In this episode of FP&A Unlocked, host Paul Barnhurst sits down with Jason Brisbane and Tanvi Lal to discuss the economics of generative AI, why traditional SaaS metrics fall short, and how finance teams can forecast AI costs. They explore token pricing, AI budgeting, and the role FP&A can play in managing AI investments.
Jason Brisbane is the CEO and Founder of FinHelm, with over 15 years of experience in FP&A technology. He developed the UA-FP&A methodology and Uncertainty Exposure Score to improve forecast reliability. Tanvi Lal is an investor at Intuit Ventures, focused on AI and fintech. Formerly at Deloitte, she is also the co-founder of VC Unleashed and specializes in AI economics and usage-based pricing.
Expect to Learn:
How AI token pricing affects project costs and profitability.
Why traditional SaaS metrics don't fully capture AI economics.
How to forecast AI spending using scenarios and cost ranges.
Why labor and orchestration costs often exceed token costs.
How FP&A teams can guide AI adoption and investment decisions.
Here are a few relevant quotes from the episode:
“Your most engaged customer is actually your least profitable with AI unit economics.” - Tanvi Lal
“We have to train our brains to not look at variance as a bad thing.” - Jason Brisbane
Jason and Tanvi explain why AI budgeting requires more than tracking token usage. They discuss project complexity, human involvement, and changing model costs. They also highlight how FP&A professionals can improve forecasting, measure uncertainty, and collaborate with business teams to manage AI spending effectively.
Follow Jason:
Website: https://finhelm.ai/
Follow Tanvi:
LinkedIn: https://www.linkedin.com/in/tlal/
Intuit Ventures: https://intuit.com/ventures
VC Unleashed: https://vcunleashed.xyz/
AI Budgeting Calculator: https://ai-budget-calc.vercel.app/
Disclosure: Portions of this episode (such as the introduction or promotional segments) use AI-generated voice narration produced under human editorial review.
Earn Your CPE Credit
For CPE credit, please go to earmarkcpe.com, listen to the episode, download the app, answer a few questions, and earn your CPE certification. To earn education credits for the FPAC Certificate, take the quiz on Earmark and contact Paul Barnhurst for further details.
In Today's Episode:
[00:00] - Trailer
[07:21] - Understanding AI Token Costs
[16:46] - Labor and Orchestration Costs
[23:56] - Forecasting AI Project Costs
[25:21] - Managing Cost Uncertainty
[30:58] - Learning From Forecast Variances
[38:59] - How FP&A Can Lead AI Strategy
[44:05] - Communicating Forecast Uncertainty
[49:29] - AI Budgeting Advice
[51:15] - Essential FP&A Skills
[56:06] - Closing Remarks & Thanks
Full Show Transcript:
Host: Paul Barnhurst (00:00):
Welcome to another episode of FP&A Unlocked, where finance meets strategy. I'm your host, Paul Barnhurst, AKA the FP&A guy. And each week we bring you conversations and practical advice from thought leaders, industry experts, and practitioners who are reshaping the role of FP&A in today's business world. Together, we'll uncover the strategies and experiences that separate good FP&A professionals from great ones, helping you elevate your career and drive strategic impact. This week, I'm joined by two guests. I'm thrilled to have Jason with me. Jason Brisbane, welcome to the show.
Guest: Jason Brisbane (00:35):
Thank you.
Host: Paul Barnhurst (00:36):
And then I also have Tanvi Lal. Welcome.
Guest: Tanvi Lal (00:39):
Thanks so much for having me. Hi.
Host: Paul Barnhurst (00:41):
Yeah, excited to have you. All right, so we're going to go ahead and jump into things. And before I get into the questions, I'm just going to take a minute and let each of our guests introduce themselves. So Jason, if you could just take a minute and maybe tell our audience a little bit about yourself.
Guest: Jason Brisbane (00:56):
Yes. Hi everyone. Jason Brisbane. My journey through FP&A, I started my career at Adobe. I was fortunate enough to be one of the first financial rotation program individuals. I spent time in their corporate FP&A group, tax, treasury, internal audit. After that, I ultimately landed on their digital media FP&A team, which essentially became the Creative Cloud. And that journey brought me through product management and product development for the office of the CFO. And now I'm building FinHelm. FinHelm is the platform that I wish I had as an FP&A professional. We need a quality metric to grade our own work, and that's what FinHelm strives to do.
Host: Paul Barnhurst (01:36):
Great. All right, over to you, Tanvi.
