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The agent security gap: 54% of enterprises have already had an AI agent incident, and most still let agents share credentials
<p>Across 107 enterprises, AI agents are being given real access to systems and data while the controls meant to contain them lag behind. More than half have already had a confirmed agent security incident or a near-miss; only about a third give every agent its own scoped identity, and most agents still share credentials; and only three in ten isolate their highest-risk agents. The security stack is overwhelmingly borrowed from the model providers and hyperscalers rather than purpose-built for agents, spending remains a thin slice of the security budget, and enterprises are evenly split on whether their defenses are keeping pace with AI-enabled attackers. The result is an agent security gap — autonomous agents proliferating faster than the identity, isolation, and enforcement controls needed to hold them.</p><p>This wave of VentureBeat Pulse Research examines how enterprises secure their AI agents: what tooling they run, how they manage agent identity and isolation, what has already gone wrong, how much they spend, and whether they believe their defenses are keeping pace with AI-enabled attackers.</p><p>The central finding is an agent security gap — the distance between the autonomy enterprises are granting their agents and the controls in place to contain them. More than half of organizations (54%) have already experienced a confirmed agent security incident (18%) or a near-miss caught before harm (36%). The structural weakness beneath those numbers is identity: only about a third (32%) give every agent its own scoped, managed identity, while the rest report that some agents share credentials or that agents mostly run on shared API keys and human or service-account credentials. When agents share credentials, a single compromised or over-permissioned agent carries a wide blast radius — and only three in ten enterprises (30%) isolate their highest-risk agents in sandboxes to bound that radius.</p><p>What makes the gap notable is how comfortable enterprises are inside it. The security stack is overwhelmingly provider-native — OpenAI’s guardrails (51%), Google’s and Microsoft’s cloud controls, and Anthropic’s managed-agent controls dominate, while the dedicated agent-security specialists barely register — and satisfaction with that borrowed stack is high, averaging 4.2 out of 5. Yet spending remains a thin slice of the security budget, only a third of enterprises believe their AI defenses are ahead of AI-enabled attackers, and a clear majority plan to change tooling within the year. Enterprises are satisfied with controls they are simultaneously preparing to replace.</p><h2>Methodology</h2><p>VentureBeat fielded this survey as part of its ongoing Pulse Research series, this instrument focused on enterprise agent security — the tooling, identity, isolation, and enforcement controls organizations use to secure autonomous AI agents. Responses are filtered to organizations with more than 100 employees (n=107; the survey’s smallest size band, 1–100 employees, is excluded), drawn from a single June 2026 wave. Because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%.</p><p>By role the sample is senior and buyer-credible: 45% are final decision-makers for AI purchases and another 30% recommenders or influencers. Managers (43%), individual contributors (24%), VPs and directors (15%), and the C-suite (11%) make up the seniority mix. By organization size the sample is mid-market-weighted: 251–1,000 (42%) and 101–250 (25%) employees lead, with 1,001–5,000 (19%), 5,001–10,000 (8%), and 10,001+ (7%) above them. Technology/Software is the largest industry at 23%, followed by Manufacturing (15%), Retail/E-commerce (14%), and Healthcare/Life Sciences (13%).</p><p>At 107 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It skews toward the mid-market, so it is best read as the view from organizations actively standing up agent security rather than from the largest operators.</p><p>Satisfaction ratings are computed on the respondents who answered each rating question; the overall satisfaction score reflects 82 of the 107 qualified respondents.</p><h2>Finding 1: The incidents are already here</h2><p><b>More than half have had an agent security incident or near-miss</b></p><p>We asked whether organizations had experienced an agent security incident — a confirmed breach, or a near-miss caught before harm. Most that run agents in production had.</p><div></div><p>This is the report’s defining number. More than half of organizations (54%) have already had an agent security event — 18% a confirmed incident and 36% a near-miss caught before it caused harm. Only 42% report nothing, and a small remainder either run no agents in production or don’t track such events. That so many report near-misses rather than only confirmed incidents is telling: enterprises are catching problems, but they are catching them close to the edge. The controls examined in the rest of this report — identity, isolation, enforcement — are what determine whether the next near-miss stays a near-miss.</p><p>Exposure scales with company size, but containment does not. The incident-or-near-miss rate rises from 49% in the mid-market (companies with 101-1,000 employees) to 63% at larger enterprises (above 1,000 employees), while sandbox isolation of high-risk agents falls from 35% to 20%, and satisfaction with security tooling drops from 4.36 to 3.97. The organizations running the most agents across the most systems carry the most incidents and the least of the one control that bounds an incident's blast radius.</p><h2>Finding 2: The identity gap</h2><p><b>Only a third give every agent its own scoped identity</b></p><p>We asked how enterprises manage the identity of their AI agents — whether each agent has its own credentials, or agents share them. Full per-agent identity is the exception.</p><div></div><p>Rolled together, the overlapping answers show 69% of enterprises (74 of 107) with credential sharing somewhere in the agent fleet. Identity is the structural weakness beneath the incidents. Only about a third of enterprises (32%) give every agent its own scoped, managed identity — the precondition for least-privilege access and clean attribution. Nearly half (48%) say some agents have scoped identities but many still share credentials, and another 32% say agents mostly run on shared API keys or borrowed human and service-account credentials. (Respondents could describe more than one pattern across their agent fleet, so these overlap.) </p><p>The consequence is direct: when agents share credentials, an over-permissioned or compromised agent can act with far more reach than intended, and forensics after an incident cannot cleanly tell which agent did what. The non-human identity problem — giving every agent its own governed identity — is the single largest unfinished piece of enterprise agent security.