How Finance Teams Are Actually Using AI | Opendoor, Datadog, PwC

**Dana Decker** (0:00)
I don't think all software is going to get ripped out. It can't. But I also think that things are moving so quickly that we are a lot more careful about diving into anything, and we are thinking about what we can build.

**CJ Gustafson** (0:11)
If you have 90 days to go and work on something, what is something that they can do?

**Micah Richard** (0:15)
Just because the word AI is in there, people are like, what do I do? The answer is just start. Your company almost certainly has a set of tools that are in place. The problem is that the longer you wait, the harder it's gonna be for you to adapt.

**AJ Ljubich** (0:27)
There's easy things like holiday, where usage can dip in a temporary period of time. The harder thing is that we also have exposure to the seasonality of our customers. If you have a video game manufacturer and they have a big release, there might be a surge in usage there, or there's streaming companies that have exposure to a certain event. So that's something that's a little bit more nuanced, that so far, AI isn't picking up perfectly, but maybe over time.

**CJ Gustafson** (0:50)
Is this thing on?

**AJ Ljubich** (0:54)
Yesterday's price is not today's price.

**CJ Gustafson** (1:05)
Welcome back to Run the Numbers.
I just did a panel. That's right. Your boy sits on stage, asks other people the tough questions. That is my job. A modern day Walter Cronkite, someone said. Rillet was kind enough, shout out to Nick Kauf and Steven Hedlund to invite me to their annual user conference. Rillet Recon, it rings a bell.
So I was on stage with Dana Decker, VP of Finance at Opendoor, AJ Ljubich, SVP of FP&A at Datadog, and Micah Richard, Principal of AI and Machine Learning at PwC. I started my career at PwC. I had about 1,000 coffees. It was called Flavia. Came in like this plastic bag and you would make it late at night. It tasted horrible, but the audits were done on time. So I had three practitioners and experts around how AI is being used within the modern CFO's office. I asked them all sorts of questions about how they're practically applying AI into their decision making and how they're actually figuring out what the ROI is. We get into a discussion on token economics, how to figure out how many tokens is too many tokens and what you're getting from it. And we also talk about pushing AI, not only through your team, but your management team and getting them to adopt. So shout out to Rillet for having me on stage at Rillet Recon. I had a blast and shout out to the panelists because they're the real stars of the show. Let's get into it.
I think we've assembled the highest hourly rate in New York City for the day. So thank you all for coming. I'm CJ. I'm a recovering tech CFO. I came up in the FP&A space, running FP&A groups at companies from 10 million to 200 million in ARR. And I helped sell a private software company that was much smaller than the folks on this stage. So I'm in awe of the momentum of all of your companies. And funny enough, I actually started my career at PwC back in the day. We're going to get into the tactics of how some of these amazing companies are applying AI day to day. But first, we're going to do some intros. So AJ, tell us a little bit about yourself.

**AJ Ljubich** (3:06)
I had the FP&A team at Datadog. I've been at the company for about three years in this capacity, but I actually boomerang back. So my roots are on Wall Street. I was at Datadog originally back in 2018, helped bring the company public and then sat in the IR seat for a while before going to another company, UiPath, leading up their FP&A team before coming back to Datadog in the last three years.

**CJ Gustafson** (3:28)
You helped take two companies public. Yeah.

**AJ Ljubich** (3:31)
Micah, what do you work on day to day?

**CJ Gustafson** (3:33)
Because it sounds like you have the coolest job ever.

**Micah Richard** (3:35)
So I'm a partner at PwC, specifically focused on AI. My background is a little non-traditional for a PwC partner. I started my career in industry leading data science teams. I did spend five years with the firm and kind of in the middle there, so I got a crash course in accounting and finance. I left to go to Facebook where I led ads ranking machine learning engineering teams and then I went to a different tech company as an engineering director. So most of my conversations with clients really focus on the practical implications of using AI. As you'd expect, a lot of those discussions are with accounting and finance teams right now.