Guest: Tanvi Lal (01:39):
Thanks so much for having me, Paul, and excited to be among good company, Jason. My name is Tanvi Lal. I'm currently an investor at Intuit Ventures, which is Intuit's corporate venture fund. So we do growth stage investments across applied AI as well as fintech. So I've spent a lot of time in and around FP&A, primarily from evaluating FP&A platforms and understanding what our product teams at Intuit need as it comes to supporting our end customers, which are primarily small and medium businesses as well as mid-market customers. So excited to dig in here. This is an area I'm really passionate about.
Host: Paul Barnhurst (02:13):
I love to dig in and Tanvi, I think it's a fascinating time for the FP&A platforms. It is for all finance software. I imagine the evaluation has changed a lot over the last few years, what you're looking for, what you see, how you think about things with AI.
Guest: Tanvi Lal (02:28):
Yeah, absolutely.
Host: Paul Barnhurst (02:30):
All right. So this is a question I like to start every episode with. And Tanvi, we'll start with you. If I asked you to define great FP&A, how would you define it?
Guest: Tanvi Lal (02:39):
Yeah. From my lens, and again, as a not FP&A professional, but just what I've observed, I think great FP&A professionals truly, truly understand the way the business works. And I mean, they know how a product gets shipped, they know which teams are involved where, and they bring that context into how they approach the financials, into how they approach thinking forward, and also into how they build trust and rapport internally. So I think understanding of the product, how the org works and how you ship and deliver as important as what the revenue stream is and how you account for that and forecast it.
Host: Paul Barnhurst (03:15):
Okay. I appreciate that answer on knowing the business. I think that you make some great points there. Jason, any thoughts?
Guest: Jason Brisbane (03:21):
I would say all the same things Tanvi mentioned. Have a great FP&A team, you're beyond managing the reports. And as we talked about, you have a pulse on the business, know how to drive the business forward and having ownership of the assumptions that matter.
Host: Paul Barnhurst (03:38):
Got it. Thank you for that. So just so our audience knows, today we're going to jump into the economics, forecasting the cost behind generative AI tokens. So we're going to spend a lot of time talking about that. And where I want to start is, and we'll go with you, Jason, and then Tanvi, how did you become interested in the economics of GenAI? How did you focus on that?
Guest: Jason Brisbane (04:03):
For me, what drove me there, Paul, is when you're thinking about platform costs, typically in FP&A, it's for the most part fixed. You know what that's going to be and you can have ownership of it. You can grab your arms around it, I would say. Generative AI, we're just in a new world where the cost is variable as soon as you commit. So that creates an opportunity for us to have a forecasting layer in terms of being able to essentially own it. And so that's kind of drove my interest here.
Host: Paul Barnhurst (04:35):
Makes sense. It's really interesting because typically, yeah, most of your platform costs are fixed. How about you, Tanvi? How did you get interested in the economics? I know you've done some reports and stuff on this.
Guest: Tanvi Lal (04:47):
Yeah, similar approach to Jason, my vantage point is really from assessing and analyzing startup performance. So I started to realize as I was digging into application layer AI companies that traditional SaaS metrics, which we relied on to assess how efficient and how well performing the businesses from a financial lens just don't lend themselves very well to AI companies. What I really want to see and understand is how efficient is your AI spend? How effectively are you driving outcomes for your customers? And what is your customer specific margin? Your most engaged customer is actually your least profitable with AI unit economics. So I started approaching it from that lens and really came to understand that there isn't infrastructure built for this or not a lot of startups are actually tracking these metrics. So that's where some of the work I've done to try to create what are the metrics I want to see and how do I want to think about this as an investor going forward to make the right bets.That's where I'm coming from with this.
Host: Paul Barnhurst (05:51):
Yeah, I would imagine when it first came out, everybody was still looking at SaaS metrics like, okay, well it's not that big of a cost. They
Guest: Tanvi Lal (05:58):
Still are. We still are rather.
Host: Paul Barnhurst (06:01):
Yeah. But I would imagine with some of these companies, it has to be a huge line on the P&L. I mean, it totally changes the economics. You want that super high gross profit you always want to see with low COGS and all of a sudden with variable, it really changes the equation real quickly.
Guest: Tanvi Lal (06:19):
Yeah, absolutely. And there's no standardization of where these costs are being held and how to really parse any AI or research spend that's in the statements.
Host: Paul Barnhurst (06:29):
Yeah. I was talking to somebody who works at pretty much the biggest social media company in the world. Most people should be able to figure out who that is. And they don't allocate any of their AI spend to cost of goods sold right now. It's all in operating expenses. And I'm like, that's kind of tough to look at. As that becomes bigger and bigger, it has to really make it hard to really understand things, I would think, to a certain extent. But I imagine we'll get standards eventually, kind of like SaaS. It was the Wild West. We never knew what bookings meant when somebody first reported it or what recurring revenue was. And you dig into it and you're like, "Yeah, that doesn't work. That's not real revenue." Where I'd love to start, we'll start with some basics. I think most people have a basic idea of what the LLM is, but I don't know that most people really know how it consumes information.
