86% of enterprises run GPUs at half capacity as AI outpaces governance

Full text

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.

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 of 50% or less

Eighty-six percent of enterprises that run their own GPUs report utilization of 50% or less. Wall Street has spent the quarter debating whether the AI buildout is overbuilt. This is buy-side measurement, from the enterprises doing the buying, and the research says the most expensive hardware in buildings of these enterprises runs at no more than half its capacity.

The measurement gap compounds it: A minority 44% rigorously track what their AI compute actually costs and returns. Everyone else is only estimating. And the enterprise shopping process continues regardless: 45% of these enterprises say the emerging compute option they are most likely to evaluate in the next 12 months is an AI-specialized cloud (CoreWeave, Lambda, Crusoe, Nebius). However, under 2% of these enterprises report using one of these neoclouds today.

Moreover, roughly one in three companies appears to be considering a hedge against Nvidia: Asked which emerging compute option they are most likely to evaluate in the next 12 months, 32% of enterprises named non-Nvidia accelerators (AWS Trainium, Google TPUs, AMD), while 28% named next-generation Nvidia GPUs. The data suggests that enterprises should measure the utilization and per-workload cost of the GPUs they already own before committing budget to new compute — whether that's an AI-specialized cloud contract, new accelerators, or more GPUs.

Most deployed "agents" do single-prompt work: 71% say a quarter or fewer complete multi-step tasks on their own

Seventy-one percent of enterprises say a quarter or fewer of their deployed "agents" can complete multi-step work on their own; the rest are single-prompt chatbots. Only 10% say true agents are the majority of what they run. To be sure, the respondents reported that they are in a position to know these things: 81% said they recommend or decide AI purchases at their companies.

That finding — that most agents are actually just chatbots in trenchcoats — lands amid adoption claims across the industry running well ahead of what enterprises are actually running. Gartner predicted 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. It also warned that the most common misconception is referring to these AI assistants as agents, a misunderstanding known as "agentwashing."

Meanwhile, Zapier's enterprise survey said 72% reported deploying or testing autonomous agents; and Writer's 2026 survey has 97% of executives saying their company deployed AI agents in the past year.

Those surveys asked whether companies have deployed something called an AI agent, and companies said yes. Our survey asked the people running those deployments a harder question: Of the agents you have in production, how many can complete a multi-step task without a person driving each step? The gap matters for two practical reasons. First, the inflated adoption figures are the benchmark boards and vendors use to pressure technical leaders into moving faster — and this data says the real bar is far lower than the headlines suggest. Second, the label determines the bill: A single-prompt chatbot with a human reading every answer needs none of the identity, evaluation, and cost controls this report covers, while a true multi-step agent needs all of them.

66% let agents push to production on automated evals alone — or are engineering toward it. 5% fully trust those evals

Two-thirds of enterprises fall into one of two camps: 34% already allow an AI agent to push a code or system change to production based on automated evaluation results alone, with no human reviewing it, and another 33% are actively engineering their pipelines to allow that within the next 12 months. Only five percent fully trust the automated evaluations that would make that decision.

The distrust is earned. Half of enterprises shipped an agent that passed internal evaluations and then caused a customer-facing failure in the past year; a quarter watched it happen more than once. Asked to name the biggest weakness in their current evaluations, more enterprises chose “poor alignment with real-world outcomes” than any other answer — 29% of respondents.

And most of the checking happens before an agent ships, then stops. Once agents are live with real users, only 23% of enterprises run real-time quality checks on the answers those agents produce. Another 51% monitor system health only — uptime, request traces, and gateway logs — which tells them the agent is running, and nothing about whether its answers are right. The first move: Before removing human review from any workflow, test your evaluations against production outcomes rather than internal benchmarks, and instrument answer quality, not just uptime.

This finding is explored in more depth in VentureBeat's related coverage of the evaluation gap , which found that larger enterprises are moving faster toward zero-human deployment while also failing more often — and outlines a regression-testing framework built on production outcomes rather than internal benchmarks.

