What your company spends on AI, and how to see all of it.
Where AI spend hides in teams, tools and invoices, how to trace every dollar to a team, a project and an owner, and how to cap it before it is committed.
- Topic
- AI cost
- Product
- Novie Control
- Format
- Guide
- Reading time
- 4 min
Seeing what your company spends on AI means recording the cost of every model call at the moment it runs, tagged with the team, project and workflow that caused it, so finance reads a ledger instead of waiting for a provider invoice. Most organisations cannot do this today because providers bill at the credential, and one credential is usually shared by many teams. The fix sits in the path the call takes, before the money is spent.
What the invoice tells you, and what it does not
A provider invoice tells you how many tokens an API key consumed last month. It does not tell you which team ran the workflow, which project the spend belongs to, or whether anyone approved it. Seeing AI spend properly means attaching those three facts to every call before the money moves.
AI spend also arrives from more directions than the API. A writing tool on a team card, a vendor's AI feature switched on inside a product you already license, an agent running on a contract signed for something else. Each is sensible alone. Together they have no owner and no single number.
Why it matters this budget season
In 2026, 98 percent of FinOps practitioners manage AI spend, up from 31 percent two years earlier, according to the FinOps Foundation's State of FinOps 2026 report of 1,192 respondents. The same report names AI cost management as the top skill teams want to add. Spend arrived before the ability to attribute it.
The visibility gap is measured, too. In its Q2 2026 AI Quarterly Pulse Survey of 204 US C-suite leaders at companies above $1 billion in revenue, KPMG found that 26 percent had full, real-time visibility into what their AI systems cost to operate. Only 36 percent had direct token or usage controls in place. The same companies planned an average of $202 million in AI spend over the following twelve months.
Budgets are growing into that gap. Forrester's 2027 Budget Planning Guides, published in July 2026, report that more than 80 percent of leaders expect their overall budget to increase in 2027. A 2027 AI line written without a 2026 baseline is a guess with a signature on it.
Where attribution breaks
Four places, in the order they usually surface.
- Shared credentials. One organisation API key serves marketing, engineering and operations. The invoice shows a total and hides who caused it.
- Spend outside the cloud bill. A writing tool on a team card, a vendor's AI feature switched on inside a product you already license, an agent running on a contract signed for something else.
- Agents that call agents. One workflow triggers three models across two providers. The cost of the business outcome is split across invoices that never meet.
- Attribution after the fact. Finance reconstructs who spent what from logs, weeks later. By then the month has moved on and the pattern has repeated.
How to see all of it: a five-step method
- Inventory the spend paths. List every route AI spend takes into the organisation: provider APIs, cloud marketplaces, SaaS features, cards. Ask engineering, marketing, operations and finance separately; the lists will not match, and the differences are the finding.
- One credential per team, or a layer that tags. Either give each team its own key, or route calls through a layer that stamps team, project and workflow on every request. Providers bill the credential, so make the credential mean something.
- Record cost as a property of the action. At the moment a model call runs, write down the team, project, workflow, model and cost. This is the ledger. Everything downstream is a query against it.
- Put the cap where the call happens. A budget in a spreadsheet cannot stop a call. A cap the runtime can read holds before spend is committed and routes the increase to a named person. Set caps at organisation, team and project level.
- Review attribution monthly, before the invoice. The measure of the programme is how quickly someone can answer what team X spent last week, on what. Aim for minutes.
What good looks like
Three measures tell you whether it is working. The share of AI spend attributed to a team and project, which should approach 100 percent. The time it takes to answer a cost question from a CFO, which should be minutes while the meeting is still running. And the number of overruns caught before the invoice arrives, which should be all of them.
If you have run cloud infrastructure, this is familiar. Cloud cost became governable when tagging moved into the request and limits moved into the platform. AI spend is earlier on the same curve, and the curve is steeper.
How Novie Control does it
Novie Control is live, and this is the job it does. Every dollar visible. Every budget governed. Every call optimised. Every agent governed.
Cost visibility: every AI dollar traced to the team, the project and the owner, at the moment it is spent. Cost control: caps that hold before spend is committed, with a named human on every increase. Cost optimisation: each call routed to the model that should take it, with failover when a provider stumbles. Governance: every agent carries a named owner, an approval trail and a full audit trail.
The result is the ledger described above, kept by the platform as the work happens. Finance reads attribution while the meeting is still running. Engineering keeps building.