Flexential has released its 2025 State of AI Infrastructure Report, revealing that businesses are increasingly planning their AI infrastructure well in advance of their traditional IT needs. Research indicates that nearly 80% of enterprises are forecasting data center capacity over a year ahead, with some extending their predictions to five years. This proactive approach reflects the substantial investments and long procurement cycles necessary for AI deployments, which require considerable resources in computing, storage, and energy.
The findings show that 62% of IT leaders now plan one to three years ahead, while 17% extend their outlooks to three to five years. However, 16% of organizations are still planning less than a year ahead, with 70% feeling confident in their preparation despite challenges in the AI infrastructure market.
Conducted among over 350 IT leaders from companies earning at least $100 million annually, the survey highlights tightening data center vacancy rates and extended lead times, warning that quick decisions are essential to avoid missed opportunities. The report stresses the shift from the traditional two-year planning cycle to a longer approach necessary to remain competitive.
Performance issues continue to afflict many organizations. Nearly 60% reported bandwidth shortages in the past year, and over half experienced excessive latency. These issues underscore the importance of network performance alongside raw computing power. Companies are increasingly turning to a mix of private and public cloud solutions, with many opting for colocation services to enhance storage and performance.
Workforce challenges also pose a significant barrier. Only 5% of respondents reported no skills gaps related to AI, with 61% identifying a particular shortage in specialized computing infrastructure knowledge. Other critical skill gaps include data science, engineering, and advanced networking technologies. Although many companies face broader recruitment and retention difficulties, only 10% cited talent scarcity as their primary obstacle to AI adoption, preferring to focus on infrastructure issues.
The report discusses the current trends in AI deployment environments, noting that the public cloud is the preferred choice for AI training data for 68% of respondents. Colocation facilities are becoming increasingly popular, utilized by 54% to meet their performance and storage needs. On-premises data centers are gradually losing relevance, with only 20% storing training data locally.
Additionally, a growing percentage of enterprises are adopting GPU-as-a-Service, which rose from 34% to 40% in a year. To combat workforce deficiencies, organizations reported implementing AI training initiatives, recognizing the need to evolve alongside their infrastructure capabilities.
The research underscores that creating AI-ready infrastructure is a long-term commitment that requires careful planning and significant investments. Companies failing to prepare risk being caught short when demand surges. Those who act decisively will be better positioned to align their infrastructure with strategic goals while embedding energy efficiency and sustainability into future capacity.
Flexential notes a particularly high demand for AI infrastructure in metropolitan areas such as Atlanta, Dallas, and Denver. As AI adoption grows, enterprises will need to extend their planning horizons further into the future. Those that treat infrastructure preparedness as a tactical advantage rather than a mere necessity will likely excel in a rapidly evolving, capacity-limited environment.
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