The opening voice on a Lyft earnings call is that of Aurelien Nolf, VP, FP&A and Investor Relations at the ride-hailing firm. He holds a dual role managing the 60-person FP&A, finance analysts, and investor team with access to billions of data points looking at the mode, price,drivers, frequency and demand for current offerings– and much-anticipated future services – such as robotaxis. In August 2025, the company saw an 11% increase in revenue to $1.59 billion for the quarter ended June 30. Its profit climbed to $40.3 million from $5 million a year earlier, driven by a rise in ridership and bookings. In this episode Nolf opens the hood into the FP&A processes and opportunities at the San-Francisco-based iconic brand.
In this episode:
- The metrics that matter with billions of data points
- The logic of combining FP&A and investor relations
- Finance hackathons, curiosity and the culture of Lyft
- Operational vs financial metrics driving Lyft’s success
- Using ML in our forecasting, variance analysis, and IR reports
- The challenge of hiring for FP&A in the Bay Area
Full blog post and transcript
Glenn Hopper:
This is FP&A Today. Welcome to FP&A Today, I’m your host, Glenn Hopper. Today I’m joined by Aurelien Nolf, vice president of FP&A, and in investor relations at Lyft, Aurelian has built his career at the intersection of numbers and narrative, starting in audit and accounting, spending more than 15 years at electronic arts in both controllership and fp and a leadership roles, and now leading both financial planning and investor relations for one of the most recognized names in mobility at Lyft, he’s helped to drive profitability scale, a global FP&A team, and shape the company’s investor story. We’re going to talk about his unique dual role, how finance leaders can bridge data with storytelling, and where fp and a is headed in an era of AI and data science. Aurelian, welcome to the show.
Aurelien Nolf:
Hi, Glenn. Great to be here. Thank you for having me.
Glenn Hopper:
So I don’t wanna dwell on the past, but I was just out in Redwood City on the electronic arts campus, which it was one of the coolest campuses I’ve been to, seeing all the video game stuff and the big Madden field out there, <laugh> and everything. I know you started in audit and accounting before moving into FPNA at electronic arts, and now you’re leading fp NA and IR at Lyft. And I guess I was quick to jump into electronic arts, but I wanna go back just before that when you were in audit and accounting. Walk me through what motivated that transition and how your accounting foundation sort of shaped your approach to fp NA and what you’ve done since then.
Aurelien Nolf:
Yeah, I spent a few years at PWC at the be at the beginning of my career. And, um, you know, there, there’s been a great foundation for, you know, who I became as a, as a final professional. You know, Audi told me a lot around, you know, verifying your numbers, but more importantly thinking about the why behind the, the numbers. You know, I remember going in the, in the corridors and meeting with all the, uh, the accounting teams and looking at the inventory, going to the warehouses. So I’ve been building, uh, pretty deep knowledge of, of the businesses and, uh, yeah, that, that was a great foundation. But, you know, the, the reason why I switched to, uh, first accounting at EA really love the business, uh, as you said, it’s a great company. It’s, it’s pretty awesome. But then transition into fp and a, just because I’m, I’m, I really love, you know, business decisions.
Like I, I am really interested in discussing about the business, being behind the numbers. Uh, I love, you know, storytelling, uh, that’s what really energized me. But the accounting is still, you know, serving me daily, honestly. Uh, you know, it’s, it’s making sure all our forecasts are granted in reality, that all the assumptions are defensible. And, you know, knowing about how a transaction is gonna flow through a balance sheet and then your, your statement of cash flow, uh, is super important for FPNA people as well. You know, my son is in college right now. He is learning about accounting, and I really push him to, uh, you know, get started with, with audit. I think it’s a great start for any career in finance.
Glenn Hopper:
I love hearing about your son. My kids, neither of them wanted to touch finance, accounting, business, <laugh>, they both, uh, so my son was a, a STEM guy in college and he’s applying to PhD programs now. And my daughter went, uh, you know, like public policy and, and political science for her dual major, and they just, they couldn’t go into business. So it’s, I love hearing about, and it’s interesting though, I talked to so many accountants whose chil and I was, you know, I came up through fp and a and finance, not the accounting side. And it seems like on the accounting side, like their kids pick it up. It’s like, well, this is a pretty practical way to go. And they, they, I, I see it a lot more than in straight accounting than finance, that having that accounting basis. And I guess it’s like Warren Buffet said, you know, accounting is the language of business. So if you speak that, you’ve got the foundation, whichever direction you ultimately go.
Aurelien Nolf:
Absolutely. Yeah.
Glenn Hopper:
One thing that I thought was interesting, and maybe this is just my anecdotal view of it, but once you get to a company, when I’ve seen people make the move, and I, I know, you know, you were at PWC before, but once you’re at a company, to move from accounting to finance within the same company, seems like it can be a difficult shift because you kind of get stuck in a, in a lane and they say, oh, well, you know, he does accounting. So that, that’s kind of the career progression. Can you walk me through how you decided to make that shift into finance? And maybe what lessons within the same company, what lessons from that experience still kind of guide how you work today? Yeah.
