Forecasting future capacity needs is an escalating concern for datacenter operators. They grapple with rising costs, power restrictions, and evolving demands driven by AI workloads. According to the Uptime Institute’s 15th Annual Global Data Center Survey, challenges in the industry persist. However, the necessity to expand could be seen as a positive challenge due to increased demand.

While outages have become less frequent, these improvements are tempered by more stringent regulations and efficiency requirements. Operators also face hurdles regarding staffing and supply chain issues.

Cost control remains a critical concern for the year ahead. Many operators plan to accommodate AI training and inference tasks, demanding further capacity planning.

The 2025 landscape is fraught with uncertainties. Operators must navigate increased costs linked to energy and market volatility within complex capacity planning frameworks. Despite the relentless growth of public cloud services, many sites are upgrading infrastructure to manage increased workloads from AI servers packed with advanced hardware.

Uptime’s report shows that 45% of IT workloads are still housed in on-premises datacenters, with 16% in colocation. The public cloud accounts for 11%, with another 10% utilizing hosted private cloud services.

While most sites keep server rack power density below 30 kW, trends show a shift. Some facilities now report densities exceeding 100 kW, although these are outliers. The average density, excluding these, has risen to 7.5 kW from 6.8 kW in 2024.

Looking at outages, impactful incidents are rare compared to IT growth, with around half of operators reporting no serious disruptions over the last three years. This decline continues, albeit slowly.

Staff recruitment remains a persistent issue. About 46% of operators find it challenging to fill roles. Senior positions are increasingly difficult to recruit for, impacting knowledge transfer to junior staff as experienced professionals retire.

In terms of AI adoption, operators are cautious, preferring tools enhancing efficiency or reducing human error. Although 73% would trust AI for data analytics, only 14% would allow AI to make configuration changes.