Large language models are moving FP&A from data aggregation to decision support. Here is how leading finance teams are already applying them.
For most FP&A teams, the last decade was defined by cloud EPM and self-service BI. The next decade will be defined by AI — specifically, how quickly finance leaders operationalize large language models across planning, reporting, and analysis.
The near-term opportunity is not autonomous forecasting. It is analyst leverage. Narrative reporting, variance explanations, board commentary, and executive Q&A are all high-frequency, high-effort tasks where a governed AI copilot delivers material productivity gains within weeks.
The pattern that works looks like this: a governed semantic layer, a retrieval architecture over trusted finance content, and a controlled copilot experience embedded where analysts already work — Excel, Teams, or the planning platform. Everything else is a science project.
The organizations moving fastest are not the ones with the largest AI budgets. They are the ones with a clean data platform, a governed KPI catalog, and a leadership team willing to redesign how FP&A actually spends its week.