Aurelien Nolf:
I’d say first and foremost, I’ve been very fortunate. You know, EA is a very supportive organization. They, they support people and career growth. And so they, they gave me the opportunity. I, I think even when I was on the accounting team at ea I was very oriented toward the business conversations, like accounting, being the translation of what happens in the business. And so it was kind of a natural thing for me to do. And the first opportunity I had for within PNA was corporate, PNA. And so the corporate sp a team within the, the, the brand of fp and a is clearly the place where accountants can transition pretty easily. ’cause you need a lot of those skillset, the, the foundations, the rigor, you know, the, the detail orientation to be successful in those roles. And so from there, I evolved and, you know, took more of a business FPA role. But, uh, the, the transition happened pretty naturally there. But again, picking the company that is gonna help you grow your career, uh, and not, you know, just grow people in their silo is a, it ma made a big difference for me. And I’ve been very fortunate, uh, to be within that organization for sure.
Glenn Hopper:
That’s great. And I love your role now at Lyft. And we talked about this a lot before the show, just ’cause I was, and, and we’ll get, I’ve got another question on that, but the, the two audiences you serve. So at Lyft, you’re overseeing both fp and a and ir. And so I guess before we talk about, there’s gotta be, I feel like there has to be challenges around that, but from your perspective and what you’ve seen in combining the roles, maybe first, was that by design initially, or was it, was one of them something you picked up after the other? But from your experience now doing both, what’s the value of combining the two functions under a single leader?
Aurelien Nolf:
Yeah, it’s something that happened over time. I, when I joined Lyft, initially my focus was on FP&A. And then over time I had, you know, conversations with our CFO Erin Brewer – Chief Financial Officer, which has been very supportive of me. And, you know, I, I wanted to, uh, combine both because it, there’s a great alignment between the two functions, right? Like, if you think about the forecast, that’s the way we build guidance, right? That’s the foundation of all of our guidance that we share externally. You know, having both under the same team, it kind of eliminates the telephone game, right? Like we, I know exactly what’s included in the, in the forecast that have been used to, to, uh, to create a guidance. Uh, it means also that I think we are able to do a better job at crafting a story that is more authentic, right? The ride share lyft business is very complicated. There’s a lot of data, many different markets, many things happen in the market. And so being able to really understand the operational drivers, not just the numbers, but the business that is behind it, uh, makes a big difference. And then the other way is also true. I get a lot of feedback from investors. Oh my God, we get a lot of feedback from them. And, uh, that fits directly into the planning process. So it’s, it’s also a two-way street that I think has been very interesting.
Glenn Hopper:
Yeah, and I <laugh> and this is what we talked about mostly before the show, it feels like that ability to partition your brain, it has to be, because there’s a, you know, there’s very different messages. And being a public company, obviously you’ve got what you can and, and can’t say to investors and, and should and shouldn’t. And then what you use for internal management and guidance, they’re, they’re very different. Like you said, they are aligned, but at the same time, sort of the controlling the information and, and keeping that audience in mind. So, is there is <laugh>, and this, I, I might be like making too big of a deal outta this, but is there a way that you have to kind of partition your brain when switching between that internal planning with executives and the external communication, uh, with the investors?
Aurelien Nolf:
A hundred percent. I mean, it’s, it’s getting easier now. I’ve been doing that for, uh, uh, a longer time, but I, at the beginning, it was my biggest challenge. The, the good news is it’s just a different lens, right? Like, it’s the same story, it’s the same truth, obviously. It’s just that internally we are going, we are going very granular, you know, we are looking at scenarios. Uh, obviously we know things in advance. You know, last week we announced a very big deal between Lyft and Waymo, which is super exciting. So I’ve been working on this for months on my FPNA role, but then investors have been asking about it, and I was like, I don’t know what you’re talking about. And so, you know, you, you have to, uh, really be able to, uh, separate the two. But the key is consistency, right? And, you know, as a company, we want to be, we want to be very transparent guide investor the right way. And so everything we say is the truth. It’s just a different level of detail.
Glenn Hopper:
Yeah, that’s a, a very important point. It’s not like you’re withholding or telling a different, you know, a different story that has different outcomes. It’s just the, the type of information. So that makes complete sense. We talk about it all the time on the show because it’s such a pivotal part of our role that when you’re just studying accounting or finance and the kinda the ivory tower of education, you don’t really start to realize how important storytelling is with what we do. ’cause it seems like, oh, we’re just learning how to do the numbers and create the ratios and the formulas and everything we’re gonna track, and this is how we do it. But being able to get that story across, whether it’s internally or on the IR side, but it’s crafting that story and then knowing the key pieces of information to pass on to whichever audience. So when you’re thinking about, I guess, both executives and investors, how do you decide what metrics matter the most and where to shine that light and where that focus is? And is it consistent? I mean, I know there are your key metrics that you track quarter over quarter, but are there factors, levers that drive, Hey, we’re gonna really focus on this one area this quarter, or, or that kind of drive that?