(07:21):
We've all heard the term token, but how does a token translate into a cost? How does the LLM absorb information? Tanvi, could you share a little bit on that? I know you've done some work there.
Guest: Tanvi Lal (07:33):
So a token, to your point, is the way LLMs consume information. And you can think of a token as more or less a word, depending on the length of the word. Maybe the word is absorbed in two tokens or three tokens. So LLMs are essentially anything you input into your ChatGPT or into Claude, your input text, any files that you're sending, any webpages that you're sending, all of that will be consumed by the LLM in the form of input tokens. And these are typically priced per million, so you'll pay a set fee for one million tokens. The same concept applies when the LLM gives you an output. So the text, the document, whatever it's generating for you is also measured in tokens, and these are called output tokens. So these are also priced similarly X amount per million tokens. However, output tokens are priced much, much higher than input tokens.
(08:37):
And the other important thing to realize is within one specific chat that you have, which is the context window for the LLM, every time you send in a new query, you're actually sending the entire chat back to the LLMs. So the LLM re-reviews everything and then gives you the output. So as you keep using the same chat, your input tokens will exponentially increase because it compounds. And so that typically is what drives up input token costs. But again, those are quite a bit smaller. I think output tokens are really where you see higher costs with the deliverable. Or alternatively, if you have hundreds and hundreds and hundreds of pages in your input, then you'll see a higher cost on the input.
Host: Paul Barnhurst (09:24):
Yeah, I would imagine, especially people who are coding, because your prompt is going to be much shorter, even if you have instruction documents and everything than the code that comes out. So I can see where the output gets really expensive.
Guest: Tanvi Lal (09:37):
Yeah, absolutely. And you need all of the context in the chat for the LLM to actually, what have you tried, what's worked, what hasn't? LLMs give you the sense that you're talking to a person or somebody who's just talking to you back and forth, but really every time it's just a net new call and there's no real relationship there. So you need to resend the information every time.
Host: Paul Barnhurst (09:58):
Yeah. Speaking of relationships, it's been interesting. I've heard something that 40% or 50% of all people have asked relationship advice from LLMs. And so it's really interesting to see how people treat it like a human and conversations, but it's zeros and ones and probability.
Guest: Tanvi Lal (10:18):
It's basically a very, very strongly enhanced prediction algorithm, and that's what you're talking to.
Host: Paul Barnhurst (10:24):
Yeah, it's basically something that's been trained on pretty much the world's data. So it's really, really good at predicting what the next word should be, what's the next output should be. Anything you want to add on the whole tokens and how they consume or any thoughts there, Jason, from your end?
Guest: Jason Brisbane (10:39):
I would say just in regards to Tanvi's work, which has given us a roadmap to be able to better understand this in terms of project estimation, it's been eye-opening to understand the impact of output tokens and how to account for tokens in regards to token ratio toward orchestration ratio in terms of implementing a project, leveraging AI.
Host: Paul Barnhurst (11:04):
Yeah, no. So great segue. I know you've done quite a bit of work on that, Tanvi. We want to talk a little bit about the project you did, because I know you did a lot of work on modeling the cost, what you did, what the takeaways from the exercise. Maybe take us through that.
Guest: Tanvi Lal (11:18):
Yeah, of course. So in my journey to better understand token economics and having seen headlines like Uber blowing through their entire token budget and Microsoft not renewing their Claude subscription, I really started thinking, I was like, listen, this tech just needs to be better managed. We need a framework to figure out what to use AI for, understand which types of asks are really driving up costs and really create some guidance and some structures so that organizations can incorporate AI in a financially responsible way. So what I did was essentially bucket out what are the main types of tasks you do in a business? So maybe you're doing coding, maybe you're doing financial analysis, maybe you're doing content, maybe you're doing something else. So I created six buckets there and I worked with Claude to come up with dry runs. So I actually ran through various asks within those six buckets and I counted, Claude counted, Claude did most of it, but through my guidance.
(12:21):
And so counted the number of input and output tokens that were coming up based on these tasks and used the latest pricing to figure out what was the actual cost of AI usage to do this task at a low complexity, medium complexity and high complexity. So that's kind of the backend model that is powering this AI budgeting calculator I put together where you can describe whatever project you're thinking through in natural language and Claude will assess, okay, what of the six categories is it? Is it low, medium, high? And it'll spit out an approximation of how much this would cost from an LLM perspective. Some caveats, when I did this work, Fable had not been released, so I'm sure actual costs are higher. And really what I wanted to get a sense of is how much does it take to run through this one time where actually in reality you're iterating through the work a few times, so you're probably going to need to compound or multiply the cost that you have for once around.
