Artificial intelligence (AI) has become the new electricity powering financial operations, and nowhere is this more apparent than in the office of the CFO.
“It’s no longer a nice-to-have,” Steve Wiley, VP of product management at FIS, told PYMNTS. “Artificial intelligence is a must-have, and that’s happened very, very quickly.”
Even as recently as a year or two ago, AI was considered a fringe benefit or experimental tool. Now, amid a backdrop of growing macro uncertainty, AI systems are becoming increasingly embedded in the core strategic infrastructure of finance departments, particularly across key functions such as treasury, payments and risk mitigation.
“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 said. “There’s an expectation now that AI-based solutions will be embedded within these financial products.”
Still, why now? Historically, the office of the chief financial officer (CFO) has lagged in forward-facing functions like marketing or customer service when it comes to technology adoption. But ongoing inflationary pressures and volatile global markets have forced the hand of finance leaders when it comes to embracing digital transformation at pace.
“AI makes technology a real differentiator for a business,” Wiley said. “And the expectation will be for CFOs to adopt those technologies and work with partners who can facilitate that.”
Read also: FIS Introduces ‘Treasury GPT’ in Conjunction With Microsoft AI
There are two main, and growing, categories of AI usage: qualitative applications, such as language-based interfaces that enhance knowledge discovery and communication, and quantitative applications, like predictive analytics, cash forecasting and fraud mitigation.
While qualitative tools can improve user engagement and reduce training overhead, it’s the quantitative applications that are radically shifting finance’s functional value.
“People were using tools like ChatGPT to formulate policies, learn best practices — all outside of the enterprise system,” Wiley said. “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.”
Take cash forecasting. Traditional methods rely on backward-looking statistical models. But generative AI can synthesize real-time market data, customer behaviors and economic signals to forecast future liquidity needs more accurately.
“Treasurers are expecting tools to improve cash forecasting,” Wiley said. “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 ready to use artificial intelligence. A wide technological maturity gap separates early adopters from legacy holdouts.
“We still encounter organizations who are living in pre-digital, really operational eras. They have inadequate technology, manual processes, limited data visibility,” Wiley said. “Those with inadequate cash forecasts are paying more for capital. They’re not able to really invest with precision, and they’re leaving money on the table.”
On the other end of the spectrum are finance departments that have already embraced cloud-based infrastructure, advanced analytics, and automation. These organizations are not only ready for AI but are demanding it.
One of the most persistent questions CFOs ask when evaluating new technologies is: how do we measure ROI? For his part, Wiley believes AI is not an exception to traditional SaaS metrics, but that it does expand the frame.
“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, also calling attention to more overlooked areas that AI can affect, such as bank fee analysis, payment security and payment efficiency — all of which add up quickly in large enterprises processing hundreds of thousands of transactions per month.
What’s next for AI in finance?
“CFOs are wanting centralized reporting and decision-making, and AI is going to facilitate that,” he said. “Historically, CFOs have observed things like liquidity, payments, receivables in isolation, but now you’ll see AI-based dashboards that look at the relationship between these areas — automatically.”
Ultimately, Wiley envisions a unified command center powered by AI that bridges previously siloed functions, adding that this evolution from functional to integrated financial intelligence promises to transform not just efficiency, but strategic decision-making.
