How CFOs Use AI and Real-Time Data to Make Faster Decisions

0:01Take that genuine curiosity that exists and embed yourself in the business model from top to bottom. That will help you be a much better CFO, not only operationally, but financially.

0:17Megan Weis: Welcome back to CFO Weekly, where we're talking with financial leaders about how to build efficiency in their teams, create time for strategy, and ultimately get results. This podcast is brought to you by Persona, the trusted leader in finance and accounting outsourcing for over 30 years. See how Persona's customized solutions can help you streamline your operations with teams that start as small as one. Visit the website at persona.com to learn more. I'm your host, Megan Weiss. Let's jump right in.

0:53Megan Weis: Welcome back to CFO Weekly. Today, I'm joined by Mark Gaudiosi, Chief Financial Officer at Burrow. Mark brings more than 25 years of finance leadership experience across technology, healthcare, retail, and high-growth companies. Throughout his career, he has helped organizations scale through rapid growth, acquisitions, and transformation, including leading finance at GoPuff during a period of extraordinary expansion, and most recently guiding Medical Guardian through profitable growth and M &A. Today at Burrow, he sits at the intersection of finance, robotics, automation, and AI, helping build the future of work in industrial and agricultural environments.

1:35Megan Weis: In this episode, we'll explore how finance leaders can use data and AI to make better decisions, build competitive advantage, and create measurable business value. In this episode, we'll explore how finance leaders can use data and AI to make better decisions, build competitive advantage, and create measurable business value while separating real opportunities from the hype. While separating real opportunities from the hype. Welcome to the show today, Mark, and thank you so much for taking the time to be here. Of course. Thanks for having me. I'm looking forward to this conversation. But to start, can you walk us through your career journey and how working across startups, large enterprises, healthcare, and now robotics has shaped your perspective on finance leadership.

2:23Absolutely. I've had quite an interesting and fun career at this point, I think. I got to start at an educational retail toy company called Zany Brainy and worked there for about seven years and got to see the entire life cycle of a company. So from startup to IPO, ultimately to bankruptcy. And so everything I learned in school went out the window and everything I learned in those seven years taught me everything that made me who I am today. Taught me everything from how to make decisions, how to view things. And that's just a lesson that I've carried with me throughout my career. And when I left there, I went to a company here in suburban Philadelphia called Wawa, and it couldn't be any more different than Zany Brainy, right?

3:15You're talking about a large, iconic company here on the East Coast, very disciplined, very operational. And I took what I learned at Zany Brainy and built on top of that how to build plans and processes. And I took that from there to Aramark, to eBay, Enterprise, and on onto GoPuff. And so that radial Aramark-EvA Enterprise was where I flipped over to the technology CFO role a little bit. But GoPuff is really the hyperscaling part of my career. So got a chance to grow revenue at an incredible rate over, I think it was 1 ,200 % over a five-year period. Growing the finance function, growing accounting, growing valuation 25 times.

4:06And that really pressure scenario, pressure tests everything I knew about what systems and process and finance and accounting were all about. Then I went to the medical device company, did a$200 million acquisition, and now moved into robotics, which kind of brings everything together. I don't know that I really meant it that way, but when you think about autonomous robots in all of the operational data that we see today in the world that we live in in 2026, I think everything that I've done up until now kind of got me ready for the AI world that we're working in today.

4:43Megan Weis: I have two questions, one about GoPuff and one about Burrow. So first of all, at GoPuff, with growth like that, how did you learn to stay ahead of it? How did everything not break? right it's a phenomenal question it's interesting and you're i always liken it to building an airplane while you're flying it right when you think about an airplane you have to look at the altimeter you need to know where you are at every second and clearly we were building and flying at the same time and on top of that we were managing capital raises with some serious investors like SoftBank, B1 Capital, Acel. And what I focused on in that time and what we shifted the organization to is the data.

5:30The data really gave us the confidence to understand, build, and grow the business. And that confidence grounded in analysis allows you to make decisions more quickly as you're going through scenario building and what if scenario analysis as opposed to just forecasting the future. And I think that's really what it was all about is that it was about disciplined thinking more so than sophisticated modeling. And I think that's where the data is so impactful and really helps you and every company grow at that stage.

6:09Megan Weis: And just real quick, Burrow, what is it exactly that you guys do? Sounds like you're doing some really cool things with robotics? So we are an outdoor robotics company. So think of an automated vehicle, fully autonomous, fully electric, doing work outdoors. So the indoor warehouse space is very crowded. People do a really good job there, but nobody's really tackled outdoors from a physical AI perspective. And that's what we've done and built over the last nine years. And so So we've sold over 800 robots across the world, and we are tackling the$2 trillion labor problem by putting an autonomous robot out there to supplement the workforce and make everything more efficient and easier to do, faster to market, and solving problems for customers.

