AI workloads as the key driver of rising data center capacity
AI is reshaping how we think about infrastructure. Over the next few years, the systems that carry your business data will face increasing strain. Not because apps are getting bigger, but because AI workloads are pushing performance demands far beyond what traditional IT architectures were designed to handle.
McKinsey predicts global data center capacity will nearly triple by 2030. The real kicker? Around 70% of that demand comes from AI. These are not gradual shifts. They’re exponential, driven by AI training and inference tasks that consume more compute power and require much faster data access than typical business applications.
The World Economic Forum confirms this shift, projecting the data center industry will grow from $242.7 billion today to nearly $584 billion by 2032. This isn’t just about more storage, it’s about smarter infrastructure. AI models feed on unstructured data: video, images, logs, text, all in random, high-volume bursts. These workloads strain traditional systems that were built for linear, predictable requests. That mismatch burns budgets and stalls timelines, your GPUs are sitting idle, waiting for data instead of doing the work you paid for.
If you’re leading a business, the message is clear: don’t scale old systems to solve new problems. AI demands a new playbook. The infrastructure that wins will be the one that anticipates load fluctuations, handles parallel processing at speed, and sustains throughput without bottlenecks. Anything less is going to lead to waste, wasted capital, wasted compute, and wasted opportunity.
Legacy storage systems are ill-equipped for the demands of AI
Most storage systems in use today were built for a different era, one where workloads were consistent, structured, and followed a logical path. AI tore that model apart. Training large models now involves thousands, sometimes tens of thousands, of processes running simultaneously. Each one hunts for different slices of unstructured data, like images or logs, demanding instant access.
Traditional storage can’t keep up. It was designed for systems where requests arrive in an orderly queue. AI doesn’t operate like that. When storage can’t deliver data at the speed required, your expensive GPUs go quiet. And when that happens, you lose time and money, fast.
This leads to a simple choice for business leaders: adapt the stack or fall behind. You can’t rely on legacy solutions that treat storage like a secondary concern. In AI, storage is core infrastructure. If it can’t match the speed of compute, every performance gain you’ve made is neutralized.
This shift isn’t theoretical, it’s affecting operational budgets right now. Enterprises burning through compute cycles without results are watching costs balloon with little return. And that’s before you factor in the project delays. You want production-ready AI? Then you need storage systems that are built to match the unpredictable, real-time demands of AI at scale. Storage that stays ahead of the workload, not behind it.
High-performance computing (HPC) provides a proven blueprint for AI-ready storage
If you’re serious about scaling AI, then it’s worth looking at the playbook that high-performance computing (HPC) environments have followed for years. These aren’t experimental systems, they’re already handling work that can’t afford downtime, errors, or inefficiencies. Think government simulations or life sciences research where continuous access to petabytes of data isn’t optional.
In the life sciences sector, for instance, the UK Biobank holds over 30 petabytes of biological and medical data on half a million people. That sort of system isn’t just large, it’s always available. And in national intelligence and defense, you’re looking at uptime requirements of 99.999%. That means even brief outages aren’t tolerable. Every second counts.
What these HPC environments show is that performance and resilience aren’t trade-offs, you need both. The most effective approach is tiered storage. That’s where high-performance systems focus on critical active data, while less urgent datasets are routed to lower-cost storage. You get speed where it matters and efficiency where you can afford it.
For AI, this is exactly the model that works. A one-size-fits-all deployment model doesn’t hold up anymore. Instead, you need fluid infrastructure that can adapt in real time. If a part of your dataset suddenly becomes critical, you don’t want it buried behind a slow access point. The architecture has to know when and where to deliver performance and have the resilience baked in.
If you’re leading infrastructure decisions, start evaluating your storage like an HPC veteran. Ensure it’s aligned to real-time performance needs and not stuck serving legacy expectations. You can’t move forward by simply upgrading capacity, you need structured, intelligent flow of data that matches AI workloads precisely.
Data durability and integrity are vital for AI project success
Here’s what people don’t talk about enough, your AI is only as good as the data it’s trained on. And that data must remain uncorrupted, accessible, and stable throughout its lifetime. If it doesn’t, the model breaks. The project fails. You lose time, money, and often trust inside the organization.