Aurelien Nolf:
Yeah, it’s actually a very important conversation because, you know, today companies have so many data points, right? Like it’s, we, we, we have so many data points every day. And so it’s easy to, to be distracted, but you know, we, we try to start with what creates the value for the company, for the business. And so in, in the, in the Lyft example, that would be active rider or frequency or, you know, margin. And then depending on the audience, you’re gonna go very deep or not very deep, right? So internally, our executive, they need, you know, early warning indicators that something is going wrong, right? So we, we focus on the same metrics, but that’s the reason why we focus on them, is that’s helping us understanding what’s going on in the business. But for investors, they’re using the same metrics, but for them it’s more about understanding growth, right? And where we are going as a company. And so it’s, uh, an evolving thing. And, you know, those metrics are meant to change all the time. But, uh, you, you know, we need metrics that are both predictive internally and that are going to be meaningful externally. And, uh, you know, usually the operational metrics are much better than just the financial ones because they tell more a story about what’s an in the underlying business as opposed to just the, the financial output, which everybody can find in, uh, in our disclosures.
Glenn Hopper:
Yeah, and, um, you talked about the way investors use it for making estimates and, and forecasts for the company. And I think one of the things that you’ve spoken about before, and this is a, such a key shift to make, because it’s easy. And I, I feel like as the CFO’s role and as the role of, of finance has evolved over the last couple of decades, it used to be, um, like you said, if you’re, if you’re doing financial metrics, it’s a lot of lagging sort of p and l metrics is what people were looking at. But to the shift and being able to focus on what is going to drive growth going forward, what are we looking at that’s more of a leading indicator, um, on where we’re headed, but making that shift, I know it’s something you’ve spoken about before, and I’m wondering with that in mind, how has that changed what you’re looking at and, and sort of the whole decision making process at Lyft?
Aurelien Nolf:
Pretty much everything. I mean, it is been a, I’d say a big transformation. You know, when I think about the way we, we close our, our books every month, and when the FPN team is driving reporting the real estate that is occupied by metrics became, you know, bigger and bigger, like, it’s clearly something that we are now focusing on. And then at the end of the day, you know, we look at the, the, the lagging indicator, which is the, the p and l. But you know, when I, when we look at our business, we really focus on, on the riders, on their frequency, the number of drivers. We look, you know, region by region, we look more and more at rider cohorts and you know, how they behave and how they spend money and how they, we retain them. And so when we do that, not only are we able to explain our financials, but we are also able to guide the business and help them make better business decisions. So it’s not just about the bottom line and the profit, but it’s more about the, the business. Now. It’s a very challenging thing to do because historically, the financial systems and the eco, everything we do as a team has not been designed, you know, to handle this complexity. It was really designed, you know, the link between your ERP and your financial, your FPNA system, very basic things. And so we have to integrate more and more of those metrics in our forecasting processes, which, you know, many new tools are now helping people do.
Glenn Hopper:
Yeah, it’s so interesting talking to anyone in finance and accounting these days, because if you think about where we were trained, I mean, it’s obviously our domain expertise in finance and accounting, but we’re more and more having to become technology experts, and we need to understand the software that we’re using. I, I guess it kind of, it goes hand in hand with data and analytics, but once you start looking at data and data governance, you end up kind of leaning into having to be a bit of a <laugh>, a data scientist almost, and, and have those approaches. I’m wondering, as, you know, as you talk about linking systems, and when you are looking for those key, you know, the, the main KPIs that you’re looking at, the things that are levers that can control the business, how much do you feel like you’re going outside of sort of the finance domain and having to be a, a data <laugh>, sleuth or investigator and understand more about analytics maybe than you initially did? Are you having to lean a lot into that and understanding the data outside of systems and then just looking at data, going across systems? How much focus do you have there?
Aurelien Nolf:
Yeah, it’s, it’s a huge focus. I don’t think we’re yet there, but honestly, the future of fp and a is clearly hybrid between, you know, finance people. And data science is clearly at the intersection of both, pretty much everything that is driving our business and helps us tell a story is not available in our financial systems, right? I mean, just if you just start with that, you can download all the information from our ERP on our forecasting system, you’re not gonna be able to tell a story, right? And so we have to go to the data science teams, to their, you know, SQL models and, and all those things to be able to, to craft a narrative and really understand what’s going on. And so, you know, it’s, historically it’s been a lot of fp and a asking questions, and then, you know, this back and forth movement between the two teams.
But clearly it’s more the, the lines are blur right now. And, and it’s clearly the future of fp and a is clearly hybrid, uh, it’s key, right? We need to connect the data and to the business story and then connect this business story to the financial, uh, results. Otherwise, you’re just a spreadsheet nerd and you don’t know anything about what’s going on. And by the way, fp a is all about creating connection with the business leaders. And if you don’t know their business well, you are useless to them. And so being relevant and having a seat at the table really requires us to, uh, make that shift.
Glenn Hopper:
Yeah, and we’re gonna, because I talked to all guests about it, we’re gonna dig into AI and automation a little bit more later, but I also think it doesn’t matter what industry or profession you’re in, you know, there’s the existential crisis right now around, um, AI and automation and is it gonna take our jobs? But to the point of what you just said, where we provide value to the business is not in being that spreadsheet jockey. It’s not in, oh, he makes really great formulas. It is, what do we do with that information? And I think that as automation gets better, and as technology gets better and systems get better, and at wrangling this data and that we’re not spending all of our time sort of assembling the data and we have it, we can lean more into that value. And you mentioned the, the storytelling and, and telling the story, whether it’s to, um, investors or internal management.
