Your AI Budget Has a Leak

You approved the AI budget. You saw the business case. You signed off on the investment.

Now the invoices are coming in they’re fragmented across three cloud providers, two API subscriptions, a handful of SaaS tools with AI features buried inside them and a growing list of team-level inference spend that didn’t appear in last year’s plan. When you ask what’s working, the answer is a confident-sounding narrative with no numbers attached.

That gap between what you’re spending and what you can account for, that’s the leak.

The number that should concern every Finance leader

Enterprise AI deployments face 20 - 40% margin compression without governance in place. That’s not failed projects or bad vendors. That’s inference spend running without guardrails; compute cycles consumed, API calls made, model outputs generated that produced nothing of measurable value.

The reason it compounds unnoticed is structural. Token spend doesn’t behave like other infrastructure costs. It doesn’t show up on a single bill. It scales quietly inside products, workflows and team experiments that no central Finance function approved or tracks. By the time it’s visible on a consolidated report, the margin impact has already landed.

cortave reduces token spend by 50 - 80%. That means gross profit is protected as usage scales and not eroded by it.

Margin protection starts with attribution

Most organisations have some version of AI cost tracking. What they lack is attribution, the ability to map inference spend not just to a vendor or cost centre, but to a workflow and an outcome.

The difference matters. Knowing you spent $180,000 on AI last quarter tells you nothing useful. Knowing that $120,000 was attributable to three workflows - two of which demonstrably improved throughput, one of which produced outputs nobody used, that’s predictable infrastructure spend. That’s a Finance function doing its job.

Attribution at the workflow level requires four things:

1.     real-time visibility into token spend by team

2.     a shared taxonomy between Finance and Engineering for how inference costs are categorised

3.     a unit economics framework that maps cost to outcome

4.     a reporting cadence frequent enough to catch token waste before it scales.

The governance gap compounds with scale

AI adoption is accelerating. Organisations that do not build cost governance frameworks now will find themselves with a significantly larger problem in twelve months.

Cloud spend followed exactly the same trajectory before FinOps disciplines were widely adopted. Costs spiralled, attribution was guesswork, Finance teams were perpetually reacting to last quarter’s bill. The organisations that built showback frameworks before the board demanded it are the ones that scale cloud spend efficiently today.

AI cost governance is in the same pre-discipline moment. The frameworks that will become standard practice in two years are being built now.

Score your organisation’s AI cost visibility

We’ve built a five-dimension self-assessment for Finance leaders who want to understand where their organisation sits on the AI governance maturity curve before the board asks the question.

The framework covers attribution, showback, unit economics, forecasting and board reporting. Each dimension is scored from one to five. The total gives you a maturity tier and a clear set of priority actions. It takes under thirty minutes.