The gap between AI ambition and operational reality has never been more apparent. According to ClearML's newly released State of AI Infrastructure at Scale 2025-2026 report, which surveyed IT leaders and AI infrastructure decision-makers at large enterprises and Fortune 1000 companies, organizations are hemorrhaging money on GPU capacity that sits idle while their AI teams queue for access.
The numbers tell a troubling story: 35% of enterprises rank increasing GPU and compute utilization as their top infrastructure priority for the next 12-18 months. Yet 44% admit they're still manually assigning workloads to GPUs or have no coherent strategy for managing GPU utilization at all. This operational disconnect translates directly into wasted capital and slowed innovation at a time when competitive pressure demands rapid AI deployment.
The Cost Control Paradox
Cost concerns dominate enterprise AI infrastructure planning. The survey found that 53% of respondents cite cost control as their primary AI workload management challenge, while 70% list it as their top infrastructure planning priority for 2025-2026. These aren't surprising figures given GPU pricing and availability constraints, but they reveal a deeper issue.
Organizations are simultaneously reporting challenges with maximizing utilization and procurement. Better GPU utilization could deliver immediate ROI on existing infrastructure investments and potentially delay the need to purchase additional hardware to compensate for poor resource management in the short term. Instead, enterprises find themselves in a cycle of acquiring more capacity to support the surging demand, while failing to maximize what they already own.
The operational bottlenecks compound this problem. Only 27% of surveyed organizations have implemented automated resource sharing dashboards. Meanwhile, 23% still rely on manual ticketing systems for compute provisioning, and 35% report that providing resource access to AI and ML teams remains "difficult" or "very difficult." In an environment where speed matters, these manual workflows create friction that delays projects and frustrates teams.
The Flexibility Imperative
Beyond utilization and cost, enterprises are grappling with strategic questions about infrastructure flexibility. The survey revealed that 44% rate flexibility and avoiding vendor lock-in as "very important" when selecting infrastructure solutions. This isn't a theoretical concern as 63% report that proprietary dependencies have already directly delayed or constrained their ability to scale AI initiatives.
This finding drives meaningful shifts in infrastructure strategy. Organizations are moving toward multi-cloud approaches (37% of respondents) and actively exploring diverse hardware options . The implication is clear: enterprises need infrastructure control planes capable of managing and orchestrating across heterogeneous environments without creating new lock-in scenarios.
AI Agents: High Ambitions, Low Readiness
One of the most striking disconnects in the data involves AI agents. While 89% of enterprise IT leaders plan to implement AI agents within six months (split between custom-built solutions at 49% and off-the-shelf options at 40%), most organizations lack the foundational capabilities to support these deployments effectively.
When asked about operational readiness gaps, enterprise IT leaders cite security and compliance concerns (53%), insufficient internal expertise (46%), and credential propagation challenges (46%). These aren't minor technical details. They represent fundamental requirements for running AI agents at enterprise scale, particularly around transparency and control over resource access.
The credential management concerns are especially notable: 58% worry about automatic propagation of sensitive credentials to compute nodes, while 38% identify credential sharing between users as a major vulnerability. As AI systems become more autonomous and distributed, these security considerations become more complex and critical.
Governance and Sovereignty Take Center Stage
Security and governance priorities are evolving beyond traditional perimeter-based models. Nearly one-third of surveyed organizations identify enforcing stronger user policies, permissions, and governance controls across data, models, and compute resources as their top operational priority.
This emphasis on governance connects to emerging concerns around AI sovereignty—the ability to prove domestic provenance, development, and deployment of AI systems. Achieving this requires complete transparency across the AI lifecycle, from data sources through model training to deployment infrastructure.
What This Means for Enterprise AI Strategy
The survey data points to three converging challenges that will define enterprise AI infrastructure success in 2025-2026:
First, organizations must resolve the operational-technical disconnect. Investing in advanced GPU hardware while maintaining manual provisioning processes undermines the value of those investments. Automation and orchestration become essential capabilities, not nice-to-haves.
Second, infrastructure flexibility needs to move from feature request to architectural requirement. With 63% already experiencing delays from vendor lock-in, platforms that preserve optionality across hardware, clouds, and deployment models will be critical.
Third, security and governance frameworks must evolve to support autonomous AI systems. The rapid adoption plans for AI agents demand infrastructure that can enforce policies, manage credentials, and maintain auditability at scale.
The organizations that address these challenges will gain competitive advantage. Those that don't will continue to pour money into underutilized infrastructure while their AI initiatives stall in queue.
The complete State of AI Infrastructure at Scale 2025-2026 report includes detailed methodology and additional findings from enterprise IT and AI infrastructure leadership at organizations ranging from 2,000 to 10,000+ employees across North America, Europe, and Asia-Pacific.