Wall Street is debating the AI buildout. Enterprises just answered: 86% say their GPUs run at half capacity or less

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Enterprise companies are running AI agents ahead of the controls needed to manage them — and they deployed that way knowingly. That is the central finding from VentureBeat Research's June survey of 573 technical leaders at companies with 100 or more employees, fielded across five parallel surveys of the agentic stack. 

Enterprises are now retrofitting to catch up with their own standards, and they are budgeting for it: Roughly six in 10 enterprises plan to switch or add vendors in each of five control layers within the next 12 months, and roughly a third — depending on the layer — plan to move within the quarter, the research finds.

There are five main layers where enterprises are building: identity for agents (which agent is allowed to do what, under whose credentials); evaluation of agent output (whether the work is any good); cost telemetry (what each agent costs to run); the context layer (the business data and definitions agents draw on to answer); and the orchestration control plane (the software that coordinates multi-step agent work).

Enterprises are already paying the price for deploying agents ahead of adequate control functions. Fifty-four percent of companies had an agent security incident or near-miss caught before harm in the past 12 months. Twenty-seven percent exercise only reactive control of agent spend — they learn what an agent costs when the invoice arrives, with no per-agent budget or ceiling in place.

About the data

573 respondents at organizations with 100+ employees, across five surveys fielded in June 2026: 101 orchestration · 157 reliability/evals · 107 security/identity · 107 infra/compute · 101 context/RAG

Samples are self-selected; read findings directionally. Trust the pattern over exact percentages — every survey, independently, points the same way, with deployment running ahead of governance, visibility and cost control.

Full reports at VB Transform, July 14–15 in Menlo Park →

Here are the five findings that anchor the set — one finding per layer of the tech stack — and what the data suggests doing first in each.

Expensive hardware is idle: 86% of GPU operators report utilization o...

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