What CFOs Are Learning as AI Changes How Finance Talent Develops (Part 2)

As AI takes on more initial work, CFOs are reconsidering what fluency looks like, how future leaders develop, and where finance talent adds the most value. 

In Part 1, we explored how AI is already reshaping everyday finance work, and the discipline required to know which investments deserve to scale. 

As the work changes, the talent questions follow close behind. What does real AI fluency look like in a finance professional? How do people develop judgment when technology handles more of the work they used to learn from? And which capabilities become more valuable as the mechanics of the job change? 

 We connected with Wunderkind CFO Fabrizio Ferronato to get his perspective. 

The Talent Question That Matters: “What Have You Done With AI?” 

As AI becomes part of everyday finance work, familiarity with the technology may not be enough to demonstrate that someone can use it effectively. 

For Ferronato, AI fluency would be an “absolute requirement” for a future finance hire. But his definition goes beyond knowing how to write a strong prompt. He wants people who can create workflows, experiment with the technology, apply it to an actual business problem, and understand when the output needs to be challenged or reviewed. He is not looking for advanced coding expertise so much as genuine curiosity. 

Unsurprisingly, many of the most productive, highest-performing employees are already using AI to create automated tools and alerts. Among job seekers, however, AI experience has yet to become a meaningful differentiator. Many candidates say they use AI for research or writing. Far fewer can point to a business problem they solved, a process they improved, or a measurable result they produced with it. As basic familiarity becomes easier to claim, the useful question shifts from whether someone uses AI to what they have actually done with it – whether they can identify the right problem, improve a workflow, and connect the technology to a business outcome. 

For boards and investors evaluating finance talent, this distinction is imperative. A question like, “Walk me through a process you changed with AI, and tell me where the technology got it wrong,” reveals far more than a list of tools or certifications. It shows how someone thinks and whether they know when an answer should be challenged. 

How Will the Next Generation of Finance Leaders Learn? 

AI’s implications for experienced finance talent are only part of the story. 

CFOs should also be thinking about what happens to professional development when technology changes some of the work through which people have traditionally learned. 

Many early-career finance assignments do more than complete a task. They expose people to the underlying data, processes, exceptions, and mechanics of the business. Over time, those experiences build judgment. If AI supports more of that foundational work, finance leaders may need to be more deliberate about developing that judgement. 

The answer is not preserving manual work for its own sake, but thinking carefully about what early-career professionals actually need to experience. Future finance leaders may spend less time on the mechanics of producing an analysis, but they will still need to understand how the analysis works, what can go wrong, which questions to ask, and how the numbers connect to the reality of the business. 

That could require earlier exposure to operators, more time inside the business, and stretch assignments that force rising leaders to explain why the numbers look the way they do rather than simply confirm that they tie out. 

When a Role Opens Up, Rebuild or Rethink? 

AI is also beginning to affect how CFOs think about the work itself. 

At Wunderkind, Ferronato has already seen the potential for technology to support portions of analytical work that once required substantially more manual effort. When a director of FP&A left the company, the transition gave him an opportunity to reconsider how responsibilities should be distributed rather than assuming the position needed to be recreated exactly as it had existed. AI was one factor in that discussion, with human review remaining part of the process. 

The point is not that technology makes finance talent less necessary; it is that the mix of work is changing. If finance professionals spend less time retrieving data, organizing information, or creating an initial analysis, CFOs have an opportunity to redirect more of their teams’ attention toward interpretation, business partnership, communication, and decision support. 

That, in turn, changes what distinguishes strong talent. As the mechanics take less time, more of the value sits in what someone can do with the analysis once it exists. 

AI Makes Human Judgment in Finance Leadership More Visible 

The same shift becomes even more important at the CFO level. 

Easier access to information puts a premium on knowing which questions to ask. Faster initial analysis puts a premium on interpretation. Changing workflows force decisions about how teams should be structured and how people will continue developing. And when technology creates new investment possibilities across the business, someone still has to determine where the economics make sense. 

None of that is primarily a technology skill. It is judgment, curiosity, communication, commercial understanding, talent development, and financial discipline – the leadership core of the role. 

For boards and sponsors assessing finance leaders, technical expertise remains foundational, but the harder questions increasingly sit elsewhere: Can someone develop a team whose work is being redefined? Can they communicate what the numbers mean to people who did not produce them? Can they make sound decisions where the playbook is still being written, and tell genuine business value apart from enthusiasm for a new tool? 

AI will change how people learn on the job. It will also make it easier to see who can interpret, challenge, communicate, and lead once the first-pass work is done.