</p><p>Moreover, a company’s agent credential posture is correlated with incidents. Organizations with credential sharing anywhere in the fleet were hit — with an incident or a near-miss in the past twelve months — at 63.5% (47 of 74). Organizations where every agent carries its own scoped identity were hit at 40.9% (9 of 22). The fully-scoped group is small, so for now the relationship is an association rather than proven causation, and the gap is concentrated in the mid-market — but within a single survey, a twenty-three point difference in incident rate suggests significance.</p><h2>Finding 3: Observe and enforce, but rarely isolate</h2><p><b>Only three in 10 sandbox their highest-risk agents</b></p><p>We asked what an organization’s agent security posture looks like in practice — whether they observe, enforce, isolate, or some combination. The control that bounds damage is the least common.</p><div></div><p>Monitoring and enforcement are reasonably common; containment is not. Roughly half of enterprises observe agent activity (47%) or enforce scoped permissions at runtime (49%), but only 30% isolate their highest-risk agents in sandboxes that bound the blast radius when the other controls fail. That ordering is backwards from a defense-in-depth standpoint: observation tells you what happened, enforcement tries to prevent it, but isolation is what limits the damage when prevention fails — and it is the control enterprises have adopted least. Combined with the identity gap in Finding 2, the picture is of agents that are watched and permissioned but rarely boxed in, which is precisely the configuration in which a single failure propagates.</p><h2>Finding 4: Security runs on borrowed, provider-native controls</h2><p><b>Guardrails from OpenAI, Google and Microsoft dominate; specialists barely register</b></p><p>We asked which agent security tooling enterprises use, and which is their primary layer. The answer favors the model providers and hyperscalers over the dedicated security vendors.</p><div></div><p>Enterprises are securing agents with tools that came bundled with their models and clouds. OpenAI’s guardrails lead at 51%, followed by Google’s and Microsoft’s cloud-native controls and Anthropic’s managed-agent controls — and when asked to name their single primary security layer, 82% name one of these provider-native offerings. The purpose-built agent-security category — Palo Alto’s Prisma AIRS, CrowdStrike, Cisco AI Defense, Zenity, HiddenLayer, Check Point’s Lakera, Okta for AI Agents, non-human identity platforms — barely registers, each in the low single digits, and only 5% run no dedicated tooling at all. As with retrieval and evaluation elsewhere in this series, the provider bundle is winning the default: enterprises reach first for the guardrails their platform ships, and the independent security layer that would address the identity and isolation gaps has not yet been adopted at scale.</p><p>The provider-default pattern is consistent across both Q2 survey waves. In April–May (n=110), usage was led by the same names — OpenAI's controls at 26%, Azure at 15%, AWS at 14%, Google at 12% — with every dedicated agent-security specialist at 3% or below and one in ten using no dedicated tooling at all. The common finding from the two surveys: Enterprises are defaulting to the solutions provided by the platform they’re using, and the specialist category vendors have yet to become big players here.</p><p>(<i>A note on reading these shares. As described in the methodology section, the respondent sample is self-selected and skews mid-market, and the usage question counted every vendor or approach a respondent has in place — so the figures measure presence in the security stack rather than spending or exclusivity. Individual vendor percentages therefore carry all the usual sample caveats. The structural pattern, however, held across both Q2 waves on two differently worded questions: provider-native and hyperscaler controls lead, and dedicated agent-security specialists remain in low single digits. Read the individual shares loosely and the pattern with confidence.)</i></p><h2>Finding 5: And enterprises are comfortable with it</h2><p><b>Satisfaction is high, even as incidents mount and identity lags</b></p><p>We asked how satisfied enterprises are with their current agent security tooling. The comfort is notably out of step with the exposure documented above.</p><div></div><p>Satisfaction with agent security tooling is high — 4.2 out of 5 overall, and 4.1 for value for money — among the most positive readings in this series. That is the striking part: enterprises are highly satisfied with a stack that is mostly borrowed provider guardrails, even though more than half have already had an incident or near-miss and only a third give their agents scoped identities. The comfort appears to rest on the convenience and low friction of provider-native controls rather than on demonstrated containment. It is a false comfort in the making — the same enterprises expressing satisfaction are, as Finding 8 shows, a clear majority planning to change tooling within the year, which suggests the confidence is thinner than the score implies.</p><h2>Finding 6: Budgets haven’t caught up</h2><p><b>Most spend under a tenth of the security budget on agents</b></p><p>We asked what share of the security budget enterprises allocate to securing AI agents. For a fast-emerging risk, the allocation is modest.</p><div></div><p>Spending on agent security is still a thin slice. The most common allocation is 6–10% of the security budget (46%), and a third of enterprises (34%) spend 5% or less; only a quarter (24%) devote more than a tenth. Given the incident rate in Finding 1 and the identity and isolation gaps in Findings 2 and 3, the budget looks like a lagging indicator — the risk has arrived faster than the funding to address it. The enterprises spending more than a tenth of their security budget on agents are a distinct minority, and they are likely the ones building the scoped-identity and isolation controls the rest have not.</p><h1>Finding 7: The arms race is even, at best</h1><p><b>Only a third think their AI defenses are ahead of AI-enabled attackers</b></p><p>We asked how enterprises assess the balance between their AI-enabled defenses and AI-enabled attackers. Confidence is far from settled.</p><div></div><p>Enterprises are split on whether they are winning. Only about a third (35%) believe their AI-enabled defenses are ahead of AI-enabled attackers; the rest are less sure — 32% call it roughly even, 21% think attackers are ahead, and another 21% say it is too early to tell. Taken together, a clear majority (53%) rate the balance as even or tilted toward the attacker. That uncertainty sits uneasily beside the high satisfaction of Finding 5: enterprises are content with their tooling yet unconvinced it is winning the contest it exists to win. In a domain where the offense is also compounding with AI, an even race is not a comfortable place to be.