Faster cash application. Fewer typos. Less manual work. For AR teams mired in manual invoice processing and payment matching, automation has been a lifesaver. It’s no wonder the market for these solutions has ballooned past $3 billion annually.
And in many ways, it’s delivered. Robotic process automation (RPA) gave teams hours back in their workweeks by taking on all sorts of monotonous tasks.
But in the rush to automate, the most important stakeholder got left behind: the customer.
Traditional AR automation works great when everything goes according to plan. But the second an exception pops up—a misapplied payment, a customer with a valid dispute—it breaks down. It doesn’t recognize the difference between a high-value client who’s late for the first time and a habitual delinquent. It can’t tell when a missed payment is a one-off oversight or a sign of financial trouble. This is when automation stops being an advantage and starts becoming a liability.
The question isn’t whether automation belongs in AR. It absolutely does. The real question is how to implement it in a way that improves efficiency without giving customers the cold shoulder.
When machines misread the room
Traditional automation like RPA has transformed the way AR teams work. It’s taken much of the grunt work off their plates, turning slow, repetitive processes into streamlined workflows.
The problem is, it’s not adaptable. It simply follows a script: if invoice X is due, send reminder Y. If payment isn’t received, escalate to step Z. In short, it’s great at reading (and following) rules. But it’s terrible at reading the room.
Impersonal customer interactions
Let’s say you have two customers who are late on payments this month. One of them has a spotless track record of on-time payments. Turns out, their AR contact has been out sick for the past week, and the invoice managed to slip through the cracks.
The other customer has exhibited more concerning behavior. This is the third month in a row they’ve been late, and you’ve heard whispers that their financial health has been steadily worsening.
To a rigid AR automation system, these two scenarios might as well be identical. Neither payment was received, so both customers get the same generic past-due notice: a templated email with a stern reminder and an escalation warning.
To the first customer, this response feels tone-deaf. They had every intention of paying and just needed a little time to get their process back on track. Instead of a friendly nudge from their account manager, they get a robotic message treating them like a repeat offender. Now, they’re questioning whether they’re a valued partner.
As for the second customer, they’ve seen this email before. A dozen times, in fact. They know exactly how to game the system—they’ll ignore the message, stretch their payment window as long as possible, and wait for the inevitable collections threat before making a move. Meanwhile, your team has no visibility into whether they’re just stalling or if their business is truly on the brink.
This is the problem with traditional AR automation—it operates in absolutes. Either the payment is on time, or it’s late. There’s no ability to read between the lines.
Overlooking nuanced customer issues
This black-and-white approach doesn’t just struggle with different types of late payments. It fails anytime a situation doesn’t fit neatly into its programmed logic.
Take overpayments, for example. A customer accidentally submits twice the invoiced amount. If an AR specialist spotted it, they’d reach out, issue a refund, or at least confirm whether the customer intended to carry a credit. But a basic automation system would simply log the payment, apply the extra funds as a credit, and move on. It wouldn’t stop to consider why this happened or whether the customer intended for that credit to sit there.
The customer, meanwhile, assumes there’s been a mistake. They check their bank statement, see the unexpected charge, and call support, only to be met with confusion because, technically, the system didn’t do anything “wrong.” What should have been a quick fix turns into a back-and-forth that wastes everyone’s time.
Overpayments aren’t the only blind spot, either. Sometimes, a customer might not even realize they didn’t pay. Maybe an invoice was lost in a spam folder. Perhaps an internal AR contact left the company, and the bill never made it to the right person. None of these are signs of a delinquent customer. But automation doesn’t know that, so it does what it was programmed to do: send a reminder, escalate the issue, and if nothing changes, shut off access.
By the time the customer realizes what happened, the damage is done. They log in to their account only to find their service suspended over a minor payment hiccup. Now, they’re stuck in support purgatory, trying to get reinstated.
The problem isn’t automation itself—it’s automation that lacks context. That’s where AI agents make all the difference.
How AI agents navigate AR ambiguity
With the help of AI agents, AR teams can enjoy the productivity enhancements from automation while actually improving their customer service. Here’s how:
Context-aware customer engagement
Traditional AR automation operates on if-then logic: if a payment is overdue, send a reminder. If it’s still unpaid, escalate. There’s no room for exceptions, context, or common sense. AI agents fill in these gaps by first taking a look at a customer’s history before taking action.
Say a customer reaches out about an invoice issue—they were supposed to receive a discount, but it wasn’t applied. A basic system sees a balance due and kicks off the standard escalation process. Before long, they’re not just frustrated about an incorrect bill—they’re getting collections threats for a mistake they didn’t even make.
An AI agent would take a more tactful approach. It recognizes that the contract includes a discount, auto-corrects the invoice before the customer even has to ask, and prevents the issue from ever escalating in the first place.
Fewer friction points
Along with accelerating workflows, traditional automation promised to remove friction throughout the payment process. But when the same script is applied to every account, that friction doesn’t disappear—it just gets transferred from your team to your customers.
AI agents eliminate this tradeoff by bringing a new level of adaptability to automation. Rather than treating every overdue invoice the same way, agents look for certain details before deciding how to proceed:
- A high-value customer who typically pays on time? A friendly, personalized reminder will probably do the trick.
- A repeat late-payer with growing delays? AI flags them as a potential risk before things spiral.
- A processing delay or disputed charge? AI can recognize the issue and adjust its approach before sending a payment notice.
This is the difference between rigid and intelligent automation. As CX Today puts it, “With advancements in AI, automation will handle most customer interactions, efficiently resolving issues, offering proactive support, and even driving upsell opportunities.”
You don’t need to choose between your customers and employees. Try a free Stuut demo and see how AI-powered AR can make everyone’s lives easier.