INTELLIGENT TECHNOLOGY
Storage economics emerging as key factor in AI infrastructure planning
A s AI has evolved into continuous, production-scale data systems, the challenge of managing the explosive growth of data storage requirements is now at the same level as compute challenges. In a new survey of a cross-section of its largest global customers and distributors, Western Digital found that enterprises are prioritising infrastructure that delivers proven reliability, predictable economics and the ability to scale data over time.
The findings reinforce a structural shift in AI infrastructure. While compute resources are reused across training and inference cycles, the data generated by AI, including training datasets, inference logs, embeddings and outputs, continues to accumulate. As organisations move from experimentation to production AI deployments, infrastructure decisions are increasingly being driven by long-term data retention and operational economics. This creates a compounding demand for storage that persists independent of short-term compute cycles.
Key survey findings: Proven infrastructure gains favour: Organisations increasingly favour operationally proven infrastructure as AI deployments scale.
• 66 % of respondents say they have deprioritised or are considering deprioritising new technologies in favour of infrastructure that delivers consistent reliability and predictable performance at scale.
Reliability and AI workloads tie as top infrastructure priorities: As AI scales, the focus is shifting towards throughput-driven infrastructure optimised for sustained data movement at scale.
• 69 % of respondents prioritise supporting AI training and inference workloads.
• 69 % prioritise improving reliability and availability.
• Latency optimisation ranked lower( 7 %) than scalability, reliability and operational efficiency.
Capacity expansion and cost efficiency drive infrastructure planning: As AI data volumes grow, cost and capacity considerations are central to long-term planning.
• 87 % of respondents prioritise capacity expansion and total cost of ownership( TCO) optimisation.
Economics drive storage decisions: Economics and scalability remain primary drivers of large-scale storage architecture decisions.
• 74 % of respondents cite TCO, capacity and scalability as primary advantages of HDD-based infrastructure.
HDD-based infrastructure remains the foundation of AI-driven data growth: HDDs continue to represent the majority of storage capacity in many data centre environments.
• 70 % of respondents with visibility into their storage mix report operating HDD-majority infrastructure.
• 35 % report environments where HDDs represent more than 75 % of total storage capacity.
The survey results indicate that many organisations are designing infrastructure to support continuous AI data systems, not just discrete workloads or short-term experimentation. The findings reinforce a broader industry shift: AI infrastructure is increasingly being designed as a longlived data system, not simply a high-performance compute environment.
“ AI is fundamentally a data systems challenge, not just a compute challenge. Our customers are on the front lines of solving it, and their needs directly shape our innovation roadmap and the technologies we build for the AI era and beyond,” said Ahmed Shihab, Chief Product Officer, WD.“ While compute is reused, data persists and grows. The organisations that win in the next phase of AI will be the ones that build infrastructure designed for continuous data systems at scale, not just peak compute performance.” •
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