Key Takeaways
- Artificial intelligence has rapidly transformed from an optional extra to an essential tool within financial operations.
- A significant number of finance professionals are now using AI, with many having adopted it only in the past year.
- Current economic uncertainties, like inflation and volatile markets, are speeding up AI integration in key finance areas.
- AI offers both improved communication tools and powerful analytical capabilities, such as more accurate cash forecasting.
- The future indicates a move towards AI-driven, unified dashboards for comprehensive financial decision-making.
Artificial intelligence is no longer a futuristic concept in finance departments; it’s quickly becoming a fundamental component. Steve Wiley, VP of product management at FIS, told PYMNTS, “Artificial intelligence is a must-have, and that’s happened very, very quickly.”
Just a short time ago, AI was often viewed as an experimental add-on. Now, against a backdrop of increasing global economic uncertainty, AI systems are deeply embedding themselves into the strategic core of finance, especially for treasury, payments, and managing risk.
“Seventy-five percent of knowledge workers, and those are people in the office of the CFO, now use AI at work, and half of those started using it in the last year,” Wiley shared. He added that the expectation now is for AI-based solutions to be built into financial products.
So, why this sudden surge? Historically, the chief financial officer’s (CFO) office has been slower to adopt new technologies compared to departments like marketing. However, ongoing inflationary pressures and unpredictable global markets have spurred finance leaders to embrace digital transformation at a faster pace.
“AI makes technology a real differentiator for a business,” Wiley stated. “And the expectation will be for CFOs to adopt those technologies and work with partners who can facilitate that.”
AI is establishing itself in finance through two primary, and expanding, applications. The first involves qualitative uses, like language-based interfaces that improve how information is found and shared.
The second, and perhaps more game-changing, category is quantitative applications. These include predictive analytics, forecasting cash flow, and identifying fraud, which are fundamentally altering the value finance departments deliver.
Wiley mentioned that people were using tools like ChatGPT for tasks such as drafting policies and learning best practices, often outside their company’s main systems. “There was an immediate opportunity to embed that within the system. Tools like Treasury GPT from FIS are leveraging that AI technology to offer that data access specifically for the treasury industry.”
Consider cash forecasting. Traditional methods typically depend on historical data. Generative AI, in contrast, can process real-time market data, customer behaviors, and economic trends to predict future cash needs with greater accuracy.
“Treasurers are expecting tools to improve cash forecasting,” Wiley noted. “Now, instead of using traditional historical-based models, treasury departments are expecting generative AI to project cash flows. And that’s already the new normal.”
Yet, not every organization is prepared to implement artificial intelligence. There’s a wide gap in technological readiness separating early adopters from those still relying on older systems.
“We still encounter organizations who are living in pre-digital, really operational eras. They have inadequate technology, manual processes, limited data visibility,” Wiley explained. He added that companies with poor cash forecasts often face higher borrowing costs and miss out on precise investment opportunities.
On the other end of the spectrum, some finance departments have already adopted cloud-based systems, advanced analytics, and automation. These organizations are not just ready for AI; they are actively demanding it.
A common question from CFOs evaluating new tech is how to measure its return on investment (ROI). Wiley believes that while AI isn’t an exception to traditional software metrics, it does broaden the scope of potential returns.
“On the receivables side, you have elements like DSO [days sales outstanding], which AI can improve. On the treasury side, it’s about liquidity optimization — improving investment performance, managing FX and interest rate risk,” he said. He also highlighted often overlooked areas AI can positively affect, such as bank fee analysis, payment security, and payment efficiency—all significant for large enterprises.
What’s next for AI in finance? “CFOs are wanting centralized reporting and decision-making, and AI is going to facilitate that,” Wiley predicted. He anticipates a shift from viewing financial areas like liquidity and payments in isolation.
Instead, AI-powered dashboards will automatically show the relationships between these different functions. Wiley envisions this leading to a unified command center driven by AI, transforming not only efficiency but also the very nature of strategic financial decision-making.
