Macro IT spend and IT budget sentiment<\/p>\n
The first chart is the one we keep coming back to because it shows a time series on macro spending sentiment. It\u2019s from ETR\u2019s quarterly drill-down survey of expected IT spending changes, going back to the COVID era, with a large sample size (N = 1,543). The story is one of a whipsaw between optimism and caution \u2013 and how quickly sentiment moves when world events get in the way.<\/p>\n
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Coming out of COVID, the data above shows a big uptick in budget flexibility. IT spending expectations surged into the 7.3% to 7.5% range. Then the air came out as rates rose and uncertainty took hold. By 2022, expectations compressed and ultimately bottomed at 2.9%, inversely proportional to interest rates. This was a reminder that when the macro tightens, IT budgets tighten with it.<\/p>\n
From there, the chart becomes a map of confidence shocks. As the Fed started to lower rates, spending expectations improved, but the recovery wasn\u2019t smooth. We saw periodic pops \u2013 4.3%, then up to 5.3% \u2013 followed by pullbacks as new uncertainties such as Ukraine and tariffs surfaced. The most recent example is seen exiting December at 4.0%, rising to 4.6% in January, then falling back to 3.6% now with the war in the Middle East. We feel that\u2019s a meaningful swing in a short period of time, especially given AI\u2019s overall momentum.<\/p>\n
We believe the right way to interpret the data is IT spending is sensitive to the business climate, and the business climate is being shaped by rates, geopolitics, policy noise and headlines. It\u2019s not always possible to prove causation with a single chart, but over many cycles the market data appears consistent \u2013 when uncertainty rises, budget confidence softens, and IT spending follows that trend.<\/p>\n
AI adoption: Productivity is the goal, decision support is rising<\/p>\n
The slide below is a check on how organizations say they are using AI in the current climate (N = 1,573), and it has been asked consistently since July 2025. The top answer is what you\u2019d expect \u2013\u00a0enhancing workforce productivity through automation or task augmentation. That answer has stayed consistently in the low 70% range range and has been durable across multiple quarters. The question we see organizations asking is: How do we make new breakthroughs beyond early use cases? In other words, firms are seeing early wins but they\u2019re eager to see them compound.<\/p>\n
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The most impressive movement is the steady rise in\u00a0supporting employee decision-making with AI-driven analytics and insights. That\u2019s a logical next step after productivity because it builds on the work organizations have already done modernizing analytics. When data is organized and accessible, AI can amplify it quickly.<\/p>\n
This is where the modern data stack players have see real tailwinds \u2013 vendors such as Snowflake Inc. and Databricks Inc. are the poster children for consolidating analytics into usable platforms, with Oracle Corp. and others such as IBM Corp. also relevant in the broader market. The data suggests more organizations are now pushing AI into the \u201cinsights\u201d layer, not just the \u201cautomation\u201d layer.<\/p>\n
Two other points stand out:<\/p>\n
The share saying\u00a0they are not currently leveraging AI in any of these areas\u00a0has fallen from\u00a010% last July to 6% now. That doesn\u2019t mean those firms aren\u2019t using AI at all, but it does reinforce the larger point that adoption is becoming near-universal, although it\u2019s uneven and sometimes implicit.
