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Society

Inside the Order-to-Cash Revolution: Interview with Independent Fintech Expert Ara Azaryan

The fintech conversation has officially shifted from "what is agentic AI?" to "how do we actually implement it without breaking things?" As we approach the final quarter of 2025, finance executives are drowning in pilot projects but starving for scalable results. To cut through the noise, we discussed with Ara Azaryan, an independent financial technology expert, to discuss the practical roadmap from awareness to execution.

With multi-year experience navigating the complexities of the Armenian financial sector—including tenures at major banks such as VTB Bank and Ardshinbank—Azaryan offers a unique perspective on how enterprise-grade AI strategies must adapt to real-world data messiness and operational realities. Here, he breaks down why so many agentic AI projects are headed for failure and how you can ensure yours doesn't make the Gartner "death list."

 

 

Lianna Ayvazyan: Mr. Azaryan, there is a lot of hype around Agentic AI. What is your opinion about the current level of acceptance of such technology by finance leaders?

Ara Azaryan: The rules of the game have changed. Traditional automation is like a train—it runs perfectly on a predetermined track, but if there is a rock on the line, it derails. It can’t handle the exception.

Agentic AI is different. It’s like an experienced driver. It understands the context of the journey. If a customer sends in a payment with a mangled remittance advice or a deduction that doesn’t quite make sense, an agentic system doesn’t just throw up a red flag for a human to manually fix. It investigates, learns from historical patterns, and resolves the exception autonomously .

In the current economic climate, finance leaders aren't just looking for efficiency; they are looking for resilience. With agentic AI, you are building a finance team that works 24/7, adapting to chaos without burning out your senior analysts.

Lianna Ayvazyan: So, if a company wants to move "From Awareness to Action" successfully, what is the first step they should take tomorrow morning?

Ara Azaryan: Stop looking at technology and start looking at your pain points. Specifically, look at your Order-to-Cash (O2C) cycle. That is where the quickest wins are hiding.

 

I advise firms to go to their Accounts Receivable managers and ask one question: "What task consumes the most manual hours every month?" Usually, it is cash application or deduction management.

Your first step isn't buying software; it is defining a narrow, high-impact use case. Maybe it’s automating the matching of payments from diverse sources—like lockboxes and virtual cards—that your current system struggles with. Pick one bottleneck. Set a measurable goal, like reducing unapplied cash by 30% in three months. That focus is how you build momentum.

Lianna Ayvazyan: Which steps do you find are the most underestimated by finance leaders?

Ara Azaryan: Without a doubt, the human factor.

There is a lingering fear in the back office: "Is this robot coming for my job?" If you don't address that head-on, you will face passive resistance that kills the project. You have to make it clear that agentic AI augments, not replaces. The goal is to free your team from the drudgery of data entry so they can focus on high-value analysis—like forecasting or strategic planning .

I also see a disconnect between IT and Finance. IT wants to ensure security and governance; Finance wants speed and results. You need a shared vision from Day One. If Finance goes rogue and buys a solution that IT can't secure, or if IT builds a fortress that Finance can't use, the project dies in the no-man's land between departments.

Lianna Ayvazyan: What does success look like once an AI agent is properly integrated into the finance team?

Ara Azaryan: It is when your collections team isn't spending the morning printing reports and dialing phones. They are strategizing about high-risk accounts because the AI agent has already handled the routine follow-ups . Ultimately, success is when you stop thinking about the "AI project" and just realize your finance operations are faster, more accurate, and more liquid than they were six months ago.

By Liana Ayvazyan