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Author: Rob Daly
Though ROI is relatively low for finance organizations, many have cracked the code for higher returns.
A recent Bain & Company survey reveals that less than half (48%) of senior financial executives have seen improvements in speed and cycle time since investing in artificial intelligence (AI) within their treasury organizations, and around a third (34%) have seen headcount efficiencies and cost reductions.
Over the past 12 months, most enterprises have discussed AI use cases in corporate treasuries for accounts receivable, treasury, and accounts payable, and have experimented extensively with AI. “They have approached it more from the concept ‘Here’s an intelligence, let’s see how we can incorporate it into our business,’” said Rami Chahine, Chief Product and Technology Officer, at treasury-automation vendor Serrala.
“This is making the office of CFO more of an AI lab than anything,” he continued. “We are not seeing real adoption of active use cases deployed within our customer base. We see a lot of our enterprise customers bringing technology or spending, in some cases, a lot of money on technology, but haven’t really turned on agentic AI to truly realize their return-on-investment in terms of speed of delivery and the speed of work.”
According to the Bain study authors, that is a common situation within finance departments. Of the survey respondents, roughly 12% of finance organizations have deployed machine learning into financial planning and analysis (FP&A) forecasting in production.
“Yet in many cases, the underlying process hasn’t changed,” wrote the authors. “Finance teams run AI-generated forecasts alongside existing bottom-up planning cycles: two processes running in parallel, neither fully trusted.”
As a result, these finance organizations do not realize the expected benefits of faster cycle times, fewer people-hours, or greater accuracy.
According to the authors, it isn’t a technology problem. “It’s what happens when AI is layered on top of existing ways of working rather than providing the impetus to change them. If this workflow debt isn’t addressed, AI and automation can multiply complexity instead of productivity,” they wrote.
Other issues that act as headwinds to AI investment include concerns about trust, data sovereignty, and the ability of firms to audit AI’s data usage.
AIs are built to learn, but CFOs are concerned that only their instance of the AI is using their proprietary data to answer only their questions, rather than teaching other AI instances to answer someone else’s questions, said Chahine.
“Everyone believes in the capability,” he said. “Everyone understands the power of agentic AI and its ability to take over some of these manual tasks in the process of financial automation and treasury. But the biggest concern that will make a true impact on adoption is whether we can trust it.”
Despite these issues, CFOs remain bullish about AI investment in their organizations.
More than half (56%) of the surveyed CFOs are increasing AI investment by more than 15% this year. That figure rises to 83% when the window is extended to two years, with 42% of respondents expecting to increase AI spending to above 30% over the same period.
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