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The biggest AI challenge facing CFOs today may not be the technology. It is the organizational culture, process readiness, and governance required to use it well.
In a recent episode of the FEI Podcast, host Heather Cole, executive advisor and FEI Member, sits down with Jennifer Witts, Partner at CohnReznick's Client Advisory Services Practice, and Kyle Vroegh, Director at CohnReznick's Client Advisory Services Practice. Together, they deliver a candid, practical conversation for financial leaders who are excited about AI but not quite sure where to begin.
What Is the Biggest AI Mistake CFOs Are Making?
According to Witts, the most damaging thing a financial leader can do is jump into AI without a clear plan. Layering AI on top of a broken process does not fix the process – it accelerates the dysfunction. The same logic applies to data: if the inputs are flawed, the outputs will be too, regardless of how sophisticated the technology is.
Before selecting tools or launching pilots, Witts recommends financial leaders first clarify what they want to achieve, audit the underlying processes, and ensure those processes are solid enough to support automation. Without that foundation, it becomes nearly impossible to diagnose whether a failed implementation was caused by the AI itself, the process, or a people issue.
Where Should CFOs Start with AI?
Both Witts and Vroegh point to the same starting place: the work that is already painful. Talk to your team, they advise. Identify the tasks that are the most repetitive, most time-consuming, and most draining – then use AI and automation to remove that burden and redirect people toward analysis, advisory, and strategic contribution.
Engaging staff in that conversation creates an added benefit: buy-in. When team members see that AI can take something they dislike off their plate, Vroegh notes, they tend to get excited rather than defensive.
How Do Financial Leaders Build a Culture That Embraces AI?
Fear of job displacement is real, and it deserves a direct response. Vroegh reframes the conversation this way: AI is not eliminating roles – it is transforming what people spend their time on. The goal is to shift from manual, routine execution toward higher-order review, pattern recognition, and business storytelling.
Witts advocates for building psychological safety around experimentation. Financial leaders should model their own learning curve, normalize failure as part of the process, and create space for people to bring use cases that do not always work. "We are all still truly learning," she says. "We just have to make that a safe place."
She also surfaces a counterintuitive finding: the most enthusiastic AI adopters in organizations are often not the most technical employees. They are the most creative ones – those who are willing to rethink how work gets done and imaginative enough to see possibilities others miss.
What Should CFOs Look for When Evaluating Outside Firms?
For financial leaders considering outside help with AI adoption, Witts offers three criteria to vet any firm:
- Platform alignment: Confirm the firm has deep expertise in the platforms your organization already uses.
- Dedicated AI expertise: Look for certified practitioners – not someone experimenting on your engagement.
- Security-first orientation: If a firm is not proactively raising questions about data governance and security, that is a red flag.
Vroegh – who has served in CFO roles himself – adds that ROI framing matters too. What is the potential for internal cost savings? Where can capacity be redeployed toward growth? Those are the questions worth pressing a vendor to answer clearly.
What Role Do Experienced CFOs Play in an AI-Forward Organization?
Senior financial executives who feel behind on AI still have a critical function: governance oversight. Blind trust in AI outputs is a real risk, and the judgment that comes from years of experience – knowing how things have historically been done and what can go wrong – is exactly what responsible AI deployment requires. That oversight role is not a consolation prize; it is a strategic necessity.
How Is AI Changing the Finance Function Long-Term?
The transactional work of finance is increasingly automated, and demand is growing for professionals who can interpret data at scale, identify anomalies, and communicate financial insights more compellingly. "We need to get much better at recognizing patterns in data," Witts notes, "and that will take a certain skill that I don't think we've necessarily developed yet."
For the next generation of financial professionals, this creates new questions about training and development in a world where many foundational tasks are automated before they ever have a chance to learn from doing them.
Key Takeaways for CFOs
- Do not layer AI on a broken process – fix the foundation first.
- Start where the pain is greatest: repetitive, time-consuming tasks with clear efficiency potential.
- Involve your team early; your best AI champions may come from unexpected places.
- Build a culture where experimentation and failure are safe and expected.
- Ask hard questions about governance, security, and dedicated expertise when evaluating outside firms.
- Start with one meaningful use case. Do it well, learn from it, and let momentum follow.
🎧 Listen to the full FEI Podcast episode »