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From Data Rails, this is Welcome to FP&A Today.
I'm your host, Glenn Hopper.
Our guest today is Maria Goudo.
Maria is the Director of FPA and Treasury at Payjoy, a global fintech expanding credit access through smartphone-secured lending.
With 15-plus years in multinational banking and high-growth startups, she's known for transforming finance into a strategic data-driven function.
She holds an FPA certification, a postgraduate certificate in AI and machine learning from the University of Texas and an MBA from the University of South Florida.
Maria is passionate about integrating AI into finance, mentoring future leaders and advocating for women in finance and technology.
Maria, welcome to the show.
Thank you, Glenn.
Really excited to be here.
Yeah, I was so excited when we talked before the show because I feel like we're really kindred spirits and we're both crusaders for the adoption of AI and finance.
So I'm really excited about diving into some of these topics with you today.
But before we do that, I love your kind of your origin story.
You started your career in Ukraine as a group brand manager in the cosmetics industry, and then you transitioned into finance and eventually became a global FPA leader.
So I'd love you to kind of walk us through how that shift happened and maybe what lessons you brought from marketing into finance.
Absolutely.
Yeah.
So, you know, I like to say that it's not me who chose finance, it's finance who chose me.
Actually, after the U.S. job market politely declined my experience and my education from Ukraine.
You see, I started, as you mentioned, in cosmetics marketing in Ukraine, which meant I was living in brand launches, consumer research and market positioning.
But when I moved to the United States at the age of 23 with 300 bucks and one suitcase, I quickly realized that my experience and my education which at that point I already had a master's in accounting and economics suddenly it didn't matter.
So i did what i had to do.
I got my mba in finance, international business, while i was working three jobs and had no day off for three years consecutively because i had to pay for my school be since the credit was not achievable for me due to lack of credit history.
So i couldn't get student loans.
And throughout my mba i realized how analytical i am and i fell in love with finance, because it's um, the control tower of the business, where every decision and every number and every trend connect to the bigger picture.
So, for going back to my experience in ukraine, what i brought from marketing is two main things.
First is storytelling matters.
Numbers by themselves are just data.
It's only when you make them relatable to the audience you can influence people.
And that leads me to the second point.
You need to know your audience.
In marketing, it's consumers.
In FP&A, it's decision makers.
The principle is the same.
Tailor the message so they care.
A hundred percent and I love The storytelling aspect because years ago in finance we didn't talk about that.
It was just get the numbers and present the numbers.
But the way you become strategic in finance is by being able to tell the story and explain what the numbers mean, and not just reading off the results from a spreadsheet.
No matter how good you are at modeling or doing stuff in Excel or Python or whatever, if you can't tell the story, where's the value that you're adding to the company?
So I love that.
Wow.
So you came to the U.S., though, $300 and a suitcase.
I mean, that's kind of the you hear that story and the ability to build that into where you are now.
That's kind of an amazing passage.
And so how long, what year was that that you moved to the U.S.?
That's 2005.
So now everybody knows my age.
Just joking.
I'm not hiding.
I never hide my age.
I'm very proud of where I am in my life.
Well, and the other interesting thing is because you had already gone through university in Ukraine, your education started.
It's that much richer because you had that experience.
And then you had the US school experience.
And I know you talked before the show about being a lifelong learner.
And I think that what you're and just from what we sounded like then that that kind of goes along with the upskilling and transitioning in your roles.
So, and I'm guessing that that's what led you into AI, but even not every lifelong learner is going to go into AI.
So I'm wondering what was it?
You talked about analytics, but what is it that sparked your interest in AI and machine learning?
And I guess I want to kind of understand your path from strict finance to AI and machine learning and then leading you to study AI and machine learning at the program at UT Austin.
That's a great question.
I always joke that I am a very lazy person.
That's why I'm automating every process possible so I don't have to do it over and over again.
But on a serious note yes, as soon as I started my corporate career in finance as a financial analyst, I was automating every single task.
Every single report are because they were not really exciting me mundane, repetitive tasks and I was trying to cut production time and increase efficiency.
I always also believe that FPA they are not just for compiling, cleaning and checking the data, because FPA is financial planning and analysis, not formatting, printing and aligning.
I have not heard that before.
I love that.
