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Companies should respond to growing data volumes with a storage plan, not simply buy more capacity when systems fill up. Forecast needs by workload, sort data by how often and how quickly it must be accessed, set retention and deletion rules, and compare storage choices over their full lifecycle—including resilience, recovery, data location and energy use. AI is increasing expected demand, but no single storage architecture fits every business.
Why are storage needs rising?
AI is a demand signal, not a universal growth forecast
A May–June 2026 survey conducted by Recon Analytics on behalf of Seagate polled 2,712 enterprise technology decision-makers in the United States, China, India, the United Kingdom, Germany, France and Japan. Seagate reported that 99% expected AI to increase their organizations’ storage requirements over the following three years, while 32% expected requirements to rise by more than half. These figures describe respondents’ expectations; they do not predict the growth rate of every company’s data.
The same survey points to a readiness gap: 38% of respondents said their organizations were fully prepared for AI’s long-term data demands. They named data quality and readiness (53%) and storage infrastructure (43%) as leading AI deployment challenges. Storage infrastructure ranked ahead of compute availability (27%) and energy constraints (24%). In a vendor-published release, Seagate executive Melyssa Banda put the distinction this way: “The next phase of AI will require capacity growth, but capacity alone will not be enough.”
Market spending is not the same as company data growth
IDC’s enterprise storage market page, updated July 1, 2026, reported $9.9 billion in worldwide external OEM enterprise-storage spending in Q1 2026, up 22.9% year over year. IDC cited deferred refreshes and AI-related workloads as market drivers, alongside component constraints, and forecast average market growth of 7.3% through 2030. These are measures and a forecast for market spending—not a forecast of how quickly an individual company’s data or storage budget will grow.
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Storage mix also varies. In a May 2026 Western Digital survey of 200 of its global customers and distributors across hyperscale cloud, cloud service provider and enterprise segments, 80 respondents held relevant infrastructure or architecture roles. Among respondents who could see their storage mix, 70% reported HDD-majority infrastructure and 35% said HDDs represented more than 75% of total capacity. Respondents cited total cost of ownership, capacity and scalability as HDD advantages. Because this was a vendor survey of its own customer and distributor population—not an independent census of businesses—it is evidence about that sample, not a recommendation that every company should choose HDDs.
What should a company assess before adding capacity?
Build a workload and retention inventory
Start with what the data is for and what the business is obligated to keep. For each major workload, identify its data owner, volume and growth pattern, access frequency, latency or throughput needs, retention period, deletion trigger, and any location or access restrictions. Separate active data used by applications or analytics from material kept mainly for audit, legal, operational or historical reasons. The distinction helps avoid paying for fast access where it is not needed—or placing frequently used data somewhere that slows a business process.
Measure actual utilization and forecast by workload rather than applying one growth percentage to the whole organization. Include copies created for analytics, AI pipelines, collaboration, testing and recovery in the inventory. The aim is to find what needs more capacity, what can move to a different tier, and what should be deleted under an approved retention policy.
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Calculate lifecycle cost, not just purchase price
Compare the cost of storing and operating data for the period it will be retained. Include capacity expansion, administration, energy, migration, resilience, and the cost and delay of retrieving or transferring data where applicable. Cloud retrieval and transfer terms vary, so verify the current contract and provider terms for the specific workload; the evidence available here does not establish a universal cloud-versus-on-premises cost winner.
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How should storage be divided among tiers?
Match access patterns to the service level
Tiering means assigning data to storage with different performance, cost, access or resilience characteristics. It can help when the operational savings from moving infrequently used data outweigh migration and management effort. A workable policy states what qualifies data to move, who approves exceptions, how quickly data must be retrievable, and how the move is verified. Without ownership and monitoring, tiering can become an unmanaged collection of exceptions.
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- Active and latency-sensitive data: prioritize the response time and throughput required by business applications, analytics or AI workflows.
- Occasionally accessed data: consider a lower-cost tier only if retrieval time and access terms meet business needs.
- Long-term retention: use a capacity-oriented option when access is infrequent and retention is justified, while preserving required security, durability and recovery arrangements.
