Nobody would argue that rule-based automation has improved efficiency in finance. Bain & Company found that companies that invested heavily in the technology “were able to reduce the cost of processes addressed by automation by 17%.” On an individual level, KPMG reports that half of workers have “improved their professional abilities” with the help of automation.
These technologies—primarily robotic process automation (RPA)—have freed human workers from hours of drudgery. But for all the progress that’s been made, there’s a long tail of automatable tasks within accounts receivable (AR) that fall beyond the rigid bounds of rule-based automation.
Take this simple example: a rule that automatically sends a late payment reminder after five days. What if a key client is facing a legitimate, temporary cash flow problem that they’ve already told you about? That automated reminder could strain a valuable relationship.
Modern AI assistants are built to handle intricate situations like these. They can learn, adapt, and make context-aware decisions, as opposed to blindly following “if-this-then-that” rules. They’re more akin to seasoned AR professionals who can understand the subtleties of each situation and respond appropriately.
Here’s how this shift from rigid rules to contextual understanding is changing the game in AR.
The limitations of traditional automation
Rule-based automation has its place in taking care of straightforward, predictable, repetitive tasks. These systems can handle the basics, but they leave a lot on the table. There’s so much more than can (and should) be automated in AR.
Fixed rules can't handle exceptions
This is probably the biggest weakness of traditional automation—it’s based on rigid logic that’s too brittle to handle edge cases. If a specific condition is met (e.g., payment is five days late), a predefined action is triggered (e.g., send a reminder). That’s great when everything goes according to plan. But as AR pros know better than anyone, the real world is rarely that predictable.
What happens when a client has a good reason for a late payment? Maybe a recent merger or acquisition temporarily disrupted their payment processes. Or maybe there was a processing error on their end. A rule-based system doesn’t care about the why; it only sees the what. It’s a blunt instrument that’s ill-suited to situations that require a nuanced approach.
Frequent manual overrides are needed
Because these systems are so inflexible, they require frequent manual interventions. When an exception arises (as they inevitably do), someone on the AR team has to jump in, override the automation, and handle it manually. This completely defeats the purpose of automation, which is to save time and reduce manual work.
Imagine a customer who’s pre-arranged a slightly later payment date due to a specific project timeline. A rule-based system, oblivious to this agreement, would still trigger a late payment reminder. This creates unnecessary work for the AR team and a negative experience for the client.
The constant need for manual overrides makes these systems less efficient than they appear on paper. They can end up creating more work rather than less.
Inability to learn from new situations
Another major limitation is that these systems can’t learn. They operate on a fixed set of pre-programmed rules, unable to adapt to changing circumstances or improve over time. They’re essentially stuck in a loop, repeating the same actions regardless of the outcome.
For example, if a new type of payment processing error emerges, a rule-based system won't be able to recognize it. It will continue to operate as if nothing has changed, potentially leading to more errors and manual workarounds. They’re simply not equipped to handle the kinds of novel situations that happen all the time in AR.
AI assistants: A new breed of automation
Modern AI assistants are the next evolution of AI-powered automation, providing a more intelligent solution that understands the context behind each situation.
Here’s how they work:
Contextual decision-making
Rather than reacting to predefined triggers, AI assistants analyze the circumstances surrounding each situation. They understand that not all late payments are created equal.
Here’s a real-world example: An AI assistant notices that Company X consistently pays late during the Q4 holiday season but consistently pays early in Q1. A rule-based system would just flag those Q4 payments as late and send out generic reminders. But the AI assistant recognizes the seasonal pattern. It realizes that Company X’s late payments during the holidays are likely due to higher volumes and extended payment terms they offer their own customers.
Instead of automatically following up, the AI assistant adjusts its payment expectations to account for the peak season, and may even proactively offer flexible terms. Doing so avoids unnecessary friction while strengthening the relationship in the process.
Adaptive learning patterns
Unlike their rule-based counterparts, AI assistants can learn and adapt over time. They develop a kind of “muscle memory” by analyzing vast amounts of data and identifying patterns within it. Just like AR teams, they’re constantly refining their approach based on what works and what doesn’t.
Here’s how they build their memory bank:
- Learning from experience (both good and bad): Rather than simply repeating what worked in the past, AI assistants are constantly building a mental map of the most effective ways to manage accounts.
- Spotting patterns: The AI can spot recurring patterns—like the holiday effect we discussed earlier—and account for it within its workflows. Along with seasonality, it can identify industry-specific payment cycles and trends related to economic indicators.
- Understanding individual client behavior: Each client’s historical payment data provides valuable context that the AI can use to predict potential delays before they happen.
Dynamic responses
Contextual understanding and adaptive learning enable AI assistants to react to unexpected situations like a human would.
Almost every AR team has encountered this situation: a normally reliable client who is uncharacteristically late on a payment. A generic, automated “overdue” reminder is impersonal and fails to acknowledge this customer’s strong track record of timeliness.
Instead, one of these assistants could look through recent communications (emails, phone logs, etc.), CRM notes, or any sudden changes in product usage or account activity to figure out if this is a temporary blip or a sign of a more serious concern. Maybe the client recently went through a major internal restructuring, or they’re facing a temporary technical snafu. This context enables the assistant to tailor its response—perhaps offering support or exploring mutually agreeable solutions—instead of immediately escalating the issue.
Why humans need to stay in the loop
At their best, AI assistants can supercharge productivity in AR. But remember—they’re assistants, not replacements. Sensitive information is part and parcel of AR, and keeping it safe requires a certain amount of human oversight. These assistants aren’t designed to eliminate human involvement entirely. They’re meant to complement what your people do best.
It’s a partnership. AI handles the heavy lifting—tedious data entry, routine follow-ups, initial analysis—freeing your team to focus on the higher-value, human-centric aspects of AR.
A more intelligent future for AR
Rule-based automation will continue to have its place handling straightforward AR tasks like generating reports. But for the complex, nuanced problems that AR teams constantly face, modern AI assistants are a much more effective solution. The adaptability and muscle memory they bring to the table can free your team from a much wider range of day-to-day activities.
Forward-thinking AR teams are already moving beyond the bounds of rule-based automation. Let’s chat about how Stuut can help you join them.