So, yeah, and in a lot of cases it is, but I was trying to limit this to minimum.
So, and in addition, somehow very early in my career, in finance career, I got pulled into IT projects and I realized how easy it comes to me working with data, working with IT.
And eventually I realized that I am fluently speaking both finance language and IT language, and which made me kind of translator between two worlds.
And as I mentioned, I always love to work smarter.
I always love to work more efficient and I was naturally gravitating towards technology.
So over time, big curiosity actually turned me into a deep interest into AI and machine learning.
And I wanted not just to understand how it works but kind of get my hands dirty and get into it and bring it into finance to solve real issues without massive IT budgets.
So that's when I decided to pursue my post-grad degree in AI and machine learning from University of Texas.
And I graduated with it at the end of October last year.
See, I knew we were kindred spirits that because my origin story is very similar.
If I had something that I was going to have to spend an hour on every day or every week or every month, if it was a periodic and repeating task, I thought oh, I'm not going to.
I would spend 50 hours trying to automate one thing.
So I never had to do it again.
And that's it's that necessity is the mother of invention.
That's the real motivator to get good at automation and technology is when you realize the time savings, and it also makes you a champion and an advocate for it, especially now with generative AI and the promise that it brings.
I keep thinking of.
There's a quote that's often misattributed to Steve Jobs.
He didn't say it.
It was I can't remember the guy's name who did, but it's.
If you want to find the most efficient way to do a job, give it to a lazy person.
That's really been my whole career.
It's not that I'm like you.
It's not that we're lazy.
This is not the best use of my time.
You're paying me to be strategic and not just do some repeating tasks.
I 100% relate with your path.
We talk a lot about AI in the show.
I guess everybody's talking a lot about AI these days.
And it's interesting to me because I wrote a book on AI and finance over COVID.
So it was published in 2021.
Of course, back then, nobody, I mean, it was a very niche book.
Nobody was talking about AI and finance because the barrier to entry was you had to learn Python.
You had to learn data science.
And there was just, it was too much.
And if you're a domain expert in finance, it's a lot to say Now go learn a whole new area.
You and I did it, I know.
But for most of us, I have life.
I have other things going on.
I have deep domain expertise here.
Now you're telling me I've got to pivot and do something new.
But I think it's like ignoring AI right now would be the equivalent of ignoring the internet in 1995, or something like that.
But there's a lot more.
The internet, it's straightforward.
It is what it is regenerative.
Ai is very opaque and confusing and there's a lot of we talked about a lot of different kinds of fear around ai and we'll get to that maybe a little more in a minute.
But when we were talking before the show, you said there's a lack of basic ai understanding among finance professionals, and i would say that's true of marketing professionals, of people you know of, of HR professionals, all industries because up until GPT was released, AI was something that happened in the background.
It was movie recommendation from Netflix or product recommendation from Amazon.
It was all happening under the hood and we didn't really think about it.
But now it's front and center.
So I'm wondering from your perspective?
Yes, obviously there is a gap, because finance pros, as you know, are very busy and they're focused on their domain.
Do you think that gap will persist?
And if it does, how is that going to impact AI adoption in the field?
Yes.
And honestly, the first time it really hit me how big the gap is was at one of the conferences where I was a part of a panel discussion.
There was a question from their audience about model training and the guy literally used their quotes.
And that's when it hit me.
And he doesn't really know what's behind model training.
How are you training?
And there's not a lot of understanding what AI is. what ML is, what generative AI is.
So, and it's understandable because most of financial professionals are trained in pre-AI world.
We train on Excel, not Python.
We train to explain variances analysis, not train the models.
And therefore, AI seems like somebody else's job, right?
Data scientist, IT, engineering, but not finance.
However, in my point of view, without basic AI literacy, finance teams right now are either overestimating what AI can do, kind of it will forecast everything for me perfectly, I'm here just to push the button or they're underestimating and thinking it's just the hype that shall pass too.
And both are actually problematic for adoption and affinity, because overthinkers, Overthinking what AI can do and not understand how it works, can lead to unrealistic expectations followed by disappointment, followed by distrust.
Understanding what AI can do actually means that teams are missing out and not investing in learning or experimenting with AI so they can miss the opportunity completely.
However, by now, we all should understand that AI is here to stay.
