AI Adoption Accelerated Enterprise Data Growth in 2026
New research shows that AI is driving massive increases in data retention and the revival of archived storage tiers.
Updated on Sept. 29, 2026 in Artificial Intelligence
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IDC research from May 2026 found that 94.7% of surveyed organizations are increasing data storage capacity due to AI adoption. Data volume growth of 25% or more was reported by 61% of companies over the past year.
Why it matters
AI adoption forces companies to re-evaluate storage strategies, as models require vast datasets and increase the perceived value of previously dormant, archived records. This shift is causing a fundamental change in how large organizations prioritize and maintain historical data.
85.4% of organizations reported growth in data lake volumes, while 59.4% cited AI-generated content as the primary driver. Additionally, 75.9% of surveyed firms have moved data from cold storage back to active, online tiers.
The players
IDC
A global market intelligence firm providing research on information technology, telecommunications, and consumer technology markets.
The details
Organizations are leveraging data lakes—centralized repositories that allow for the storage of vast amounts of raw data in its native format—to feed AI training and inference pipelines. As these models generate their own outputs, this AI-generated data is becoming a primary source of volume growth. To meet these demands, firms are pulling from cold-tier data, which refers to infrequently accessed or archived storage environments, and migrating it back into active, online environments for model training and validation.
Timeline
May 2026: IDC conducted the quantitative survey.
Past 12 months: Reported growth in data lake volumes occurred.
Next three years: Data volumes are expected to grow 25% or more.
The Tech Race
This trend marks a clear departure from the historical practice of moving unused data into low-cost, permanent archives. It underscores how the race for high-quality training datasets is effectively ending the era of 'dark data' by making historical archives essential components of active AI pipelines.
IT professionals and system architects should prepare for significantly higher storage overhead as data retention policies shift toward longer, active lifecycles. Companies are increasingly forced to prioritize data discovery and retrieval speed over the cost-efficiency of cold-tier archival.
The takeaway
The necessity of feeding models with historical data is forcing a major reinvestment in storage infrastructure. Organizations should monitor their internal data growth benchmarks against the industry standard of 25% annual expansion to ensure their infrastructure can support the next cycle of AI model training.
Further reading
For more context on how machine learning is reshaping enterprise infrastructure, visit Artificial Intelligence.
Source note: This article includes information reported by Frontier Enterprise.
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