(13:23):
And in addition to this, what I also did was trying to estimate the labor spend that goes into it to see, hey, are you actually using that less labor spend? Is the overall cost more or less? And really what I found is that even though your token costs can be high, it's always, always going to be the personnel and the labor costs that far outweigh the costs of tokens. So really at the end of the day, the concept isn't how do you effectively lower your tokens, it's how do you create a better process with what AI can do so that you are creating work faster and the aggregate cost is lower with human intervention.
Host: Paul Barnhurst (13:59):
So it sounds like you mentioned the token cost is really in the big scheme of things for specific tasks, not when you're experimenting or big huge runs multiple times, but just the basic task is usually really not very expensive. If you're using the right model, you've optimized the process. So it's really the labor and more about the optimization of the process with AI than the token cost. Is that kind of what I'm hearing?
Guest: Tanvi Lal (14:29):
Yeah, yeah, that's accurate. And I think the type of task matters a lot. I mean, content creation tasks are basically free. They're so low costs, but coding, for example, just continuously running your Claude or your Codex in the background all day running coding prompts, that is going to eat up a lot of tokens. Really deep or comprehensive financial analysis actually generating a comprehensive Excel takes up a lot of output tokens. So I do think the tasks that you are doing matter as well. And I think this is where the ROI comes up of, okay, if I'm spending $10,000 on coding tokens, what am I actually seeing and is it worth it or not?
Host: Paul Barnhurst (15:08):
Yeah. And you make a great point. We'll get to you here on a question in a second, Jason, but I think when you mentioned Excel files, I hear a lot of people saying it did hardly anything in Excel and I blew my budget. Well, you're basically asking - Well, my dad said that too. Work in a free form file where it can go anywhere and one formula, unless you're using dynamic arrays, can spill across a thousand places. That consumes a lot of tokens in a hurry.
Guest: Tanvi Lal (15:33):
Yeah, absolutely. I'm just laughing. My dad had that recently where he's using Claude's free version and he was trying to format a doc and he is like, "Well, before it even spit out the doc, it ran through my token usage." I was like, "Yeah, well, it takes a lot of tokens to output a formatted document. The formatting is actually really tough for the LLMs. You could spit out a really ugly version, but the formatting takes time and tokens and effort.
Host: Paul Barnhurst (15:58):
Kind of funny in that when it first came out, Microsoft released, they built an LLM that was all around designed to analyze Excel files. The first one they did is about three years ago. I remember reading the article. It couldn't handle formatting. There was a long list of things it couldn't do. It had reduced consumption for reading it by 97% by the way they were parsing it and everything. But compared to what they can do today, that I was like, okay, they're not getting there anytime soon on spreadsheets. And the next thing you know, you're like, oh, they can do it, but it's costly. So Jason, would love to get your thoughts. We've talked a little bit, the human cost is expensive, but from a finance standpoint, as companies are looking at GenAI, because FP&A, everybody's seeing the Ubers of the world and all these others that blow their budgets or token maxing and all this stuff.
(16:46):
What are some of the costs? How should we be thinking about this beyond tokens?
Guest: Jason Brisbane (16:51):
I would say in terms of cost of labor, cost of resources. So I actually ran an exercise on Tanvi's calculator. I took a typical FP&A project. I just asked it, "Hey, give me an estimate of building a three scenario cash runway model." When I ran the estimate, it was amazing. I got a range first off, and I got a confidence level at that range. But in terms of token cost to orchestration, I would say the ratio was 99 to one in regards to, hey, less than 1% of it was token versus orchestration was the other 99%. So it really points to when you're thinking about this from an FP&A perspective, but then also as an organization, you want to leverage that on exercises that are going to provide some sort of value. But hopefully when you do that, it's going to navigate your resources to really provide a value add versus, hey, manual things that the LLM you can hand off to.
Host: Paul Barnhurst (17:56):
And when you say orchestration, just so our audience is kind of clear, elaborate on that. You say 1% is token. What do you consider all part of that orchestration?
Guest: Jason Brisbane (18:05):
Guiding the LLM, doing checks, putting into inputs, some of those things that Tanvi mentioned in regards to, hey, we're inputting, we're having a chat back and forth. We're iterating, hey, maybe a second person needs to come in and take a look at this, accounting for that in terms of the total project estimate scope.
Host: Paul Barnhurst (18:25):
But I thought I could just do everything. You're telling me it can't? I have to actually still work. I
Guest: Jason Brisbane (18:30):
Wish we're not there yet. You hear some doomsday rumors, but we're not there yet.