It's not going anywhere.
And the sooner we, as financial professionals, embrace it, the sooner we can use it to solve real business problems instead of debating whether it's worth our time or not.
And I think it's not just us folks responsibility to learn AI, it's also organization responsibility to create opportunity to learn, to create training for the finance and other teams to at least have a one-on-one knowledge what AI is and how it works.
We use slightly different language, but I think we're both saying the same thing.
Will you tell me about what you were saying about the two main camps in finance, about their approach to AI?
So what I observed, at least in my experience, there's two camps, as you named it, called them.
The first camp is excited, but clueless.
And the second one is cautious but also clueless, but also they are convinced that the Skynet is coming for their job.
And the bridge to solve this problem is education.
Because if people don't understand it, they will not trust it.
And if they will not trust it, they will not use it.
And you don't need to be a data scientist to thrive in AI-powered finance.
But you do need to understand enough to ask the right questions, guide the outputs and build trust in the results.
So when I talk about AI and when I explain my folks about the models I'm using, I talk in plain kind of finance language, without jargon, without tech, and show examples.
So once people see how it connects to their everyday work, how it simplifies it, the fear kind of starts to fade and they understand it a little bit better.
And that's when the real adoption can possibly begin.
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All right, I'm going to ask you a favor.
I'm going to ask you to help make my argument for me.
So I've been training on AI for a long time because, just like you, I think that's how we overcome the fear is by understanding it.
Because if you don't know how it works, I've had people tell me when ChatGPT first came out this feels sentient.
How does it know how to respond?
And you walk through the statistical model and the probabilistic and how.
It's basically the same thing as the autocomplete text on your phone, but with a much bigger context window and the transformer model and all that.
And then they start to glaze over and start ignoring you.
They don't really want that level of detail.
But when I was first doing training I would dig into okay look, This is what machine learning does.
You know, you're going through classification, regression, clustering, all that.
This is what the algorithms look like.
This is how they learn on data.
And people would shut down.
They would say, I don't, I'm not trying to be an engineer.
I'm not trying to be a data scientist.
And I would say, yeah, but you have to understand this.
And I've, I've since I mean I still have my last book was very much on uh, It wasn't on how to prompt chat GPT.
It was more on what AI's true classical machine learning, is doing under the hood and how it could be expanded to use across finance even more.
But I'm sure you sort of encounter the same thing.
What do you say to someone if you're trying to tell them, oh, this is the great power of AI.
Let me explain to you how it's working.
And then you get that same sort of glazed overlook.
How do you approach that?
So actually I approach it in a very simple way, because I draw a parallel and I call AI a junior analyst.
And I'm telling people like, imagine you're hiring a junior analyst, just the one that never sleeps and doesn't require coffee.
So, the same as junior analysts, you train AI model, you provide it with historical data and with the correct answers.
And as the journalist will learn, the machine learning is kind of the same way.
And we kind of check the results and provide their feedback.
But as junior analysts grows in their career, They acquire more and more experience.
They can spot the arrow in numbers just by looking at numbers, not even looking at their what's happening?
Underlying formula.
They just see that numbers are off and so does AI.
So I draw really easy parallels to make it more engaging for finance folks and make it more understandable how the process works in the background.
Yeah.
And I think also it's that classic, if you're writing a book, you want to show, not tell.
And I think that really, where people start to get interested and want to learn, wait.
How did you do that?
Is when you actually have an AI project that's running successfully and they're seeing the results from it.
And we talked before the show.
I know you've done a lot since then, but I think for people who are listening, who maybe have never taken on a project like this, Tell us about your first hands-on AI project with automating the vendor reporting.
You kind of swabbed through that.
What led you to it?
Obviously, we're not going to make the sausage on air here, but what you did, what your approach was and what the results were.
Sure.
So when I started my post-grad degree and I was coding away without prior coding experience, I didn't do it to switch my career or to become a coder, just to understand the process and how it works.
However, this part really paid off and really quickly, because my team had a big pain point, big headache.
Every month we were producing a vendor expense detailed report by vendor for each function had.
It sounds simple.
But here's the reality.
We operate across 10 countries and only three of them are fully integrated within our ERP.
The rest are closing the books in their own ERPs and then mass uploading their data.