69% run credential sharing somewhere in the agent fleet — and those companies get hit far more often

Sixty-nine percent of companies allow agent credential sharing somewhere in their agent fleet during runtime – meaning multiple agents operating under one API key or service account. Those companies were far more likely to get hit: Organizations with credential sharing anywhere in the fleet experienced a security incident or near-miss at a 63.5% rate (47 of 74), against 40.9% (9 of 22) where every agent has its own scoped identity.

The takeaway for enterprises is this: Give every agent its own scoped identity, starting with the agents that touch production systems.

57% traced a confident, wrong agent answer to their own missing or inconsistent business context

Fifty-seven percent of enterprises traced at least one confident, wrong agent answer in the past six months to missing or inconsistent business context: wrong metrics, stale definitions, absent documents. Most of them watched it happen more than once.

Most enterprise companies are fixing this, even though they’ve moved forward with agent deployment already: 25% already run a governed semantic layer, or one governed definition of the business that every AI reads from, in production. However, 34% are still building one, and 41% haven't started. The takeaway: Govern the definitions your agents answer from, metrics and entities first, before scaling the agents that depend on them.

The quarter where agent technology “portability” became a priority

One more shift is worth reporting with its limits stated plainly. In our spring orchestration survey wave, the top concern about provider-controlled orchestration was security and permissioning limits (32%). By June, vendor lock-in led at roughly a third, with security limits at 28%.

Those are two snapshots one quarter apart, and here’s one possible explanation for why portability became a top issue for enterprises. Our June survey went into market after a June 12 U.S. Commerce Department export order took Anthropic's Claude Fable 5 offline for enterprises for roughly three weeks. Meanwhile, Chinese company Z.ai released GLM-5.2's open weights under an MIT license on June 16 at roughly one-sixth of GPT-5.5's price; and Tencent's Hy3 arrived July 6 under Apache 2.0; and OpenAI previewed GPT-5.6 on June 26 to a small group of government-vetted partners, opening it broadly on July 9 after the government's review cleared. The open-weight releases in particular promise enterprises more control over their agents, and while we haven't established a causal link here, the timing is worth noting.

The posture data matches the mood: 51% now expect their primary control plane for enterprise agents to be hybrid — provider-native plus external orchestration — by the end of 2026, up from 34% in the spring survey wave. Enterprises reporting that they rely purely on provider-managed agent services fell from 12% to 7%.

Five layers, no incumbents, 12 months

The synthesis across all five surveys reveals a huge “buying” window. In each of the five control layers, 57% to 64% of enterprises plan to switch or add vendors within 12 months — 64% in infrastructure and in evaluations, 59% in agent security, 57% in retrieval and context — and 26% to 38%, depending on the layer, plan to move within a quarter. No layer has an established incumbent: The most common evaluation tooling is the model provider's built-in evals, tied with no dedicated tooling at all (17% each); 82% of respondents name provider-native or hyperscaler controls as their primary agent security layer; and provider-native retrieval leads the context technology layer (RAG, etc) as well.

Most enterprises are defaulting today to the built-in tools that ship with the big AI platforms they already use: Anthropic, OpenAI, Google, Microsoft, and AWS. That holds true across every one of these agentic technology layers: enterprises are looking to their primary cloud and model providers to supply the guardrails, evaluations, and retrieval solutions already bundled into those providers' offerings.

Those defaults are winning on convenience, and they're also what the coming spending decisions will test. The survey didn't ask which direction that money moves — toward the platforms' built-in tools or toward the specialists challenging them — which is exactly why every contract in these five layers is worth watching over the next four quarters.

The Q3 survey wave will measure whether the enterprises made good on these budget plans: whether their agents gained scoped identities, whether evaluations got tested against production outcomes, whether GPU utilization rose, and whether the semantic layers under construction shipped.

VentureBeat will release the full Q2 reports across all five VB Pulse trackers at VB Transform , July 14–15 at Hotel Nia in Menlo Park, where we convene enterprise technical leaders building autonomous agents in production.

Disclosure: VentureBeat produces both this research and VB Transform