And I’m wondering, with all that information, whatever the state is now, where you have to, you still have to spend a lot of time aggregating and, um, consolidating and, and coalescing data to, um, to get to the numbers you want, but then you have to shift and be able to convey that to someone in a way that isn’t just a wall of numbers and ratios and, and, you know, sort of too much information. So I’m wondering, once you gather all that and have that information, how do you connect that financial data with narrative to make it resonate with very different audiences between FP and a and ir?
Aurelien Nolf:
Yeah, I mean, it, it’s all about leveraging the data. I mean, in the, in the fp and a world, we do two things. We, we tell the story about what happened last quarter, right? And so using data and, you know, systems to be able to understand what happened in a very granular way, being able to identify outliers like market, uh, stage mode within your, your mode mix or things like that where things are going wrong, uh, being able to detect that very quickly and easily with, with data is gonna make a whole lot difference in the way you’re gonna tell the story about why you missed or beat your quarter. And then when, when, when we think about how we can use that, when we look forward and when we create forecasts, well, uh, the same, the same inputs, the same decisions, the same data points are gonna influence the way you shape your forecast.
And so you can just forecast and say, Hey, this is what’s gonna happen, or you can use this data points to influence the business and tell them, well, if you change this in that market, this is the alternative path and this is the, the, the better outcome for the company and for your p and l and, and, and, uh, for your business. And so, using dashboards that have the right data points that it is focused on the key metrics as we discussed earlier, that’s, that’s gonna help surface, you know, all those issues and all those opportunities and help people coming up with better insights, uh, and not just gather data. Because one danger for SPNA is to have want to own and know everything about everything, and then you create, you know, spreadsheets that nobody can use. ’cause it’s just too much.
Glenn Hopper:
I wanna drill into that a little bit more, because as a data nerd, I gotta say, I’m, I’m a little envious of the amount of data you must have access to. I mean, Lyft has to be generating just billions of data points daily. So there’s, I, you know, the, the, the quantity and quality of data out there has to be, but to your point, it’s a lot of it can be just noise if it doesn’t drive anything that you’re looking at. But finding those correlations and finding ways to tie it into forecast and coming up with something new maybe that you hadn’t seen before of, oh, wow, this is, you know, X is a predictor of Y or, you know, whatever you didn’t know before by having all that data. So I’m wondering first off, maybe what’s the makeup of your team between analysts? Do you have data science people on the team, or do you work with the data science group? What’s the internal makeup of, of your team?
Aurelien Nolf:
We, we are a team of roughly 60. And, you know, most people are finance people, you know, pure finance people. But you know, as I think about our analysts and our senior analysts on the team, you know, they’re very curious and very curious and very, you know, tech oriented. They love new technology, they love data. They’re very interested in the business. And so they partner with the data science team. So the data science team is doing all the heavy listing for us. I mean, as you, as you said, we have billions of data points every day. Like think about a ride. Uh, we know where, where you’re coming from, when you are going, who you are, you know, you are spending habits. And then we look at the mode, the price, the demand, the, so like, it’s, it’s huge. And so part of the challenges we have as a company is to make sure we, uh, really organize this data, make sure it’s consistent, it’s, and it’s usable, right?
And so it’s a team of curious people, and they, you know, they keep automating the processes. We have a finance hackathon, uh, you know, that our CFO, uh, got started a couple quarters ago, which is super fun. We have all those, uh, finance people on the accounting, FPNA side that are getting together and coming up with new ideas about how they can make us better, more efficient and worked, you know, in a more automated way. So I don’t have data scientists today on the team, although I think it’s gonna, it’s coming like if I don’t have them today. But I’m fairly sure that if you and I discussing in a couple years, uh, we, we will have more data scientists on the team, but we have curious people that are embracing those new tech and, you know, those new tools. Uh, some of them are, you know, learning about SQL and, you know, querying, uh, we have very complex databases and some of them are directly querying them, uh, with, with complex code. So, uh, that’s, I think it’s the future of, of FPN area.
Glenn Hopper:
Yeah, and I think you nailed it. And I’ve had, um, like a couple weeks ago, we had, um, two teams. We had the head of fp NA and the head of data science from Wasabi, a big, uh, web hosting company, had them on and talked about how they work together, and they spoke to the same issue, that curiosity. And I think that that curiosity is what makes you good at fp and a asking why, why, why, until you dig down to the bottom. And when you’re asking questions like that, and you’re intellectually curious and you’re driven to get the answer that’s going to open up new doors and avenues to you where you’re gonna figure out how to get the answer, and maybe it’s a stretch and something you haven’t done before, whether it’s a complex Excel formula or a new way to approach forecasting and or learning about machine learning or you know, what, whatever the case is, that curiosity is gonna drive you.
Aurelien Nolf:
Exactly. And you know, I, I’ve been through a similar revolution of tech disruption, uh, 25 years ago. I mean, I’m that old that when I started my career, you know, people were telling us, oh, you are, you are all gonna lose your job because of the internet, right? And look at us, we’re still here. And I think AI is gonna be just the same, which is, it’s more of a new tool. We’re gonna give our teams to be more efficient and do a better job. It’s not something that is, you know, replace business acumen. It’s not gonna replace, you know, business strategy and the fact that we need people that are hybrid between finance, data science and business, you know, advisory. Uh, that’s not gonna be replaced by any tool. It’s just gonna augment the, the team capacity.
