Managing AI Agents at Scale Across BFSI Operations - with Yoav Naveh of Reindeer AI

**Daniel Faggella** (0:12)
Welcome everyone to the Emerge AI in Business Podcast. Today's guest is Yoav Naveh, co-founder and co-CEO at Reindeer AI. Reindeer AI is an enterprise automation platform that deploys custom AI agents to handle labor-intensive back office workflows, including payment approval, supply chain tracking, and invoice processing. Yoav examines how large enterprises in financial services have moved through three waves of AI adoption, from customer support tools to AI co-pilots to full operational agents, and why the third wave demands a fundamentally different approach to governance. Today's episode is sponsored by Reindeer AI. In this episode, we cover how enterprises in banking and financial services are operationalizing agentic AI inside regulated workflows. To go deeper on this topic and learn how banks are using RPA to reduce operational costs in repetitive workflows, and applying deviation detection to identify fraud in real time, download our free PDF report AI in Banking Executive Cheat Sheet at emerj.com/bcs1.
That's emerj.com/bcs1 to download your copy. Now the conversation with Yoav.
Yoav, welcome to the Emerge AI in Business Studio.

**Yoav Naveh** (1:47)
Great to be here, thanks for having me.

**Daniel Faggella** (1:48)
Yeah, I'm excited to get into AI in financial services today, but from a different perspective than we've been talking about in the last couple of months and weeks. And we're gonna start where we've been hearing from the operational side of things that there's a real shift, right? So AI is no longer experimental in financial services, but it also feels like the pressure to operationalize is just getting more and more and more. And in the middle of this, we have agentic AI that also landed, and it landed without a shared definition almost. And you've been building these systems inside workflows where ambiguity isn't an option, right? So where compliance and legacy infrastructure and human oversight, they have to coexist in the same operations.
How have institutions evolved in their thinking and where does agentic AI actually fit in?

**Yoav Naveh** (2:42)
Yeah, I think it's a really interesting way to frame this. The way I see this, we've kind of been experiencing three waves of AI adoption in the enterprise. I think at first, the initial experimentation started at the call center, the customer support, which is there's one nice thing about it. It's kind of one big call center that has a lot of repetitive tasks that are kind of very similar. If someone is asking a question and they're getting an answer. And I think the second wave came into play with kind of a very high adoption of assistance.
You get ChatGPT or Copile or Claude in the organization. And it's still kind of the point is let's take someone that has, maybe it's an expert employee in my company, have them ask a question, ask AI a question and get some assistance. But near the day, the person is still responsible for the job. Super important, there's a lot to do there. I think the third wave, which is what we're seeing now is like, okay, we started to understand how we can use AI, but how can we actually use it on the very broad sense of operation, which as you said, that could mean so many different things. It could mean accounting, it could mean treasury, it could mean in financial crimes.
I think one of the interesting thing that's happening there is that it's not just a question and answer. You actually have to take a task across multiple systems, multiple teams, and it's a lot more difficult to just close a task. It's not like I've got asked a question, here's the response, and then someone would be able to do something with it. So the agent-human aspect becomes much more important. Exceptions and edge cases become much more frequent. I think that is such a promising opportunity for enterprise, because operation touches 70 percent of what they're doing, the ability to provide services faster to their customers, the ability to capture revenue faster, also the ability to introduce cost efficiency. But the level of complexity in doing that is also dramatically bigger than the two waves.

**Daniel Faggella** (4:30)
Yeah. I like the picture that you painted there, saying that it's no longer just a question and answer, context is very important there.
In this environment, the compliance constraint is the obvious one. But I suspect that that's not the primary bottleneck. What's the thing that trips institutions up that they don't usually anticipate?

**Yoav Naveh** (4:49)
I think compliance is really important in the sense that there's trust that has to be built, and governance potentially requires you to track everything the AI is doing. I actually think that's today almost coming out of the box. It's very easy with these new AI tools to see what the AI is doing and understand the reasoning. I think that the biggest challenge in the market is, first of all, it's incredibly noisy. People ask us, who are your competitors? It's not so much about who's competing with us, it's about the entire noise. How do you actually build a strategy around all these different tools? I think when we started Reindeer, that was the question. It was very obvious AI is going to make its way to every function in the enterprise. The question we became obsessed with is, how do you actually build a strategy around that?