Beyond the Prompt: The Hidden Data and Storage Implications of Generative AI
IDC ·
Generative AI (GenAI) is accelerating how much data organizations generate, straining storage and governance frameworks built for a slower-growth era. Most GenAI-using organizations now expect increases in data volume over the next two years, and infrastructure and governance strategies often struggle to keep pace. The scale of the challenge According to IDC’s Global DataSphere, consumers […] The post Beyond the Prompt: The Hidden Data and Storage Implications of Generative AI appeared first on IDC .
Generative AI (GenAI) is accelerating how much data organizations generate, straining storage and governance frameworks built for a slower-growth era. Most GenAI-using organizations now expect increases in data volume over the next two years, and infrastructure and governance strategies often struggle to keep pace.
According to IDC’s Global DataSphere , consumers and organizations worldwide will generate more than 274.4 zettabytes (ZB) of data in 2026 alone. That figure is forecast to nearly triple to 718.5 ZB in 2030, a compound annual growth rate (CAGR) of 26.9%. Enterprise data is growing significantly faster than consumer data (31.1% CAGR versus 13.5%), and it matters most here because it’s the data organizations are responsible for governing and protecting, and expect to get value from. It’s also the data most directly implicated in AI workloads.
Storage is expanding in parallel. IDC’s Global StorageSphere shows the installed base of storage capacity roughly doubling, from about 13 ZB in 2026 to more than 26 ZB in 2030. Even before accounting for AI, the enterprise data challenge was already heightened and the governance surface area was already growing faster than most organizations could manage.
In IDC’s 2026 Worldwide Data Utilization Survey , 81.7% of the 714 GenAI-using respondents said they expect GenAI to increase their organization’s data volume over the next two years. Only 4.8% expect a decrease.
Of those anticipating an increase, 47.7% expect data volumes to grow by 25% or more, and 13.4% expect growth exceeding 50%.
Much of what GenAI produces is ephemeral, and a share of AI-generated content replaces existing workflows. In some cases, GenAI can reduce data volumes, such as by eliminating raw footage through synthetic generation, or by converting unstructured data into structured, actionable formats that can then be selectively retained or deleted.
These dynamics leave the overall growth trajectory intact. For organizations running active GenAI programs, operational telemetry, model logs, RAG corpora, and fine-tuning data sets can add up in ways that initial planning assumptions rarely account for.
There are at least four distinct layers, and the net effect varies by industry, organization, use case, and deployment maturity:
Knowing that data volumes will grow is not the same as being prepared for it. IDC’s research points to three structural gaps:
None of these gaps is insurmountable, but they widen quietly in the background while organizations focus on deploying AI.
There’s a second cost to letting data volumes outrun your strategy: the value you never get out of the data. Value depends on how effectively data becomes insight or advantage. Vast quantities of poorly structured, unlabeled, or siloed data may hold almost no practical value despite their volume, so every RAG index, fine-tuning corpus, and log stream that no one has cataloged or assigned an owner is data you hold but cannot use.
Most GenAI-using organizations expect data volumes to rise. The open question is whether infrastructure and governance strategies are built for that reality. Preparing takes a deliberate, systematic approach, and these five steps are the place to start:
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