Glenn Hopper:
I want to get to generative ai, but I’m trying to, I want to kind of close the loop on this. First, I’m imagining with as much data as you guys have, and you talked about, you know, a lot of times it’s, it’s not the financial metrics that are driving where, where the company’s heading. It’s the operational metrics. And with the amount of data you have, I would imagine that you guys have probably for years, are you using machine learning in your, in your forecasting, not just the traditional statistical models? So you’re using ML to bring in exogenous factors and all these, you know, multiple variables and everything in, in those forecasts?
Aurelien Nolf:
Yes. All the operational drivers are forecast, uh, through a machine learning process, yes. Okay. Yeah.
Glenn Hopper:
And is that done? Is your setup, since you don’t have data scientists on the team, do you become a customer of the data team that is putting those together? You work together to sort of build the model and understand what the Yeah, that’s right.
Aurelien Nolf:
The, the process is the, the rev ops team that includes, you know, a lot of data scientists. They are using machine learning to create those very complex forecast. And then we translate that, those data points into a, a financial forecast, and then we look at both together and we challenge each other on all the assumptions.
Glenn Hopper:
So yeah. Yeah, that’s kind of the perfect setup too. FP and a today is brought to you by Data Rails. The world’s number one fp and a solution data rails is the artificial intelligence powered financial planning and analysis platform built for Excel users. That’s right, you can stay in Excel, but instead of facing hell for every budget month end close or forecast, you can enjoy a paradise of data consolidation, advanced visualization reporting and AI capabilities, plus game changing insights, giving you instant answers and your story created in seconds. Find out why more than a thousand finance teams use data rails to uncover their company’s real story. Don’t replace Excel, embrace Excel, learn more@datarails.com.
And you talked earlier about a lot of your analysts are learning, you know, how to write SQL queries, and I’m sure dabbling in in Python as well. And with generative ai, you know, everybody’s vibe coding now. And if you can’t write a SQL query, you could put in, you know, the, the basics of it and have, uh, AI write the query for you and save a lot of time. Which is funny because I’m, you know, I always say no one would ever pay me to sit in front of a blinking cursor and write code for a job, but necessity’s kind of the mother of invention. And through my career, I’ve had to find ways to get access to systems. And sometimes that meant writing SQL queries and, uh, learning a little bit, bit of python or, or whatever the case was. But because that wasn’t my day job, you know, it would take me two and a half hours to write a, a long SQL query to get me what I want, but then it wouldn’t run because I left a comma out somewhere or something <laugh>. And I think about going back then, if I had generative AI to just Q <laugh>, qc, my, my code, my query, how much time I would’ve saved. And I’m wondering, is anyone on your team right now, are you using generative ai maybe for doing some vibe coding to get some additional kinds of reporting? Or, or how are you using, uh, generative AI right now across your organization?
Aurelien Nolf:
Yeah, it’s obviously, uh, just the beginning, but, you know, we are using AI to automate reporting, improve our analysis. So think about, you know, variance analysis and then improving our comms, right? So for example, we use AI to drive, you know, some initial variance explanations. Like you can dump the entire model and start to get a first pass. Like, I mean, honestly, it’s not yet something you can just follow to your bottom of recals, right? And so it’s, it’s the beginning, but it’s very helpful because helps you get to this first pass, which saves a lot of time. And then other things, like in on the IR side, Lyft has 47 covering banks, the, the analysts that are covering our stock, and they are promising so many reports, very hard for us to digest everything. You know, we now dump all those reports, uh, into an AI model and ask all the summaries, the risks, the opportunities, highlight, you know, the issues, highlight everything we, we need to know.
And in three minutes you get a very robust AI is really, really good with, with those reports and being extracting the, uh, the information. So we use that also for consensus analysis. You know, all those models that all those ana, uh, analysts are producing, we can use that to summarize and show out all the outliers, et cetera. So, you know, for now, it’s clearly about augmenting the team. I’m not reducing the size of the team because of ai. Like, we’re not yet there at all, and I don’t think we’d be there for a very long time. But clearly every quarter we see new tools, and those tools have new capacities that are helping us, you know, augment and, and with and, um, accelerate the way we produce information and insights.
Glenn Hopper:
Yeah, it sounds like you guys have, have figured out where, you know, obviously keeping human in the loop, but where you can get benefit. And I’ve gotta say, I’m right there with you. One of my favorite uses of generative AI is kind of the, even even when I was CFO, the first thing I do, I get the, the monthly financials and I see the, the variance analysis, and it’s all gone through and it’s been highlighted with, you know, uh, where all those variances are. But I’m gonna go through and do my quick summary of it and, and make my notes off to the side. But having generative AI go through and do that first pass and say, you know, hey, you know, revenue’s up 4%, but cogs are down 16%, something’s off here, or whatever. And, you know, highlighting stuff like that and having that first pass, it’s, it’s pretty amazing how quickly it can do that.
