Artificial intelligence seems poised to change everything, although naturally a great deal of attention tends to be paid to the cool things it makes possible. AI can also make the humdrum less tedious and even transform the dullest of back-office operations into something more meaningful. For example, AI can take accounts receivable automation to the next level.
Infusing AI into managing the process of collecting money from customers can significantly increase the efficiency of what is often a highly manual process. More importantly, it can also
In its most basic form, A/R automation has become increasingly important as business-to-business selling has grown more complex. Buyers want a simpler and more streamlined experience like they expect as individual consumers, but it takes technology to make the complexities of today’s B2B selling appear simple. Processing transactions is more difficult than ever because the structure of these transactions can be complex. One-time sales are straightforward, but B2B purchases may be covered by a negotiated annual contract that sets pricing, discounts, terms and conditions. Customers can have complex invoicing requirements, such as wanting to be billed at individual business units or locations, while others require a single bill for a division or the company as a whole. Some outsource the process to a third party.
There are a growing number of accounts payable portals that buyers use to streamline processes on their side of the transaction. For sellers to interact with those portals, especially in managing a digital relationship, requires automation. Companies that still rely on printing and mailing bills face escalating costs of using paper-based systems. These variables can complicate a transaction and lead to errors and disagreements with the customer that can be avoided when order-to-cash is managed digitally.
End-to-end automation increases efficiency by making it easier for individuals to keep track of what they must do because, almost always, the software gives managers and executives a dashboard that alerts them when tasks are overdue, when they need to intervene in some process and how well the system is working or not. Data integrity is maintained from beginning to end because all data movements are managed programmatically, significantly reducing errors caused by data entry mistakes when amounts are rekeyed going from one system to another. This also increases efficiency because it cuts the time spent finding and correcting errors and eliminates many of the checks and reconciliations that staff accountants might be doing to ensure that bills are accurate.
AI-enabled end-to-end A/R automation amplifies the benefits of technology investment. The core business case for A/R automation is that it reduces costs by making people more efficient. AI in all its forms can increase the efficiency of end-to-end A/R processing, beginning at the point of document ingestion. There are an increasing number of software providers that offer systems with optical character recognition processing, which is a no-hands approach to entering document data into an accounting system. These documents might be in paper form or a digital file like a PDF or an email. Depending on the quality of the AI model and its training, the data captured by the system can require no human intervention, meaning that all necessary data is reliably posted.
Pure automation increases control because, these days, OCR and other forms of digital ingestion substantially reduce errors compared to manual data entry. It also makes it feasible to capture more information from documents and emails than would be practical otherwise. AI is probabilistic, so it can be sensitive to ambiguity in the document, and automation can have guardrails when a transaction meets a certain condition (such as those above a certain value or specific customers). To deal with those circumstances, organizations can craft a preferred method for dealing with them, including approvals and signoffs if necessary.
With AI, A/R automation can improve the customer experience. For example, by analyzing customers’ payments using straightforward statistical analysis, enterprises can identify those who pay promptly after receiving an invoice. If, based on statistical parameters, payment has not been received past a certain date, A/R can contact the customer to see if something is wrong that needs straightening out. In contrast, if payment has been withheld because of a problem, getting a dunning notice a few weeks later will only enrage a good customer. Getting ahead of an issue and being able to resolve it quickly addresses the problem and probably accelerates payment.
AI can backstop a customer relationship management system by serving as a “defector detector.” Payment systems can flag customers whose purchases have declined compared to their normal pattern, suggesting they’re taking their business elsewhere. By taking frictions out of making payments, an enterprise enhances its reputation of being easy to do business with. And by swiftly and accurately identifying perennial late payers, the system can help the credit allocating process to reduce risk.
Accounts receivable automation is rarely strategic, but it’s one of the tasks where AI can increase the business value of a cost center. I recommend that finance executives who do not already manage their accounts receivable processes end-to-end do so. I also strongly recommend that they quickly adopt AI-enabled capabilities as they become available to support a more customer-centric approach to the receivables process.
Regards,
Robert Kugel