AI-Native ERP vs. Legacy ERP: What's the Difference? with Santiago Nestares, Founder & CEO of DualEntry

**Ray Rike** (0:08)
Welcome to today's episode of the AI to ROI podcast. Yes, formerly known as the Metrics Measure Up podcast. Today, I am joined by Santiago Nestares. He is the founder and CEO of DualEntry. And I'll be covering four topics with Santi today. Number one, traditional ERP versus AI native ERP. What is the difference? How to approach ERP migrations? Where does the ROI actually reside in an AI native solution? And a little bit about the auditability of financial decisions driven by AI. So with that, Santi, I hope it's okay that I call you Santi. Can you give a brief background of your journey to becoming my guest here on the AI to ROI podcast?

**Santiago Nestares** (0:57)
Thank you, Ray. I appreciate you having me. And yes, Santi, Santi sounds great. I always say that our journey for Dual Entry actually might have started before Dual Entry without us knowing because in our last company, we were using a legacy ERP that ended up being a catastrophe. And we jokingly said at the time, if we ever start a new company, it's going to be one to build a modern version of this. And Chad GPT-3 still hadn't had a launch at the time. But there's certainly a need for something modern and intuitive. We were stuck in this implementation for over nine months. We had a team of 12 people in finance that we had to parachute into this implementation. You can imagine hundreds of thousands in costs. And there was a joke internally that if you do like an ACV to NPS ratio, in our software expenses, it would be the highest. We were spending ten times more in ACV for legacy ERP than the next software spend that we had. Yet, it was the lowest NPS score in the entire stack. So the ratio was like through the roof.
And we jokingly said if we ever build a new company, it's going to be a modern version of this. And here we are.

**Ray Rike** (2:04)
There's a new metric for me, the ACV to NPS ratio. I'll have to think about that one.
Okay, so there's a lot of talk out there about systems of record. Now, by the way, nothing is more of a legacy system of record than ERP, right? The legacy ERP. So even the word ERP, it freaks me out a little bit because a lot of what I'm seeing today is AI native platforms being more accounting than ERP. But let me step back from that. Maybe you can address that later on today. But what differentiates an AI native ERP from a legacy ERP? We're already adding AI to their legacy ERP systems out there.

**Santiago Nestares** (2:48)
Yeah. Well, I mean, adding AI to a legacy system is like the equivalent of trying to run a on-prem system with a CD on the Cloud. It just never worked out because AI is not really a specific feature. You can't say like this is ERP and this is ERP plus AI.
It's not a feature that you just plug into it. Some people think Copilot is like the version of that feature, but it really isn't. Copilot is just another interaction layer. AI is really the use of these magnificent models that we stumbled upon as humanity to make more gooey, less deterministic guesses about what the next step or what the next action from an account should be. So that shows up everywhere in the product. To give you the silliest example, as in DualEntry, every time you click on a drop-down, AI is already pre-computing. What are the things that you've chosen in that screen? Can I guess based on those things, what is the most likely drop-down choice for this drop-down? So that's a small example. That's not a big feature. You're not going to advertise that on your website, but it's just everywhere. It's embedded in it. In order to do that, you need to make sure that the data and the context is available and I'm sure we can talk about that more later. But whether it is to run a report and to tell the system, hey, give me a report segmented by these departments, by these regions, that's an interaction layer. It could also be like it pre-writes the flux analysis, so it's already doing a lot of the commentary work for you. It's not a single thing. It also be categorizing. Categorizing is probably the most obvious example. But it is how do you rethink something? By the way, some UIs are even different in the AOI. How do you rethink a system from the ground up that is already taken into consideration that a non-deterministic layer can give you the best suggestion? And what guardrails do you want to build around it, whether it's permissioning or approvals, so that you can ensure that not even a 1% transaction error rate can make its way through the system?