</p><h2>Finding 8: A security reshuffle is coming</h2><p><b>Nearly six in 10 plan to adopt or switch tooling within a year</b></p><p>We asked whether enterprises plan to adopt a new, additional, or replacement agent security solution, and which they are considering. Few intend to stand pat.</p><div></div><p>The security stack is not settled. While 41% have no plans to change, a clear majority (59%) intend to adopt a new, additional, or replacement agent security solution within twelve months, and 29% within the next quarter — a strong signal that, high satisfaction notwithstanding, enterprises know the current stack is provisional. Incidents are what start the buying cycle. </p><p>Among organizations that have been hit, 42.1% plan to adopt, add, or replace agent security tooling within the next ninety days, against 14.0% of organizations with no incident — and after a confirmed incident it becomes majority behavior, at 52.6%. Getting hit also changes the threat assessment: 33.3% of hit organizations say AI-armed attackers are ahead of their defenses, against 8.0% of the unhit. Experience, in this data, is the strongest predictor of both urgency and pessimism.</p><p>The consideration set still leans provider-native (OpenAI 34%, Google 30%, Anthropic 29%, Azure 25%), but the dedicated security vendors — Cloudflare, Cisco, Palo Alto, Okta, Check Point’s Lakera — draw early interest in the mid-to-high single digits, more than their current footprint. </p><p>What the shopping does not yet include is the identity layer specifically. Twelve percent of the respondents include an agent-identity product — Okta for AI Agents, Microsoft Entra Agent ID, or a non-human identity platform — anywhere in their consideration set, and among the credential-sharing organizations that have already had an incident, identity consideration is essentially unchanged, at roughly one in ten. The control most directly implicated by the incident data is the one largely missing from the purchase plans. Whether this wave hardens the provider-native default or finally opens the door to purpose-built agent security — the identity and isolation controls the incidents call for — is the question this series will keep tracking.</p><h2>The bottom line: A security gap that autonomy will test first</h2><p>Organizations with more than 100 employees are giving AI agents real reach into systems and data while securing them with controls built for something else. More than half have already had an incident or near-miss; only a third give every agent its own scoped identity, and most still share credentials; only three in ten isolate their highest-risk agents; and the stack doing this work is overwhelmingly borrowed from the model providers and hyperscalers rather than purpose-built for agents.</p><p>The uncomfortable pairing is confidence with exposure: satisfaction with the current tooling is among the highest in this series, yet spending is a thin slice of the security budget, only a third believe their defenses are ahead of AI-enabled attackers, and a clear majority are already planning to replace what they have. At 107 respondents in a single wave this is a directional read, skewed toward the mid-market — but the direction is clear: agent adoption is running ahead of agent security, and the controls that matter most when something fails — scoped identity and isolation — are the ones enterprises have built least. The agent security gap is not a coverage problem that a provider guardrail will close on its own; it is a problem of identity, isolation, and enforcement built for autonomous software. The open question for later waves is whether enterprises close it deliberately — or whether a confirmed incident closes it for them.</p><hr/><p><i>Based on survey responses from 107 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. This is a directional read, not a precise measurement — the sample is self-selected and skews mid-market, so it's best read as the view from organizations actively standing up agent security rather than from the largest operators. Respondents are senior and buyer-credible (45% final decision-makers, 30% recommenders/influencers), spanning managers through the C-suite, and drawn primarily from Technology/Software, Manufacturing, Retail/E-commerce, and Healthcare/Life Sciences.</i></p>
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The AI compute gap: Enterprises are buying infrastructure faster than they can measure what it costs
<p>Across 107 enterprises, AI infrastructure spending is accelerating well ahead of the ability to see or steer its economics. Most organizations run their AI on a familiar base of hyperscalers and model-provider APIs, yet the next dollar is aimed at specialized compute almost none of them use today; a majority intend to switch or add providers within the year, many within a quarter. Buying decisions turn on integration and total cost of ownership rather than headline token price — which is fortunate, because most enterprises cannot yet see their unit economics clearly: GPUs sit at half utilization or less, and fewer than half rigorously track what their compute actually costs. The result is a compute gap — heavy, fast-moving investment running ahead of the visibility needed to control it.</p><p>This wave of VentureBeat Pulse Research examines enterprise AI infrastructure and compute: where organizations are in their deployment journey, what they run AI on today, how satisfied they are, what would make them switch, where they plan to evaluate their investments, and — most revealingly — how well they can measure and control the economics of the compute underneath it all.</p><p>The central finding is a compute gap — the distance between how aggressively enterprises are investing in AI infrastructure and how little of its economics they can see. Only about one in five (21%) run AI in production at scale, yet spending intentions are outrunning that maturity: the single largest planned area enterprises plan to evaluate over the next year is AI-specialized clouds (45%), a layer almost none of these enterprises use today. Meanwhile the compute already in place runs cold — 83% report GPU utilization of 50% or less — and fewer than half (44%) can rigorously track what their AI compute costs. Enterprises are buying more infrastructure faster than they can account for what they already own.