And that’s without even tying into pulling in GL data. And if you’ve got a system that’s in your general ledger can actually tell you, oh, hey, by the way, we didn’t have this big payment. You know, whatever the, the, the variance is that explains it, that’s where you start seeing real power. But then the other use that you mentioned is taking that like massive data dump of, of between, you know, financial statements, tables, and all the narrative and all the uh, uh, descriptive text that goes in and, and organizing and consolidating. I could see that being a huge time save for ir ir kind of reporting. Yeah,
Aurelien Nolf:
Yeah. It’s, it’s, it’s clearly, uh, I think gonna gonna change the game. By the way, when I discuss with analysts, they are also mentioning they are using it more and more on their side as well. I, I think it’s just making everybody more efficient.
Glenn Hopper:
Yeah, a hundred percent. And I’m sure, obviously with all the reporting requirements and, and compliance and all that, making sure that the models aren’t hallucinating is, is a very, uh, important part when you’re doing that. So you keep a human in the loop. Are there any things that you’ve found or any ways to sort of combat or identify when they do hallucinate? And are you seeing it, the model’s getting better just because of whether it’s the, the way the models are built or the way that your team is, is using ’em? How do you deal with hallucinations when you’re dealing with LLMs?
Aurelien Nolf:
Yeah, I, I don’t think we are seeing too much of that, but that’s the big reason why we need the people that are using those tools to be the experts in their field and domain first, right? And so I think it’s gonna be a challenge for the, the, the coming, you know, generations is you have to learn the basics because you have to be able to spot the issue in whatever the AI tool is, you know, speeding back at you. And so that, that’s why, you know, people are not going anywhere, it’s because you still need this expertise being able to understand the fine point and adjust whatever the answer is based on your own knowledge and expertise. It happens to all of us. Like you, you ask a question to ai, and like sometimes I ask, are you sure? And you’re like, oh, good point. It’s, it’s, uh, it’s very interesting to see, uh, even on the GPT models, et cetera, we say it will spot their own mistakes, but if you don’t ask, they won’t. So again, very important to keep, uh, experts in the loop, uh, for sure,
Glenn Hopper:
A hundred percent. Yeah, it’s interesting right now because I am seeing more companies today that have budget for big implementations of generative ai, you know, integrated into workflows, not just the employees using, uh, whichever tools they’re using. And I think the year started, so last year people wanted to learn about AI and, and how it might work, and then with all the pressure from leadership and, and boards and investors, okay, we’re gonna lean into AI now, and I think they’ve started to be budget, but you’re also seeing a lot of studies that some of these projects aren’t working. And, you know, the, the kind of the big massive projects are, uh, there was that MIT study that there are faults with, you know, but it showed something like 95% of AI projects fail. And I think it’s, it’s interesting right now because I think at the senior, at the leadership level, there are kind of two fears that are dictating what happens.
One is we’re hearing about competitors, how they’re using ai. We’re, there’s this FOMO of we better do AI or, or we’re gonna get left behind. And then there’s the other one that is that fear of hallucination or fear of what could happen to your data and all that. But then, and I, I think that that all kind of sits in sort of these top down big AI projects, but then there’s the bottom up where employers are putting the tools in the employee’s hands and they’re getting more and more comfortable with how to use them. And we’re really starting to see efficiencies and maybe, you know, it doesn’t, it’s a software expense, it’s not a big capital investment. So sort of labeling those efficiencies and understanding ’em, maybe it’s hard to see if there is ROI on a, you know, recurring user-based software expense or, or whatever. But how have you seen, even on your team employee usage and adoption, do you get feedback? Are there still some holdouts who are, don’t want to use ai, or is everybody kind of now starting to see it? What’s the kind of the mix there between excitement? And there may be an existential threat too, of like, this is really cool, it’s probably gonna take my job. <laugh>,
Aurelien Nolf:
What’s interesting is everybody’s curious and excited. Many, many people get back to their Excel model five minutes after they started, you know, they, they are experimenting with ai and then they’re like, oh, now I need to do my real job and I’m back in Excel. I mean, Excel is very deep in the, uh, PNA encrypt. It’s actually very surprising to see even people that are very tech savvy, they’re like, oh, don’t take my excel away from me. Right? And so I think it’s gonna be a journey. I think the, the, the worst thing we could do as leaders is to choose the tools for our teams and then tell them, now you’re gonna use this. I think that’s the recipe for failure. Uh, I, I mean, guilty, I’ve done it. I’m seeing it today. We have a lot of success with a workflow automation tool.
And, you know, the, the team is adding more and more processes to this tool, leveraging a tool to cut a lot of the processing time. You know, when we have complex spreadsheets, you have 20 spreadsheets, you need to put everything in one table. Now all of that is being automated as we speak, at least. And there’s a team behind it. And, and trust me, they are the people that are doing the work. They are not a VP coming in and saying, just because they’re boss, put pressure on them on you have to use AI or you are gonna be irrelevant. That never works, right? The, what works is when the team comes up with ideas. And so I think our challenge as leaders is to empower our teams and foster a culture where, you know, they’re gonna try and test things, uh, that are not necessarily expensive, expensive, but again, at least we have this finance hackathon, and honestly, the best ideas we had over the last two years are coming from that specific event is coming from the team themselves.
