AI Storage Planning Beyond Compute

Last updated: October 22, 2026

AI projects can stall before a model reaches production because the infrastructure around it wasn’t designed for the data it creates, stores, retrieves, or protects.

The latest reporting points to a shift in enterprise priorities. Compute still matters, but storage planning, data readiness, security, and energy requirements are becoming equally important to dependable AI delivery.

• PCQuest says 99% of surveyed enterprise technology leaders expect storage demand to rise over the next three years.
• Only 38% of respondents believe their organizations are fully ready for AI’s long-term data requirements.
• CIO.com describes agentic AI as creating unpredictable, continuous demand for computing environments and secure data access.
• Shattered.io highlights a Western Digital measurement showing that one 3.97 MB generated video produced more than 500 MB of associated backend data.

Why AI storage planning now belongs on the roadmap

For many project teams, AI planning still begins with access to GPUs or other accelerators. That focus is understandable, but the wider infrastructure problem extends beyond processing power. Training data, checkpoints, inference inputs, outputs, logs, caches, intermediate renders, backups, and compliance records can have different storage and retention needs.

PCQuest, citing Seagate’s 2026 Data Infrastructure Readiness Report, says 53% of respondents identified data quality and readiness as a deployment challenge. Storage infrastructure followed at 43%, ahead of compute availability at 27% and energy constraints at 24%. Those figures don’t prove one issue causes another, but they show that organizations see several linked constraints rather than a purely compute-led problem.

This is where AI storage planning becomes a delivery concern, not merely an IT purchasing exercise. A team may have a promising model and enough compute, yet still struggle to retrieve the right data quickly, govern sensitive information, or control the cost of retaining intermediate artifacts. A project plan that treats storage as a later-stage concern is carrying avoidable risk.

The same research says 98% of respondents believe AI is turning storage into a strategic business capability. Security and compliance led investment priorities at 44%, while data management and governance reached 43%. AI and GPU infrastructure and storage hardware refresh were both selected by 39%.

Agentic workloads change the capacity conversation

CIO.com’s analysis focuses on a different operating model: autonomous agents that can run continuously, call APIs, consult enterprise systems, delegate work, and iterate on a task. That pattern makes demand harder to predict than systems used only during predictable working hours.

Agents can create bursts of activity whenever a request arrives, potentially triggering several temporary execution environments at once. Keeping enough idle capacity for every possible burst may be expensive, while too little capacity can slow delivery or force teams to limit automation.

CIO.com argues that isolated, on-demand environments may become a practical unit for running agents. Such sandboxes could be created when needed and retired afterward, helping teams control capacity and isolate untrusted code. That is an architectural proposal rather than a universal prescription, but it highlights a leadership question: how much contingency capacity should an AI service maintain, and who owns the trade-off between availability, security, and cost?

CIO.com also says intelligent execution may need to move closer to enterprise data. A knowledge catalog can map data assets so agents can discover and query relevant information across hybrid environments. For project managers, the implication is concrete. A backlog item that depends on an agent finding authoritative information is also dependent on data ownership, catalog quality, access permissions, and retrieval design.

The hidden cost can be much larger than the output

Shattered.io reports an example from an internal Western Digital measurement, originally reported by MIT Technology Review. An eight-second, 720p generated video clip delivered at 3.97 MB left more than 500 MB of related backend material across the infrastructure. That is approximately 130 times the size of the user-facing file.

The larger footprint included several categories with legitimate purposes: inference logs, cached context, initial renders, backups, and safety records. The lesson is not that every AI output should carry a 130-fold storage burden. It is that the visible file is an incomplete measure of the system’s storage demand.

For project leaders, this changes the questions asked during planning. How long will intermediate artifacts remain useful? Which records are required for security or compliance? What can be deleted safely? Which objects need fast retrieval, and which can sit on lower-cost storage? Without those decisions, a team may underestimate infrastructure cost and responsible deletion.

That matters when comparing AI video with other workloads. Shattered.io notes that text, image, and video inference can all generate logs, cached states, and intermediate representations. Video makes the issue easier to see because the final file is already large. Other workloads may produce a less dramatic-looking file while still accumulating substantial backend data.

What this means for project teams

AI storage planning should be treated like any other cross-functional dependency. Bring infrastructure, security, data, legal, and product stakeholders into the decision before a pilot becomes a production commitment. PCQuest reports that 32% of respondents expect storage requirements to grow by more than 50%, so the cost conversation may arrive early.

Seagate’s findings also show geographic variation worth noting rather than flattening into one global average. PCQuest reports that all Indian respondents expect storage volumes to rise, 91% see moderate or significant returns from AI, and 89% consider their organizations fully or mostly prepared. About 62% use AI to determine storage and operational requirements, according to the same report.

Those figures require care. The research covered 2,712 enterprise technology decision-makers across seven countries between May and June 2026, and Seagate commissioned it. PCQuest notes that the results should be read as enterprise sentiment rather than an independent storage-demand forecast. They remain useful for identifying concerns that leaders should investigate in their own organizations.

A practical scenario for your next AI initiative

Say you’re leading a pilot that generates training videos for a global team. The stakeholder asks for longer clips, higher resolutions, and multiple language versions. The visible deliverables may look manageable, but Shattered.io’s reported measurement suggests you should ask about backend artifacts before approving the estimate.

First, define what must be retained: final files, source assets, intermediate renders, logs, safety records, or backups. Second, assign an owner and retention period to each category. Third, model the cost of both the output and the process that creates it. Finally, test whether agentic features, if planned, will increase demand beyond the initial video workload.

What to watch next

Compute will remain central to AI infrastructure, but capacity planning increasingly has to include storage, governance, power, security, and execution environments. PCQuest reports that 77% of respondents said sustainability or energy concerns delayed or changed an infrastructure expansion, with power consumption cited by 62% when assessing storage hardware’s environmental impact.

For leaders, the practical move is simple: make the full lifecycle of AI data visible before it reaches the production budget. Don’t approve a storage assumption based only on the file a customer receives.

Key takeaways

• Include storage, data governance, security, and energy in AI project planning rather than treating them as later infrastructure concerns.
• Model backend artifacts, not just final outputs; a small visible file may not represent the full storage footprint.
• Give agents controlled execution environments, defined data access, and clear retention rules before they operate across enterprise systems.