</p><p>Enterprises are not settled on their infrastructure vendors, either: A clear majority (64%) plan to switch or add an infrastructure provider within twelve months, and 38% within the next quarter — unusually high churn intent for a category this foundational. When they choose, they choose on integration with the existing stack (41%) and total cost of ownership (35%), not on headline price: cost per million tokens is the deciding factor for just 8%. And the frontier constraint that will shape the next round of decisions — the shift from GPU compute to memory bandwidth as inference scales — is barely on the radar, with roughly one in five enterprises either unaware of it or yet to address it.</p><h2>Methodology</h2><p>VentureBeat fielded this survey as part of its ongoing Pulse Research series, this survey focused on enterprise AI infrastructure, compute, and inference economics. Responses are filtered to organizations with more than 100 employees (n=107; the survey’s smallest size band, 1–100 employees, is excluded), drawn from a single Q2 2026 (June) wave. Because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%.</p><p>By organization size the sample concentrates in the mid-market: 101–250 employees (36%) and 251–1,000 (27%) lead, with 1,001–5,000 (22%), 5,001–10,000 (8%), and 10,001+ (7%) above them. By role it spans managers (38%), individual contributors (28%), VPs and directors (19%), and the C-suite (13%); on purchasing authority it is buyer-credible, with 45% final decision-makers and another 30% recommenders or influencers for AI solutions. Technology/Software is the largest industry at 26%, followed by Healthcare/Life Sciences (15%), Financial Services (13%), and Retail/E-commerce (12%).</p><p>At 107 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It also skews toward the mid-market and toward earlier-stage adopters, so it is best read as the view from organizations actively building out AI infrastructure rather than from the largest hyperscale operators.</p><h2>Finding 1: Ambition outpaces production</h2><p><b>Only one in five run AI in production at scale</b></p><p>We asked where organizations sit in their AI deployment journey. Most are still building toward production rather than operating at scale.</p><div></div><table><tbody><tr><td><p><b>38%</b></p></td><td><p><b>are experimenting — running proofs of concept, not yet in production</b></p></td></tr><tr><td><p><b>37%</b></p></td><td><p><b>have some workloads in production, but not across the organization</b></p></td></tr><tr><td><p><b>21%</b></p></td><td><p><b>run AI in production at scale — the mature minority</b></p></td></tr><tr><td><p><b>4%</b></p></td><td><p><b>are not yet running AI workloads at all</b></p></td></tr></tbody></table><p>The maturity curve is front-loaded. Three-quarters of enterprises (76%) are either experimenting or running only some workloads in production, and just 21% describe AI in production at scale. This matters for everything that follows: the infrastructure decisions in this report are being made largely by organizations still early in deployment, whose compute footprint — and whose costs — are about to grow. The evaluation and switching intentions in Findings 3 and 4 are the leading edge of that build-out, not the settled preferences of operators who have already found what works.</p><h2>Finding 2: Enterprises run on hyperscalers and model APIs</h2><p><b>The specialized GPU clouds barely register — today</b></p><p>We asked which providers and platforms enterprises currently use to run their AI. The answer is a familiar one: the incumbents.</p><div></div><table><tbody><tr><td><p><b>48%</b></p></td><td><p><b>use Google Cloud — the most-used platform overall (Microsoft Azure 29%, AWS 22%, Oracle Cloud 22%)</b></p></td></tr><tr><td><p><b>41%</b></p></td><td><p><b>use Google’s Gemini models, with OpenAI close behind at 40% and Anthropic at 12%</b></p></td></tr><tr><td><p><b>6%</b></p></td><td><p><b>run their own on-prem or co-located GPU clusters; 4% a custom open-source self-managed stack</b></p></td></tr><tr><td><p><b><2%</b></p></td><td><p><b>each use the specialized AI clouds — CoreWeave, Lambda, Crusoe, Nebius, Together, Fireworks and peers</b></p></td></tr></tbody></table><p>The current stack is hyperscaler-and-API. Google Cloud leads at 48%, and the general-purpose clouds (Google, Microsoft, AWS, Oracle) together with the major model APIs (Gemini, OpenAI, Anthropic) account for essentially all current deployment. The specialized “neocloud” GPU providers that dominate AI-infrastructure headlines — CoreWeave, Lambda, Crusoe, Nebius and peers — register at or near zero among these enterprises today. Only 6% run their own on-prem GPU clusters and 4% a custom open-source stack. Enterprises are, for now, running AI on the providers they already buy from — which makes the evaluation intentions in Finding 3 all the more striking.</p><p><i>(A note on reading these shares. As described in the methodology section, this sample is self-selected and skews mid-market, and this question counted every provider a respondent uses — an average of 2.1 selections each — so the figures measure presence in the stack rather than spending or primary status. A sample built this way will show a different provider mix than a spend-weighted census of the broader market; Google's strength here, for example, is consistent with its long-standing position among smaller enterprises building on AI. Read these shares as a portrait of what this AI-active cohort runs today, and treat gaps between these figures and industry-wide market share estimates as a property of the sample rather than a contradiction of either.)</i></p><h2>Finding 3: The next dollar goes to infrastructure they don’t yet run</h2><p><b>AI-specialized clouds top the evaluations list</b></p><p>We asked where enterprises planned to evaluate AI infrastructure over the next 12 months. Their answers point away from the stack they run today.</p><div></div><table><tbody><tr><td><p><b>45%</b></p></td><td><p><b>AI-specialized clouds (CoreWeave, Lambda, Crusoe, Nebius) — the top planned evaluation area</b></p></td></tr><tr><td><p><b>32%</b></p></td><td><p><b>non-NVIDIA accelerators (AWS Trainium, Google TPU, AMD Instinct, Intel Gaudi, in-house ASICs)</b></p></td></tr><tr><td><p><b>28%</b></p></td><td><p><b>Nvidia Blackwell (GB300) / next-generation GPUs</b></p></td></tr><tr><td><p><b>16%</b></p></td><td><p><b>decentralized or distributed compute networks</b></p></td></tr><tr><td><p><b>11%</b></p></td><td><p><b>sovereign or region-specific compute; 9% say none of the above</b></p></td></tr></tbody></table><p>Here is the report’s sharpest tension. The single most-cited planned evaluation area — AI-specialized clouds, at 45% — is the very category almost none of these enterprises use today (Finding 2). Nearly a third (32%) intend to evaluate non-Nvidia accelerators, and 28% in next-generation Nvidia silicon; even decentralized compute networks (16%) and sovereign compute (11%) draw meaningful interest. Read against current usage, this is not incremental — it is the leading edge of a re-platforming. The direction-of-travel question tells the same story: every infrastructure approach is net-expanding, but specialized AI clouds carry the highest net momentum (+24), edging out even the hyperscalers (+22). Enterprises are preparing to move a meaningful share of AI compute off the general-purpose cloud.