Vibe Coding Comes to Finance as CFOs Embrace Conversational AI

Businesses and balance sheets have long been built on the finance function’s ability to close.

Close the month. Close the quarter. Close the year. The faster the books are closed, the more successful the finance team is considered to be. For years, it has been accuracy, control and timeliness that has defined the office of the chief financial officer.

However, a new era appears to be emerging for the finance department, one built around narratives and not just numbers, powered by conversational artificial intelligence. Datarails announced Wednesday (Jan. 21) that it raised $70 million in a Series C funding round to expand its AI-driven offerings across financial planning and analysis, cash management, month-end close, and spend control.

Vibe coding, or the ability to express intent in plain language and have AI systems translate that intent into functional outputs, is moving into the finance stack and is set to change not just how finance teams work, but how they think.

The office of the CFO is, on paper, a perfect fit for vibe coding. Finance has always been data-rich and time-poor. Modern organizations generate enormous volumes of financial and operational data across ERP systems, planning tools, data warehouses and point solutions.

The challenge has never been access to this data, but the friction involved in interrogating it. Queries require technical expertise, models require specialized knowledge, and presentations require manual assembly. Each incremental step adds latency between questions and answers.

However, vibe coding and conversational AI promise to help collapse much of that friction.

Read also: Three New Year’s Resolutions CFOs Are Making About Data and AI

From Closing the Books to Explaining the Business

Traditional financial systems excel at producing outputs like reports, spreadsheets and dashboards. But outputs alone do not create value. What matters are outcomes such as better decisions, clearer communication and faster responses to change. The promise of AI-enabled finance lies in its ability to close the gap between the two.

Instead of writing SQL queries, building pivot tables or manually assembling slides, finance leaders can ask questions the way they think about them. Why did margins decline in the Northeast last quarter? What assumptions are driving this forecast variance? How does this scenario change if customer churn increases by 50 basis points?

This shift matters because it aligns technology more with how finance leaders tend to operate. CFOs do not think in formulas or schemas but in narratives, risks and trade-offs. Conversational AI interfaces can help systems meet finance where it is, rather than forcing finance to adapt to the logic of machines.

“Folks are just starting to understand that AI isn’t just automation with kind of sexier marketing,” Finexio CEO and founder Ernest Rolfson told PYMNTS in December. “Embracing it as infrastructure lets you use your data as a strategic asset.”

The AI marketplace is also increasingly positioning itself toward corporate use cases, with two of the most prominent AI startups, OpenAI and Anthropic, reportedly set on competing for enterprise clients this year.

It’s not just AI-native firms, either. SAP is pushing agentic AI deeper into its ERP suite. Oracle is rolling out AI agents, while Salesforce is packaging industry-specific copilots and autonomous workflows into its ecosystem. AWS, Google Cloud and Microsoft Azure each offer their own toolkits for building agentic systems at scale.

According to the PYMNTS Intelligence report “Time to Cash: A New Measure of Business Resilience,” 70% of firms surveyed already use at least one AI tool to manage cash flow. The most advanced, those using agentic AI, capable of autonomous decision-making, have automated up to 95% their accounts receivable processes, compared to just 38% among firms without AI integration.

See also: How AI Is Supercharging the Tools CFOs Already Trust

Revealing the Narrative Behind the Numbers at Machine Speed

At its core, finance exists to impose coherence on complexity. As businesses generate more data and operate under greater uncertainty, that task becomes harder, not easier. Tools that reduce friction help, but they do not absolve finance of responsibility.

Faster, AI-powered analysis can also raise governance challenges. When models and reports can be generated quickly, the opportunity for unexamined assumptions increases, meaning that traditional controls, such as manual reviews of formulas and reconciliations, may not scale well in an AI-accelerated environment.

In response, governance may shift upward. Instead of validating mechanics, finance leaders can validate logic and ensure that data sources are reliable, assumptions are documented, and outputs are explainable.

Whether AI truly transforms the finance tech stack will depend on execution. The history of enterprise software is littered with well-funded platforms that failed to change behavior. The difference in this case may be alignment. Conversational AI maps more closely to how finance professionals already think and work.

“It’s no longer a nice-to-have,” Steve Wiley, vice president of product management at FIS, told PYMNTS in May. “Artificial intelligence is a must-have, and that’s happened very, very quickly.”

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