Today we’re speaking with Dominic Jainy, an IT professional whose expertise lies at the intersection of artificial intelligence and enterprise technology. With a deep background in AI, machine learning, and blockchain, he has a unique perspective on how these powerful tools are not just theoretical concepts but practical solutions reshaping core business functions. We’ll be exploring how AI is revolutionizing accounts payable for companies using Microsoft Dynamics 365, moving beyond simple automation to create truly intelligent financial workflows. Our conversation will touch upon how this technology drastically cuts down invoice processing times, the mechanisms behind its impressive learning capabilities, and its role as a vigilant guard against fraud. We’ll also discuss pragmatic first steps for implementation and the critical importance of a seamless connection with an ERP system to unlock an AP team’s full potential.
The claim that AI can shrink invoice processing time from days to just hours is quite a powerful one. Could you walk us through the traditional bottlenecks in a Dynamics 365 AP workflow that AI targets and perhaps share a story of how a company saw such a dramatic change?
Absolutely. The traditional AP process is riddled with manual stop-and-go points. An invoice arrives, someone has to physically scan it or key in the data, verify it against a purchase order, figure out the correct GL codes, and then email it around for approvals. Each of those handoffs is a potential delay. What AI does is create a fluid, continuous flow. It automates the data capture right at the beginning, validates the entries, and intelligently routes the invoice to the right person without anyone having to lift a finger. I recall a finance leader at a large healthcare company who was just buried in this kind of work. After bringing in an AI-powered system integrated with their Dynamics 365, they told us they had nearly halved the time spent on coding and approvals alone. That’s a real-world testament to removing those human–powered bottlenecks.
You mentioned that the AI can learn from a company’s history to suggest invoice coding with up to 90% accuracy. Can you break down how the system actually learns, and what the experience is like for a user who needs to validate or correct the AI’s suggestions?
It’s a fascinating process that’s much more intuitive than it sounds. The AI essentially studies your company’s entire history of paid invoices within Dynamics 365. It analyzes patterns, connecting specific vendors to certain GL accounts, departments, and cost centers. So, when a new invoice from a familiar vendor arrives, the AI has already learned, for instance, that “Staples” usually codes to “Office Supplies” for the “Marketing Department.” For the user, the experience is designed to be a partnership. The AI presents its coding suggestions, and if they’re correct, it’s a simple one-click approval. If a suggestion is off—maybe a specific invoice is for a special project—the user just makes the correction. The beautiful part is that the AI learns from that correction. It’s a continuous feedback loop that combines machine efficiency with human expertise, getting smarter and more accurate with every invoice processed.
Beyond just catching duplicate invoices, how does AI strengthen fraud prevention? What kinds of subtle, unusual behaviors can it detect, and can you paint a picture of a scenario where it might flag a suspicious invoice that a person could easily miss?
This is where AI becomes a true safeguard for the business. It’s constantly analyzing transaction patterns in the background, building a profile of what’s “normal” for each vendor. It looks for anomalies that would be almost impossible for a human to spot in a sea of transactions. For instance, imagine a vendor you pay regularly suddenly submits an invoice with new banking details, or the invoice amount is three times higher than their historical average. An overworked AP clerk might not notice, but the AI flags it instantly as unusual behavior. It’s like having a digital detective on your team that never sleeps, protecting your cash flow by catching these red flags before a payment is ever made.
Many finance teams are intrigued by AI but nervous about a massive, disruptive overhaul. Your advice is to start small. Could you lay out the first few practical steps a team would take to implement AI for just one specific area, like invoice capture and routing, within their Dynamics 365 setup?
Starting small is definitely the key to building confidence and momentum. The very first step is to pinpoint your biggest headache. Let’s say it’s the mountain of PDF invoices that have to be manually entered. So, you decide to focus AI just on invoice capture. The team would work with a provider to establish a secure connection between the AI tool and their Dynamics 365 environment, which is often a very straightforward process with cloud-based systems. Then, you’d start feeding it invoices. The AP team gets to see the AI extract the data in real-time. They aren’t losing control; they are simply validating the AI’s work. As they see the accuracy and feel the relief of not having to do that tedious data entry, the adoption happens naturally, and they become the biggest champions for expanding its use.
It’s often said that AI allows AP teams to scale their operations without scaling their headcount. Could you share a story of a client who successfully managed a major increase in invoice volume, detailing what their workload looked like before AI and the kinds of metrics they used to measure success?
I’m reminded of a client in the property management space that was growing rapidly through acquisitions. Their invoice volume effectively doubled overnight. In the past, their only option would have been to immediately hire two or three more AP clerks, a process that’s both costly and time-consuming. Instead, they leaned on their integrated AI solution. Before, their team was constantly playing catch-up, but the AI was able to absorb that entire new workload. They tracked a few key metrics: invoices processed per full-time employee and average time-to-approval. Within just a few months, they saw their automation rates for routine invoices nearly double, and their approval times were actually faster than before the acquisition, all without adding a single person to the team. It proved that you can scale the business without scaling the back-office burden.
You’ve highlighted the importance of a seamless ERP connection. How exactly does a solution like Rillion’s AI integrate with Dynamics 365 Finance or Business Central to create that smooth data flow, and what are the common pain points for companies when their AP tool is poorly integrated?
A seamless integration means the AI platform and Dynamics 365 are in constant, two-way communication. It’s not just about pushing data in one direction. The AI can pull real-time vendor information, purchase orders, and your chart of accounts directly from Dynamics 365, which is how it makes such accurate coding suggestions. When an invoice is approved, all the data flows back into the ERP perfectly, ready for payment. When that connection is poor or “bolted-on,” it’s a nightmare. You end up with data silos, teams manually exporting and importing spreadsheets, and constant reconciliation headaches to figure out why the numbers don’t match. A clunky integration completely defeats the purpose of automation; it just swaps one type of manual work for another and creates immense frustration for the finance team.
What is your forecast for AI in accounts payable automation?
Looking ahead, I see AI moving beyond just being an efficiency tool and becoming a true strategic partner for finance departments. We’re already seeing this with features like the AI Assistant, which provides instant answers and guidance, but the potential is much greater. The future is about predictive insights. Imagine an AI that not only processes invoices but also analyzes spending trends to forecast cash flow with incredible accuracy, suggests opportunities for early payment discounts, and even identifies potential supply chain risks based on vendor payment patterns. It will transform the AP team from a cost center focused on processing transactions into a hub of financial intelligence that actively drives business strategy.