\nOn labor, the data is consistent with what we\u2019ve been seeing elsewhere \u2013 that\u00a0AI is limiting future headcount growth\u00a0more than it\u2019s driving immediate headcount reduction. Companies will often message layoffs as \u201cAI-driven,\u201d but the data suggests the reality is mostly headcount avoidance and slower hiring rather than dramatic cuts directly attributable to AI.<\/p>\n
The bottom line in the data is AI is in the building. Productivity remains the primary use case, decision support is gaining momentum, and the job impact is showing up first in hiring plans, not sudden mass reductions. Our take is these are predictable and relatively straightforward early wins, but they\u2019re not game-changing. Later in this post we posit an emerging new architecture that can support more dramatic organizational change as AI becomes simpler and safer to adopt.<\/p>\n
ROI reality check: Pilots everywhere, scale still rare<\/p>\n
The chart below gets to the heart of the agentic gap \u2013 what kind of ROI organizations are actually reporting from AI initiatives so far (N = 1,573). ETR splits the data into two approaches:\u00a0building in-house solutions\u00a0on the left and\u00a0buying external vendor solutions\u00a0on the right. In both cases, \u201cno adoption or traction\u201d is declining, but it remains meaningful \u2013 especially on the in-house side. Embedded AI and vendor-delivered capabilities appear to have an path to adoption, which shows up in the lower \u201cno traction\u201d bars on the right-hand side.<\/p>\n
The more telling story is what happens after initial adoption:<\/p>\n
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On the in-house side,\u00a0\u201cadoption, ROI not yet realized\u201d\u00a0sits around the 30% range and has been stuck. That implies a meaningful slice of organizations are building, experimenting, and learning \u2013 but not getting payback yet.<\/p>\n
Then you hit the dominant category in both sides of the chart:\u00a0ROI in pilots or limited use cases, but not yet at scale.\u00a0It\u2019s roughly\u00a033%\u00a0for in-house and\u00a039%\u00a0for vendor solutions. That is the clearest indicator that AI is working in pockets, but most enterprises are still struggling to industrialize it.<\/p>\n
Finally, the metric everyone cares about \u2013\u00a0sustained ROI at scale\u00a0\u2013 sits in the low to mid-teens in both cases, about\u00a013%. That\u2019s the headline. Whether organizations build or buy, only a small minority say they have durable ROI at scale.<\/p>\n
This supports the broader point we\u2019ve been making in that vendors are moving fast \u2013 from RAG-based chatbots to reasoning to agentic workflows \u2013 and enterprises are absorbing that shift more slowly. The constraint is not enthusiasm for AI or lack of vision. Rather, it\u2019s operational readiness \u2013 AI governance, safety, security, integrating data, hardening processes and building repeatable deployment muscle memory so pilots can convert into production outcomes at scale.<\/p>\n
The new AI software stack: Closing the \u2018agentic gap\u2019<\/p>\n
We have argued in\u00a0previous Breaking Analysis segments<\/a>\u00a0that as we moved from on-premises perpetual software models to software as a service, it changed firms\u2019 technical, operational and business models. We further argue that a more profound change is coming with AI that will affect not only IT departments but entire organizations. We\u2019ve written extensively about the infrastructure shift from general-purpose computing to accelerated architectures.<\/p>\n In this section we go further up the value chain and drill into the emerging AI software stack. Here we specifically project the technical layers we see emerging that will support more rapid AI adoption.<\/p>\n The slide below ties the ROI data to a deeper architectural shift \u2013 the enterprise is moving from an app-centric world to an intelligence-centric one. The diagram lays out a four-layer topology and, in our view, it explains what\u2019s missing in today\u2019s software stack and what has to emerge to help simplify adoption, support new business models and help organizations that are stuck in pilots. Organizations are enthusiastic about AI and they\u2019re funding it. That\u2019s not the constraint. The problem is that most enterprises are trying to bolt agentic workflows onto yesterday\u2019s software stack, while the stack itself is being rearranged.<\/p>\n At the top sits the\u00a0Frontier Model\u00a0\u2013 the scarce, capital-intensive layer that produces tokens. It runs on the most advanced hardware, improves rapidly and is increasingly concentrated in a small number of providers (OpenAI Group PBC, Anthropic PBC, Google LLC and xAI Holdings Corp.). For most enterprises, building this layer is not a viable objective. The economic reality is that frontier models are enabled by AI factories \u2013 and most companies will consume them, not replicate them.<\/p>\n The more underappreciated layer is the\u00a0Cognitive Surface. We have often referred to this layer as the\u00a0System of Intelligence or SoI<\/a>. This is where intent gets shaped, context gets assembled, constraints get enforced and outputs get turned into actions. It is also where the enterprise requirements live \u2013 security, policy, compliance, auditability, latency control and integration to existing systems.