Scaling Regulated Data Workflows Without Lock‑In - with Juan Orlandini of Insight

**Daniel Faggella** (0:12)
Welcome everyone to the Emerge AI in Business Podcast. Today's guest is Juan Orlandini, CTO of North America at Insight. Juan joins us to discuss how finance leaders can move beyond manual data mazes by using AI for compliance heavy lifting. Our conversation also explores optimizing workflows through data engineering to clear data swarms and leveraging existing SaaS tools to ensure mathematical accuracy and long-term ROI. Today's episode is sponsored by K1x. Please note that the views expressed by Juan Orlandini do not necessarily reflect the official position of Insight or its leadership. Position your brand alongside the Fortune 500 leaders defining the Enterprise AI roadmap for the opportunity to showcase your solution to the executives currently funding and scaling global initiatives partner with Emerge to reach the decision makers holding the strategic mandate. Secure your partnership at go.emerge.com/partner. That's G-O dot enerj.com/p-a-r-t-n-e-r. Let's dive into the conversation with Juan.
Juan, thank you so much for joining me on Emerge's AI in Business Podcast today.

**Juan Orlandini** (1:18)
Thank you, Wande. Thanks for having me.

**Daniel Faggella** (1:20)
It's great for us to have you as well. So our discussion today is going to be very relevant to the finance guys. And I think you people would agree that in a highly regulated area like tax and financial reporting, the data lives in chaotic states. We've got PDFs and we've got spreadsheets and we've got legacy portals that should have been phased out years ago already.
And I know that you've written about mistakes that businesses make when deploying AI enterprise apps. One of which is trying to build on AI to a maze. So we're starting with the maze and we're just putting the AI on top of the maze. How should a CFO look at their architecture so that they aren't just throwing more people at a manual data problem, but actually building a system where AI handles the heavy lifting of compliance?

**Juan Orlandini** (2:08)
So that's a really good question. And so, you know, one of the things that I always caution CFOs and any anybody that's in the financial side of any organization is that these generative AI models, which is what most people talk about today when they talk about AI, they're really not good at math. They're terrible at it. In fact, if you ask them to do some basic math stuff, they will very much sound convincingly true in giving you an answer, but they're truly giving you a statistical response. They're not giving you math. What we live in in finance is math. You need to be aware that when you're using these tools, you have to put them in the right place where the superpowers that they have, operating the constraints of math and the things that you as an organization have built to make sure that the math works out at the end of the day. You have a balance sheet, you have all the things that have been built in financial operations forever.
Use those. Don't throw them out because these things tells you convincingly that it's got the right answer when it might or might not. You don't know. That's what they call hallucination.
It's very deliberate use of the tool in the right place.

**Daniel Faggella** (3:29)
Makes sense. So I know that for the CFO, automation always comes or AI always comes with this promise of, oh, your life is going to be easier and everything's going to be automated.
But manual steps do creep back in. Why, from your experience, have you seen where the workload often result in adding more manual input or more people instead of just fixing the underlying architecture?

**Juan Orlandini** (3:55)
Well, yes and no. All right, so absolutely it can happen, right? Because of the verification that's inherent in the operations of any financial organization. And if you have a tool that comes in and it is maybe collecting PDFs from one source and CSV files from another and you're trying to ask it to reconcile the stuff and all of a sudden, there's an error in that reconciliation.
All of a sudden, you might be asking your people to do not just double the work, because they have to do that reconciliation, but then figure out how this thing went wrong in the first place, that they might not even know how it works. So absolutely that can happen. And that's why I caution is make sure that if you're building an automation that it's in the places where these tools have matured.
And there's some places where they're amazing right now. Absolutely mind-blowingly good.
I'll give you a personal example. And this will give you an idea of the crazy superpowers that you can leverage these tools.