sector.

Enhanced cybersecurity and cyber-resilience

Having a storage infrastructure optimised for taking advantage of higher-quality data that is regularly updated from company datasets, databases and files, puts your enterprise into a position to mitigate the impact of AI hallucinations. But that’s not all. At a time when cybersecurity is the biggest threat facing business continuity for enterprises, the right enterprise cyber secure storage also bolsters cybersecurity in multiple ways - by enabling automated security responses to be triggered immediately if threats are detected. Advanced AI powered storage solutions can rapidly identify anomalous access patterns and potential security threats in real-time. They also use machine learning algorithms to detect potential cyber threats and automatically initiate protective measures, such as creating immutable snapshots or isolating affected data.

Through the adoption of advanced enterprise storage solutions, organisations can deploy their AI powered systems with confidence. They can not only access a robust and cyber-resilient infrastructure capable of supporting their desire to develop advanced internal LLMs, but also be confident in the accuracy of AI powered intelligence. In doing so, they can benefit from the enhanced business agility that comes with early AI adoption and the ability to compete more effectively in today’s data-driven world.