Host: Paul Barnhurst (18:35):
Oh, we hear doomsday rumors all day, every day on LinkedIn, but that's another story.
Guest: Jason Brisbane (18:40):
Yeah, right.
Host: Paul Barnhurst (18:42):
And I see you both smiling and laughing a little bit at that. That's when you know it's true. So Tanvi, if someone wants to see your calculator or get ahold of that, just real quick, where would they find that? Have you put that out there publicly? Is there a place they can go look at that?
Guest: Tanvi Lal (18:54):
Yeah, yeah. It's on my LinkedIn and I'll share the link with you if it makes sense to add it in the session notes, but it's free. It's online. Please use it. I would love your feedback and your thoughts, and clearly I need to make some updates with the new model releases. And I also would like to run this back with Codex and ChatGPT to also see how it works across different models. That'd
Host: Paul Barnhurst (19:16):
Be great. How long did it take you to do it?
Guest: Tanvi Lal (19:18):
It took me about, I would say 10 days, like a week to 10 days. And even with the Claude Pro plan, I was kind of hitting the session limits, so I had to wait - Was
Host: Paul Barnhurst (19:29):
That the $100 a month pro or the $200 a month?
Guest: Tanvi Lal (19:33):
I know, right? You're like, "How much must I pay to actually assess these things?" But yeah, I mean, even with the Pro Plans, they have limits and some of the tasks actually took quite a while to run. They would take 20 minutes to run. So yeah, just got it out and still much less time than if I had to do it all by myself with an Excel.
Host: Paul Barnhurst (19:51):
Isn't it funny how now we complain, "I had to wait for my LLM for 20 minutes," and it's something we could have never done before. We wouldn't even have attempted, and we're complaining that we had to wait a whole 20 minutes to get something that was beyond our capability.
Guest: Tanvi Lal (20:05):
Well, I was just like, "Hey, my laptop's overheating. Come on, Claude. Work faster."
Host: Paul Barnhurst (20:10):
It is so true. I was having edits a PowerPoint the other day and I was just like, "Come on, finish. Why is this take..." And it was doing stuff would've taken me three, four hours to do, and it took 20 minutes or whatever, and I was just sitting there complaining and I'm like, "Man, first world problems is what I though."
Guest: Tanvi Lal (20:30):
But it's interesting. The LLM still, it takes some time to figure out how humans do it. For example, I was trying to get Claude Code to download a file from a document repository, and it kept running into issues. It was like, "I'll try this, I'll try this, I'll try this." And 10 minutes later, I'm like, "Hi, do you need my help? Let me help you." And so I think it iterates in a very different way and will find issues and you know how to troubleshoot the issue, but the LLM still takes 10 different tries to figure it out.
Host: Paul Barnhurst (21:03):
That's one of the most interesting things for me is you read. I feel like every day I see something fail and the first time you see that you're thinking, "All right, the LLM sucks," but it comes back with the right answer. It just keeps iterating, "Oh, I'll do this approach. I'll do that approach." It's almost like it has this checkbook, this checklist, and it just keeps working through every possible solution until it finds the one that works.
Guest: Tanvi Lal (21:26):
Yeah, and you're like, "Just ask for help. I'll tell you which one's going to work."
Host: Paul Barnhurst (21:30):
There are definitely times. There's times where I've stopped it. I was like, "Okay, stop searching. Here's how you..." "Oh yes, that will work." And you're like, "Thank you. I knew it would work because I've done it that way before."
Guest: Jason Brisbane (21:41):
Yeah. Have you ever stopped mid-project while he's thinking and then check your usage? I've done that a lot to make sure I'm not running over.
Host: Paul Barnhurst (21:51):
I haven't done that one. I haven't stopped mid-project on my usage, but I'm pretty good on usage. I used to be running over a lot when I was on the $20 a month plan. Now that I've upgraded to a hundred, I've bumped into the limit a few times on some coding projects, which tells you I just need to use AI more. I'm a slacker, but we won't get into that. Today's sponsor is Lineos. Most finance teams I've talked to have built something impressive in Excel. Real models with years of institutional knowledge sitting in the formulas. Then the data underneath goes stale the moment someone posts a journal entry and somebody spends a morning rebuilding it. Lineos provides reporting intelligence by connecting your ERP data directly to Excel. Reports refresh with current data, your formulas stay exactly as you built them, and you can trace any number back to the transaction behind it.