<\/p>\n This is the layer that turns \u201ca smart model\u201d into something operable inside a regulated enterprise. It is also the layer that determines switching costs, because policy, semantics and tool integration get hardened here. As such, switching vendors will become much harder, in our view.<\/p>\n We expect this layer to be distributed \u2013 but controlled. Large enterprises will want instances closer to their data for latency, sovereignty and regulatory reasons. But they won\u2019t own the evolution of the cognitive surface. They\u2019ll configure it, operate it and integrate it \u2013 within guardrails defined by the frontier model provider. That preserves enterprise control over data and policy while preventing semantic drift.<\/p>\n Below that sits the\u00a0Transactional Substrate\u00a0\u2013 the systems of record. This layer is essential because it stores truth and executes transactions. The change we project is that intelligence migrates upward. The apps and databases don\u2019t disappear, but their role focuses around state, service level agreement guarantees and execution.<\/p>\n Finally, the\u00a0Edge\u00a0eventually becomes important because sensing and physical execution happen there. Capability at the edge will lag initially, but it becomes strategic as agents and automation demand local action and local autonomy when disconnected. This is where smaller language models will thrive, in our view.<\/p>\n The other key point is that the lack of a mature cognitive surface contributes to the agentic AI gap. We see this in the ROI data. Enterprises are being asked to move to a world where intelligence is produced in AI factories as tokens, accessed through application programming interfaces and governed through a cognitive surface. Until organizations (and SaaS players) build the control, governance and integration muscle memory in that middle layer, they\u2019ll keep shipping pilots \u2013 and struggling to turn them into repeatable ROI at scale.<\/p>\n We see this model evolving and the four frontier labs will be fundamental in supporting this new software stack. We believe OpenAI, Anthropic and Google will aggressively compete for enterprise traction, while xAI is best positioned for edge workloads, in our view \u2014 leveraging Elon Musk\u2019s flywheel of Tesla, Optimus and SpaceX.<\/p>\n The token business model: Throughput, interactivity and \u2018where you sit on the curve\u2019<\/p>\n We believe the deeper shift underway is not just architectural \u2013 it\u2019s economic. AI is catalyzing a model where intelligence is manufactured in AI factories as tokens, accessed through APIs, and paid for as a first-class line item. At the macro, today\u00a0firms spend approximately 4% of their revenue on technology. We believe this figure will rise to 10% or more within the next decade.\u00a0Spend will move away from general-purpose computing toward accelerated computing \u2013 supported by\u00a0extreme co-design\u00a0across central processing units, graphics processing units and networks \u2013 with power as the governing constraint.<\/p>\n This is why Jensen said the slide below from GTC was the most important. The vertical axis is throughput normalized by energy (tokens per second per megawatt). The horizontal axis is interactivity \u2013 responsiveness that is broader than simple latency but latency is the driver of user experience. In a power-constrained world, moving up the curve on the vertical axis means dollars to operators. This what we wrote about as\u00a0\u201cJensen\u2019s New Law\u201d<\/a>\u00a0in a previous Breaking Analysis.<\/p>\n Access to the latest and greatest systems from Nvidia can be the difference between a stalled AI program and one that scales. Hyperscalers, AI clouds and frontier labs have known this for years. Getting on the Nvidia technology curve is critical for leadership. The annual cadence from Hopper to Blackwell to Rubin is the key \u2013 massive step-function improvements on a 12-month cycle, not an 18- to 24-month Moore\u2019s Law clock. The 35X improvement called out on the slide below is the kind of delta that changes unit economics overnight if you have a fixed power budget. The big capex spenders know this and the dynamic will migrate to enterprises as described by Jensen and shown on the horizontal axis.<\/p>\n That\u2019s the other part of the chart where the business model starts evolve. Training monetizes primarily on the vertical axis \u2013 maximize throughput per megawatt and customers \u201cbuy more, save more\u201d or \u201cbuy more, make more\u201d depending on whether they\u2019re building models or selling capacity to model builders.<\/p>\n Interactivity is a second monetization opportunity on the X-axis. It creates tiers \u2013 free, medium, high, premium, ultra \u2013 where users pay more for better responsiveness, and where the most demanding workloads drive the highest willingness to pay. Low-latency inference becomes a priced product and a service delivered through software.<\/p>\n That\u2019s where the Groq integration and Nvidia\u2019s $20 billion Groq investment starts to makes sense. Rubin + LPX extends the curve further to the right \u2013 preserving throughput while improving interactivity. The implication is that the platform that can move the curve right without collapsing the curve down gets to charge for use cases that are sensitive to responsiveness, especially in agentic workflows and edge inference. The spectrum goes from freemium (free ChatGPT) to paid ($20\/month) to higher-paid tier ($200\/month) to coding assistance to super-low-latency agentic\u2013 ultra-expensive but worth it because it drives revenue.