Vibe coding is a way of building software without writing code. You describe what you want, the AI builds it, you give feedback, and it refines. Describe, generate, refine. That loop is the whole thing, and it does not require an engineering background. You do not have to know how the code works. You have to be able to say clearly what you want.
What comes out is not a screenshot of a chat or a one-off answer. It is real, working software: interactive dashboards, automated reports, workflows that run on a schedule. Things that persist and that you use again next month. The first time you build something this way, it is a genuine shift in what you thought was possible without an engineer in the room.
Think about what a finance team actually does. The close, the budget, the P&L, the scenarios the board sees. Every major decision, on hiring, pricing, investment, and expansion, runs through finance's analysis. Finance is the operating system of the company.
Yet the tooling has barely changed in decades. Spreadsheets, a few specialized systems, and a lot of manual work stitching data across sources that do not agree. Up to 80% of the week is still assembly, not analysis. The headcount plan gets re-pasted into a fresh tab. The variance chart gets remade because a late actual moved. The board deck gets rebuilt slide by slide the night before.
Vibe coding closes that gap. The reporting and workflows a team always wanted but could never get built now take an afternoon instead of a sprint cycle that never comes. The manual assembly gets automated, and the time goes back to the work that actually moves the business.
The examples that landed hardest were the ones people recognized from their own week. An automated month-end reporting package with written commentary on the variances. A Monday morning variance alert that lands in Slack on its own. Agent workflows that map new GL accounts or flag data that came in wrong. And the one we spent the most time on: an interactive budget versus actuals dashboard you filter by department, period, and vendor, instead of rebuilding a static spreadsheet every cycle.
Build the tool once, refresh it forever. The work is all in the setup. After that, every month is a click.
The fastest way to a good result is to slow down at the start. The instinct is to start building immediately. The better move is to plan first, because a clear plan up front means fewer revisions, less back and forth, and a result that is right the first time.
The approach we shared on every build is the same. State your goal clearly, because if you cannot describe what you want, the AI cannot either. Then, before it builds anything, tell it to ask you clarifying questions first, and to summarize the plan in plain English for your approval. You read the plan, you approve or edit it, and only then does it build.
One more habit worth keeping: use the right model for the right job. A stronger model for the planning conversation, where the architecture gets decided, and a faster one to execute once the plan is set. Better output, lower cost.
Everything above is only useful if the numbers are right. AI can make calculation errors, miss line items, and produce a figure that looks completely correct and is not. That is the part that makes AI feel risky for finance, because the output does not look wrong. It looks board-ready, right up until someone checks it.
The reason comes down to what the AI is reading. Point it at a spreadsheet and it has to interpret, guess, and assume. Point it at a governed layer and it retrieves clean, structured data where every figure already traces to the source transaction.
The Excel version came back about 60% accurate. It got the big numbers right, then quietly missed line items and got period comparisons wrong. Connected to Cube as the data layer, the same build was 100% accurate, because the AI was reading clean, structured data where every figure traces to the source transaction. General AI is probabilistic. Finance is deterministic. Building on ungoverned data is a faster way to build the wrong thing. Vibe coding on messy data is still messy. On clean, governed data, it is reliable.
This matched what attendees said about their own experience. When we polled the room on what trips them up most when building with AI, trusting the numbers was the runaway answer, ahead of knowing where to start, getting data in, and rolling out safely.
Building something once is the easy part. Making it survive is what separates a demo from something a team relies on.
The guardrails we walked through: one source of truth, so the AI is not inventing its own data model. Citations on every number, so you can trace it back rather than trust a paraphrase. Version control. A clear line on who reviews and approves before anything ships. Read access before write access. And a review mode that catches jobs that fail silently, because AI will sometimes claim it did the work when it did not.
We also covered what your security and IT teams will want to know before you deploy, with language you can copy and send. The point of all of it is the same one that runs through the whole session: AI you can put in front of the auditors.
This was the first session in a series built for finance teams, with more on the way. If you want the full walkthrough, including the live dashboard demo and the prompts we shared, the recording is here:
Watch the recording
And if you want to see what building on governed data looks like on your own numbers, book a demo. Bring a question from your own close, and we will run it through Cube and open the answer down to the transaction.