So basically, 80 of data, 80 of transactions, which is hundreds of thousands on a monthly basis, were missing the vendor name in a dedicated field.
And it was possible that it was mentioned in some other fields like description and memo, but all of them are freeform.
There's misspelling.
There's a lot of different text across those fields.
So producing these reports or any kind of meaningful information for their department heads, for the function heads, was very, very extremely difficult and was taken 40 hours a month.
One of my analysts was spending basically a quarter of his working time just to clean the data.
Therefore, I kind of scratched my head and I'm like, well, I think I know enough by now.
And I built a process that cleans and translates and normalizes the data because, as I mentioned, we're in 10 countries, so a chair on top was different languages on top of everything else.
And then my model.
I'm using their pre-trained model named Entity Recognition that pulls the potential vendor names out of this string of clean data and normalizes it.
And, as a last step, it basically clusters all the other transactions that were not identifiable with a vendor name into groups based on their description.
So now the entire process is taking several hours, including the populating publishing reports, versus 40 hours a week, 40 hours a month.
It was taken prior.
And this time we're actually using to work with the accounting team to fix the underlying data and to fix their process which, in my point of view, is going to add far more value in the long run than spending it on cleaning the data.
I love that.
And to me, the untold story in there is the analyst who was having to spend 40 hours a month doing that.
How relieved he must have been to get out of this mindless task and get to something that is meaningful and adding value to the company instead of doing that.
And i think that's you just laid out the perfect example of the promise of what we can do with automation, and i know there's all the talk about oh, what happens when it gets smarted up?
It's going to take all of our jobs.
It's i.
I don't know.
I mean i i don't have the crystal ball to see that, but i know every other technological change that has come along.
Some particular jobs have gone away, but new jobs are created and the efficiency gains.
We have it and i just nobody goes and gets their master's in finance.
I say this all the time.
Nobody goes and gets a master's degree in finance or accounting because they really love data entry.
Nobody loves data entry.
They're trying to add value there.
So that's a perfect example.
And I think when people see something like that working, that's when their ears perk up and finally they're like okay, tell me about this AI thing.
Oh, absolutely.
Yeah, we actually called it a vendor reporting week.
So we were dreading it.
And obviously, the person who doesn't have to do it anymore, very happy of not doing it.
And I think everybody's sort of grappling this with right now.
But from your perspective, I know there's rescaling and upscaling that has to happen.
But what are kind of the table stakes?
What are the must haves if your FP&A team is looking at rolling out some kind of AI system?
So I'm talking like data structures systems, whatever integrations.
You have to do what needs to be there first so you can start moving down the road with AI.
Yeah, you called it.
I call it.
Actually, before you start thinking about AI, implementing AI and what tools to use, you need to make sure you have your foundation in place.
Because I always say that AI is a roof, but your foundation is three main things.
Your data, your process, and your systems.
So what I mean by that is, your data has to be clean consistent connected, like same naming conventions, same timestamps, same metric logic.
You cannot have different calculations for the same metric across different teams.
Same data definitions across organization, that's very important.
So create a data dictionary and lock it.
Make sure your data is consistent because consistency is the key.
Without it, the model won't know what to trust and neither will you.
Second, you need to have integrated systems between HR, finance sales operations because, if you don't like, AI models will only see the half of the picture.
And if you're trying, for instance, to produce a forecast without knowing the sales pipeline and your inventory, you're just guessing and you're creating perfect forecast that, in reality, is impossible to accomplish.
And the third is you need to standardize your processes.
You need to have the same accrual methodology between the countries.
You have to have the same, you know, processes, how you book in, CapEx versus OpEx.
And because AI is learning on the pattern.
So if there is no pattern, if your accrual methodology is changing month over month, AI is going to just be confused right and produce you a very interesting result that will have no correlation with reality.
So make sure the process is standardized.
And if you get these three things right before you start even thinking about AI, your rollout of the automation of models will have a real shot inside.
So bottom line, don't get distracted by the AI hype.
Build the foundation first and the roof will hold.
And this, I mean, that all sounds beautiful to you and I.
And then some of our listeners might say oh, it must be nice to be in that ivory tower where you can talk about this.
But I'm going to follow up because I know we talked about this before the show as well.
And then there's like the change management piece.
