The bill has arrived. After years of AI experimentation, enterprises are now staring down real invoices – and for many, the numbers are deeply uncomfortable. <\/p>\n\n\n\n
Gartner\u2019s latest forecast puts worldwide AI spending at $2.67 trillion in 2026, up 49.5% from 2025. Yet McKinsey finds that only 37% of survey respondents attribute any EBIT impact to AI. That gap – between what the market is pouring in and what organisations can connect to earnings – is the defining business challenge of this moment. <\/p>\n\n\n\n
For every organisation, the question is no longer \u201cshould we invest in AI?\u201d It is: \u201cCan we prove this investment is working?\u201d<\/p>\n\n\n\n
Tokens (and credits) are a core unit of AI consumption and an important driver of cost. Every prompt sent, every document processed and every agent action taken can consume tokens. But tokens are not the whole bill: licences, infrastructure, retrieval, storage, tool calls, monitoring and human review can also contribute. Unlike traditional fixed-price software, much of this spending varies with use, making it harder to forecast. <\/p>\n\n\n\n
The volatility is catching enterprises off guard. In an independent February 2026 survey conducted by Sapio Research for DoiT, covering 500 finance leaders at large US and UK organisations, 79% reported AI-related cost overruns in the previous 12 months<\/strong>. A separate July 2026 WitnessAI survey of 300 US business executives found that 68% said at least some AI initiatives had run over budget. <\/p>\n\n\n\n One widely reported example involved Uber\u2019s engineering organisation and Claude Code. Adoption expanded rapidly across roughly 5,000 engineers, with agentic-tool usage reaching 84% by March 2026. By April, Uber had reportedly exhausted its full-year AI tooling budget<\/strong>. Reported monthly costs ranged from about $150-$250 for average users to $500-$2,000 for power users. One rollout, four months, a full-year budget.\u00a0<\/p>\n\n\n\n This is not an outlier story. It is a warning signal.<\/p>\n\n\n\n The shift to agentic AI – systems that can plan, reason and execute multi-step tasks – is making cost management significantly harder. Gartner analysis cited across industry reporting estimates that agentic workflows can consume 5 to 30 times more tokens per task than a standard chatbot query. Separate reporting indicates inference now exceeds training within AI-optimised cloud infrastructure spending; however, the exact share of an individual enterprise\u2019s total AI budget will vary.\u00a0<\/p>\n\n\n\n Gartner\u2019s June 2026 research predicted that AI coding costs could surpass the average developer\u2019s salary by 2028 as token consumption rises. It also warned \u201ctoken discipline will not emerge through developer choice alone, as developers tend to optimise for speed and convenience over cost efficiency.\u201d <\/p>\n\n\n\n Meanwhile, Gartner\u2019s July 2026 analysis projected that up to $234 billion in enterprise application software spending could be exposed to \u201cagentic arbitrage\u201d by 2030 – roughly 20% of enterprise software-as-a-service spending – as agents complete tasks across systems and reduce reliance on traditional interfaces. This is a forecast, not an observed loss, but it signals that the economics of enterprise software are being rewritten. <\/p>\n\n\n\n Here is the core problem that underpins nearly every AI budget blowout: organisations are scaling spend faster than they are building the visibility to understand it. <\/p>\n\n\n\n Only 26% of organisations report full, real-time visibility into what their AI systems cost to operate, according to KPMG\u2019s US Q2 2026 AI Quarterly Pulse survey of 204 senior leaders. KPMG\u2019s separate Global AI Pulse survey of 2,145 senior leaders across 20 countries and territories found that organisations with strong cost visibility were five times more likely to report established ROI than those without it (15% vs. 3%). <\/p>\n\n\n\n The Tokenomics Foundation\u2019s 2026 State of Tokenomics survey, based on 472 organisations across 11 industries representing $4.6 trillion in combined revenue, makes the gap even starker: 96% use frontier model providers, yet roughly three in four cannot confidently connect AI spend to a measurable business outcome their CFO would accept. <\/p>\n\n\n\n If you cannot show your CFO where the money is going, you are