GPU power isn't the only thing that the AI revolution is built on; data storage is just as important. It's a critical part of the enterprise AI story that companies often overlook in their rush for computing power.
However, most eventually come to realize its importance, which is why Omdia's research company ESG found eighty-three percent of IT departments planning storage overhauls in the next 2 years.[1] But where's the gap? What stops legacy enterprise data storage from helping organizations meet their AI aspirations?
Where legacy storage lets AI down
There are several data storage stumbling blocks that eventually crop up in the race to get AI projects over the line.
Capacity
Data collection and preparation face a massive storage capacity problem. AI projects dramatically inflate data volumes, especially during data preparation and planning, where 52% of companies cite capacity as the main challenge (per ESG). AI data storage requirements can also fluctuate wildly, creating unpredictable capacity demands. So while traditional storage assumed predictable annual growth, AI inference demands flexible data storage solutions.
Modern storage platforms combine speed with guaranteed data reduction outcomes to make capacity planning easier. Meanwhile, many legacy systems bolt on measures like deduplication and compression as afterthoughts.
Performance
Storage performance is also critical for data collection and preparation in AI projects. Mid-sized shops and neocloud providers might obsess over GPU usage, but slow storage risks leaving those GPUs idle some of the time. Performance tops the list of challenges during model training, with 42% of companies telling ESG that it is their top problem. And while security teams want granular data tagging before models go live, legacy storage makes this painful.
Modern all-flash arrays that use NVMe from end to end boost performance dramatically, slashing latency and raising IOPs numbers.
Integration
While companies might assume legacy architecture handles AI workflows well, they eventually come to realize it doesn't. ESG survey data found that 71% of organizations can't get their storage to play nicely with AI pipelines. Legacy storage treats each data as a separate silo, which means physically copying data between namespaces.
Conversely, modern storage platforms unify data into single namespaces. They support block, file, and container workloads simultaneously, even extending to cloud environments. That creates simpler, automated environments where data is always close to where it should be in AI pipelines.
Security and compliance
European firms face a stark reality: regulators demand that data stays within borders. Cloud providers can't guarantee this. The sovereignty squeeze creates demand for on-premises infrastructure that acts like cloud storage solutions. This makes security and compliance a big problem for AI workloads, showing up mainly in the inferencing stage according to ESG, where it ties with performance as the main blocker.
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Download NowThe tension between compliance and capacity is evident in the ESG data too. While 80% push AI data to the cloud to help mitigate their legacy storage architecture's capacity and performance issues, almost as many (76%) insist their data crown jewels stay on-premises.
To get the best of both worlds, hybrid storage solutions are critical for enterprise AI. Modern storage platforms bridge the gap by running natively across AWS, Azure, and Google Cloud while retaining on-premises control. Their native migration tools and multicloud backup capabilities make hybrid deployments practical.
The path forward: modernizing storage for AI inference
Everyone eventually realizes that legacy data storage won't cut it, but the later in an enterprise AI initiative that happens, the more disruptive it will be. Over time, AI projects are likely to become more sophisticated and expansive as CIOs gain confidence, and the sooner the constraints of legacy storage will bite. Smart organizations will create a strategy for this early on.
Buying modern storage certified against NVIDIA's program will remove guesswork, as will working with data storage vendors that offer guarantees on capacity, longevity, and the support life cycle.
Get this right, and you can look forward to productive training runs and smooth AI inference scaling. Move too late, and you could end up watching your AI ambitions suffocate on yesterday's infrastructure.
[1] Source: Enterprise Strategy Group Complete Survey Results: The Critical Role of Storage in Building an Enterprise AI Infrastructure, September 2025 All research stats in their article are from this study.