</p><p>This continues a trend we saw in our April-May survey wave. Back then, usage of the AI-specialized clouds was equally marginal — CoreWeave at 3%, Lambda at 4%, Crusoe at 2% of enterprises. When we asked enterprises what change they planned in their AI infrastructure strategy over the next twelve months, the most-cited answer was moving workloads to specialized AI clouds, at 33%. Asked in April-May which emerging compute option they were most likely to evaluate AI-specialized clouds again drew the most responses. Two waves, two differently worded questions, one consistent picture: the type of cloud enterprises are most eager to assess is the type they have barely begun to use.</p><h2>Finding 4: A switching wave is building</h2><p><b>Six in 10 plan to change providers within a year — many within a quarter</b></p><p>We asked whether and when enterprises plan to switch or add an infrastructure provider. Very few intend to stand still.</p><div></div><table><tbody><tr><td><p><b>38%</b></p></td><td><p><b>plan to change within the next 0–3 months — tied for the most common answer</b></p></td></tr><tr><td><p><b>36%</b></p></td><td><p><b>have no plans to change</b></p></td></tr><tr><td><p><b>22%</b></p></td><td><p><b>plan to change within 3–6 months</b></p></td></tr><tr><td><p><b>7%</b></p></td><td><p><b>plan to change within 6–12 months</b></p></td></tr></tbody></table><p>For a category as foundational as compute, this is a remarkable amount of intended movement. Only 36% have no plans to change, meaning a clear majority (64%) intend to switch or add a provider within twelve months — and 38% within the next quarter alone. Where that interest points is telling: the providers drawing the most switching consideration are again the incumbents — Microsoft Azure and Google Cloud (33% each), OpenAI (30%), and Gemini (22%) — which suggests much of the near-term movement is reshuffling among the majors and consolidating spend rather than defecting to new entrants. The neocloud interest in Finding 3 is a 12-month evaluation thesis; the switching in the next quarter is mostly incumbents trading share.</p><p>(<i>Method note: Respondents who selected both "no plans to change" and a specific switching window are counted as switchers, on the logic that naming a timeframe is the more specific answer; three respondents were reclassified under this rule.</i>)</p><h2>Finding 5: Nobody buys on token price</h2><p><b>Integration and total cost of ownership decide — not sticker price</b></p><p>We asked what matters most when enterprises select an AI infrastructure provider. Headline price finished last.</p><div></div><table><tbody><tr><td><p><b>41%</b></p></td><td><p><b>integration with the existing cloud and data stack — the top factor</b></p></td></tr><tr><td><p><b>35%</b></p></td><td><p><b>total cost of ownership (TCO)</b></p></td></tr><tr><td><p><b>24%</b></p></td><td><p><b>performance — latency and throughput</b></p></td></tr><tr><td><p><b>19%</b></p></td><td><p><b>each cite security/compliance, autoscaling for spiky workloads, and GPU access/availability</b></p></td></tr><tr><td><p><b>8%</b></p></td><td><p><b>cost per 1M tokens — the least-cited factor</b></p></td></tr></tbody></table><p>Enterprises do not buy AI infrastructure on pricing, which is the place vendors compete on hardest. Integration with the existing stack (41%) and total cost of ownership (35%) dominate, while the headline metric — cost per million tokens — is the deciding factor for just 8%, dead last. The pattern is coherent: buyers are optimizing for how a provider fits and what it truly costs to operate, not for the advertised unit rate. It also foreshadows Finding 7 — enterprises say TCO matters most, yet most cannot yet measure it rigorously. The stated priority and the measured capability are out of step.</p><h2>Finding 6: Expensive GPUs, idle most of the time</h2><p><b>83% report GPU utilization of 50% or less</b></p><p>We asked what share of their GPU capacity enterprises actually utilize. The answer is a well-known but rarely quantified inefficiency.</p><div></div><table><tbody><tr><td><p><b>37%</b></p></td><td><p><b>run at 26–50% utilization</b></p></td></tr><tr><td><p><b>34%</b></p></td><td><p><b>run at 10–25% utilization</b></p></td></tr><tr><td><p><b>15%</b></p></td><td><p><b>run under 10% utilization</b></p></td></tr><tr><td><p><b>12%</b></p></td><td><p><b>run over 50% — the efficient minority</b></p></td></tr><tr><td><p><b>8%</b></p></td><td><p><b>don’t measure utilization at all; a further 7% consume via API and run no GPUs of their own</b></p></td></tr></tbody></table><p><i>Disclosure: Band percentages count every selection against all 107 qualified respondents; 14 respondents selected more than one band, so bands overlap. At the respondent level, 83 of the 100 GPU-operating enterprises reported utilization at or below 50%</i></p><p>The compute already in place runs cold. Adding the bands at or below half capacity, 83% of enterprises that operate GPUs report utilization of 50% or less, and nearly half (49%) run at 25% or below. Only 12% clear the 50% mark, and a further 8% do not measure utilization at all. Idle accelerators are expensive accelerators, and this is the clearest single measure of the compute gap: enterprises are planning to buy more GPUs and specialized compute (Finding 3) while the capacity they already own sits substantially unused. The efficiency headroom in the current fleet is large — and largely unmeasured.</p><h2>Finding 7: Spending fast, measuring slowly</h2><p><b>Fewer than half rigorously track what their compute costs</b></p><p>We asked whether enterprises can quantify the cost and return of their AI infrastructure spend, and how satisfied they are with what they run. Confidence in the ledger lags the spending.</p><div></div><table><tbody><tr><td><p><b>44%</b></p></td><td><p><b>track compute cost and ROI rigorously</b></p></td></tr><tr><td><p><b>39%</b></p></td><td><p><b>track it only partially</b></p></td></tr><tr><td><p><b>20%</b></p></td><td><p><b>can’t quantify it yet</b></p></td></tr><tr><td><p><b>6%</b></p></td><td><p><b>say it isn’t a priority</b></p></td></tr></tbody></table><p>Measurement trails money. Fewer than half of enterprises (44%) rigorously track the cost and return of their AI compute; the majority track only partially (39%), cannot quantify it yet (20%), or have not prioritized it (6%). That gap is consequential given Finding 5, where total cost of ownership was the second-ranked buying criterion — enterprises are choosing providers on an economic basis they mostly cannot yet measure. Satisfaction with current infrastructure is moderately positive but not enthusiastic: on a five-point scale, overall satisfaction averages 4.0, with ease of implementation (3.8) and value for money (3.9) trailing slightly — the softness landing, tellingly, on cost. Enterprises are spending quickly and accounting slowly.