not managing an AI strategy – you are managing an AI expense. <\/p>\n\n\n\n One of the most important shifts in AI financial thinking in 2026 is a change in the unit of measurement. Tracking cost per token is like measuring a factory’s efficiency by counting how many bolts it uses rather than how many finished products it ships. <\/p>\n\n\n\n The more meaningful metric is cost per completed task<\/strong> – the total attributable cost of useful work that meets defined acceptance criteria, including model usage, retries, failed attempts, tool calls and human intervention. The FinOps Foundation\u2019s unit-economics framework supports connecting technology cost to business units such as a transaction, case resolved or workload, rather than treating a raw consumption metric as the outcome.\u00a0<\/p>\n\n\n\n As a September 2026 AIwire analysis argued, optimising cost per token alone can encourage organisations simply to use less AI, rather than use it more effectively. More durable measures include cost per completed task, workflow or resolved unit of work, assessed alongside quality and compared with the equivalent human or legacy-software cost.\u00a0<\/p>\n\n\n\n Research from the Tokenomics Foundation and KPMG points to several operational disciplines associated with stronger AI value and ROI: <\/p>\n\n\n\n The State of Tokenomics survey found that companies with a clearly defined owner for AI economics were 3.7 times more likely<\/strong> to show their CFO a measurable outcome from AI spend. Accountability is a structural advantage. <\/p>\n\n\n\n Not every task needs a frontier model. Routine, high-volume work such as classification, extraction and summarisation may be suitable for smaller or open-weight models, while complex reasoning and high-stakes work may justify frontier models. The Tokenomics Foundation found that 86% of respondents were evaluating or using model routing, and router users were four times more likely to be able to show CFO value. Routing is a major cost lever, but its benefits depend on workload quality, risk and performance requirements. <\/p>\n\n\n\n The organisations seeing returns have moved from tracking “AI usage” to tracking business outcomes: tickets resolved, documents processed, hours saved, revenue influenced. The metric that matters is not how many tokens were consumed – it is what changed in the business as a result. <\/p>\n\n\n\n KPMG\u2019s Q2 2026 US survey found that 61% of organisations had approval processes in place, but only 36% had direct token or usage controls. The practical lesson is to attach a cost model, an outcome target and a review cadence before an AI workload moves into production. <\/p>\n\n\n\n AI investment is not slowing down. The question is whether your organisation is positioned to turn that spend into demonstrable value – or whether you will find yourself explaining to the board why the AI budget tripled and the results are still intangible. <\/p>\n\n\n\n Token spending without governance is not a technology problem. It is a business risk. And the organisations that are pulling ahead are not necessarily spending more – they are spending more deliberately. <\/p>\n\n\n\n At Insentra, we work with organisations to move from AI experimentation to AI accountability. Meaning we help organisations build the visibility, governance frameworks, and outcome-measurement practices to turn AI spend into a line item you can defend, optimise and scale with confidence.\u00a0<\/p>\n\n\n\n If your AI investment is not showing up in your outcomes, it is time to change the conversation. <\/p>\n\n\n\n Insentra\u2019s AI Momentum programme trains and coaches your people to use AI where it delivers real value, with the governance to keep spend under control. Capability your team owns, and outcomes you can defend. Explore AI Momentum<\/a>. <\/p>\n\n\n\nAgentic AI Is Amplifying the Cost Problem<\/h2>\n\n\n\n
The Visibility Gap: You Cannot Manage What You Cannot See\u00a0<\/h2>\n\n\n\n
Rethinking the Metric: Cost Per Task, Not Cost Per Token\u00a0<\/h2>\n\n\n\n
What Organisations Getting ROI Are Doing Differently\u00a0<\/h2>\n\n\n\n
1. They Have Defined Ownership<\/strong>\u00a0<\/h3>\n\n\n\n
2. They Are Routing Intelligently\u00a0<\/h3>\n\n\n\n
3. They Measure Outcomes, Not Activity\u00a0<\/h3>\n\n\n\n
4. They Have Embedded Cost Reviews Into Governance\u00a0<\/h3>\n\n\n\n
The Bottom Line\u00a0\u00a0<\/h2>\n\n\n\n