AI deployments introduce power demands and other challenges that most infrastructure budgets and facilities were never designed for, says the 2026 State of AI Infrastructure Report from DDN.

“Energy consumption, cooling capacity, and inefficient data movement have become real operational constraints – often limiting progress long before compute capacity or GPU availability,” the company says.

Specifically, the report says that:

• 65% of infrastructure sits idle while still consuming power.
• 93% of respondents are actively working to reduce AI’s energy footprint.
• 47% cite energy and cooling as their top inefficiency.
• Only 41% report efficiency gains from recent AI investments.

Complexity in AI infrastructure was cited as another top challenge, as: 

• 98% of respondents report a skills gap related to AI infrastructure.
• 65% say their AI environments are already too complex.
• 54% say they have postponed or cancelled AI initiatives.

Read more at DDN.