</p><h2><b>Finding 8: The next bottleneck few are watching</b></h2><p><b>As inference shifts from compute to memory, the field scatters</b></p><p>Finally, we asked how enterprises would address the emerging constraint in large-scale inference — the shift from GPU compute to memory, specifically KV-cache capacity. The responses reveal a frontier that is not yet a priority.</p><div></div><table><tbody><tr><td><p><b>31%</b></p></td><td><p><b>would rely on Dell (PowerScale / Project Lightning) — the leading single answer</b></p></td></tr><tr><td><p><b>16%</b></p></td><td><p><b>would rely on Nvidia (Dynamo / ICMSP)</b></p></td></tr><tr><td><p><b>18%</b></p></td><td><p><b>are not aware of this as a constraint (9%) or haven’t addressed inference-memory limits yet (8%)</b></p></td></tr><tr><td><p><b>10%</b></p></td><td><p><b>Hammerspace (Tier Zero); 9% DDN (Infinia); the rest split across open-source KV-cache tooling, model-level efficiency, VAST Data, and WEKA</b></p></td></tr></tbody></table><p>The memory frontier is real but barely governed. Asked which approach they would rely on as the binding constraint in inference shifts from compute to memory bandwidth, enterprises scatter: Dell leads at 31%, Nvidia follows at 16%, and the rest fragments across storage vendors, open-source tooling, and model-level efficiency techniques. Most telling is that roughly one in five (18%) either do not recognize the constraint or have not begun to address it. For a shift that will reshape inference cost and architecture, this is an early and unsettled market — and, consistent with the measurement gap in Finding 7, one where many enterprises simply do not yet have a view. It is the next chapter of the compute gap, arriving before most have closed the current one.</p><h1><b>The bottom line: A compute gap that faster spending will widen, not close</b></h1><p>Organizations with more than 100 employees are investing in AI infrastructure faster than they can measure it. Most are still early in deployment, yet their spending intentions point past their current stack — toward specialized clouds and alternative accelerators almost none of them run today — and a clear majority intend to change providers within the year. They buy on integration and total cost of ownership rather than headline price, which is rational; the difficulty is that most cannot yet see those economics clearly.</p><p>The visibility gap is concrete. The GPUs enterprises already own run at half utilization or less for the overwhelming majority, and fewer than half can rigorously track what their compute costs or returns. Satisfaction is decent but unenthusiastic, softest on value for money — the dimension hardest to judge without measurement. And the next constraint, the shift from compute to memory in large-scale inference, is arriving while most enterprises are still unaware of it. At 107 respondents in a single Q2 wave this is a directional read, skewed toward the mid-market and earlier-stage adopters — but the direction is consistent: the appetite to spend is running well ahead of the instrumentation to spend well. The compute gap is not a capacity problem that more hardware will solve on its own; it is, first, a problem of seeing what the hardware already costs. The open question for later waves is whether enterprises build that visibility before the re-platforming arrives — or buy the next layer of infrastructure as blind to its economics as the last.</p><hr/><p><i>Based on survey responses from 107 qualified enterprise respondents (100+ employees), drawn from a single Q2 2026 (June) wave. Because this is one wave rather than a pooled multi-month sample, the results read cross-sectionally rather than as a month-over-month trend, and at 107 respondents this is a directional signal rather than a precise measurement — the sample is self-selected, skews mid-market, and leans toward earlier-stage adopters rather than the largest hyperscale operators. Respondents include managers, individual contributors, VPs/directors, and the C-suite, with buyer-credible purchasing authority, across Technology/Software, Healthcare/Life Sciences, Financial Services, Retail/E-commerce, and other industries.</i></p>
The AI context gap: Enterprise AI organizations have a trust problem, not a retrieval problem — and most are still building the fix
<p>Across 101 enterprises, the infrastructure that feeds AI agents their business context is being built faster than it can be trusted. Retrieval-augmented generation is already the default context source, and provider-native retrieval has quietly overtaken the dedicated vector databases that define the category — yet a majority of enterprises have already watched their agents produce confident, wrong answers traced to missing or inconsistent context. A governed semantic layer is emerging as the fix, but most are still building it; the field is converging on hybrid retrieval; and even as provider-native tools lead in practice, a plurality say they intend to keep best-of-breed. The result is a context gap — agents that sound authoritative running on a foundation their owners do not yet fully trust.</p><p>This wave of VentureBeat Pulse Research examines the enterprise RAG and context layer: what feeds AI agents their business context, which retrieval systems enterprises run, how they buy and measure them, where the architecture is heading, and — most revealingly — how often that context is already failing them.</p><p>The central finding is a context gap — the distance between how confidently enterprise agents answer and how reliable the context beneath them actually is. A majority of enterprises (57%) report that in the past six months their AI agents produced confident but wrong answers they traced to missing or inconsistent business context, and more than half of those said it happened more than once. This is not a fringe failure: retrieval is the primary context source for 38% of enterprises, more than any other approach, so when retrieval is thin or inconsistent, the errors it produces are wearing the agent’s authority. The infrastructure to fix it is being built — 58% already run or are building a governed semantic layer — but for most it is not yet in production.