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Budget season is starting and most finance teams are finding the AI line is the most evasive line on the page. You lived through the year. AI spend came in higher than planned and moved in ways nobody could foresee or forecast. And when the board asked what it produced, the honest answer probably was “we’re working on it.”

People run Codex on their own laptops. When Codex is signed in with a ChatGPT subscription, OpenAI’s own admin console shows who used it and how much: messages and credits. What it doesn’t show is what any of that usage was for, or how it compares to what your team spent on other AI tools. The CloudZero desktop agent for macOS installs on a Mac, sees the traffic from AI coding tools, and prices what those tools use.

AI is expensive because the model bill is only part of the cost. Three components set the floor: model subscriptions, per-token API pricing, and infrastructure. Three more make it move: adapting models to your business, catching and fixing errors, and rising energy and datacenter costs. Efficiency doesn't fix it, because cheaper AI gets used more, not less. Businesses are willing to spend on AI. Research from Deloitte found that in 2025, 85% of organizations increased their AI investments.

Months have different numbers of days, and a monthly cost chart built on raw totals mixes that calendar difference into the trend. A 28-day February next to a 31-day March shows a 10.7% increase even when daily spend never moved. The same math works in reverse: real growth in a short month can look flat, hiding an increase worth investigating. That costs you time in two places. The first is triage.

AI budgeting is the process of planning, allocating, and forecasting an organization's AI spend: model and API costs, AI infrastructure, tooling, and the people running it all. It differs from traditional budgeting because AI spend is usage-based, scales with product success rather than headcount, and often spans multiple providers.

If your engineering teams run on ServiceNow, incidents are where they get work done. Putting cost work into an incident gives it the same path to resolution as any other work item your team handles. When a cost anomaly arrives as an incident, your teams route it, assign it, and resolve it on their usual SLAs. When a savings recommendation arrives as an incident, an engineer owns it and acts on it. Now you can send either straight into ServiceNow.

A strong AI business case ties a specific goal to a measured outcome and a fully-loaded cost. Most fail because they skip one of the three: no clear mandate, an over-broad "AI fixes everything" scope, or a cost estimate that ignores adaptation and error-correction. Build it in six steps: define goals, identify uses, break work into tasks, evaluate models, assess total cost, then launch and refine. Most companies are now spending on AI. Far fewer can show what they got back.

You shipped a skill. It worked. You closed the tab. That’s the whole problem. Model choice is a decision you make once, at the moment you’re least equipped to make it: before the skill is even authored. Then you never revisit it, because the skill stopped being interesting the day you got it working. So go back and check. Here’s how.

LLM cost management is the practice of tracking, allocating, budgeting, and governing large language model spend so every dollar maps to a feature, team, and business outcome. It has five levels: provider visibility, business allocation, unit economics, model governance, and a continuous optimization loop. It matters because 68% of companies say AI initiatives ran over budget last year, and per CloudZero's 2026 survey, 30% of finance leaders still reconcile AI spend manually.

If you spend even fifteen minutes reading about AI’s impact on the future of work, you’ll take in a lot of fear-based analysis. The fears are real — 40% of workers fear losing their jobs (Metaintro), 60% believe AI will eliminate more jobs than it creates (Yardi Kube), and 52% generally worry about the impact of AI in the workplace (Pew Research) — but the analysis is all wrong.