So you have your systems in place and you have your data as good as it's going to get and you're ready to move forward.
But I think in your case I know Payjoy you're in 10 countries all with startup-like processes and it's a lot of moving parts there in that environment.
How do you introduce AI and machine learning into such a mixed maturity environment without sort of the human toll of overwhelming the teams or breaking processes?
Honestly, Glenn, very slowly.
Because...
You know, automation is always competing with other priorities that nobody canceled, like bonds, unclosed reporting results to executives and investors, preparing for the board meetings you name it.
NPJ grew significantly over the past several years, but our processes across the account in capital markets and FPA are still largely very manual.
I cannot just come and drop a big AI project and expect it to take off and solve all our pain points magically.
But the way I approach any process of automation is quite structured actually, as I learned, I think too, through other projects and experiences in automation.
So, first things first, I identify the real pain, something that makes the team grow every month, every week.
And once you know the pain, you truly need to understand the cause of the pain.
Otherwise, you will be treating the symptoms and not the cause.
Second, you need to involve the right people from the beginning, people who really understand the cause.
And they can translate it to the IT and clearly describe what we are trying to achieve.
Because tech people can actually build a solution or workflow that does not translate into reality and doesn't solve our business needs.
Third, you need to be agile.
So in our environment implicit pay joy.
Business needs change faster than we can say balance sheet honestly.
Tech cannot just disappear for three months and come back with a solution that we don't need anymore.
You need to build constant check-ins, very short cycles, testing by the end users, and pivot, pivot, pivot, adjust till you get it right.
And finally, quick wins.
You need to share your quick wins.
Don't wait for the full rollout and share them with your team and with your management, because it actually shows the results.
And even if it's all just a little part of the pain, this is a visible progress, turns skeptics into believers and creates momentum for even bigger change.
Yeah, I love that.
And that's very similar to how I work with clients every day.
And I love Agile as an approach.
I mean, I've seen historically so many projects that have this waterfall approach, where you're just waiting and waiting for this one behemoth project that keeps having scope creep and bloat and it just keeps getting pushed out and you wonder what are we doing here?
Because people are inconvenienced by the development of it, but they don't see the results.
So 100%, I think you're spot on there.
Yeah.
And involving the right people and users is very, very important, because I've lived through the pain when an executive was sold on the tool and the tool was implemented for like five months and was hyped and it was supposed to solve all our problems and was the best thing after the sliced bread.
And when they rolled it out, it thought nobody used it because it was just theory, not practice.
We have our existing teams and our existing teams have a skillset.
And I think for the past several years more and more have had more of going from pure FPA to broader BI.
And maybe a lot of people have team members with some data science skills on their team too.
And I'm seeing in the future.
To me it looks like all those skills are going to be even more important.
But for right now, if you're, if an FPA leader is looking at their current team and they know they've got some great analysts and some people are great at Excel and there's probably some people in there who can write SQL queries and, you know, do power pivots and power BI and all the skills that they have there.
But if you're an FPA leader and you're looking to upskill and reskill your current teams, or if you're hiring new talent, which is a, I think we are going to start seeing budget in there for bringing in new positions as well.
But how do you get your current team or staff to have a team that is ready to effectively move forward with AI and automation?
You're absolutely right, because their skill mix is changing fast.
Their hard skills are evolving, but soft skills, they are becoming just as important, maybe even more.
First, let's talk about hard skills.
As I mentioned earlier, you need enough literacy and AI and AI models and what they're saying you to understand them.
Also, you need to be data smart.
You need to be comfortable with tools like Power Query SQL, even a little Python just enough to have meaningful conversations with other teams.
NIT team specifically, you need to know process automation and workflows, URPs and APIs.
Know the concepts that stand behind AI, like what regression is, what overfitting means and how to tell if your model is drifting.
And always remember.
Also, we work with the sensitive data, so governance and ethical use are non-negotiable in the future world.
So then there's soft skills, the ones that make you actually strategic advisor and not just data producer.
Storytelling is huge because executives, they don't care how pretty your report is, they care what it matters to them, what it means to them and what it means for the business.
And you need to be the one with the answers.
You need to be also very strong in cross-functional collaboration and speak not just finance language, but also product risk fraud engineering, et cetera.
You have to have very strong business acumen.
