A growing number of data center operators are embracing AI to boost efficiency and productivity, but they’re still approaching its use in critical systems with caution.
Cost and capacity planning are the top management concerns, according to Uptime Institute’s 15th Annual Global Data Center Survey 2025. Interest in AI workloads adds further complexity as data center owners and operations perform or plan to perform some AI training or inference in the future, the data center advisory group reports.
Uptime Institute’s 2025 data center survey was conducted online from April 2025 to May 2025 and collected responses from more than 800 data center owners and operators and more than 1,000 vendors and consultants. Respondents shared the issues that have them very or somewhat concerned about their digital infrastructure management in the next 12 months. The most pressing issues are:
- Cost issues: 76%
- Forecasting future data center capacity requirements: 71%
- Improving energy performance for facilities equipment: 67%
- Power availability: 63%
- Supply chain disruptions: 65%
- A lack of qualified staff: 67%
With respect to capacity planning, there’s been a notable increase in the number of operators who describe themselves as “very concerned” about forecasting future data center capacity requirements. Andy Lawrence, Uptime’s executive director of research, said two factors are contributing to this concern: ongoing strong growth for IT demand, and the often-unpredictable demand that AI workloads are creating.
“There’s great uncertainty about … what the impact of AI is going to be, where it’s going to be located, how much of the power is going to be required, and even for things like space and cooling, how much of the infrastructure is going to be sucked up to support AI, whether it’s in a colocation, whether it’s in an enterprise or even in a hyperscale facility,” Lawrence said during a webinar sharing the survey results.
The survey found that roughly one-third of data center owners and operators currently perform some AI training or inference, with significantly more planning to do so in the future. As the number of AI-based software deployments increases, information about the capabilities and limitations of AI in the workplace is becoming available. The awareness is also revealing AI’s suitability for certain tasks. According to the report, “the data center industry is entering a period of careful adoption, testing, and validation. Data centers are slow and careful in adopting new technologies, and AI will not be an exception.”
Perceived benefits of using AI in operations
Survey respondents shared how they currently believe AI would most benefit their data center operations. The responses reveal three primary drivers: improved efficiency, reduced human error, and increased productivity.
- Increased facility efficiency: 58%
- Lower risk of human error: 51%
- Increased staff productivity: 48%
- Improved IT performance: 40%
- Lower risk of equipment failure or outages: 39%
- Reduced maintenance/service costs: 36%
- Improved IT resiliency: 26%
- Reduced staff levels: 22%
- None: 5%
Confidence in AI for operations
When asked what type of operational decisions operators would allow AI to make in the data center, respondents revealed that the trust in AI depends directly on the use case. “The results show trust in AI as a tool varies considerably depending on the specific application of this technology,” the report reads. Respondents ranked their confidence in AI with the following data center tasks:
- Operational sensor data/alarm analytics: 73%
- Predictive maintenance tasks: 70%
- Generating document text: 55%
- Controlling data center equipment: 35%
- Staffing issues/shift arrangements: 21%
- Configuration changes: 14%
- None: 6%
“Data center operators are very, very happy to do certain things using AI, and they will never, never trust AI to do certain other things. That is interesting. What I think is happening is the industry is becoming more aware of what AI can do. It is becoming aware of the different types of AI. And they’re not created equal,” said Max Smolaks, research analyst with Uptime Institute, on the webinar. “Some types of AI are a little bit better at doing precise work that needs to be done, like in mission-critical environments, and some are better for generating copy or generating documents, but they’re perhaps not as suitable for controlling equipment.”
Source:: Network World