Flexential released its 2025 State of AI Infrastructure Report, finding that enterprises are shifting to long-term planning horizons to secure the data center capacity and expertise needed for AI workloads. Among surveyed organizations, 79% are planning more than a year ahead, with 62% mapping out capacity one to three years in advance and 17% looking as far as three to five years. The report warns that shorter planning cycles leave enterprises exposed as vacancy rates tighten and lead times stretch.
The survey of more than 350 IT leaders, including 100 from companies with revenues above $2 billion, revealed that while 94% expressed confidence in their planning, many may be underestimating the difficulty of securing infrastructure. In the past year, 59% reported bandwidth shortages and 53% excessive latency, underscoring that network performance is as critical as capacity. Workforce gaps compound the challenge: 61% cited shortages in managing specialized computing infrastructure, 53% in data science and engineering, and 47% in advanced networking. Only 5% said they faced no staffing challenges related to AI.
Deployment strategies continue to diversify. Public cloud remains the leading option for AI training data, used by 68% of respondents, while 54% also rely on colocation. On-premises deployments are waning, with only 20% storing training data locally. GPU-as-a-service usage rose to 40%, up from 34% last year. Every surveyed organization is investing in AI workforce development, primarily through embedded tools, in-house training, and external certifications.
• 79% of enterprises plan AI infrastructure at least one year in advance; 17% plan three to five years out
• 94% are confident in planning, but shortages in bandwidth, latency, and workforce challenge that outlook
• 61% face skills gaps in managing specialized compute; 53% in data science; 47% in advanced networking
• Public cloud leads in AI training data storage (68%), with growing colocation (54%) and GPU-as-a-service adoption (40%)
• Only 20% of organizations store AI training data on-premises
“Vacancy rates are tightening, and lead times are extending as AI requirements grow,” said Ryan Mallory, President and COO of Flexential. “What used to be a two-year runway is now the minimum planning horizon to stay competitive. Providers and enterprises must act early and be flexible because waiting for certainty will cost time, capital, and opportunities.”
🌐 Analysis: Flexential’s findings highlight the growing disconnect between AI ambitions and the infrastructure and workforce needed to support them. The pivot to multi-year planning reflects the reality that data center construction, power procurement, and interconnect provisioning cannot keep pace with sudden surges in demand. Competitors like Equinix, Digital Realty, and CoreWeave are making similar points as they rush to add high-density GPU-ready capacity. For enterprises, early planning is becoming not just a cost management strategy but a prerequisite for AI readiness.
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