This year has been a year of AI implementation. AI is now an unstoppable force, a silver bullet for automating processes and solving complex business dilemmas. What once was experimental is now operational with business leaders racing to deploy systems that promise transformative returns. Yet, beneath the surface of this urgency lies a possible miscalculation: the massive amounts of data that AI requires and generates.
Building an appropriate infrastructure for AI is a big investment, and it’s becoming increasingly clear that for AI to be successful, leadership teams need to work smarter, not faster. This starts with understanding how infrastructure choices align with AI data requirements, and whether existing data is even ready for AI use. In most cases, the reality is nuanced. AI’s appetite extends far beyond what many infrastructure strategies anticipate, and the consequences of underestimating storage requirements can compound quickly.
Beyond the basics: Strategic questions leaders often miss
Many executives understand that LLMs, AI tools, AI agents, and AI training are all built on enormous amounts of data at every stage of the AI cycle. Fewer appreciate the architectural decisions that determine whether that data becomes an asset or a liability. The difference lies in asking harder questions upfront: is existing data centralized or fragmented across siloed systems? What does data versioning and lineage tracking look like at scale? How do latency requirements shift as models move from training to inference? Which workloads justify cloud infrastructure versus on-premises investment?
These aren’t IT minutiae. They are strategic choices that shape return of invest, agility, and competitive positioning. By 2030, advanced AI workloads are projected to represent 70% of global data centre demand. Organisations that treat storage as a day-one strategic priority will likely operate fundamentally differently than those that retrofit later.
It all starts with data hygiene
A digital data hygiene strategy is a quiet discipline behind a resilient AI infrastructure. Digital data hygiene – the practice of maintaining the cleanliness, accuracy, relevance, and security of data throughout its lifecycle – has become a cornerstone of resilient data strategies, yet it often remains an overlooked discipline in enterprise IT.
Legacy systems, unstructured repositories, unmanaged cloud storage, and duplicated files contribute to “data bloat“. This creates friction in the AI pipeline and can inflate costs and slow decision-making. AI makes high-quality data more important than ever because it trains on massive volumes of structured, semi-structured, and unstructured information. The quality of its insights depends directly on the quality of the data it consumes.
Inaccurate, irrelevant, or biased data can compromise model performance and results. Moreover, the AI workflow generates even more new data that is often fed back into training datasets, creating a compounding effect if the data is flawed. By having no or few data hygiene practices in place, enterprises risk unintended consequences in their AI output. Putting an appropriate data strategy in place can allow businesses to handle the increasing demands of AI at scale and overcome this challenge.
Data cleaning tips and tricks
To maintain effective data hygiene, IT teams should set clear standards for data quality like using consistent formats, removing duplicates, and validating information at the point of entry.
Solid governance also matters. Assigning a clear owner to each dataset, managing who can access or modify information, and maintaining proper version histories all can help ensure traceability and trust. Regular audits, automated anomaly detection, and ongoing cleansing routines can prevent data drift and performance degradation. Finally, aligning storage, labelling, and metadata practices helps ensure AI models learn from well-organized, policy-compliant information.
As data hygiene improves, businesses are tackling one of the hurdles to ensuring the success of their AI investments. The next step is making sure all that clean, reliable information is stored in a way that AI can use it. Thoughtful storage choices can shape how quickly models can learn, adapt, and scale. That’s why a clear, future-ready storage strategy that can scale becomes a logical next step.
Data storage: The engine behind AI
According to IDC, the annual volume of data generated is expected to more than double to 527.5 zettabytes (ZB) in 2029 (IDC Source: Worldwide IDC Global DataSphere Forecast, 2025-2029, May 2025, Doc #US53363625). This is unsurprising given how much data is created in the world every day. Given the explosion of data creation, a solid storage strategy must be a top priority.
AI demands are putting a whole host of new pressures on current IT infrastructure, which has up until this point largely fallen under direct attached storage (DAS), storage area networks (SAN), and network-attached storage (NAS) systems. These traditional architectures, however, weren’t designed for the extreme throughput and scalability requirements of modern AI workloads, making it often difficult to meet both performance and capacity needs.
A prominent solution to accelerate performance and scaling resources are hyperconverged infrastructures (HCIs) – a unified model that combines compute, storage, and networking resources into on integrated systems. But with the introduction of AI and AI workloads, such singular infrastructures risk bottlenecks as they can become overwhelmed with the huge amounts of unstructured data demanded by these new tasks. This is because tightly coupled systems can struggle to scale one dimension or resource without overprovisioning the other, creating inefficiencies and architectural rigidity. To counter this, leaders can consider disaggregated storage as a viable solution to the sudden surge in AI workloads.
Scaling AI with disaggregated storage
Disaggregated storage is rapidly becoming an essential data architecture for enterprise AI. Unlike HCI, it separates compute, storage, and networking, providing greater flexibility, efficiency, and scalability. For example, organizations can add GPU capacity for training cycles without upgrading entire storage arrays or expand storage density for datasets without unnecessary compute overhead. This way, a disaggregated storage architecture can reduce overprovisioning and prevent vendor lock-in, giving organizations more control in tailoring systems to their specific AI requirements.
For AI specifically, disaggregated infrastructures can offer meaningful advantages. Physically separating the components creates additional space within servers—allowing for more powerful processors like GPUs and DPUs or expanded capacity through high-density HDDs and SSDs. Both are crucial to meeting the demands of modern AI workloads.
This flexibility of future-ready IT infrastructure can make it easier to support the massive data volumes and high-performance processing required by current and emerging AI models. It can give leaders a scalable, efficient foundation for accelerated computing at a time when AI adoption shows no signs of slowing.
As AI becomes central to modern business, the organizations that thrive will be those that treat data and storage as strategic assets and not technical afterthoughts. Clean data, thoughtful governance, and flexible, scalable storage architectures form the backbone of a successful AI initiative. By investing in these foundations today, leaders can unlock faster innovation and build AI systems that grow with their business.
