BusinessThursday, July 16, 2026· 2 min read

Enterprises Race to Build Smarter AI Infrastructure

TL;DR

A new VentureBeat Pulse survey shows enterprises are moving quickly to expand AI infrastructure, with many evaluating specialized clouds and new providers. The findings highlight a maturing market where companies are beginning to prioritize integration, total cost of ownership, and better compute visibility—key steps toward more efficient AI at scale.

Key Takeaways

  • 1Enterprise AI infrastructure investment is accelerating, even though only 21% of surveyed organizations currently run AI at production scale.
  • 2AI-specialized clouds are gaining momentum, with 45% of enterprises planning to evaluate them over the next year.
  • 3Companies are prioritizing practical deployment factors like integration and total cost of ownership over headline token pricing.
  • 4The survey reveals a major opportunity to improve GPU utilization and cost tracking as AI adoption grows.
  • 5A majority of enterprises expect to switch or add AI infrastructure providers within 12 months, signaling a dynamic and competitive market.

Enterprise AI Enters a New Infrastructure Phase

VentureBeat’s latest Pulse Research points to a major shift in enterprise AI: organizations are investing rapidly in the compute foundations needed to bring AI into everyday business operations. While only about one in five surveyed enterprises currently run AI in production at scale, many are already preparing for the next stage of adoption.

The most encouraging signal is momentum. Forty-five percent of enterprises plan to evaluate AI-specialized clouds over the next year, even though few use them today. That suggests companies are actively looking for infrastructure designed specifically for AI workloads, not just adapting traditional cloud setups.

The research also shows that enterprise buyers are becoming more sophisticated. Rather than focusing only on headline token costs, respondents said their decisions are driven by integration with existing systems and total cost of ownership. That shift is important because it points toward more sustainable, production-ready AI deployments.

  • 83% of respondents report GPU utilization at 50% or less, revealing room for major efficiency gains.
  • 44% can rigorously track AI compute costs, creating an opportunity for better tooling and governance.
  • 64% plan to switch or add an infrastructure provider within a year, opening the door for innovation and competition.

Overall, the survey captures a market moving from experimentation toward operational maturity. The compute gap is real, but it also represents a clear path for progress: better measurement, smarter infrastructure choices, and more efficient AI systems that can scale across the enterprise.

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