(22:45):
Everyone works from one version of the numbers, including whoever is building the board deck. Your team moves from assembling reports to analyzing them when you leave it to Lineos. Learn more at www.insightsoftware.com/lineos/solutions/reporting-intelligence. That's www.insightsoftware.com/lineos/solutions/reporting-intelligence. So let's talk a little bit of new projects. Obviously, finance is going to be asked whether it's, "Hey, we're working on this new software thing. We're doing something internally. We're creating these agents." There'll be a lot of them having to figure out estimating, which not something I ever did as an FP&A professional, obviously, now haven't been in my own business for five years, and not something most people have experience in. So would love to get some of your thoughts. So where should they begin and what are some of the key inputs? So Tanvi, we'll start with you, your thoughts on that, and then we'll go to you, Jason.
Guest: Tanvi Lal (23:56):
Yeah, I think the first thing to consider is what is the type of work the team that you're budgeting for has done? Are they coding? Are they creating... Are they either parsing large amounts of input data? So let's say the data analytics team, they're looking across data lakes and all these different BI tools, their inputs are going to be very heavy. So either looking at teams which have very high volume of inputs or high complexity inputs or teams that have high volume or high complexity outputs. So I think teams which have either or both would require more tokens. And the other piece I would look at obviously is head count, and actually getting a clear sense of how many people in this team are actually going to be using the tool versus are going to be reviewing outputs of the tool. And then I think as always, it's important to talk to the team, understand how they are, how they plan to use, but with the obvious understanding that when folks have budgeting conversations, they will inflate their actual requirements.
(25:00):
So that would be my thought is to focus on either complexity of inputs, volume of inputs, or complexity and volume of outputs those teams, deadlines would require the most.
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Host: Paul Barnhurst (25:08):
Yeah, which makes a lot of sense. It's really kind of understanding the project and what's the key drivers to the cost, which are usually how much output or how much input do you expect. Jason, other thoughts on this? What else would you add here?
Guest: Jason Brisbane (25:21):
Yeah, I would just add on. I would say just overall as the organization, the hygiene and the behavior has to change where I would say as an organization at this point, you have to move away from what I would call false precision. As we say at FinHelm where we're just reporting from a single number perspective, I think now a range is critical in owning, hey, what's the floor, what's the expected, what's the blowout, and manage to that band. When the actuals come in, log it and just get the band tighter as you go.
Host: Paul Barnhurst (26:00):
No, you make a great point because the cost can be substantially different. If you assume that 80% of the project was going to be Opus and it turned out that 80% was Fable 5-1 for whatever reason, those are very different costs. And so little changes in assumptions because there's so many different ones that can go into this can have a pretty big impact on what that cost is, especially from a software product platform. You talk about your best users in some cases are your most expensive now. So do you really want all your best users, if you define that by usage of the AI, blowing out your cost? There's just some of those things you have to think about is, is it the same? What does that mean in ways that you didn't have to before?
Guest: Tanvi Lal (26:49):
I think the other wrinkle here is as AI monetization models evolve, this is going to look different, right? Startups and companies are starting to charge only for successful outcomes, which means that you're still going to have a cost line item of unsuccessful items that then lead up to that. So obviously that's on the product team for building a product and the end team for building a product that actually meets the output metrics that you're stating. But I think that's the other trend that I'm seeing coming down the line and I actually need to talk in more depth with startups that are approaching pricing like this to understand how they're thinking about it internally from a cost perspective.
Host: Paul Barnhurst (27:26):
Yeah. A couple months ago I had on Ebony... I'm trying to remember her name now. I'd have to go look it up, but I had on a pricing economist specialist in that, and we talk about outcome-based pricing, right? It's an area that everybody talks to holy gail as that value or outcome-based pricing, but that comes with some real risks. You got to have a high level of confidence that you can deliver, otherwise it can be a disaster if you're not delivering. I think we all like the idea of paying on an outcome because it's much easier to say, okay, how much is a successful outcome worth to me and I'll pay this versus I pay this for unlimited usage or I pay this for per token and there's more uncertainty.
Guest: Tanvi Lal (28:06):
Yeah. The other thing I'm seeing is folks are charging different costs for the type of work performed. So if you're doing a simple query or things that are testing before go live, maybe you'll charge a certain amount less. And then if it's a production ready customer-facing agent, you're charging a little bit more. So lots of different levers and approaches I think we're going to see.
Host: Paul Barnhurst (28:29):
Yeah, which makes a lot of sense, customer-facing. I mean, I think the higher the risk around the agent, the more QC you have to be, at least in theory, the more you would expect to pay.
Guest: Tanvi Lal (28:37):
In theory.
Host: Paul Barnhurst (28:38):
Yeah. You like the in-theory part because we all know, how do I like to say it? There's theory in reality and rarely. And anything else on this subject, Jason, before you move forward? Any other thoughts here?