</p><p>Underneath, the market is consolidating in a direction that surprises. Provider-native retrieval — OpenAI’s file search (40%) and Google’s Vertex AI Search (38%) — already leads every dedicated vector database, and enterprises expect hybrid retrieval to dominate by the end of 2026 (34%). Yet a plurality (36%) say they intend to keep best-of-breed standalone tools rather than consolidate onto a provider’s native context stack, and a majority (57%) plan to switch or add a provider within the year. Stated preference and actual usage are pulling in opposite directions — the market is buying provider-native while insisting it wants independence.</p><h2>Methodology</h2><p>VentureBeat fielded this survey as part of its ongoing Pulse Research series. This survey focused on enterprise RAG infrastructure and the context layer — the retrieval systems, semantic layers, and context sources that feed AI agents. Responses are filtered to organizations with more than 100 employees (n=101); the survey drew no responses from organizations of 100 or fewer, so the full sample qualifies. All responses are from a single Q2 2026 (June) wave, so the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%.</p><p>By organization size the sample concentrates in the mid-market: 251–1,000 employees (31%) and 101–250 (31%) lead, with 1,001–5,000 (20%), 5,001–10,000 (12%), and 10,001+ (7%) above them. By role it spans managers (39%), individual contributors (27%), the C-suite (16%), and VPs and directors (14%); on purchasing authority it is buyer-credible, with 46% final decision-makers and another 26% recommenders or influencers. Technology/Software is the largest industry at 20%, followed by Healthcare/Life Sciences (11%) and a broad spread across retail, transportation, financial services, manufacturing, and education.</p><p>At 101 respondents this is a modest sample and should be read as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It is best read as the view from organizations actively standing up RAG and context infrastructure rather than from the largest operators.</p><h2>Finding 1: Confident and wrong</h2><p><b>More than half have traced agent errors to bad context</b></p><p>We asked whether, in the past six months, enterprises had traced a confident but wrong agent answer to missing or inconsistent business context. Most had.</p><div></div><p>This is the report’s defining number. A majority of enterprises (57%) have already had an AI agent produce a confident, wrong answer they traced to bad context — wrong metrics, stale definitions, or missing documents — and more than half of those have seen it happen more than once. Only 28% report no such failure, and a small remainder either don’t run agents on enterprise data or don’t trace root cause closely enough to know. </p><p>The failure mode is specific and dangerous: the model is not obviously hallucinating; it is confidently wrong because the context feeding it was thin or inconsistent. Everything else in this report — what enterprises retrieve, how they govern it, and what they plan to build — is downstream of this problem.</p><h2>Finding 2: RAG is the default context source</h2><p><b>Retrieval feeds more agents than any other method</b></p><p>We asked what an enterprise’s AI agents primarily use to understand its data. Retrieval leads by a wide margin.</p><div></div><p>Retrieval is the backbone of enterprise context. For 38% of organizations, RAG over documents or a vector index is the primary way agents understand the business — nearly twice the share of the next approach, a governed semantic layer or ontology (21%). Mixed approaches (14%), direct live-system queries (10%), and long-context loading (6%) fill out the rest, and only 2% let agents run on the model’s general knowledge alone. The concentration matters in light of Finding 1: because so much enterprise context flows through retrieval, the quality of that retrieval is the quality of the answer. When RAG is the default source, thin retrieval is not an edge case — it is the main failure surface.</p><p>One approach is notable for its absence from these answers: customizing model weights, also known as fine-tuning. Every leading source of business context is injected at run time. Our most recent direct measurement of fine-tuning comes from our April–May survey wave (a separate survey, n=136), where fine-tuning capabilities ranked last of six factors in model selection at 5% — even as 26% of that sample still named fine-tuning and customization an investment they expect to grow. Fine-tuning has fallen out of the primary selection conversation; context injection is how enterprises make agents knowledgeable about their business.</p><h2>Finding 3: Provider-native retrieval already leads the vector databases</h2><p><b>OpenAI file search and vertex AI search top the dedicated tools</b></p><p>We asked which retrieval systems enterprises run in production today. The answer favors the model providers and hyperscalers over the specialists.</p><div></div><p>The dedicated vector database is no longer the center of the RAG stack. OpenAI’s file search (40%) and Google’s Vertex AI Search (38%) lead — provider-native and hyperscaler-native retrieval — ahead of every purpose-built vector database. Among the specialists, the most-used is the one enterprises already run for other reasons (Elasticsearch/OpenSearch, 20%) and the open, embedded option (pgvector, 12%); the pure-play vector databases that define the category — Weaviate, Qdrant, Pinecone, Milvus — each sit in single digits to low double digits. Notably, 13% of enterprises say they still run no production RAG at all. As with the platforms in the parallel infrastructure wave, enterprises are gravitating to retrieval that comes bundled with tools they already buy.</p><p>The shape of this finding held across both Q2 waves. In April–May (n=161), provider-built retrieval led usage there too, while every dedicated vector database remained marginal — the most-used standalone vector database peaked at 8% of that sample — and the hybrid, pluralistic future was already the consensus expectation (34% expected hybrid retrieval to dominate, with another 29% expecting multiple architectures by use case). Two waves, consistent picture: the category that coined the “vector database” term is being collected by the platforms enterprises already buy from.</p><h2>Finding 4: But they say they want to keep best-of-breed</h2><p><b>A plurality resist consolidating onto a provider’s native stack</b></p><p>We asked how enterprises will respond as model providers bundle retrieval, memory, and orchestration into their platforms. Their stated intent cuts against their current usage.</p><div></div><p>Here is the tension at the heart of the stack. Even as provider-native retrieval leads in practice (Finding 3), a plurality of enterprises (36%) say they intend to keep best-of-breed standalone tools rather than consolidate onto a provider’s native context stack — well ahead of the 21% who plan to consolidate. Another 21% expect a mix, and 9% intend to build and own the layer themselves. The gap between what enterprises run and what they say they want is the strategic question of the category: they are adopting bundled retrieval for convenience while asserting they will preserve independence. Which impulse wins — the pull of the provider bundle or the stated preference for modular control — will shape the retrieval market more than any single tool.