And on top of that, you have to be very adaptable.
You have to have critical thinking and good listening skills.
And that's the mix that will actually make future finance professionals successful, because hard and soft skills now share the driver's seat.
One shows you the way and the other one gets you there.
Yeah yeah, that's great guidance.
Um, let's step back on.
I'm i'm hung up on this agile thing too, so i think it's important, when you're looking for those quick wins, for people to understand what these high impact ai projects are, and i think that there's two parts of it.
You've got to understand the complexity of of the projects and is it feasible?
And then, on the other hand, what is the impact of?
And you kind of have to balance those.
But i'm wondering if you're evaluating projects to run, what guidelines do you use?
Are there some examples beyond uh, for like forecasting that we always talk about?
But are there other areas where ai can deliver measurable results?
Yes absolutely and, to be honest with you, we are Still pretty new in implemented AI in my current company because we developed so fast.
So as I mentioned, our foundation is not there yet.
However, I did do another project and that was in terms of forecasting, because we were forecasting in USD across all the countries, because our functional heads allocated in US.
So it was easy for them to look at the consolidated level and just project their travel expenses and other expenses into the future and use the consolidated level and not split it by countries.
However, in reality it works in Excel.
But in reality it really didn't work, because If Mexican Peso appreciates our USD, expenses translates in USD actually higher, but so is our revenue.
And another problem was granularity, because we were just projecting kind of a consolidated GL level and not by any kind of detail.
So once I did the vendor reporting and I cleaned the data, we were able to actually have this base to implement their forecasting, to build an AI model that forecasts our fixed expenses using the data and it's forecasted on their vendor level and in local currency.
So even if the vendor is working with different countries, it will forecast the vendor for each country specifically in their local currency.
And it looks at the past seasonality and trend, also incorporates their inflation, local inflation.
And then, as a last point, it summarizes.
It translates everything in USD and then provides us with a summary by GL, by a function, as well, as we have their detailed information in the forecast for each vendor.
So now Function has still see their forecast the same way they used to.
Nothing changed for them, but we as an FPA has a full breakdown behind every number.
And instead of just reporting variances, we actually can explain that what's driving the variance to the budget and segregate the effects impact from a specific vendor or project.
And talk a lot about quick wins and change management.
And on that change management, a big part of that.
And I always, whenever I'm training on this, like for you and I, it came very naturally to us.
We are AI enthusiasts.
And I know you've talked about selling AI to the enthusiasts first, because if you or I just come in and we're rattling our saber over and one corner and are just by ourselves, it's harder to get people to adopt the process.
But if we have some evangelists out there that are helping spread the word for us, I think that goes a long way.
Sometimes it happens naturally to sort of light those flames.
Getting those evangelists out there early will help with the change management.
So could you walk us through kind of what your quick win strategy is for building trust and momentum inside the finance teams and getting those AI enthusiasts out there?
Absolutely.
And it's not rocket science what I'm doing.
It's pretty simple.
I start with the enthusiasts and I bring them in early.
I involve them.
I engage them.
I make them accountable.
I teach them so they understand.
And that's the people who actually will help you to steer the ship of change, right?
From there, I look for the quick wins.
And what quick win for me is something that can be delivered fast, right?
Like in a couple of days weeks, not months or years, and has results that you can literally show in before and after comparison right?
Like before Jenny Craig and after Jenny Craig.
The trick is to make the benefits so obvious that no one can argue with them.
So then, when I have a couple of those quick wins, I bring skeptics on board and I skip the hype, the buzzwords and the tag jargon and I go straight to what matters to them.
For instance, look this process that used to take you hours, sometimes even days, now takes you minutes.
That's when the enthusiasts who are already trained also step in and help me delivering this message.
And that's when the light bulb goes on and they see that, oh yeah, that actually helps and that actually can move the business into the right direction.
And that's why I communicate in even the smallest wins I have, because that's helps to really build the momentum and trust.
Yeah, that's great.
Again, it's when people see it, understand it, it's having an impact on them.
That's when you start converting the new evangelists, I guess.
All right, we're getting close to time here, because I know you've thought about this a lot.
If you could design the FPA function of the future just completely from scratch, how would AI and machine learning be embedded from day one?
If you just had the magic wand and you could set up the perfect environment and where this was going.
