60 CFOs Can’t Be Wrong … AI Can Help Accounts Payable
May 2024
Efficiency can be key for accounts payable, minimizing delays and fueling faster payments, among other wide-ranging benefits. Yet despite continual technology advancements — including those in the AI field — data from 60 CFOs spanning more than 10 sectors reveals that many big companies have not streamlined their AP cycles and rely on too many tools.
• Nearly 60% of large firms use at least five systems for AP.
• Using AI automation to complete the source-to-pay cycle with one tool can fuel faster AP payments and lessen human involvement.
• Seventy-eight percent of enterprise CFOs surveyed say access to AI technology in AP is important or extremely important.
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Despite the inefficiency, most CFOs surveyed from firms with at least $1 billion in revenue report using multiple systems to manage their source-to-pay cycles. Just 13% of the companies surveyed use just one accounts payable (AP) system, the most efficient method. Nearly 60% of large firms (those with between $10 billion and $20 billion in annual revenues) use at least five AP systems. Even the largest companies surveyed, those earning more than $20 billion, employ two to four systems for AP on average.
Why does this matter? Needing more systems opens the door to interoperability and incompatibility problems, as more things can go wrong. Currently, they do, as delays are common in AP processes. The data — which comes from enterprise CFOs directly, representing a wide range of industries — reveals that delays affected at least one area in the source-to-pay cycle in the past year for nearly two-thirds of CFOs. They most commonly cited payment execution and authorization delays in the past year.
Involving fewer systems in AP cycles often leads to fewer issues and payment delays. Resetting this paradigm using artificial intelligence (AI) automation tools could reduce these firms’ need for human involvement and fuel faster payments.
Manual AP handling is still the norm. Just 17% of enterprise firms run their source-to-pay systems largely without human involvement. This share suggests the remaining firms’ AP systems — more than 4 in 5 — could benefit from automated, single-source AP. However, while CFOs know the potential AI offers, those using a larger number of AP systems or software may underestimate the advantages of truly streamlined automation — and, therefore, be slower to implement it.
These are just some of the findings detailed in “Accounts Payable Cycles: The Potential for AI,” a collaboration with Ottimate, the second edition of PYMNTS Intelligence’s “The 2024 C(AI)O Project.” This series offers a monthly recap of the players and innovators using AI to revolutionize how they manage key parts of their businesses. This edition examines the potential for AI in source-to-pay cycles. It draws on results from a survey of 60 CFOs from U.S. firms that generated at least $1 billion in revenue last year, held from March 5 to March 15.
The Full AP Story
The opportunity is ripe for integrating AI with AP cycles: just 17% of firms run their source-to-pay cycle mostly without human involvement.
Most CFOs from firms surveyed — all with at least $1 billion in revenue — report using multiple systems to manage their source-to-pay cycle. Thirteen percent of companies overall use just one system for AP. More than two times as many use five or six systems for AP.
Firm size appears to correlate with the number of systems used for AP. Those earning between $1 billion to $5 billion in revenue are most likely to use just one system. Twenty-one percent of these “small” enterprises do so.
The data reveals that just 17% of enterprise firms run their source-to-pay cycle largely without human involvement. This suggests that the remaining firms’ AP systems — 83% — could benefit from automated, single-source AP.
Payment execution is the most human-centered part of the source-to-pay cycle. Forty-two percent of all surveyed firms use the human touch for most or all of this part of the process. Conversely, the least human interaction stems from invoice capture, receipt and approvals, which nearly half, or 48%, of companies automating these tasks. Companies are most likely to employ automated systems for tasks that are mostly routine. In turn, tasks requiring more human judgment, such as payment execution and supplier onboarding, have more human involvement.
42%
of firms use humans for payment execution — the least automated of all AP cycle steps.
Using fewer source-to-payment systems translates into fewer payment delays.
A more streamlined AP process should mean payers pay invoices at a faster rate. The data backs this up.
Firms using only one system throughout the source-to-pay cycle report 48% fewer days of delay in days payable outstanding (DPO). Overall, firms with just one system for AP had an average of just nine days of delay added to their DPO, while companies with five or six different systems reported an average of 24 days of delay. And remember: More than one-third of enterprises in our sample use five or six systems.
This is yet another support that having one system capable of running the source-to-pay cycle is the most effective way of reducing delays in DPO.
Firms that have more AP systems — and more delays — are less likely to value the potential benefits of AI technology.
Overall, most CFOs surveyed agree that access to AI technology is very or extremely important. That perception is true for 78% of the CFOs. Just 5% report that AI automation in their AP cycle is not at all or only slightly important.
The data suggests that the integration of AI could help streamline AP systems. Yet some CFOs may be slower to implement it depending on their past experiences with automation, which disfavors those with a larger number of systems in their AP processes. Among the CFOs who reported that accessing AI for source-to-pay systems was not at all or slightly important, all have two or more different AP systems.
Conclusion
More than three-quarters of CFOs surveyed (78%) consider access to AI for accounts payable systems important or very important. A key potential benefit is the chance to better fight delays in the AP cycle, which 63% of CFOs experienced. These delays include the areas of supplier onboarding and invoice matching, which can adversely affect product or service delivery. And as more systems are involved in the source-to-pay cycle, more delays are added to DPO.
However, only 13% of enterprise firms are using just one system to operate their source-to-pay cycle, even though these firms face 48% fewer delays added to DPO. While CFOs understand the importance of optimizing their systems, few have been able to do so. These firms have an opportunity to embrace AI technology to streamline their systems, but they may need more time or a solution to help them get there.
Methodology
“Accounts Payable Cycles: The Potential for AI” is based on a survey of U.S. CFOs conducted from March 5 to March 15. The report explores the potential for AI integration with source-to-payment cycles. Our sample included interviews with 60 CFOs from U.S. firms that generated at least $1 billion in revenue last year from more than 19 different industries.
Ottimate (formerly Plate IQ) is the leading AP automation AI. Ottimate is AP automation AI that provides a smarter way for AP managers, approvers, controllers, and CFOs to work through the entire invoice lifecycle. With mature deep learning capabilities, Ottimate gets to know your business and AP process down to the line-item, supporting a custom approval and payment workflow.
Ottimate not only eliminates over 90% of the manual accounting process but also provides insights into invoices and spend, helping finance professionals uncover opportunities for growth. This means more strategic business decisions for CFOs and a better day-to-day for the entire team.
Don’t Just Automate AP. Ottimate It.
PYMNTS Intelligence is a leading global data and analytics platform that uses proprietary data and methods to provide actionable insights on what’s now and what’s next in payments, commerce and the digital economy. Its team of data scientists include leading economists, econometricians, survey experts, financial analysts and marketing scientists with deep experience in the application of data to the issues that define the future of the digital transformation of the global economy. This multi-lingual team has conducted original data collection and analysis in more than three dozen global markets for some of the world’s leading publicly traded and privately held firms.
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