HDDs can be a capacity-oriented part of a design, as the Western Digital survey illustrates for its respondent sample. That does not make HDDs the right choice for every workload. Compare actual performance requirements and lifecycle economics rather than treating a device type or tier label as a complete strategy.
Should a company use cloud, on-premises or hybrid storage?
The right location depends on workload, data movement, contract terms, geography, operating model and existing skills. Gartner’s public 2026 enterprise-storage abstract recommends platform-based models that embed intelligence and security in the data layer, with efficiency and resilience as aims. Its public 2025 roadmap abstract describes data proliferation, AI, cyberthreats and consumption-based infrastructure as forces changing enterprise storage. Those public abstracts support considering the whole data platform, but they do not provide a public, apples-to-apples comparison of vendors or deployment economics.
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| Approach | When it may fit | What to evaluate |
|---|---|---|
| On-premises | Workloads that benefit from local control, existing infrastructure or close integration with local systems. | Refresh and expansion needs, operating and energy costs, staffing, failure domains, and how quickly capacity can be added. |
| Cloud | Workloads whose scale, operating model or location needs align with a cloud service. | Retention-period cost, access and retrieval performance, transfer terms, contract conditions, jurisdiction, and recovery arrangements. |
| Hybrid | Organizations that need different locations or operating models for different data and workloads. | Consistent classification and access controls, interoperability, migration effort, visibility across environments, and the complexity of operating multiple tiers. |
Use the same workload assumptions and retention period when comparing alternatives. Model likely growth, data movement and recovery needs, and test the effect of changing those assumptions. A choice that looks inexpensive for storage alone may not remain so once operations, retrieval, resilience and migration are included.
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How should governance, security and recovery shape the design?
Make data lifecycle rules explicit
Assign an accountable owner, classification, permitted users, retention period and deletion rule to each important data set. Governance helps prevent indefinite retention of data that no longer has a business or legal purpose, while making it easier to locate and protect material that must be kept. Build access controls into the data layer and consider jurisdictional requirements when choosing where data and recovery copies reside.
Design for restoration, not just backup completion
Storage architecture should account for independent failure domains, backup separation, recovery-time objectives and recovery-point objectives. A backup job reporting success does not establish that a business can restore its data in time. Test restoration of representative data and applications, record the results, and revise recovery plans when systems or dependencies change.
Gartner’s March 2026 public data-protection abstract highlights identity backup, ransomware survival, cloud-native recovery, AI-driven backup and sovereignty requirements. Those themes make recoverability and jurisdiction design inputs, rather than tasks to defer until after storage is selected.
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How can companies manage energy and extend infrastructure life?
In the Seagate/Recon Analytics survey, 77% of respondents said they had delayed or restructured AI infrastructure expansion because of sustainability or energy concerns, and 97% agreed that extending infrastructure lifecycles significantly improves sustainability. These are survey responses, not measured savings or a finding that every refresh should be delayed.
Use company-level measurements to decide: track utilization, energy use, hardware condition, capacity headroom, support requirements and retention needs. Extending a lifecycle may avoid premature replacement, but only where performance, reliability, support and recovery requirements remain acceptable. Conversely, retaining underused data and infrastructure without a business reason can consume capacity and energy without improving readiness.
What is a practical adaptation plan?
- Inventory: map major data sets, owners, applications, access patterns, growth, retention rules and location requirements.
- Measure: establish baselines for utilization, capacity growth, energy, operating effort and restoration performance.
- Classify: define active, occasional-access and retention categories, with named owners and documented movement and deletion rules.
- Compare: evaluate on-premises, cloud and hybrid options for representative workloads using lifecycle cost, resilience, access, jurisdiction and migration effort.
- Pilot: test tiering or a location change on a bounded workload, verifying performance, access controls, cost assumptions and restoration before expanding.
- Review: update forecasts and placement decisions as workloads, data policies, energy conditions and contracts change.
Western Digital chief product officer Ahmed Shihab described the broader challenge in a vendor-published statement: “AI is fundamentally a data systems challenge, not just a compute challenge.” For companies, the practical implication is to treat storage as part of data readiness and business continuity—not as a capacity purchase in isolation.
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