Enterprise investment in AI compute is accelerating faster than organizations can track the cost of the hardware they already own, according to survey data that exposes a widening gap between spending ambition and financial visibility. Most organizations run their AI workloads on hyperscalers and model provider APIs, yet their next dollar is aimed at specialized GPU clouds that almost none use today. The result is a compute gap: heavy spending moving ahead of the measurement needed to control it.

What You Need to Know

The survey fielded by VentureBeat in June 2026 covers 107 enterprises with over 100 employees. Only 21% run AI in production at scale, yet 45% plan to evaluate AI-specialized clouds within a year. GPU utilization sits at 50% or less for 83% of respondents, and fewer than half rigorously track compute costs. These figures suggest enterprises are investing ahead of their ability to manage returns.

The Visibility Gap

The central finding is a disconnect between infrastructure spending and cost visibility. Fewer than half of respondents (44%) can rigorously track what their AI compute costs. GPU utilization remains cold: 83% report utilization of 50% or less. When enterprises cannot see unit economics, they risk overprovisioning and wasted capacity. The gap is especially striking because enterprises are buying more infrastructure faster than they can account for what they already own.

  • GPU utilization: 83% of enterprises run at half capacity or less, indicating idle compute.
  • Cost tracking: Only 44% can measure compute costs rigorously, leaving most in the dark.
  • Planned evaluation: 45% intend to assess AI-specialized clouds despite near-zero current use.

Vendor Turnover and Selection Criteria

Enterprises are not settled on infrastructure vendors. A clear majority (64%) plan to switch or add a provider within twelve months, and 38% within the next quarter. This high churn intent signals dissatisfaction or a search for better economics. When choosing, enterprises prioritize integration with the existing stack (41%) and total cost of ownership (35%). Headline token price matters for only 8%. The frontier shift from GPU compute to memory bandwidth as inference scales remains largely unaddressed, with roughly one in five enterprises unaware of it.

Why This Matters

The compute gap carries real consequences. Enterprises that invest without cost visibility risk wasting capital on underutilized hardware, eroding ROI for AI initiatives. The high switching intentions mean vendors face pressure to improve measurement tools and integration. As inference workloads grow, the shift to memory bandwidth will demand new infrastructure strategies. Companies that fail to close this visibility gap may find themselves locked into expensive, inefficient compute setups that hinder AI adoption rather than accelerating it.