</p><h2>Finding 5: Hybrid retrieval is the consensus bet</h2><p><b>Vector-only retrieval is already seen as insufficient</b></p><p>We asked which retrieval architecture enterprises expect to dominate their production RAG systems by the end of 2026. The field is converging — with a large share still unsure.</p><div></div><p>The architecture is settling on hybrid. A third (34%) expect hybrid retrieval — embeddings combined with reranking and access controls — to dominate their production systems by the end of 2026, three times the 11% who expect vector-only retrieval to prevail. That is a notable signal: the pure vector-search approach that launched the category is already viewed as insufficient on its own, superseded by pipelines that add reranking for accuracy and access controls for governance — the very access controls whose absence produces the failures in Finding 1. Tellingly, the second-largest answer is uncertainty: 17% simply don’t know, and another 14% expect to move beyond a dedicated vector layer entirely toward tool-first or long-context retrieval. The consensus is not a single tool but a layered pipeline — and it is not yet fully formed.</p><h2>Finding 6: The governed context layer is being built now</h2><p><b>Most run or are building a semantic layer — few in production</b></p><p>We asked whether enterprises use a governed semantic or context layer to give agents and BI a shared understanding of their data. Most are on the path; fewer have arrived.</p><div></div><p>The fix for the context gap is under construction. Well over half of enterprises (58%) either run a governed semantic layer in production (25%) or are piloting and building one (34%), and a further 17% are actively evaluating — meaning three-quarters are engaged with the idea in some form. But the balance is telling: more are building than have shipped, so for most enterprises the shared, governed definition layer that would prevent the "confident but wrong" failures of Finding 1 is still a work in progress. The semantic layer is the industry’s answer to inconsistent context; this wave catches it mid-construction, ambition well ahead of production.</p><h2>Finding 7: Bought on ingestion and simplicity, watched for correctness</h2><p><b>Selection favors operability; monitoring favors correctness and security</b></p><p>We asked what matters most when enterprises choose a retrieval system, and what they track once it is running. Both answers lean practical.</p><div></div><p>Enterprises choose retrieval systems on operability. Ease of data ingestion (36%), latency and performance (32%), and operational simplicity (29%) lead the selection criteria — ahead of retrieval accuracy and access control (23% each), the two factors most directly tied to the failures in Finding 1. Once systems are running, the emphasis shifts toward trust: the most-tracked metrics are response correctness (42%) and security and access control (38%), ahead of latency (28%), operational stability (27%), and answer relevance (23%). </p><p>Satisfaction with current systems is moderately positive but not enthusiastic — on a five-point scale, overall satisfaction averages 4.0, with ease of implementation and value for money both near 3.9. Enterprises buy for how easily a system runs and watch it for whether it can be trusted.</p><h2>Finding 8: A retrieval reshuffle is coming</h2><p><b>A majority plan to change providers — and the vector specialists are gaining interest</b></p><p>We asked whether enterprises plan to change or add a retrieval provider, and which they are considering. The consideration set differs from today’s stack.</p><div></div><p>The retrieval stack is not settled. While 43% have no plans to change, a small majority (57%) intend to switch or add a provider within twelve months, and a quarter (26%) within the next quarter. The consideration set is where it gets interesting: provider-native retrieval still leads what enterprises are evaluating (OpenAI 22%, Vertex AI Search 21%), but the open-source vector specialists punch above their current footprint — Qdrant (14%) and Milvus (13%) draw more switching interest than their present usage (10% and 6%) would suggest. Read with Finding 4, the picture is a market in flux: enterprises run provider-native today, are evaluating a broader field, and say they want to keep their options open. The reshuffle ahead will test whether best-of-breed intent survives contact with the convenience of the bundle.</p><h1>The bottom line: A context gap that more retrieval alone won’t close</h1><p>Organizations with more than 100 employees are wiring agents into their business faster than they can guarantee the context those agents run on. Retrieval is the default source of enterprise context, and it increasingly comes from the model providers and hyperscalers rather than the dedicated vector databases — yet a majority of enterprises have already watched agents answer confidently and wrongly because that context was thin or inconsistent. The failure is not exotic; it is the predictable result of pointing authoritative-sounding agents at an unreliable foundation.</p><p>The industry’s answer — a governed semantic layer, hybrid retrieval with reranking and access controls — is being built but is mostly not yet in production, and enterprises are pulled between the convenience of provider-native bundles and a stated preference for best-of-breed independence. At 101 respondents in a single Q2 wave this is a directional read, skewed toward the mid-market — but the direction is clear: the context layer is the next contested tier of the AI stack, and right now agents are running ahead of it. The context gap is not a retrieval-volume problem that more documents or bigger indexes will solve on their own; it is a problem of governed, consistent, access-aware context. The open question for later waves is whether enterprises finish building that layer before the confident-but-wrong failures move from the lab into decisions that matter.</p><hr/><p><i>Based on survey responses from 101 qualified enterprise respondents (100+ employees), drawn from a single Q2 2026 (June) wave. At this sample size the results should be read as a directional signal rather than a precise measurement — it's a self-selected sample, not a probability sample, and skews toward the mid-market. Respondents include managers, individual contributors, VPs/directors, and the C-suite, with strong purchasing authority, across technology, healthcare, retail, transportation, financial services, manufacturing, and education.</i></p>
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