6:56Megan Weis: And how long has this company been around? So the company's been around for about nine years, so really six years of commercial success where we've deployed those 800-plus robots. And looking back over the last 25 years, what's been the biggest shift in how finance leaders use data to influence business decisions? I mean, it's pretty miraculous how far we've come in 25 years. As I think about my career, I don't think about myself being around in the workplace that long, but it's actually been longer than that. And what I've seen in my career change a couple of times, but really in the last 10 years or so.

7:36is the finance and the CFO role moving away from what I call a historian to really a co-pilot and a navigator, to go back to my airplane analogy again, has been the biggest shift in using data. I think about some of my career. When I was working at Wawa, I was managing a$5 or$6 billion budget. And that's a lot of discipline. That's a lot of work. That's a lot of backward looking. You close the books, you look at the report, and by the time you think about the answer that you have, you've already surpassed the decision. And I think what I've learned from the beginning and what's transformed is learning to make better decisions with imperfect information and not having the right answer all the time.

8:26And so I think the shift to real-time data and utilizing that in the AI-driven world that we live in today and not just historical documentation is really the change. I think I'll also mention a second shift that I've seen in my career is who owns the data, right? If you think about it, for most of those 25 years, it was really IT. Whether or not the CFO ran IT or had a hand in IT is a different story, but IT was the data owner and everything for data was IT's problem. finance could have asked for something we could have never got it and i think because now finance teams and cfos in particular sit so close to the data that allows us to not wait for things and actually have access to the real-time data immediately to run the business and of course

9:23Megan Weis: ai is dominating almost every business conversation today so from where you sit what's genuinely different this time compared to previous waves of technology innovation. I mean, I can think back to the beginning of my career and that was like the start of the internet and seems like the trajectory of technology is just crazy right now. Just those two letters, AI, mean so many different things in the marketplace today, in the world today. I think you mentioned internet. I think back to my own beginnings at Zany Brady and we were just trying to put a web store together when nobody even really knew what a web store was, right?

10:08When you think about internet, you think about ERPs, you think about big data, data analytics, all of those things are significant in the ecosystem of growth and all had the same type of hype in the time period that they existed that got well ahead of day-to-day reality of the situation. Think about AI and really as a whole, but you think about Gen AI specifically, it's not just processing, but it's reasoning at the same time, right? If I go back and think about the couple of things I mentioned that you mentioned, right? Internet, big data, ERP, all of those things are about different ways to access and save and store information.

10:56when you think about data analytics and some of the things that we were using with Looker back in the beginnings of my GoPuff days, it's really kind of visualization. AI really helps, at least how we use it here and now, I think most of the world is beginning to use it. And it helps with the interpretation. It can see all of that. And really for finance and accounting, it's a totally different leverage situation to take advantage of. That's one. I think the other thing I think about just because I'm a finance person is the cost. I mean, if I think about, I've gotten a chance to implement various ERP systems, implement data analytics when I was at GoPuff, you're talking about multi-million dollar projects that span 18 plus months.

11:45You think about AI today, I have two or three engineers that sit across from me here in the office in a couple of days, you can pilot something and put it into practice immediately at an enterprise level, which is just phenomenal to me today and how fast that moves.

12:02Megan Weis: And it seems like there's enormous amount of pressure on executives to have an AI strategy. So do you think that pressure is causing companies to start with technology rather than what their actual business problem might be? I think that is likely the case. If you think about any of the AI type companies that don't have real world data in their toolbox, so to speak, I think that's one. I think companies want to jump on this AI wave sooner than they might be ready for and are not actually tackling a problem. And so when you think about Burrow, we're tackling a$2 trillion labor problem that exists in the outdoor markets today, where labor is scarce and or expensive in agricultural settings and industrial settings.

12:56Sure, we can enable cannabis vehicle and the AI that it sees and feels in the world to enable that to solve that problem every day. But you have the problem to begin with that you're tackling. I think everybody is focused on adopting, whether it's Claude or OpenAI or ChatGPT, everybody's jumping to grab hold of something to say that they're utilizing it and not really doing it in a very effective way to solve a real-world problem that successful businesses, for that matter, need to be built on.