Glenn Hopper:
And I do wanna, uh, dive into your, your leadership a little bit more in a second, but one last, uh, question around ai, and I think I love to hear the way that you guys are using it and the way that you’re approaching it, because I do, in my day job, I’m, I’m doing implementations of, of AI to help, uh, the office of the CFO. Um, and I always kind of groan when somebody comes in and they, their mission with ai, I understand it’s, it’s technology, it’s automation, and it is gonna be more efficient. And if we can find ROI by doing more with fewer people, that’s great, but I always hate it when people lead with, we need headcount reductions outta this. It just feels not right. And it, I don’t mean, I don’t mean from a philosophical standpoint, I mean, it’s just, it’s a big ask right now if you think of a robot is gonna fully take over the, the entirety of someone’s job.
Now there are tasks, and we could find efficiencies there, but I’m, you know, we maybe look at your crystal ball right now. And, and I think we’re, we’re both using generative AI in very similar ways where it’s a tool for efficiency in what we’re doing right now. But if you look out in the future, does it fundamentally reshape, and I’ll say this about fp and a and about IR since you’re, you’re over both, but do you think it reshapes, I know they’re gonna stick with the Excel, but maybe they’re, whether it’s the time to deliver the amount of delivery, the, the depth of the analysis, do you think it reshapes how they formulate and, and, and deliver insight?
Aurelien Nolf:
A hundred percent. I, I, I think over time we’re gonna cut the, the processing time in half. I mean, I’m, I’m just making up those numbers by the way. I don’t have any data to back that up. But my, my, my intuition is over time, we, we are gonna cut the processing times for the forecast, all the reporting. We are gonna do a better job at, you know, detecting patterns earlier and, you know, bring that insight to the business teams. So yes, I think it’s gonna impact our work in a very meaningful way, but I think it’s just gonna grow the business. It’s as opposed to just shrink the teams, right? With I, the, the biggest, the most important part of the SPNA job is to sit next to a business leader. And that’s not go, gonna go away, even if we have, uh, all the AI tools in the world.
Glenn Hopper:
Yeah, a hundred percent. Before we start wrapping up, I do want to talk about leadership, because even been with a couple of great companies at Electronic Arts and at at Lyft and, uh, leading a large team right now, and again, this is another I love, that’s why I, I just love finance because you have to have this domain expertise, you have to have the technical expertise, you have to have the data expertise now more and more, but you also, there’s still that soft skill. These are still people we’re dealing with. We have to manage teams. So I’m wondering, do you have a philosophy on sort of building and developing your fp and a organizations, whether it’s your approach to how they’re set up to what sort of training path is, and I know you mentioned at EA having a great development path, and maybe that’s carried through, and I’m sure Lyft does something similarly. Could you talk a little bit about your team structure and your philosophy around that?
Aurelien Nolf:
Yeah, we, uh, operate in a very competitive environment, right? Hirings, FP&A teams in the Bay Area is very challenging, right? There’s a lot of competition, many great companies, compensation can be very high in some companies. And so for a company like List, it’s all about, you know, what we can offer in terms of environment to our, to our team members. And so, uh, when we hire, we really look for curious people that are gonna be able to, uh, adapt and, you know, that are gonna surface the problem pretty early. We are trying to foster a great environment in terms of, you know, safety, I mean, people feeling safe about raising issues, uh, challenging the business assumptions, having healthy debates with the business. They need to understand the impact of what they’re doing, right? And so asking the same people to prepare the same reports on and on and on, then they don’t know what’s done with it, doesn’t, doesn’t help.
And so we have a fairly flat structure here at Lyft, and so, you know, we promote direct access from the analyst to the CFO, trying to make sure everybody has exposure to the management team. We are also promoting rotations, uh, which, you know, is always challenging, but something we, we really like to do this week, we had someone from my business, FPA team going into my corporate FPNA team. And that to me is what success looks like, right? Because when you think about a career, there’s maybe, like if someone wants to stay in the corporate world, there’s maybe five levels, right? And so it’s not about being promoted, it’s about learning different things, learning, you know, learning about different parts of the business. And so, uh, that’s what we offer at least. And, and it’s this career path. And then, you know, our challenge is to give them the right tools. And so we, we discussed, um, about ai, it’s part of it, but, um, that’s the culture we’re trying to foster.
Glenn Hopper:
That’s great. I used to always joke that the reason I was able to move up in, uh, in finance was because I was really good at Excel and PowerPoint. And I didn’t realize until later that being good at PowerPoint meant I was able to actually tell the story and, and, you know, the Excels do the numbers and PowerPoints tell the story. And if you ask me at the beginning of my career, or even in my first CFO role, you know, what skills people starting out in finance needed to focus on and hone in, it would’ve been one thing. And I know we all love Excel, and I don’t think, think Excel’s going anywhere and having that ability to do what we need to in Excel to get to the numbers is important. But there are new ways that people can get to those numbers now.
