Skip to main content

Data Centers Preparing for Unpredictable AI Workload Demand

The data center industry is innovative and resilient, but also facing rising costs, worsening power constraints, and challenges in meeting the demands for AI, according to the Global Data Center Survey 2025 from Uptime Institute.

As operators expand and modernize to meet power and density requirements, they must address availability, efficiency, staffing challenges, supply chain delays, and unpredictable technological advances.

"Our data shows operators are tasked with managing a lot of big strategic challenges at the same time. These include anticipating multiple technological changes, planning for expansion in spite of major constraints on power availability, and preparing for and supporting unpredictable AI workload demand," said Andy Lawrence, Executive Director of Research, Uptime Institute. "This is a time where senior level experience is critical. But for the first time, more operators are finding it harder to recruit and retain senior people than people at an earlier stage of their career. There is a management shortage, with many experienced leaders retiring just as another phase of dramatic growth gets underway."

Roughly one-third of data center owners and operators currently perform some AI training or inference, and a significantly greater proportion plan to do so in the future. But much of this is early stage and cautious. Uncertainty over the appropriate or likely venues for AI workloads, and apprehension over the power demands of projected NVIDIA GPU systems, is likely contributing to capacity concerns.

Now in its 15th year, Uptime Institute's annual survey is the most comprehensive and longest-running study of its kind. The findings of this report highlight the practices and experiences of data center owners and operators in the areas of resiliency, sustainability, efficiency, staffing, cloud, and artificial intelligence.

Key findings from the 2025 report include:

  • Cost issues remain the top concern for digital infrastructure management teams in 2025 — but worries around forecasting future capacity requirements have grown significantly.
  • Average PUE levels show little change for the sixth consecutive year, with improvements constrained by legacy infrastructure and some climate specific limitations to efficient cooling.
  • Average server rack power densities continue to rise, with greater adoption of racks in the 10–30 kW range. Few facilities exceed 30 kW, and extreme densities are as yet rare.
  • The collection and reporting of key sustainability metrics have not improved in 2025, which is likely due in part to commercial pressures to support AI, and easing regulatory pressure in some regions.
  • Trust in AI for data center operations depends on the use case: most would allow its use for analyzing sensor data and predictive maintenance tasks, but not configuration changes, controlling equipment, or staffing issues.
  • Impactful data center outages are gradually becoming less frequent — but one in ten still cause serious or severe disruption, underscoring the need for continued investment.
  • Enterprises continue to adopt hybrid IT strategies, spanning cloud, colocation on-premises data centers. On-premises data centers remain foundational for those with large, mission critical processing needs, with 45% of IT workloads still residing in corporate facilities.
  • Staffing challenges persist in 2025. Nearly two-thirds of operators report difficulty retaining staff, finding qualified candidates, or both.

Methodology: Uptime conducted this year's Annual Global Data Center Survey online and via email from April to May 2025 and collected responses from more than 800 data center owners and operators. For the third consecutive year, Uptime's survey asked data center operators to identify their management team's top concerns related to digital infrastructure. In 2025, new response options were added to reflect the evolving challenges surrounding power availability, supply chain disruptions, and demand for AI.

The survey participants represent a wide range of industry verticals in multiple countries. Nearly half (43%) are located in North America and Europe. Approximately one in five respondents work for professional IT / data center service providers — that is, staff with operational or executive responsibilities for a third-party data center, such as those offering colocation, wholesale, software or cloud computing services.

The Latest

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

Data Centers Preparing for Unpredictable AI Workload Demand

The data center industry is innovative and resilient, but also facing rising costs, worsening power constraints, and challenges in meeting the demands for AI, according to the Global Data Center Survey 2025 from Uptime Institute.

As operators expand and modernize to meet power and density requirements, they must address availability, efficiency, staffing challenges, supply chain delays, and unpredictable technological advances.

"Our data shows operators are tasked with managing a lot of big strategic challenges at the same time. These include anticipating multiple technological changes, planning for expansion in spite of major constraints on power availability, and preparing for and supporting unpredictable AI workload demand," said Andy Lawrence, Executive Director of Research, Uptime Institute. "This is a time where senior level experience is critical. But for the first time, more operators are finding it harder to recruit and retain senior people than people at an earlier stage of their career. There is a management shortage, with many experienced leaders retiring just as another phase of dramatic growth gets underway."

Roughly one-third of data center owners and operators currently perform some AI training or inference, and a significantly greater proportion plan to do so in the future. But much of this is early stage and cautious. Uncertainty over the appropriate or likely venues for AI workloads, and apprehension over the power demands of projected NVIDIA GPU systems, is likely contributing to capacity concerns.

Now in its 15th year, Uptime Institute's annual survey is the most comprehensive and longest-running study of its kind. The findings of this report highlight the practices and experiences of data center owners and operators in the areas of resiliency, sustainability, efficiency, staffing, cloud, and artificial intelligence.

Key findings from the 2025 report include:

  • Cost issues remain the top concern for digital infrastructure management teams in 2025 — but worries around forecasting future capacity requirements have grown significantly.
  • Average PUE levels show little change for the sixth consecutive year, with improvements constrained by legacy infrastructure and some climate specific limitations to efficient cooling.
  • Average server rack power densities continue to rise, with greater adoption of racks in the 10–30 kW range. Few facilities exceed 30 kW, and extreme densities are as yet rare.
  • The collection and reporting of key sustainability metrics have not improved in 2025, which is likely due in part to commercial pressures to support AI, and easing regulatory pressure in some regions.
  • Trust in AI for data center operations depends on the use case: most would allow its use for analyzing sensor data and predictive maintenance tasks, but not configuration changes, controlling equipment, or staffing issues.
  • Impactful data center outages are gradually becoming less frequent — but one in ten still cause serious or severe disruption, underscoring the need for continued investment.
  • Enterprises continue to adopt hybrid IT strategies, spanning cloud, colocation on-premises data centers. On-premises data centers remain foundational for those with large, mission critical processing needs, with 45% of IT workloads still residing in corporate facilities.
  • Staffing challenges persist in 2025. Nearly two-thirds of operators report difficulty retaining staff, finding qualified candidates, or both.

Methodology: Uptime conducted this year's Annual Global Data Center Survey online and via email from April to May 2025 and collected responses from more than 800 data center owners and operators. For the third consecutive year, Uptime's survey asked data center operators to identify their management team's top concerns related to digital infrastructure. In 2025, new response options were added to reflect the evolving challenges surrounding power availability, supply chain disruptions, and demand for AI.

The survey participants represent a wide range of industry verticals in multiple countries. Nearly half (43%) are located in North America and Europe. Approximately one in five respondents work for professional IT / data center service providers — that is, staff with operational or executive responsibilities for a third-party data center, such as those offering colocation, wholesale, software or cloud computing services.

The Latest

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...