Guest: Jason Brisbane (28:52):
I just think we're in an era now where the FP&A team has to think in bands beyond a single point on this and communicating in bands, what's the expected, what's the floor, what's the blowout, I think that earns the confidence within the organization to, what I would say, have arms around this line item.
Host: Paul Barnhurst (29:13):
Well, I think what's great is with LLMs and the fact they can do Python, they can do deterministic solutions, whether it's in Excel or other applications, it's much easier to do a Monte Carlo simulation. A lot of people would've struggled to build one or to do a bunch of sensitivity or scenario, but most people can understand the basics of it. You may not understand all the statistics, but you can understand it. So I think it's much easier now for people to get those ranges with some statistical confidence around them. Does that make them perfect? No. But knowing it's 10 to $50, 90% chance that's my range versus someone just saying $30 is really helpful or 30 million versus 10 and 50 because sometimes those ranges can be high. We've all seen it with finance. How confident Are you in that number? I wouldn't stake my life on it, if that's what you're asking.
(30:08):
We've all put forward those numbers where you're like, "I think we'll land here." See, you're learning how we really work when we do forecasts. You want to come over and join us?
Guest: Tanvi Lal (30:22):
I had another experience when I'm doing my financial model. I was like, who knows? I have two years of historical data and I'm forecasting what a 15-year exit or a 10-year exit. So I feel you want that.
Host: Paul Barnhurst (30:34):
Yeah, yeah. I could imagine forecasting, especially if you're working with high growth companies, there's not a wide range in those outcomes, is there?
Guest: Tanvi Lal (30:44):
There can be, but the good outcomes, no. There's a pretty specific range you want.
Host: Paul Barnhurst (30:48):
Yeah, there's a pretty specific range of what you want. Unfortunately, reality tends to have a wider outcome.
Guest: Tanvi Lal (30:55):
Exactly. Expectation versus reality again.
Host: Paul Barnhurst (30:58):
So Jason, I want to ask a question. When we were chatting, if you remember when we were preparing for this episode, you mentioned a term or you said a phrase that I liked. You said treat variance as a learning. It kind of reminds me of the whole idea of failure leads to success. We're supposed to treat failures as a learning. So talk a little bit about that. What does that look like in the terms of AI? How do you think people should look at that?
Guest: Jason Brisbane (31:22):
Great question, Paul. I think what you want to do is, and we talked about it previously here, where when you move past false precision, when you can communicate the number in bands, you manage within the band. And from there we get learnings from the band and a miss one way or another is not failure. There's nothing bad. There's something to learn. And then we come back and we score it and we come back and test ourselves against it. And then we're getting into a calibration type of exercise. And so I think if any organization that can install that, that's where you really create an environment to what I said in the pre-call, treat variance as learning.
Host: Paul Barnhurst (32:06):
So I'm going to ask a follow-up here. I want to drill down. I agree with the concept, but we're often have to be held to a budget. There's a number that the board has given often external. So how do you manage between that range you need to do for projects and making sure you deliver in total? Because at the end of the day, we give Wall Street a pretty tight range or hey, we expect to be 115 or we give our board of directors, we expected 150 million in revenue. Typically, we don't give them a wide range. Usually it doesn't work there. So how do you manage between these variances and make sure you're not blowing the top end off of what's expected when it all is said and done?
Guest: Jason Brisbane (32:52):
Well, in what we talked about operating in bands, what I would say own and beginning to get your arms around what I would call the trip wire, owning the assumptions around that and being able to calibrate and adjust. I think when you're able to do that, you're able to have those levers you can turn and pull what I would say on a dime. Because as you said previously, we have these tools now. So now we just need to bring them in-house and manage to them and own things in terms of like, hey, what are the drivers with the trip wires on these drivers and being able to adjust accordingly.
Host: Paul Barnhurst (33:31):
Makes a lot of sense. And I agree with you. I like something you said in the sense you have to be able to know the dials because just like with any budget, there's going to be areas you blow, there's areas you're going to do way better, and you're going to have to adjust and move throughout all of it. It's just another level of complexity, so to speak. Today's episode is brought to you by our sponsor, Lineos. Lineos brings live ERP data directly into Excel, allowing your team to build reports with fresh data, no stale files in sight. More on that in a few minutes. So I'm going to ask you guys a question, something I didn't have in here, but I'd love to get your thoughts. I want to start getting into the role finance and FP&A should play in AI because I think there's a huge role they can play.
(34:16):
And I want to get your thoughts. So Glenn Hopper, I don't know if either of you know him, but I do a podcast called Future Finance with him and he just released his or he is releasing in October. He wrote his book, The AI Ready CFO. And he makes an argument because of the intersection of governance, which finance already has to do a lot of, and data, the AI adoption should be owned by the CFO and finance. And I'd just love to get your guys' thoughts on this from a perspective. So that's a big argument he makes in his book to CFOs. So Tanvi, we'll start with you. What are your thoughts on that?
