AI and AR – Top trends now or coming soon

By Doug Hathaway, VP of Engineering, Versapay

Over three-quarters – 77% – of CFOs reported being behind on invoice-to-cash reconciliation, according to a survey of 300 CFOs by Wakefield Research for Versapay. This research also found that the respondents’ companies held an outstanding balance of $4 million in uncollected monthly receivables.

Historically, collaborative portals have been found to save CFOs an average of $1.6 million annually in collections – but perhaps the most important finding in this research relates to the ROI of collaborative payment portals. Wakefield’s research validates that collaborative payment portals are the best way to reduce the challenges associated with disputes and increase the velocity to cash in accounts receivable.

Beyond the AR Disconnect

The gap between buyers and suppliers presents a key challenge in the traditional AR process, which we call the “AR Disconnect.” The AR Disconnect can manifest in many ways, such as an invoice that gets lost or received by the wrong person, collections notices that get buried in your customer’s inbox, a check that’s “in the mail,” unexplained short payments, payments with missing or incomplete remittance information, and invoices with insufficient supporting documentation.

In the recent past, there was a great need in the market to not only have collaborative AR – being able to offer your customers the ability to pay online – but also to offer customers the ability to automate legacy payments. The goal was not necessarily to change customer behavior in making payments but to work smarter in the current channels, such as check payments and complex electronic payments.

With that in mind, considerable effort has been invested in helping solve the common problems in cash application across many industries – issues that include manual steps and multiple touchpoints. Because companies like Versapay have introduced significant innovation in cash application automation broadly, the scope of what remains to be automated has narrowed to more niche use cases. This is great for businesses looking to automate cash application, but we haven’t reached the end of innovation in cash application or AR functions in general – not by a long shot. So, what’s next?

Humanizing AR

Think about what’s most important when leveraging AI applications in AR. A starting point is seeing that AI can transform AR and make it “more human” by removing much of the legwork needed to apply payments. When this happens, it’s more engaging, you can access more data, making it less mundane. Then, consider what constitutes the heart of great CX. Great CX is simple at its core: it involves presenting a human face to your customers.

AI helps an AR team focus on solving complex customer issues that require empathy, imagination, and creativity, while AI handles document-heavy and time-consuming manual tasks like cash application. AI also increases personalization by alerting a company to changing patterns in customer behavior, which helps AR teams proactively resolve any invoice challenges that have emerged or escalated during the invoice-to-cash cycle.

Within this context, traditional AI’s clear benefits in AR include improved efficiency and accuracy, enhanced cash flow, and better CX. Yet, there are challenges to using AI in AR, primarily in data quality, security, and privacy. Historically, the players in this space have built their own platforms.

So, a challenge moving forward is that there are so many different models out there, and companies need to consider security and privacy concerns with AI. A big challenge for the future is: How do you take advantage of some advancements in AI while still providing the same level of security and privacy that you have with internally developed platforms?

Predictions for the industry

What does the future look like for AI and AR? The first thing to do is park the idea that we’re only using AI to automate busywork – while that’s a bread-and-butter application, it’s nothing new, as that’s been on the table for a decade.

I’ve seen firsthand that many companies are entrenched in their old-school enterprise resource planning (ERP) system – whether a company makes a few million in annual revenue or billions. I’ve been blown away at the use of legacy systems at these very large companies, yet they rationalize it because the cost and risk for them to switch ERPs can feel insurmountable. Less progressive companies end up stuck despite how much this limits them.

This creates a widening gap between what’s needed from ERPs and what’s possible with AI/ML. In the future, I expect that we’ll see many more companies leveraging AI that can run on top of these legacy ERPs to give them more life out of their older systems.

Look for AI layers

A key trend to watch for is using AI as a layer between systems, and even between people and your payment data, as a new, more efficient way of getting the answers you want. This avoids the need for wholesale replacement of an old ERP – instead, layering AI with the old systems can still make it ten times faster or better.

There’s a new trend in which AI is just emerging in AR. It involves using AI to connect legacy systems that don’t generally “talk to each other.” This means a middle layer of AI does data transformation between these systems without much legwork, unlike in the past, when connecting two legacy systems required advanced extract, transform, and load (ETL) technology and monitoring. Simply having a layer of AI in the mix to figure things out with minimal oversight extends the life of these legacy systems, including legacy ERP systems or various payment networks.

How will companies interact with their payments data going forward? This often involves interacting with their ERP to run reports and get some basic data. Depending on whether they have visualization and if it’s giving them what they want, there may be various levels of sophistication when it comes to the ERP. Some organizations may have some type of data lake or analytics engine that they pump data into.

But with AI, we’re starting to see a layer of AI on top of that data – you’ll be able to just talk to the AI and ask it questions, prompting it to get back what you’re really looking for. Think about the fact that when you’re looking for information, you usually have a specific question, and you’re turning to charts and graphs looking for answers. AI will allow you to skip that middle layer of charts and graphs – you can just ask it the questions you’re looking for, very specifically, and it will be able to formulate a response. You can think of it as a different way to interact with your data. Rather than clicking around and looking at information, you can interact – and that’s already happening with some products.

Tech trends to watch

Technologies to watch are those that work on top of existing ERP systems because those will be the easiest to adopt and grow the fastest. Companies don’t have to upgrade or change their current ERP – it’s just additive to get you the answers you want. Look for technologies working on top of entrenched software systems that are difficult to replace.

In the payments space, there are legacy rails of payments like checks and ACH that aren’t going away, as well as legacy entrenched ERP systems with a very high replacement cost. That’s why AI technologies on top of legacy rails will grow the fastest, because they won’t require switching out anything – it’s just plug and play.

A proactive approach can help companies be ready to handle the future of AR and payments with the help of AI. Every week, there’s a new AI technology that comes out. Will all of those be around in 10 years? No – but the ones that solve real problems will still be around, which will evolve as this hype continues or intensifies.

Sometimes, the AI buzz is treated like a solution looking for a problem. But forward-thinking companies are looking for problems proactively and then applying AI to that problem space. In the future, they are the ones that will dominate.

About the Author

Doug Hathaway is VP of Engineering at AR software as a service fintech Versapay. He was previously Co-founder and CTO of Dade Systems and was a Senior Software Engineer at Lockheed Martin. Doug is a University of Central Florida graduate with a Master of Science degree in computer sciences.