The finance function is evolving fast, and the skills required to thrive in it are evolving even faster. According to recent research, a staggering 100 percent of U.S. financial leaders plan to leverage or pilot AI within the next three years, a steep rise from 71 percent in 2024. Adoption of generative AI is expected to more than double in that time, from 46 percent today to 97 percent. In other words, this shift isn’t coming. It’s already here.

AI is no longer confined to IT departments or data science teams. With GenAI now embedded in everyday tools like Excel, PowerPoint, and enterprise planning platforms, finance professionals are being pulled into a new era of work where adaptability, data fluency, and cross-functional collaboration matter as much as traditional accounting expertise.

Yet despite the momentum, real barriers remain. According to Oracle, the top hurdles CFOs see to the adoption of GenAI are technical skills (65%) and fluency (53%). In other words, the challenge isn’t access to AI, it’s readiness.

For finance leaders, the message is clear: in the coming years, your team’s ability to confidently and competently use AI will matter far more than simply having the tools. Here’s a closer look at six essential AI skill sets every finance professional will need to develop to stay relevant and lead in this new landscape.

1. Prompt Engineering in Finance: The Language of Productivity

Prompting isn’t just how finance professionals interact with GenAI, it’s how they unlock its full value. Whether summarizing a quarterly forecast, generating board visuals, or drafting commentary, the quality of the prompt directly impacts the relevance and accuracy of the output. Prompt engineering requires a structured approach: knowing the right formats, context, and tone to guide GenAI toward meaningful results.

Gartner identifies prompt engineering as the single most important GenAI skill for finance professionals today. Efficient prompting is a core productivity tool, and it sits at the top of the list of in-demand genai skills for finance professionals.

2. GenAI Storytelling for Finance: Turning Data into Direction

Numbers alone rarely drive action, but stories do. That’s why AI storytelling for finance is becoming a must-have capability.

Finance professionals must learn to guide GenAI in transforming raw data into tailored, audience-ready narratives. This means shaping outputs that combine clarity, context, and the right visuals for things like dashboards, investor decks, or executive updates.

According to Gartner, 76% of CFOs ranked improving storytelling and insights as a top priority. GenAI provides a unique opportunity to meet that goal, helping even junior team members create polished, data-driven stories that resonate with stakeholders.

3. Experimentation: The Hidden Power Skill

Traditionally, finance has favored precision. But in the AI era, experimentation is a strategic advantage. AI experimentation in finance – trying out new use cases, iterating on prompts, testing models – is how teams identify high-impact, organization-specific applications. It also builds confidence and fluency, especially in environments where teams are encouraged to explore safely.

Finance leaders of the future must be able to reframe experimentation not as risk, but as a capability. Gartner highlights experimentation as a defining capability for finance functions seeking to lead rather than follow in GenAI adoption.

4. Responsible Testing: Guardrails for GenAI

GenAI isn’t perfect, and finance professionals need to be well aware of the stakes. That’s why rigorous testing of prompts, outputs, and assumptions is non-negotiable.

Testing involves pressure-checking outputs, benchmarking results across inputs, and building standard operating procedures (SOPs) for accuracy and consistency. According to Gartner, organizations that apply structured testing protocols are far more likely to deploy GenAI successfully and avoid “hallucinated” insights or misinformed decisions.

Structured testing is one of the AI skills finance leaders need to take seriously if they want trustworthy outcomes from AI tools.

5. Data Model Preparation and Customization

As finance teams mature in their use of AI, off-the-shelf solutions won’t be enough. Teams will need to engage in data model preparation for finance AI, curating and structuring internal data for meaningful use.

Techniques like retrieval augmented generation (RAG) in finance let models reference proprietary knowledge, while fine-tuning GenAI for finance ensures outputs are aligned with the team's domain language and goals.

While these tasks will often fall to more technical staff or embedded data experts, Gartner recommends that finance leaders develop basic fluency in these methods, so they can better evaluate vendor offerings, collaborate with IT, and set appropriate guardrails.

6. Empowering the Finance Citizen Developer

Thanks to low-code platforms and AI assistants, finance professionals are increasingly stepping into the role of citizen developers in finance; building automations, dashboards, and workflows without writing a single line of code.

Gartner projects that the percentage of citizen developers contributing to digital initiatives will rise from just 10% in 2025 to 70% by 2029. For finance, this shift opens up huge potential: streamlining reporting cycles, accelerating analysis, and reducing reliance on tech teams for everyday improvements.

The rise of the finance citizen developer also marks a new chapter in leadership skills finance professionals need: the ability to guide and support innovation from the ground up.

Final Word: Invest in Skills, Not Just Software

It’s tempting to think of AI as a technology investment. But the real return comes from enabling people.

The most valuable AI skills for finance teams won’t come from one-off training. They’ll emerge from real-world use, collaboration, and iteration. CFOs who build these capabilities, from prompt engineering to responsible testing, will future-proof their teams and unlock a new level of strategic impact.

Because in the end, the defining finance skills of the future won’t be learned in a classroom. They’ll be built at work, in the rhythm of daily decision-making.

It’s no longer about choosing whether to adopt AI or fear being replaced by it. The real differentiator will be whether your team has the insight and initiative to lead with AI – shaping smarter strategies, stronger decisions, and a more agile finance function.