The Generative CFO: Leading finance into an AI-driven future

Snippets from the HFS Roundtable in NYC in partnership with TCS

In today’s evolving business environment, the CFO’s role is no longer confined to managing and reporting numbers. The “Generative CFO” is emerging as a leader tasked with reshaping finance by harnessing the power of artificial intelligence (AI). However, this transformation isn’t easy—CFOs face structural, technological, and talent-related challenges. AI offers an enormous opportunity to unlock growth and innovation, but the journey to capitalize on it remains fraught with obstacles.

At a recent HFS roundtable discussion in New York in partnership with TCS, CFOs candidly shared their experiences adopting AI. The conversation revealed excitement over AI’s potential and frustration with the roadblocks standing in the way of a genuinely AI-enabled finance function.

Navigating the structural challenges in finance

The modern CFO sits at the intersection of strategic growth and operational efficiency, but outdated systems and slow processes are a major hindrance. GenAI is certainly on the agenda of the board and CFO, but almost two years into its availability, most participants feel they are still just beginning their AI journey. Roundtable participants rated their GenAI maturity at a modest 3.8 out of 10, citing struggles with legacy systems and lack of IT support. While AI promises to accelerate everything from financial forecasting to fraud detection, many finance teams remain tethered to clunky infrastructure and disjointed processes.

Exhibit 1: Biggest challenge holding back CFOs from making progress on GenAI—according to roundtable participants

Source: HFS Research, 2024

One CFO said, “We’ve been talking about automating finance for years, but the systems we’re working with are often 15-20 years old. How can we talk about AI when we’re still operating on SAP from the early 2000s?”

The consensus was clear: progress is stunted not by a lack of desire but by an inability to modernize systems fast enough. Legacy infrastructure, while functional, is a deadweight, preventing CFOs from fully exploring AI’s potential. CFOs emphasized that addressing process inefficiencies and technology debt is essential to unlocking AI’s true value.

Progressing on AI’s practical applications in finance

Despite these structural hurdles, CFOs remain optimistic about the promise of both GenAI and broader AI applications. Real-world applications in finance are starting to show potential. From streamlining forecasting to automating routine functions such as accounts payable and receivable, AI is beginning to prove its worth. The three most significant areas of GenAI impact roundtable participants anticipate include forecasting and business planning, financial reporting and compliance, and fraud detection and risk management (see Exhibit 2).

Exhibit 2: Three most significant areas of GenAI impact on finance according to the roundtable participants

Source: HFS Research, 2024

One CFO shared an example of how AI enhances analytics: “We’ve started using AI for revenue forecasting, and it’s given us insights we never had before. It’s helping us identify trends and anomalies far quicker than any manual process could.” Another CFO noted that AI is playing a pivotal role in fraud detection, adding, “The real advantage is not just in catching fraudulent transactions but predicting where fraud is likely to occur next.”

However, there was broad agreement that the most significant challenge lies in moving beyond pilots and proof-of-concept projects. Another participant observed, “We’re seeing a lot of interesting experiments, but what we need is for AI to be embedded into our everyday operations, not just something we trial and forget about.”

CFOs must shift their focus from experimentation to integration. AI and GenAI will have their greatest impact when they’re seamless parts of finance operations, driving continuous improvements rather than isolated breakthroughs. As one CFO put it, we need to move beyond “random acts of brilliance.”

Talent and skills: Building the workforce for AI-enabled finance

Perhaps the most critical factor in making AI successful in finance is not just the technology itself but the people making decisions based on it. The discussion revealed that talent, more than any other factor, will make or break the adoption of AI and GenAI.

CFOs agreed that finance teams must evolve rapidly to keep pace with AI. “The finance team of the future won’t just be great at numbers—they’ll need to be data scientists, storytellers, and business partners,” said one CFO. Another echoed the sentiment: “The biggest challenge isn’t AI; it’s getting my team to think differently. They need to stop focusing solely on reporting the numbers and start analyzing what those numbers mean for the business.”

Upskilling existing staff was a key concern for CFOs. While many teams have traditional accounting expertise, few have the data skills to leverage AI effectively. The group discussed the need to invest in continuous learning and cross-functional collaboration. AI will require finance professionals to work closely with data scientists, IT teams, and other departments in ways that may be new to them. One of the examples shared by a high-tech CFO was how he got his finance teams to learn SQL to understand data better and collaborate more effectively with technical teams.

Beyond upskilling, CFOs emphasized the need for a cultural shift within finance teams. The traditional, risk-averse mindset must be replaced with one that embraces experimentation and agility. CFOs who can foster this culture will unlock the full potential of AI in their organizations. One example shared by a CFO was how their organization is now rethinking job descriptions to emphasize the cultural shift toward agility, sharing the expectation, “Now I need you to be creative, but still have accounting integrity.”

Save to invest to grow: Self-funding model for driving change

GenAI promises to shift the operations paradigm remarkably by automating an entire operations value chain. It will provide real operational insights, allowing CFOs to spend more effort on driving business growth. Participants do fear, though, that the early state of GenAI application within finance, if miscued, has the potential to create a spending juggernaut without realizing benefits.

As a participant shared, the leverage of partners to “save” from existing operations and “invest” in modernizing the finance and operations estate, allowing CFOs to drive enterprise “growth,” provides a viable win-win model.

The Bottom Line: CFOs who succeed with GenAI will invest equally in upgrading legacy systems and cultivating a finance team that blends deep analytical skills with a bold, innovation-driven mindset.

Finance leaders can no longer afford to wait for perfect conditions to begin adopting AI. The key to making AI and GenAI a reality in finance is tackling the underlying issues—updating legacy systems, reducing tech debt, and creating a workforce ready to embrace something new. CFOs must drive this transformation not solely by investing in the latest tools but by fostering a culture of innovation and collaboration within their teams. Only then will AI move from being a buzzword to a powerful engine of growth for finance.