2 posts tagged with "finance"

Most finance teams filed their first LLM bill in the wrong drawer. It arrived during the experimentation phase, when a handful of engineers were prototyping with an API key, and it looked like exactly what it was at the time: research and development. A few thousand dollars a month to figure out whether the technology worked. So it went into R&D, mentally and sometimes literally, and nobody thought harder about it.

Then the feature shipped, usage climbed, and the same line item that was a rounding error in Q1 became the fastest-growing cost on the cloud invoice by Q4. The problem was never the dollar amount. The problem was that the cost had quietly changed categories — from a fixed bet on building something to a variable cost of serving every customer who used it — and the mental model hadn't moved with it.

That misclassification is the single most expensive accounting mistake in AI products right now, and it's not really an accounting mistake at all. It's a forecasting one.

Every engineering team shipping AI features eventually hits the same wall: finance wants a spreadsheet that justifies the spend, and the spreadsheet you built doesn't actually work.

The problem isn't that AI features lack ROI. The problem is that AI economics break every assumption the standard ROI model was built on — fixed capital, linear cost curves, predictable timelines. Teams that treat AI spending like SaaS licensing get numbers that either look deceptively good before launch or collapse six months into production. The ten-fold gap between measured AI initiatives (55% ROI) and ad-hoc deployments (5.9% ROI) comes almost entirely from whether teams got the measurement model right before they shipped.