Absolutely.
So at first I want to talk a little bit about past and present, because a lot of finance professionals spend about 80 of their time on compiling and fully compiling cleaning, checking the data if the numbers are right, and 20 of that on actually understanding what these numbers actually mean.
AI flips this ratio, allowing us to actually spend most of the time in understanding the numbers influencing strategy and turning insights into actionable advice for business.
So if I would build a FA team from scratch, I obviously would embed AI from day one into their data-heavy repetitive tasks.
But the F&A of my dreams can be built in isolation.
We are not a siloed team.
And AI-centric team, like F&A team, requires a strong foundation, as we talked.
And much of that foundation is actually built across organization by different teams.
So it really takes a village to create an AI-ready business.
And to be honest, I'm still learning every day.
I'm traditionally a trained finance professional, but one thing that I learned so far is that not everything that counts can be counted.
And AI still needs human guidance to focus on what truly matters.
So I don't think AI will replace us soon, but knowing and being curious, experimenting with that actually can shift FPA from being just budget gatekeepers to one of the most strategic future ready functions in our organization and move FPA from there cost center to profit center.
And that's not just a tech update.
It's a true, real mind shift in what FPA is and its role and the role it plays in driving business forward.
I love that.
Yeah.
And based on the number of calls I'm having every day, I know that's where finance teams are trying to go.
Wow.
I can't believe how quickly this went by.
And there's two questions that we ask every guest.
So I can't let you go till we bring this home with our boilerplate question section here.
But the first one, what is something that not many people know about you?
And this is something that wouldn't be on your LinkedIn profile or we couldn't find just by Googling here.
Oh, absolutely.
One thing is growing up I was a complete introvert, so much so that I was terrified to talk to people, even people I knew.
And it came across a little bit sometimes with people.
They misunderstood me and thought that I'm uninterested or even arrogant.
So when I started my corporate job at 17, I really quickly realized that it wasn't serving me well.
So in my early 20s, especially when I moved to this country, it combined with my accent and me being very, extremely anxious about my accent.
So I decided to change it and I pushed myself, and I pushed myself hard to talk in front of the audiences, to go to the events, to speak to people I didn't know.
So over time, I turned myself into self-created extrovert.
In fact, like people who meet me right now, they cannot believe that this is true.
That said, the introvert in me never fully went away because after being in the environment, around a lot of people, big events I need solitude.
And in a lot of times my husband, my daughters and I, we just escape to the mountains, to the woods for weekends, so I can reset and recharge.
Yeah, I completely relate.
And this may sound crazy as a podcast host, but I am a complete introvert too.
And I just today got back from speaking to 60 finance professionals at an AICPA two-day event where we were doing training and workshops and discussion.
And now we're recording this podcast and I've got one more this afternoon.
And then I think I'm just going to go crawl in a hole.
Actually, I do this sensory deprivation thing.
There's a space around the corner where you go and it's just you're floating and no lights and everything.
So I'm going to do a long session this weekend with that, I think.
Yep.
Sounds familiar.
Okay.
Everybody's favorite question, and I know you're a Python person and you use other things, but also I know every data scientist I've talked to.
No matter what other skills you have, it's amazing how much time we spend in Excel.
So I bet you have a good answer for this one.
I'm going to ask, what is your favorite Excel function and why?
That's a tricky one because I don't have one.
Because I have two that I use a lot and building reports and models still use, still my favorites.
So the first one is VLOOKUP plus Match.
It's my go-to when I need to have to make reports where columns or dates always moving or changing.
So Match finds the position of the column I want to look up and pulls the right value for me.
The second is Offset.
And honestly, I
Did not like when pivot tables, I had to update the data range all the time.
So tables was one option, but then you hate tables because writing formulas with tables is oh my God.
So I learned that offset can actually help you to automate the data range for your pivot table.
And it automatically will grab new columns or rows when the data range changes.
But it also helps you to look up to the left. when you need it.
So it's kind of my other secret weapon.
So between those two, I kept my reports automated and easy to update.
Love it.
Love it.
Two excellent, excellent answers.
So Maria, I really appreciate you coming on.
I appreciate you out there spreading the same gospel that I am and keep up the great work.
And yeah, I've really enjoyed having you on the show.
Thank you.
