8 mins read
We asked finance operators a blunt question. What role does AI in accounts receivable play right now, and is it genuinely delivering? Their answers cut through the marketing noise fast. Across small teams and venture-backed finance shops, the verdict converged. The technology earns its keep in narrow, measurable jobs. Meanwhile, the broad promises still run ahead of the proof. So three leaders agreed to share their hype-free read on what works.
AI in Accounts Receivable Starts With Smarter Prioritization
For leaner teams, the first win rarely involves full automation. Instead, it involves triage. AI in accounts receivable helps decide which overdue accounts deserve a gentle reminder, which demand a phone call, and which can wait a few more days. That sorting once swallowed hours of manual review every week.
For example, Mike Khorev runs a smaller AR function, and he sees prioritization as the clearest payoff. His system flags accounts by payment behavior, invoice size, and whether a customer ever responded to past chasing. As a result, his team stops burning effort on accounts that were never going to pay sooner. Moreover, the same ranking keeps his staff consistent, which is hard when headcount is tight.
While I am a business owner overseeing a smaller AR team than larger organizations, I’ve used AI for accounts receivable collections on a less extensive basis, i.e., an incomplete credit application or cash application, etc. But I have discovered AI’s most significant area of impact has been in helping to prioritize the accounts that we pursue. For example, the AI system can correctly flag each account for us based upon payment behavior, the amount of the invoice, and whether or not there has been any previous response to communications from us or any other company attempting to collect from them. This way, we can identify which accounts require a reminder, which require a phone call to collect, and which require additional days before making any contact. Using AI as outlined above has improved our team’s ability to spend less time pursuing accounts that we incorrectly identify as needing to be contacted, thus improving our level of follow-up attempts toward the consistency we need as a small AR staff.
Is AI helping us? Yes; however, primarily regarding workflow efficiency. AI has also provided us with improved turnaround times for triage, fewer missed follow-up attempts, and improved sorting accuracy; however, I would not feel comfortable allowing AI to conduct collections without some level of human interaction. My experience suggests that AI is best at completing the identification of patterns and sequences, while humans can choose communication style, define exceptions, and use their judgment regarding relationships with customers. Therefore, I recommend focusing solely on the collection management dunning and prioritization functions until you have measurable metrics corresponding to days sales outstanding or response rate. Once you have verified clean data and your people trust the data from the automated functions, then implement the automation.
Mike Khorev, SEO and AI Visibility Consultant, Mike Khorev
His caution matters as much as his praise. He trusts AI in accounts receivable to rank and route the work, yet he keeps a person on every customer conversation. That instinct lines up with current analyst guidance. For example, Forrester’s 2025 research names collection management as the top AI use case in receivables, precisely because prioritization scales without touching the relationship. In short, AI in accounts receivable shines when it tells people where to look first. Even so, the payoff is operational rather than magical. AI in accounts receivable trims wasted contact attempts and steadies follow-up, which is exactly what a thin team needs most.
Where AI in Accounts Receivable Delivers Hard ROI
Beyond triage, the strongest evidence sits in cash application. Cash application is where AI in accounts receivable proves itself. Matching incoming payments to open invoices is tedious and error-prone, so it suits pattern-matching software perfectly. Here the time savings turn real and repeatable.
Meanwhile, Gary Jain has run AI in accounts receivable workflows for ecommerce and SaaS clients across two years. Still, he stays candid about the ceiling. The wins are concrete, yet he warns that teams routinely inflate them.
We have been applying AI-powered cash applications and dunning sequence management with some of our ecommerce/SaaS customers for two years now. Honestly, there’s a definite ROI here but very limited, and it is almost always overstated by the majority of teams.
However, the areas where we can see tangible results from applying AI technologies in finance operations today are cash application. The process of matching payments to invoices could take our account receivable teams 6-10 hours per week before; today with AI software such as Vic.ai and automation scripts in NetSuite it takes under 90 min.
Regarding dunning management, there is no denying that sending automated messages based on predictive algorithms increases your chance of receiving responses as compared to standard follow-ups performed on the 30th, 60th and 90th days after invoice submission. Nevertheless, it works efficiently only in case you have clear customer data.
As far as credit scoring is concerned, I would suggest to be careful about expectations from AI applications. The truth is, most of small business-oriented software solutions use artificial intelligence simply as fancy terminology for describing rules-based engines. In order to achieve tangible results, you will need to perform large transactions with various types of customers.
Gary Jain, CEO, Ledger Labs
Jain’s figures track with the wider market. Tools that pair document AI with NetSuite automation now post auto-match rates above 90 percent on clean data. Even so, the headline ROI rarely matches the sales deck. A Zuora and Harris Poll survey found that only 28 percent of finance teams report measurable financial impact from their AI tools, despite near-universal spending. Therefore, his “definite but overstated” framing reads less like cynicism and more like field data. AI in accounts receivable returns the clearest gains where the task is structured, high in volume, and rich with history.
Dunning timing tells the same story. Predictive reminders that read each customer’s history beat rigid 30, 60, and 90-day chasing, though the edge holds only when the underlying data is clean. Feed the model messy records and that advantage fades fast. As a result, data quality, not model choice, decides most outcomes.
Why AI in Accounts Receivable Credit Claims Need Scrutiny
Jain saves his sharpest warning for credit scoring. Many small-business platforms dress rules-based engines in AI language, a habit the industry calls AI washing. Gartner coined that term years ago, and regulators have since started acting on it.
In 2024, the SEC charged two advisers for overstating their AI use. That lesson carries straight into receivables. Genuine machine learning for credit risk needs huge, varied transaction data to beat a well-tuned rule set. By contrast, a vendor serving a few hundred customers cannot train a real model from scratch. So buyers should ask a simple question first. How many customers and how many defaults sit behind any AI credit claim? When the honest answer is “a few thousand,” the label is mostly marketing. AI in accounts receivable can score risk well at scale, though scale is the catch most SMB tools quietly lack. For that reason, demanding proof is the cheapest safeguard a buyer has, so ask for live model details rather than a glossy claim.
AI in Accounts Receivable Still Needs a Human Hand
One theme threads through every answer. Automation handles stable, repeatable patterns well, and it stumbles on anything new. Here, Ranjith Raghunath put this plainly when he described his invoicing setup.
We use AI specifically to automatically generate and send invoices to established clients. Because it’s quick and automatic, it helps to accelerate our cash flow, and especially once we have established payment procedures, it makes the entire process fairly seamless. There’s always some trial and error when implementing this with new clients, though. We still check for errors regularly and develop custom prompts to make sure the automation process goes smoothly.
His workflow models the sensible middle path. In practice, the new-client friction he describes is the model meeting unfamiliar patterns. Give it history and the accuracy climbs, yet throw novelty at it and a human still has to step in. He lets AI in accounts receivable handle repetitive billing for known clients, then keeps people watching the edges. That balance reflects a wider truth across AI in fintech, where the hype runs loud but the real payoff hides in narrow, well-governed tasks. Late payments still drain SME cash flow faster than any single tool can repair, so the stakes stay high.
So is AI in accounts receivable delivering? On cash application and prioritization, the answer is a clear yes. On autonomous collections and instant credit scoring, the honest answer is not yet. The operators winning with AI in accounts receivable treat it as a sharp assistant, never a stand-in for judgment. For now, that disciplined approach beats the hype every time.
