We spoke to the FP&A leaders of a Fortune 500 company about what they want from AI.
They didn’t need AI to tell them revenue fell in a segment.
Their analysts already catch that. What they wanted was the next step:
So what are we going to do about it?
What are the opportunities?
How do we correct or redirect?
Very little AI in finance is built for that question yet. Here’s what this team asked for and what worries them. The company and the people stay anonymous.
Read on.
1. They got the data ready first
This team didn’t start with a chatbot.
They started with the data.
They had already built an ontology, a data layer that lets language models sit on top of their financial data. It covers the P&L and every slice of profitability: by customer, industry, product, volume and revenue type, and cost.
Most teams skip this step.
FP&A is no different. F
P&A still spends 47% of its time on low-value work like collecting and validating data.
AI can only read your P&L by customer, industry, and product if someone has built that layer. If they haven’t, the model is guessing.
2. FP&A wants AI at three levels
The team described what they want as an agent with their own FP&A discipline, working at three levels:
Below, we break down what makes AI output trustworthy enough for a board pack, the worries that stop FP&A teams from using it and what to do next. Paid readers also get an early invite to see finstory live before launch on Nov 18.
