The Three Questions That Reveal If Your AI Finance Tool Actually Works

The Demo Problem

Every AI finance vendor has a great demo. Upload a spreadsheet, ask a question in plain English, watch a chart appear in three seconds. Executives nod. Contracts get signed. Then, six months later, finance teams are still building the same manual variance reports they always did — the AI tool sits open in a browser tab, mostly unused, mostly unquestioned.

The problem isn't that the technology doesn't work. It's that most buying decisions are made on the wrong evidence. A slick demo tells you the tool can produce an answer. It tells you nothing about whether that answer is correct, whether it would have changed a decision, or whether a human still has to redo the work to trust it. Those are three completely different questions, and almost no evaluation process asks all three.

Question One: Was the Output Actually Correct?

This sounds obvious, but it's the question most teams skip because it requires effort — you have to check the AI's work against ground truth, not just against plausibility.

A forecast that looks smooth and confident isn't the same as a forecast that's accurate. A commentary generator that produces fluent, well-structured prose about