13:29Megan Weis: And as we mentioned at Burrow, you're helping build autonomous robots that generate large amounts of operational data. How has working in AI-enabled business changed the questions that you're asking as a CFO? So for me, it's changed it, first of all, very quickly, but also a little dramatically, right? If I think about every other company I've worked for, whether it was Medical Guardian or Wawa or Zeni Brainy, the financial questions are all the same. It's just as you could probably round them off. Revenue, margin, headcount, return on vested capital. All that still matters for me here at Burrow.

14:10But if I think about a robot running continuously in a real-life scenario every single day, generating terabytes and terabytes and terabytes of data, that operational data seen the same way as financial data is really the change for me. And that leads me to very different directions in terms of questions and answers. And I think about something for me and us here as a company that's really impactful is fleet utilization. How often is the fleet being used? How many miles has it run? It's the input to the value prop for the customer. And back to financials, right? Revenue, cost, margin, unit economics.

14:53If the robots are running at a 20 or 30 % utilization as opposed to a 70 or 80 % utilization, that has a huge implication on customer ROI and really our ability to demonstrate that we are the right solution for the business. And so the shift for me is asking more and more questions about data. I think I've said to people, I am now an operationally minded CFO helping drive the business forward as opposed to a true financially minded CFO that is just here to help make decisions. I think that really makes a difference. The other thing is that I think about a lot is, as most CFOs do, is risk. But Burroughs really changed the way I think about risk.

15:39If you think about Medical Guardian, right? Operational risk typically lags, right? You see, close the books, see the numbers. All of a sudden you're like, oh my gosh, how about that problem that we had 35 days ago? In real-time AI data in a physical AI robotics business, you're seeing it literally as it happens. So I can watch robots running in the world today, signaling issues, visualizing obstacles, and build that into decisions immediately. And so that's changed what I look at. It's also changed the question that I ask, which is, what decision does this information change? It's less about what decision am I making and more about what decision would I've made that changes with the data and operational metrics that I see in real time today.

16:39Megan Weis: And you mentioned being an operational CFO. For a CFO out there who's stepping maybe into a new industry, how do you go about understanding operations? What's one piece of advice you would give someone who's stepping into the role to really, truly understand operations? So what I've seen in my career with my peers and what I've seen just genuinely in myself is that a curiosity about the business, number one, you have to have genuine curiosity about the business you're going into. Sure, the financial representation is great, but the best peers that I've worked with and mentors that I've worked with don't just understand the numbers, right?

17:29You need to understand why customers buy, what makes operations tick, what the team is worried about that doesn't show itself on a spreadsheet or a dashboard. At GoPuff, I went and spent time in the warehouse understanding the fulfillment operations. At Barrow, it means sitting with a robot in a 101 degree field in Peru, understanding what people are going through and what that data is really telling us. The curiosity is so important because it allows you to ask the right question. And when you think about asking the right question, that's how you get a useful answer from all the data that you can see.

18:13So my advice would be take that genuine curiosity that exists and embed yourself in the business model from top to bottom. That will help you be a much better CFO, not only operationally, but financially.

18:28Megan Weis: And along those same lines, you've led finance through periods of hyper growth, acquisitions and transformation. How has data helped you to make better decisions during times when there wasn't necessarily a clear playbook? So I'll take GoPuff as an example and I'll go back to something I mentioned before is the data, utilizing the data to give you confidence when there's periods of uncertainty is what helps, has helped me and I think helps everybody make better decisions. So growing at 25 times valuation, there's no playbook for that. There's very few companies that have been able to do that in a short period of time.

19:09And in that hyper-growth scenario, you need to spend more time making, I want to call them gut decisions, but it is disciplined decisions with the data that exists as opposed to relying on the past. When you're looking at an acquisition and acquiring, doing a$200 million healthcare tech acquisition. It's less about what worked in the past and more about what will work in the future. And so to do that, my way book had been not to rely on sophisticated modeling, really rely on modeling, but not sophisticated modeling, but really sit on the data and make those future scenarios credible rather than speculative.

19:54And I think that's really what's helped me through those periods of transformation and acquisition and clearly hyper growth at GoPuff and what I'm using here at Barrow to take us through the scaling phase.

20:08Megan Weis: And as a CFO, when you're evaluating AI investments, how do you distinguish between projects that create real business value versus those that just simply sound cool or innovative? By the way, there's a lot of things that sound cool. I'm going to go back to the statement I made before. And then this is really what I live by is what decision does this change? And I put everything through that lens. And if my teams can't answer that clearly, if they give me what I perceive to be a fluffy answer, like, well it's going to make us more efficient or it alleviates the ability to hire today or we become more capable i don't really do well with those types of answers and i keep pushing what i want somebody to say is today we do this it takes this long and this is the decision we make and tomorrow or with this particular investment, we will be able to do this, this, and this that allow us to make a different decision that is better for the company.

