The AI infrastructure conversation has largely been defined by compute, but new global research from IDC reveals another critical factor organisations must address as they scale AI — data that persists and compounds long after individual compute cycles are complete.
The research comprised two components: a quantitative survey and a series of qualitative in-depth interviews.
The quantitative survey was conducted online using a computer-assisted web interviewing methodology in May 2026. A total of 763 qualified respondents completed the survey across seven countries — the United States, Kingdom of Saudi Arabia, Germany, India, China, Japan, and South Korea.
All respondents held director-level or above positions within their organizations and had direct involvement in AI infrastructure or data storage decisions. Participating organizations employed 500+ people.
In a white paper sponsored by WD, IDC said that among surveyed organisations, AI is creating a structural expansion in data storage requirements.
Organisations are generating more data, assigning greater value to it, retaining it longer, and increasingly bringing historical data back online for new AI workloads.
The result is a compounding data cycle: AI requires data and increases the potential value of data already stored.
While compute requirements vary by workload and investment cycle, the data created by AI persists and accumulates, making storage capacity, accessibility and economics increasingly important considerations in AI infrastructure design.
Findings show that AI is creating a compounding data cycle as 94.7% of surveyed organisations are storing more data because of AI and generative AI adoption over the past 12 months.
Also, 61% 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 years.
Further, 85.4% reported growth in data lake volumes over the past 12 months, and 59.4% identified AI-generated data, including synthetic data, inference outputs and model logs, as the leading driver of data lake growth.
Nearly 95% say the value of their organisation’s data has increased as a result of AI and GenAI adoption
About three-quarters or 74.3% say AI and generative AI have caused them to retain data longer and 75.9% report bringing increasing volumes of archived cold-tier data back online to support AI workloads.
