Enterprises are investing in AI infrastructure but can't measure costs
AI infrastructure spending outpaces economic visibility.
1AI infrastructure investments are accelerating
Organizations are investing in AI infrastructure faster than they can measure costs. A survey of 107 enterprises shows spending is outpacing economic visibility. Most run their AI on hyperscalers and model APIs, highlighting a gap between investment and oversight.
2GPU utilization remains low
83% of enterprises report GPU utilization at 50% or less, indicating inefficient resource use. Fewer than half (44%) rigorously track AI compute costs, making financial control challenging. This gap between spending and accountability risks unchecked expenses.
3High intent to switch providers
64% of organizations plan to switch or add infrastructure providers within 12 months—38% within a quarter. Decisions prioritize integration (41%) and total cost of ownership (35%) over headline pricing. Token cost per million influences only 8%, reflecting long-term operational priorities.
4Specialized clouds barely used today
Most enterprises rely on hyperscalers for AI workloads, with niche GPU providers near 0% adoption. Yet 45% plan to evaluate AI-specialized clouds next year—a stark shift from current stacks. This signals impending market disruption as compute needs evolve.
5Integration outweighs pricing
When selecting providers, enterprises prioritize stack compatibility (41%) and TCO (35%) over per-token costs (8%). This underscores the importance of seamless operations in AI infrastructure, where long-term efficiency trumps short-term savings.
6Limited production-scale AI
Only 21% run AI in production at scale, while 76% remain in experimentation or partial deployment. Infrastructure decisions are thus being made by early adopters whose costs will grow substantially, not by operators with mature deployments.
7Memory bandwidth awareness lags
The coming shift from GPU compute to memory bandwidth as inference scales remains off-radar for 20% of enterprises. This looming bottleneck—critical for next-gen AI—highlights a knowledge gap in future-proofing infrastructure investments.
8Mid-market leads adoption
Survey respondents skew toward mid-sized firms (101-1,000 employees), with Technology (26%) and Healthcare (15%) as top sectors. This reflects AI infrastructure build-out by active adopters rather than hyperscale operators, shaping early-market dynamics.
9Decision-makers drive purchases
45% of participants hold final purchasing authority for AI solutions, with 30% as influencers. Their focus on integration and TCO reveals how operational needs—not just cost—guide infrastructure choices in this nascent market.
10Cost tracking remains weak
Fewer than half of enterprises rigorously monitor AI compute expenses, risking unchecked spending. Improving cost visibility is critical for sustainable investment—especially as GPU utilization hovers below 50%, signaling wasted capacity.
11Future challenges emerge
Rapid AI infrastructure growth brings cost control and efficiency to the forefront. Organizations must develop better measurement tools and strategies to align investments with returns, ensuring scalable and economically viable AI deployments.
12AI infrastructure management grows critical
The speed of AI investment makes governance indispensable. Enterprises need new approaches to optimize resource use and maximize ROI. Mastering this balance will define who sustains competitive advantage in the AI era.