AI Adoption Triggers 25% Data Growth as Storage Becomes Strategic Priority, IDC Report Shows

AI Adoption Reshapes Data Storage Requirements

A new IDC report sponsored by Western Digital reveals that AI adoption is fundamentally changing how organizations approach data storage. The research, titled "Built for Scale: The Enduring Role of HDDs in the AI Era," finds that 94.7% of surveyed organizations are storing more data because of AI and generative AI adoption over the past 12 months1

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. This structural expansion in data storage requirements marks a shift from the traditional focus on compute power to the economics and accessibility of persistent data.

The study shows 61% of organizations experienced data growth of 25% or more over the past year due to AI, while 74% expect data volumes to grow 25% or more over the next three years1

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. Data lake volumes are expanding rapidly, with 85.4% of organizations reporting growth over the past 12 months. The primary driver is AI-generated data, including synthetic data, inference outputs and model logs, identified by 59.4% of respondents as the leading cause of data lake expansion2

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The Compounding Data Cycle Creates New Value

AI infrastructure is creating what the IDC report characterizes as a compounding data cycle. Unlike compute cycles that complete and move on, data created by AI persists and accumulates, becoming input for future AI applications. Nearly 95% of organizations say the value of their data has increased as a result of AI and GenAI adoption1

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. This increased value is driving organizations to retain data longer, with 74.3% reporting extended retention periods specifically because of AI and GenAI2

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Historical data that once sat dormant is reentering the active data lifecycle. The research shows 75.9% of organizations are bringing increasing volumes of archived cold-tier data back online to support AI workloads1

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. This shift is changing expectations for how data is retained, managed and made available when needed. Looking ahead, 96% of organizations anticipate needing faster archive retrieval to support AI inference and retrieval-augmented generation (RAG) applications2

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Storage Economics Becomes Critical for AI Success

The full data lifecycle now demands strategic attention as organizations architect their AI infrastructure. For surveyed organizations, 74.6% of enterprise data resides in warm, cool and cold storage tiers, with more than 60% of data lake volume consisting of cold or infrequently accessed data1

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. This distribution underscores why storage a strategic priority has emerged as organizations balance performance, capacity, accessibility and economics according to workload requirements.

Total cost of ownership per terabyte has become a decisive factor, with 98.2% of organizations considering it important or very important when making storage decisions2

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. Irving Tan, CEO of WD, emphasized this shift: "For the last few years, the AI infrastructure conversation has centered on compute. But AI runs on data. Organizations are generating more data, keeping it longer, and finding new ways to create value from the information they already have."1

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The research points to HDDs playing an enduring role in managing storage economics at scale, particularly for the vast amounts of warm, cool and cold-tier data that AI workloads increasingly need to access. As data volumes compound and retention periods extend, where data lives and what it costs to store and access becomes an architectural consideration that will determine how far organizations can scale their AI initiatives.