Finance teams are embracing vibe coding, a new approach where plain English descriptions enable AI to assemble software tools, potentially revolutionising data handling and reporting accuracy, provided data governance is maintained.
Finance teams are increasingly being pitched a new way to build software: by describing what they need in plain English and letting artificial intelligence assemble the tool. Cube Software says this approach, often called vibe coding, can produce working dashboards, reports and workflows without an engineering background, provided the underlying data is reliable. The appeal is obvious in finance, where teams spend large amounts of time stitching together numbers for the close, the budget and board reporting.
That promise matters because finance still relies heavily on spreadsheets and manual rework. Cube says much of the week can be lost to assembling data rather than analysing it, with teams repeatedly rebuilding the same models when assumptions change. Related guidance from Aleph notes that vibe coding can be especially useful for one-off analysis, internal calculators and disposable dashboards, while more sensitive systems still demand traditional controls and review.
The most persuasive examples are the ones finance teams already recognise from daily work: automated month-end packs with commentary, Slack alerts for budget variances, and workflows that flag new general ledger accounts or bad data. Cube also points to interactive budget-versus-actual dashboards that let users filter by department, period and vendor, then drill down to the transaction level. The point, the company argues, is to build once and refresh indefinitely.
But the technology is only as good as the numbers behind it. Cube says its own test showed a clear difference between AI working from a spreadsheet and AI working from a governed data layer: the spreadsheet version was only partly accurate, while the governed version produced correct results because every figure could be traced back to source transactions. That distinction is echoed by vendors such as OneStream, IBM and Flowwiz, which are all trying to position AI as useful for finance only when it is tied to controlled data and human oversight.
The broader lesson for finance leaders is that trust does not come from a more polished prompt. It comes from disciplined setup: one source of truth, clear approval steps, version control, read-only access before write access, and a review process that catches failures before they reach auditors. Cube says the workshop that introduced the approach was the first in a series for finance teams, reflecting a wider push to make AI practical in day-to-day finance work rather than merely impressive in demonstrations.
Disclaimer: This article is intended to inform and educate, not to recommend or endorse any financial product, investment or strategy. Please consider your own financial circumstances and seek professional advice where appropriate before making financial decisions.
