The role of a CFO has expanded significantly beyond that of financial gatekeeper. Finance chiefs are now required to operate across finance, technology and strategy—particularly when it comes to AI.
Scott McDermott, CFO of Middleton, Wisconsin-based finance software provider Esker, gives his take on how finance leaders can best navigate these increasing organizational demands, how he uses AI himself and how to strike a balance between “too much” AI and “not enough.”
How has the CFO role become much more complex?
Historically, the role of the CFO was a bit narrower, and they were focused on control, compliance and GAAP reporting. Those are still core to the foundation of the CFO, but their responsibilities have grown in line with increased technology and AI investments. Now, instead of just being known as “financial gatekeeper,” they’re expected to lead organizational transformation and guide strategy.
They’re also becoming an even more strategic partner within the C-Suite, as CEOs look to finance teams for broader organizational decisions, like guiding capital allocation and evaluating technology investments like AI. That means every CFO needs to define and stand behind AI strategies, including how it creates value, how it influences investment decisions and how it ultimately ties to valuation. That’s a significant shift, especially since most CFOs did not start their careers thinking about how factors like AI and geopolitics tie into valuation.
At the same time, the operating environment has become more demanding. We’ve come out of a period where capital was relatively abundant, and growth was the priority. Now, there’s a lot more scrutiny around ROI and how resources are being distributed. Expectations have increased significantly, but many organizations are still operating with fragmented systems and processes that make it harder to move quickly and make decisions with confidence.
What we’re seeing is that the CFO role is under pressure because expectations have outpaced the tools available to support them. That’s part of what’s fueling burnout and turnover.
How do you think about the volume of decisions CFOs are expected to make today and what impact does that have on how they prioritize and approach decisions?
Not only has the volume increased, but the nature of decision-making has changed. A lot of what CFOs are being asked to weigh in on is AI investments and pricing models, which do not come with clear frameworks or immediate financial impact. You’re often trying to connect inputs to outcomes that are difficult to measure.
While it’s true that the volume of decisions is increasing, a big part of the challenge lies in making decisions in areas where the data is less structured and often buried in temporary distractions that don’t change the long-term health of the business.
This is where the role of the CFO as the “information arm” of the business becomes critical, and finance leaders have to be able to use their data, filtering it and structuring it in a way that helps drive clear decisions.
From there, it really comes down to capital allocation. Where are we deploying resources? What are we prioritizing, and why? If you’re disciplined in that thinking, it helps cut through a lot of noise that can cloud decision-making, like temporary revenue fluctuations or the hype around trends in the industry.
At the same time, you have to stay flexible. The environment is changing quickly, and you need to be open to adjusting your decisions as new information comes in.
A big topic right now within finance teams is AI upskilling. How are you personally upskilling?
I use AI every day, testing out different prompts and monitoring what outputs I get. That has been the biggest driver of learning for me. Even six months ago, the quality of output was not great, but it has improved quickly, and you can do things now that genuinely change how finance teams operate.
Historically, the most successful finance teams were those with strong technical skills like modeling and reporting. With AI automating several of those more technical tasks, the expectation is that teams spend more time on insight, judgment and decision-making. Those are the skills that are going to bring the most value to an organization while helping finance professionals stay competitive in their own careers.
There is also a leadership component. CFOs need to be visible adopters and champions of AI by using it themselves. I use it, but I also focus on building a structured approach within Esker by identifying a power team of “wizards” who come on the AI journey with me and infuse it throughout the organization.
These wizards help with the transformational change by assisting in training sessions and workshops. They present the AI learnings they’ve adopted over the past few weeks and share them with others to improve comfort level with the tools. My goal is to build intentional time and systems that help my team and I adapt to how AI is changing our roles, or we risk falling behind fairly quickly.
What roadblocks are you most concerned about when it comes to upskilling and how can finance leaders be prepared?
My biggest concern is that as AI becomes more of a core tool in our work, and as new grads enter the workforce, finance teams may over-rely on AI outputs without enough underlying judgment to understand what’s good and what’s bad.
AI makes it very easy to get to an answer quickly, but that does not mean it’s the right answer. And in finance, the nuance matters. You are dealing with trade-offs, incomplete data and organizational dynamics that AI does not fully understand. If people rely too heavily on AI without building that critical thinking capability, it can create real risk.
At the same time, if we don’t adopt AI, we’ll fall behind. That means finance leaders have to strike a balance between too much AI and not enough, and that opens up an opportunity to change the way we integrate and encourage AI upskilling and use.
Finance teams are notoriously cautious, so when it comes to adopting new technologies, we tend to be slower than other departments. However, since a portion of finance work today is spent handling menial tasks like emails and reconciliations, AI can take over those that require less human judgment and input to allow employees to focus on higher-value work.
You can tell when someone is using AI, which is good—we want people to be using it. But, you also know when they have not really challenged the output or understood the logic behind it. You want to encourage adoption, but you also need to balance upskilling investments so teams simultaneously develop the judgment and business understanding that are even more valuable in the AI age.
At the end of the day, AI can help you get to an answer faster, but it is still up to the finance team to make sure it’s the right decision. We as finance leaders should be intentional in how we train our teams, making sure that we never lose sight of the value of human judgment.
