Beyond Buzzwords: Turning AI Into ROI for CFOs

Jul 17, 2025

Artificial Intelligence is everywhere. It’s flooding headlines, powering product features, reshaping workflows. For CFOs, it’s easy to feel both overwhelmed and underwhelmed by it at the same time.

Overwhelmed because the possibilities seem endless but undefined. AI tools promise to transform finance, forecasting, and productivity, but…how? Underwhelmed because many practical uses can feel gimmicky or limited to surface-level tasks, like writing emails or summarizing meetings. And for a role built around precision, risk management, and strategic insight, hype isn’t enough.

Many CFOs are unsure how AI applies to their role. AI can seem too broad, too technical, or too far removed from the financial decisions they’re making today. Others may see it as a passing trend, filled with more buzz than impact.

AI is a tool, not a trend. And CFOs, more than most executives, are positioned to lead that transition by framing AI not as a shiny object, but as a problem-solving resource.

The CFO’s Lens: Start With the Problem, Not the Tool

CFOs are responsible for aligning resources, reducing friction, and increasing visibility across the organization. Their work revolves around questions like: Where are we wasting time? Where are we leaking profit? What are we not seeing?

These questions are the perfect entry points for applying AI. But many organizations begin with the inverse. They look for ways to “implement AI” without a clear understanding of what they want it to do.

The better approach is to identify a friction point or inefficiency, then explore how AI might support or accelerate improvement. That mindset shift – from tech-first to problem-first – is essential to calibrate your entire approach to AI.

Realistic AI Use Cases for CFOs

AI doesn’t need to be transformational to be valuable. In fact, the most effective implementations tend to be modest and tactical at the start. A few ways CFOs are already putting AI to use in ways that deliver clear, measurable benefits include:

• Scenario planning: Using AI to simulate different market conditions, stress-test revenue assumptions, or identify financial blind spots that may otherwise go unnoticed.
• Process automation: Streamlining repetitive tasks like invoice matching, reconciliation, or forecasting prep, especially in environments where manual effort slows down the team.
• Pattern recognition: Identifying anomalies or trends in large datasets, helping teams find what matters without hours of spreadsheet diving.
• Prompt-based analysis: Acting as a sounding board to pressure-test assumptions. For example: “Here’s our revenue model. What might we be overlooking?”

Overcoming the Two Most Common Barriers to AI

In most organizations, resistance to AI comes from one of two directions:

1. Overwhelm: The belief that AI requires a massive transformation, new systems, or deep technical knowledge.
2. Skepticism: A fear that AI is more sizzle than substance, with unclear ROI or relevance to the business.

Both of these are fair but solvable.

To address overwhelm, just start small. Choose a specific area of inefficiency and test how AI might help. Don’t make the objective to implement “AI across the business.” Instead, explore how AI can improve one task, one process, or one decision. Then build from there.

To address skepticism, shift the focus to results. Think of AI as a tool, just like like a forecasting model that helps project cash flow, a CRM that tracks pipeline movement, or an analytics dashboard that surfaces performance trends. Each one supports better decisions by making complex information easier to understand or act on. AI belongs in that same category. It doesn’t matter whether it’s called artificial intelligence, machine learning, or robotic process automation. If it saves three hours per week or eliminates a bottleneck, it’s worth evaluating.

Build a Culture of Curiosity, Not Compliance

For AI to become naturally integrated, it can’t be seen as someone else’s initiative. It has to be worked into how the finance team thinks, experiments, and learns. That doesn’t necessarily require formal training or a dedicated expert, but rather a willingness to ask new questions.

Some CFOs have embedded this curiosity into leadership approach. At ProCFO Partners, for instance, team meetings include a simple recurring question: What have you tried in AI this week? It’s not a performance metric. It’s a prompt to think differently and share discoveries.

Others are assigning exploration to one team member, asking them to evaluate AI tools that might support specific roles. Some are beginning with standardized questions and scenarios that can be tested in tools like ChatGPT or Claude. The structure doesn’t matter as much as the intention. The goal is to normalize experimentation and invite small risks that can lead to big efficiencies.

Focus on Augmentation, Not Replacement

One of the most misunderstood ideas about AI is that it will replace jobs. While some functions may shift, the greater opportunity is in augmentation. AI can help you and your teams work faster, see more clearly, and focus on higher-value tasks.

This is especially true in finance, where accuracy, oversight, and context are the nature of the work. AI can help categorize expenses, but it still needs a human to interpret anomalies. It can suggest strategies, but it takes a leader to choose one. The most powerful use cases are the ones where human judgment is enhanced, not bypassed.

A Starting AI Framework for CFOs

Here’s a simple framework to begin using AI in meaningful ways:

1. Identify a bottleneck: Where is your team spending time on tasks that feel repetitive or manual?
2. Frame the challenge clearly: Translate that task into a question: How could we speed this up without sacrificing accuracy?
3. Explore one tool: Choose a trusted AI platform and test a simple prompt. Don’t look for perfect answers – instead, collaborate with AI to explore or solve a problem.
4. Evaluate with intent: Did the tool help you think differently? Did it reduce time or improve clarity? How? If you can be specific about its value, you’ll have a clearer vision of how to continue taking the AI effort wider or deeper in your organization.
5. Share the learning: Build a habit of sharing small wins. Encourage others to test, apply, and adapt.

This approach to AI reduces risk and helps you build a foundation for more impactful use of AI over time.

AI is a capability to develop. For CFOs, that means leading with questions, looking for leverage, and staying focused on outcomes. Not every tool will deliver value. But the ones that do can reshape how finance teams operate, think, and lead.