And I, I think I, I wanna ask this, maybe this is a two-part question. One is on the technical sort of hard skill side, and the other is on the soft skill side. So if you were talking to, with your, uh, with your kid in college right now in accounting, talking to that next generation of finance and accounting professionals, other than the basics that they’re getting in their, their core domain expertise, education, what skills should people be focusing on to make themself kind of future-proof and to be prepared for the coming, whatever evolution happens in, in fp and a,
Aurelien Nolf:
There’s things that will never change. So as, as you said, the, the core, the foundational skills around, like, if you are a finance professional, you have to understand accounting, you have to understand the statements of cash flow. You have to understand all those things. Like, it, they, it’s not a, maybe you have to do it, even if you are only focusing on FPNA, that’s very important. Then the healthcares where they, they depend on, you know, your role and, and what you want to do. But clearly, you know, tech, data manipulation, complex formulas, all of that, super important. It was important 20 years ago. It, it’s still important today. It’s just the tools are different, but this mindset that you are gonna leverage technology to do your job is super important. But then I think when I look at, you know, who’s, who’s successful over a career versus who’s not really successful, I mean, it’s all about communication and business acumen, right?
So being curious, understanding the business, and then being able to tell a story. You know, I’ve seen so many of my colleagues when I was, uh, you know, more junior, they were like spreadsheet nerds, and they could, you know, they, those people that are doing all those very complex formula with a, without even looking at their keyboard and, you know, everything moves on the screen and look super cool, and you’re like, oh, am I behind? But then you realize that over time, that doesn’t make, that’s not very important, right? Because that’s what’s really important is your ability to connect with the business teams, understand what they’re doing, what’s important to them, how you can help them grow their business, and then how you can communicate those, uh, challenges and those opportunities. So number one, focus on your communication skills. How do you interact with, with, with leaders? That’s number one.
Glenn Hopper:
So key. So key. It’s, it’s sometimes easy to forget when you get very good at excel. You, you know, what is going on under the hood where you’re building all this stuff, but it’s the outcome that you’re being hired for is not the, uh, really cool formula. It is the insight that you provide from it. So I, I do think as automation moves on the, the ability, it really shifts your focus to what am I doing here? What does the, why, why do I work for this company? What do they want from me? Being able to provide that value rather than just building them a cool, uh, you know, nested if statement or whatever in a, in an Excel sheet. Yeah. Yeah. Okay. Well now I’m, we’re gonna, we’re gonna get to the part of the show where we bring it home with our two standard quest, uh, questions that we ask everyone. And the first one is, what is something that not many people know about you? Something that we couldn’t learn from your LinkedIn profile or online presence?
Aurelien Nolf:
So my family know, knows about it very well. ’cause I, I am pretty obsessed, but, uh, I think in, in the work environment a little bit less, but I’m, I’m a passionate outdoor guy, so I do a lot of, uh, very long, you know, running, uh, races a hundred miles up to a hundred miles. Uh, so I’m, I am very big about, you know, going out there in the woods, seven, eight hours every Sunday. And, you know, I’m kind of this weekend wire with, uh, my very modest abilities. But, uh, I’m very passionate about, you know, pushing myself, uh, out there. So, uh, something is very important part of my life.
Glenn Hopper:
That’s great. What a great balance too from being just so focused on, on finance and all the accounting and being able to get out in the woods and go do a, a long, uh, long trail run. That is, that’s awesome. So, um, any, any events coming up that, uh, are on your calendar?
Aurelien Nolf:
The Ultras? Yeah, I, so I just completed my first triathlon ever. And so that was super awesome in Santa Cruz. And, uh, we have a marathon with my wife in December, so, uh, very excited.
Glenn Hopper:
Oh, that’s awesome. Yeah. Wh which marathon?
Aurelien Nolf:
Uh, in Sacramento, the California now. Okay. In the marathon.
Glenn Hopper:
That’s great. That’s great. Well, good luck to you on that. Um, and are you guys, are you guys gonna run together?
Aurelien Nolf:
Yes, that’s absolutely, we are training together. Uh, it’s pretty awesome.
Glenn Hopper:
Very cool. Very cool. Um, okay, now everybody’s favorite question to close this out. What is your favorite Excel function and why?
Aurelien Nolf:
Before I answer that specific question, let me tell you that my team hates when I’m in their Excel models. So I’m kinda at a point where I’m not using Excel that much, but I would, I mean, index match is probably the obvious, uh, you know, this kind of a more sophisticated version of the vlookup. Uh, you know, I’ve been using that a lot, uh, you know, in my earlier, uh, in the earlier part of my career. But, uh, yeah, that’s pretty amazing what you can do with the X match.
Glenn Hopper:
A hundred percent. And I’m really dating myself now ’cause my first CFO role was back in 2007. I’m vlookup till I die, but I’m, if, if somebody’s relying on me to get to get into an Excel spreadsheet at this point, there’s probably a problem. They’ve probably come to the wrong person at this, at this point. But yeah, I know, and I’ve heard all the wonderful things about index match, but uh, until I have to use it, I’d just loo up. Is one of those things you just quickly do because, because I was doing it back when I had to. So <laugh>, uh, well, Aurelian, I really enjoyed, uh, having you on the show and, and hearing about Lyft and, and electronic arts and, and your experience and, uh, just really appreciated all your insights today. Thank
Aurelien Nolf:
You, Glenn. Appreciate it. And, uh, I hope we’ll, uh, discuss again soon.