21:21I think that is the most important thing and that's the lens that I force everybody here to go through. They don't like me at times, but they know that we are all making the business decision that grows the company faster before we lose the ability to get through that window. So in conjunction with that, I think accountability is also something that I focus on. AI in particular tends to not have responsibility and accountability. I don't know why. I haven't put my finger on why yet other than it's cool, it's innovative. Everybody wants to be part of that initiative, but I don't think there's a lot of accountability and ownership responsibility there.

22:03So for me, everything needs to have a named owner and a measurable target and a smart way to spend that money so that we can focus on achieving the goals that we set out to. But those two things, I think, in conjunction with each other is really how I look at that alleviating the fluff and the cool that comes out of evaluating AI investments.

22:28Megan Weis: And one challenge that CFOs face is getting the organization to trust data-driven decisions. How do you build that culture without losing the value of experience and intuition? Yeah, I think that's an interesting question because I think that I use intuition a lot more than I ever thought I would in my roles that I've had. I think intuition is all around compressing all the experience that I've had or that anybody's had and utilizing that to get something stronger than not utilizing the intuition. So I'd say what I tell the organization is use that intuition, like don't discount it. Because when people say that something doesn't feel right, I actually think that there's, that's not just an irrational statement.

23:22It's really something that they've learned over years and years of experience. So I think it needs to start with acknowledging that intuition needs to be used. So I always do that first. after that and when i think about trusting and allowing the information the organization to trust data-driven decisions around a cultural aspect is talking to the organization about what we call intellectual honesty because the data is either going to confirm or deny the intuition and so you have to acknowledge the intuition and then utilize the data to prove it right or wrong and I think what we've built here from a cultural aspect is that we don't use the data to justify conclusions that everybody wants, whether it's founder, whether it's me, whether it's our COO.

24:16I think I've seen a lot of failures in my past life at using data to justify conclusions. I think it's really the ability to change decisions when the evidence shows it. By the way, in the forefront, at an all-hands meeting, recognizing that I'm just as bad as everybody else is at making decisions. But if you do that over and over again, then people really buy into the data-driven decisions because you can acknowledge that their intuition is real, utilize the data to prove it or disprove it, and then show real-life examples of that working and not working. that gets the full buy-in of the organization.

25:01That's what I've been successful with for my career.

25:04Megan Weis: Yeah, that's great advice. So looking ahead, how do you see the role of the CFO evolving in the next three to five years? And what new skills or mindsets will be critical for the CFO of tomorrow? So I mentioned earlier about not looking backwards and looking forwards and being a pilot, so to speak. At the end of the day, the scorekeeper, if you want to call it that, That role's not going away. It's never going to completely go away. Everybody's going to need to close the books. Everybody's going to need to run reports and read reports and talk to the board of directors. But I think this, even more so in the last couple of years, this complete shift to forward-looking, data-driven decision-making, even more so than when CFO roles changed to come from controllership to coming from FP &A.

25:58That was kind of the first wave of this. And I think now, so if I think about where my time goes, it's still a lot of it goes to reporting and variance analysis and some compliance stuff. But that's clearly going to be automated, if not in the next five years, certainly before the next 10. And so if I think about the next 10, it is that pilot or co-pilot next to the board and the CEO to think about making complex decisions for the business with AI-enabled data. Somebody has to translate the complex and make it clear. And I think everything I've learned from Zany Brainy to eBay Enterprise to GoPuff to Burrow has really translated, allowed me to make the translation of that to the organization.

26:52And that's going to be the norm in a go forward five to 10 years. that technology is the CFO's problem and issue today. And getting at the data with the AI that exists and will exist is really going to give this role an advantage in the future.

27:13Megan Weis: Mark, thank you so much for taking the time to be here today to share your experience and knowledge. This has been great. Thank you very much for having me. It was a pleasure. Really, really enjoyed it. And to all of our listeners, please tune in next week. And until then, take care. Thank you for listening to this episode of CFO Weekly. We hope you picked up some useful insights and strategies to help move your business forward. If you enjoyed the conversation, be sure to subscribe on your favorite podcast platform so you never miss an episode. And if you found it valuable, we'd love for you to leave a review and share it with your network.

27:48Megan Weis: It helps us bring even more great content to finance leaders like you. For more episodes, resources, and to see how Persona can support your accounting and finance needs, visit persona.com. See you next week.