What CFOs Are Learning as AI Moves from Experimentation into Everyday Finance (Part 1)

Inside finance organizations, AI is already turning days of work into minutes. We recently spoke to CFOs across our network to hear how they’re putting that capacity to work – and deciding which AI investments to scale. 

For most CFOs, the AI conversation has moved beyond what might eventually be possible and now centers on where it works today – and the resulting decisions they must make. 

Which higher-value work should get the time AI gives back? How closely should teams scrutinize the output? Which applications are delivering enough value to expand, and where does further investment make sense? 

AI is increasingly capable of doing more of the work. But speed does not make analysis any less important. Finance teams still have to understand what the numbers mean, challenge what does not look right, and decide what the business should do next.  

 As executive recruiters, we have a unique vantage point into the emerging trends and challenges shaping the finance function through our ongoing conversations with leading CFOs. Recently, we spoke with several finance leaders about how AI is impacting their organizations, including Wunderkind CFO Fabrizio Ferronato, who shared his perspective on where AI is making an impact today and where it may be headed next. 

When the First Pass Takes Minutes, Not Days 

Ferronato’s team faced a cumbersome challenge involving partner agreements. Payment terms were spread across old and new contracts, including scanned documents, while an existing spreadsheet summarizing those agreements had not been consistently maintained. 

The team used an AI tool to extract key terms from the contracts and organize the information, then validated the results against the original documents. By combining those terms with revenue-share data, they created a weekly partner-aging report that gave the business a clearer view of payment timing and outstanding obligations. 

Ferronato estimated that producing the report manually could have taken multiple people two or three days. Today, he can generate it in roughly three minutes. 

“It doesn’t save me hours; it saves me days,” he said. 

Other CFOs we have spoken with have identified similar opportunities, including using AI tools to pull and analyze operating data to help finance identify process gaps affecting performance. 

Speed matters, but it is not the whole story. Data that had previously been difficult for finance to obtain has now become usable. 

Leveraging AI to perform more of the initial analysis enables finance professionals to spend more time interpreting what happened, why it happened, and supporting the business on what comes next. 

That raises a key management question for CFOs everywhere: when the first pass takes minutes instead of days, where should the recovered time go? 

Faster Analysis Still Requires Human Judgment 

The division of labor is a challenge today’s CFOs must confront: AI can pull and analyze large quantities of data, but “human logic” is still needed to complete the story. 

Ferronato believes AI is highly effective at what he calls “level 1 and 2 analysis”: organizing information, identifying patterns, connecting data, and producing an initial view. Its limitations become more apparent as the work moves closer to interpretation and decision-making. 

“Any report that comes my way, whether it’s created by a human or a tool, gets reviewed and tested,” Ferronato said. 

That responsibility begins with the underlying data. As companies rely more heavily on AI, ensuring master data accurately reflects the business will become even more critical. Ferronato’s contract example shows why. His team did not simply accept the information the AI tool produced; they checked the output against the original documents before incorporating it into a recurring finance process. 

AI shortens the path to an answer, but it does not relieve anyone of the obligation to know where the data came from, where the analysis could be weak, and whether the conclusion holds up against what is actually happening in the business. 

AI Investment Still Has to Earn Its Return 

AI may be a newer investment category, but the CFO’s obligation to demonstrate value is not. Many CFOs are planning dedicated AI investments in the coming years. Generally, the goal is to accelerate adoption in areas where the business believes the technology can generate a measurable return. 

Successful AI applications elsewhere in the broader portfolio can make the opportunity more tangible, creating greater interest in identifying where the technology could generate meaningful value within finance. Greater interest, though, is not a mandate to invest indiscriminately. An increasingly important part of the CFO’s role is ensuring AI continues to generate a positive ROI rather than pursuing “AI for the sake of AI.” 

Ferronato sees the same discipline applying to day-to-day spending. Employees want access to the latest AI tools – a level of curiosity he sees as positive. But premium licenses can become expensive quickly as access expands. He compared the dynamic to cloud hosting costs: an initial investment creates efficiencies, but spending can quietly balloon as usage expands without ongoing discipline around what’s actually being used. 

For CFOs, this is shaping up as one of the defining tensions of adoption: leaving enough room for experimentation to find the applications that matter, while maintaining the discipline to know which investments actually deserve to scale. 

The Work Is Changing. The Next Question Is What That Means for Talent 

Some of AI’s most meaningful impact on finance shows up not in transformation initiatives but in ordinary work. A report that took days now takes minutes, data that was hard to reach becomes usable, and an initial analysis comes together with far less manual effort. 

CFOs are leaning into those possibilities – experimenting, expanding use cases, and looking for places where AI can make their teams more effective. At the same time, they are still learning what broader adoption will mean for the finance function. 

As the work changes, another question arises: what does it mean for the people doing it? 

In Part 2, we explore how CFOs are thinking about AI fluency, the development of future finance leaders, and the capabilities that may become more important as the mechanics of finance evolve.