Artificial intelligence continues to capture our attention for its mind-boggling pace of advancement. Just take a look at what’s happened since the launch of ChatGPT, which lit a fire across the internet, commanded countless headlines and even got the attention of Capitol Hill as people from all walks of life opine and prognosticate about what AI’s current and future state holds for society as a whole.
Whatever the public’s opinions are on AI, there’s no doubting the technology’s benefits from a business standpoint. Indeed, AI holds a lot of promise to accelerate processes, improve efficiency and control costs — especially for those highly automated and data-rich applications where the sky is truly the limit for how fast and streamlined they can become.
One of these applications is the order-to-cash cycle. This is, of course, an area that’s generated its own share of headlines with the threat of a recession highlighting its value and the need for faster cash flow. Demand for AI-powered processes, as a result, is rising as organizations and specifically AR teams look to safeguard their financial health. We saw this during COVID-19, too, when accounts receivable teams turned to AI to inject a much needed dose of predictability into their order-to-cash processes as they battled historically high rates of days sales outstanding.
With this in mind, let’s take a look at the two most crucial components of the order-to-cash process and how they can benefit from the power of AI.
Accelerating cash application with machine learning
According to recent research, AR teams typically dedicate almost a quarter (22%) of their time to manual cash application, making it the most time-consuming activity within the order-to-cash cycle.
When you consider all the elements that make up the cash application process, this isn’t all that surprising. Indeed, a company’s cash application function is one of its most influential assets. Although it seems straightforward on paper — it’s the process of matching a payment from a customer to its corresponding invoice — it becomes much more complex when you consider the amount of payments an AR department has to process.
An enterprise might send out anywhere between 5,000 to 2 million invoices per month, for example. Now imagine having to manually match these invoices to their remittance. Needless to say, it would be incredibly time-consuming and costly.
Machine learning, however, can play an integral role in helping AR teams accelerate their cash application efforts. This type of automation works like a digital lockbox, enabling teams to take remittance data from different sources across check, ACH, direct debit, wire and credit cards, and standardize it for fast and easy cash application. Even if a payer sends a decoupled remittance, suppliers can leverage AI to automatically match it to its appropriate payment, freeing up cash application staff to take on those tasks that need more of a human touch.
For too long, manual cash application has threatened to minimize AR’s value. With AI, though, they’re able to apply cash much faster and ultimately build their organization’s resilience against any economic challenges that come their way.
Supercharging the role of the collector
Establishing a streamlined collections program is one of the most important things an organization can do, regardless of whether they’re operating in a flourishing economy or anticipating a downturn. But with the past few years teaching us the dangers of late payments, it’s easy to see why the role of the collector is intensifying.
Just look at the collapse of Silicon Valley Bank, an event no one saw coming and which left countless organizations without access to cash to pay their bills. While a recession can, for the most part, be predicted, there’s always a chance that something else will pop out of the blue to prevent your customers from meeting payment terms.
Indeed, businesses are operating in a landscape that’s rife with uncertainty. At the same time, they’re also working in an environment where predictable cash flow has never been more important. The good news for collections and AR teams is that AI has the power to help them overcome these challenges.
For example, with AI, collections teams can access historical data to predict when an invoice will be paid by a customer, and even how much of that invoice the customer will pay. This is incredibly important, as it not only enables collections teams to forecast cash flow, but it empowers C-suites to make faster, better-informed decisions and improve cash management — something that is, of course, crucial as they prepare to launch new business models to help them get through this rocky economic terrain.
Moreover, in what is a huge draw for collectors working within smaller businesses or teams, AI can empower them to achieve much more with far fewer resources as it reduces repetitive tasks and makes time for more complex jobs. This enables them to manage only the most important issues, such as dealing with delicate clients or finding solutions for customers with complex financial problems. This, in turn, also helps collectors scale client communications in a way that directly contributes to the quality of their organization’s overall customer experience.
Arming AR with the tools to succeed
Perhaps what’s most interesting about AI’s value in the order-to-cash cycle is how it’s amplifying the value of AR professionals. There has, of course, been a lot of talk about AI and its potential to take human jobs. One thing that’s clear is AI will never replace the invaluable work of AR teams. It does, however, have the potential to make them more effective and efficient by boosting their ability to maintain their organization’s cash flow at a time when external challenges post enormous threats.
So, just as it’s become the defining market trend of 2023, we should celebrate its exciting prospects for order-to-cash. After all, any tool that can contribute to faster, better-informed decision making and improved cash management should be a top priority for AR teams.
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