<\/p>\n The takeaway for enterprises is that this is not something they can absorb overnight. They have to pick the use cases that matter, build the systems, validate safety and controls, operationalize them, prove ROI, then scale. At the same time, the cost model changes. Token spend becomes part of cost of goods sold \u2013 the way cloud costs became part of SaaS COGS \u2013 and organizations start managing token budgets as a core operating discipline.<\/p>\n This is why cautious IT budget sentiment can coexist with AI enthusiasm. Customers don\u2019t want to over-invest in legacy spend, and they don\u2019t want to over-rotate into the new spend until they understand where they sit on the curve \u2013 and how to translate throughput and interactivity into unit economics, outcomes and predictable revenue returns.<\/p>\n Going forward this will create new revenue models and begin to break down organizational silos that exist today because of technology constraints. Many departments build their own custom tech stack to support their specific mission. Processes are developed and organized around this tech stack. Data lives in their siloed department and humans then integrate the data via extract\/transform\/load processes, data pipelines, data science workflows and the like.<\/p>\n Increasingly, we believe these silos will dissolve to a great extent as organizations gain access to intelligence in the form of tokens to power their agentic enterprises. They will build digital representations of their organizations and the operational model will evolve to support this new reality.<\/p>\n Jensen said something profound at GTC.\u00a0Every CEO must understand where they sit on this Pareto curve<\/a>. Are you monetizing on the vertical axis, the horizontal axis or both? Today a new employee gets a laptop and access to systems. In the future they will get a token budget to direct their revenue-producing agents.\u00a0A software engineer paid $300,000 to $500,000 who spends only $5,000 annually on tokens would be like a chip designer eschewing modern design tools and using graph paper instead. They would be fired.<\/p>\n That sounds absurd, but the analogy holds for the future of business. Profit-and-loss managers, sales pros, operational staff, logistics planners, finance pros and others will all be managing armies of agents and burning tokens. Closing the agentic gap requires new technology, business and operational models that can be executed securely and safely.<\/p>\n That day is coming. Where do you sit on the pareto and how fast can you get there?<\/p>\n Image: theCUBE Research\/Gemini Support our mission to keep content open and free by engaging with theCUBE community. Join theCUBE\u2019s Alumni Trust Network, where technology leaders connect, share intelligence and create opportunities.<\/p>\n 15M+ viewers of theCUBE videos, powering conversations across AI, cloud, cybersecurity and more About SiliconANGLE Media<\/p>\n SiliconANGLE Media is a recognized leader in digital media innovation, uniting breakthrough technology, strategic insights and real-time audience engagement. As the parent company of SiliconANGLE<\/a>, theCUBE Network<\/a>, theCUBE Research<\/a>, CUBE365<\/a>, theCUBE AI<\/a> and theCUBE SuperStudios \u2014 with flagship locations in Silicon Valley and the New York Stock Exchange \u2014 SiliconANGLE Media operates at the intersection of media, technology and AI.<\/p>\n Founded by tech visionaries John Furrier and Dave Vellante, SiliconANGLE Media has built a dynamic ecosystem of industry-leading digital media brands that reach 15+ million elite tech professionals. Our new proprietary theCUBE AI Video Cloud is breaking ground in audience interaction, leveraging theCUBEai.com neural network to help technology companies make data-driven decisions and stay at the forefront of industry conversations.<\/p>\n","protected":false},"excerpt":{"rendered":"The geopolitical dislocations ripping through the stock market are filtering down to information technology budgets in the form…\n","protected":false},"author":2,"featured_media":370701,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[220,218,219,166376,17523,61,60,13065,80,166375],"class_list":["post-370700","post","type-post","status-publish","format-standard","has-post-thumbnail","category-artificial-intelligence","tag-ai","tag-artificial-intelligence","tag-artificialintelligence","tag-enterprises-crawl","tag-guest-author","tag-ie","tag-ireland","tag-siliconangle","tag-technology","tag-the-agentic-ai-gap-vendors-sprint"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts\/370700","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/comments?post=370700"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts\/370700\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/media\/370701"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/media?parent=370700"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/categories?post=370700"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/tags?post=370700"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"\"](\"https:\/\/www.newsbeep.com\/ie\/wp-content\/uploads\/2026\/03\/311-_-Breaking-Analysis-_-The-Agentic-Gap_-Vendors-Sprint-Enterprises-Crawl-4.jpg\")
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