CIO AI
2026-08-28 05:57 UTC
Score 30.0
USR-0125-20260828-global-ai-ne-2a541694
Every CFO I talk to right now is under some version of the same pressure: the board wants AI, the business wants faster answers, and the finance team is often still reconciling spreadsheets. The promise of AI in finance is real. But so is the gap between that promise and what most organizations are able to deliver. I believe finance leaders need to be asking not simply, “How do we use AI?” but “What would make our data trustworthy enough for AI?” That distinction matters. AI-ready finance data is intentionally shaped for a specific business outcome, so we can trust what AI produces from it. In finance terms, it’s the difference between having transactions and being able to defend the numbers. Finance data is uniquely messy, and important Finance data is messy for rational reasons. We pull from multiple systems — ERP, CRM, payroll, procurement, planning tools, banks, data warehouses, and yes, still spreadsheets. We live through reorgs, acquisitions, new products, and chart of accounts changes. And when the business cannot wait, we create manual workarounds to keep moving. That complexity is the context in which we’re now being asked to use AI. It’s no wonder that so many initiatives stall. The non-negotiables of AI-ready finance data When Alteryx talks about AI-ready data , I translate it into a few non-negotiables. For finance leaders, this is where the concept becomes practical. Purpose-built, not “all the data” – AI-ready data should be scoped to the decision or workflow a…