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 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.
The AI inflection point: What IT leaders are telling us
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.
Why GenAI drives data growth
There are at least four distinct layers, and the net effect varies by industry, organization, use case, and deployment maturity:
- Model training and fine-tuning data: Customizing models on proprietary documents, transaction records, or customer interactions creates derivative data sets that must be stored and governed, including every version.
- Retrieval-augmented generation (RAG) corpora: Every document ingested into a RAG pipeline generates an embedding stored in a vector index, and that corpus needs ongoing refresh. RAG can also have a reduction effect by lessening the need to retain full-length source materials indefinitely.
- AI-generated output data: Much of it is ephemeral, and only finalized outputs tend to be stored, but where compliance mandates retention, the footprint grows predictably over time.
- Operational and observability data: Every inference request generates metadata, including prompts, outputs, latency, and token consumption. This layer tends to be underestimated in early planning, and agentic AI compounds it with richer, more continuous logs and reasoning traces.
The governance and infrastructure gaps
Knowing that data volumes will grow is not the same as being prepared for it. IDC’s research points to three structural gaps:
- The governance gap: Most frameworks were built for structured, human-generated data. AI-generated content often falls outside them, creating compliance exposure in regulated industries.
- The storage architecture gap: AI inference, training, and RAG pipelines need high-throughput access, low-latency embedding lookups, and cost-efficient tiering for training archives. Organizations that haven’t revisited their architecture may pay a performance or cost penalty.
- The retention and life-cycle gap: Retention policies at most organizations predate the GenAI era, and keeping a prompt-response pair, a fine-tuning data set, or an embedding raises questions those policies never anticipated.
None of these gaps is insurmountable, but they widen quietly in the background while organizations focus on deploying AI.
Volume isn’t value: Why runaway data is harder to use
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.
Beyond the prompt: What to do next
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:
- Audit the AI data footprint before it becomes unmanageable. Inventory training sets, fine-tuning corpora, RAG indexes, and telemetry, including where each lives and who owns it.
- Extend data governance explicitly to AI data assets, with updated classification, retention, and lineage tracking, and engage legal and compliance early.
- Align storage infrastructure planning with the AI road map by developing capacity plans together with AI and data science teams.
- Revisit retention policies with AI data types explicitly in scope.
- Model the economics of AI data storage across cloud and on-premises options. Cloud is growing nearly four times faster than noncloud, but high-volume, persistent data may be more cost effective on-premises or in colocation over a multiyear horizon.