Durability and integrity are directly impact your success rate. According to Gartner, by 2026, 60% of AI projects that lack AI-ready data will be abandoned. Right now, only 48% of AI projects actually make it into production. The numbers paint a clear picture, most organizations are not solving this problem.
The cost is real. Bad data quality costs enterprises between $12.9 million and $15 million every year. And when your data pipeline breaks, you’re losing $300,000 per hour on average. That’s $5,000 per minute in lost output, delayed insights, and broken SLAs. This isn’t inefficiency, it’s a financial liability.
For executives guiding AI initiatives, it’s time to stop viewing storage as static capacity and start viewing it as a living component of your AI lifecycle. You need robust data protection, seamless fault recovery, and zero tolerance for silent corruption. Achieving that means building storage architectures that prioritize integrity, implement frequent validation protocols, and recover from disruption without delay.
Your AI outcomes depend on being able to trust your data. Any compromise here stops the entire machine. Leaders who handle this now won’t just avoid delays, they’ll put their teams in position to lead when execution matters most.
Advanced storage technologies and practices are required for cost-efficient AI deployment
Running AI at scale is not just a compute challenge, it’s a storage challenge. And if you’re not investing in the right technologies on that front, you’re making everything downstream harder and more expensive. You can’t solve latency issues by throwing more GPUs at the problem. You solve them by optimizing the pipeline that feeds those GPUs data without delay or failure.
Today’s smarter approach involves a mix of flash and disk, modular deployment, and advanced fault tolerance. Hybrid flash-and-disk systems balance the need for ultra-low latency with the financial pressure to control infrastructure costs. You don’t always need the speed of flash, but when you do, it should be available without bottlenecks.
Another critical shift is adopting multi-level erasure coding (MLEC). That gives you better fault protection than traditional RAID configurations. It ensures the system can handle simultaneous failure scenarios without risking data or performance. This matters when you’re pushing 24/7 AI workflows into production and can’t afford retraining due to minimal data loss.
Scalability, too, needs to be modular. You don’t want to overhaul your system every time your demand spikes or models grow more complex. Modular designs give you the agility to scale performance or capacity in short cycles without disruption to existing operations.
On the operational level, preventative measures matter. Automated data integrity checks catch corruption before it enters training stages. Scheduled recovery drills help teams react fast when something goes wrong. These aren’t optional, they’re operational minimums for AI to deliver real ROI.
The cost of downtime, failed inference, or corrupted training is measurable. When pipeline failures cost you $300,000 per hour, as current estimates show, the investment in intelligent storage architecture pays for itself fast. Executives focused on growth and scale need to lock in these safeguards early. Otherwise, you’re spending heavily on AI systems that won’t deliver consistent value. AI isn’t just about faster answers, it’s about reliable infrastructure that never breaks when you need it most.
Key executive takeaways
- AI is driving exponential infrastructure demand: AI now accounts for the bulk of data center growth, with McKinsey projecting a near-tripling of capacity needs by 2030, 70% of which will come from AI workloads. Leaders should prioritize scalable, high-throughput infrastructure to avoid capacity bottlenecks and inefficiency.
- Legacy storage is slowing down AI progress: Traditional enterprise storage, built for linear, structured workloads, cannot meet the low-latency demands of modern AI systems. Executives must rethink storage as a strategic component, not a back-end cost center.
- HPC strategies offer a tested model for AI: High-performance computing environments run petabyte-scale workloads with near-zero downtime, using tiered storage to balance performance and cost. CIOs should apply HPC-informed architectures, and adopt hybrid systems that prioritize high-speed data access for AI.
- Data resilience is non-negotiable for AI success: Lack of durable, AI-ready data is the top reason AI projects stall or fail, with only 48% making it to production and data issues costing millions annually. Leaders must invest in systems that ensure data integrity, availability, and recoverability as foundational elements of their AI strategy.
- Smart storage tech delivers performance and cost control: Approaches like multi-level erasure coding, hybrid flash-disk systems, and modular scaling reduce risk and keep AI pipelines moving. Organizations should make these technical upgrades now to protect against costly downtime and delayed value realization.