FP&A guy here. We'll get back to the show in a minute. This section is unscripted. When I started my business, I had two main goals, to be trusted and for my content to come across as authentic and helpful. I've heard from many of you that I've accomplished those. Today, I have three goals, to be trusted, authentic, and have the best beard in finance. All right, enough with the jokes, enough with the fun. What I'm asking for is I'm asking for you to help me out. If you've enjoyed the show, if you've enjoyed an interview, if you found it helpful in your career, can you please leave a rating and review? That helps more people find the show, helps me grow the show, helps me grow my business. So I'd really appreciate it if you could take a moment and leave me a review. You can do it on Apple, Spotify, YouTube, wherever you listen, or you can go to my website, https://www.thefpandaguy.com/. That's F-P-A-N-D-A guy.com. Go ahead and go to Walla Love, go down to the bottom, and you can click to leave a testimonial. Thanks for doing that. Thanks for listening.
Guest: Tanvi Lal (34:50):
I guess I'm in two minds because I see the logical reason why the CFO should own it because I think the biggest issue we have today is what is the ROI from AI? And frankly, that is the buck stops with the CFO. If it's a huge line item on my expenses list, I need to be able to assess whether or not there's enough ROI to justify it. So I think from that angle, I really, really see the use case for it, for the CFO owning it. I think the con on the flip side there is the CFO is not the one who's actually in the weeds using the product and understanding exactly how things are being used across the board. So this is where I think FP&A and the finance team really needs to understand how work is being done and why the costs are what they are in a more unique way than you have in the past, because usage based means you have to understand how the work gets done.
(35:44):
So I think to the extent the CFO is embracing that, and maybe we can talk about this later too, just you don't want to manage costs but stifle the innovation or stifle the way the work gets done. So I think you need a very specific type of CFO who's ready for that, which I'm sure he gets into in the book and all of that. I think from an ROI perspective that tends to sit with the CFO when it comes to the tooling you're using. So that makes logical sense to me.
Host: Paul Barnhurst (36:10):
Jason, any thoughts on that?
Guest: Jason Brisbane (36:12):
Yes, I would agree. I think there's two pillars for the CFO. I think pillar one is at least in terms of your top LLM model that you're using, having a grasp of token costs and within that for each model. But then also going back to what we said earlier when we were talking about Tanvi's calculator in regards to the ratio of token costs versus orchestration costs for a project, the CFO is often tasked with directing what projects are done within the organization. So I think from that lens, the CFO has to manage that piece, but then also manage, hey, what is this costing from an overall LLM perspective? So yeah, I would agree, but I would just put it on those two pillars.
Host: Paul Barnhurst (37:01):
Thank you for that. I appreciate it. I was just kind of curious where you sat on that. So when we chatted before the -
Guest: Tanvi Lal (37:06):
Where do you stand on that, Paul? I'm curious to get your take. We can't move on just yet.
Host: Paul Barnhurst (37:16):
I tend to agree that the CFO needs a lead adoption and not just from the ROI standpoint, but any company that's going to be successful beyond efficiency gains has to have a good foundational data. And the neutral person that should be helping define that data dictionary is really finance. If you let marketing define it, they're going to take one angle. If you let another group define it. If you have a data group, yes, they're going to be involved. Yes, obviously the IT, but I think you got the ROI side, but I also think you have a big data. There's a lot of governance in there that finance is really good at. So I think it's a natural spot. Do I think that means every situation should be that way? No. Does company size and complexity depend? If you're a company that's huge in AI, it's a big part of everything you do in your products.
(38:06):
Okay, maybe there's portions I'm going to have the CFO own, but is he really owning all of it? Probably not. But I think in general, the philosophy makes sense just because the one who has to make sure that data dictionary is right for the external board is your finance. Even if IT owns some of that, they're the ones that are going to... The CFO's going to pay for it if he looks bad in that board meeting or something's reported wrong. And so I think having that data, having that governance, and then the ROI side, it is kind of a natural fit for the CFO to help lead out that adoption and make sure the data and governance along with that ROI are balanced in the process. But there are areas where they would have to get deep into the weeds and that may require somebody else, you're going to have the right team around it.
(38:51):
So I don't think it's just a simple, "Hey, it's always the CFO," but I think the argument makes a lot of sense.
Guest: Tanvi Lal (38:57):
Appreciate your perspective. It's good points.
Host: Paul Barnhurst (38:59):
Yeah. No, you're welcome. All right, so when we chatted, both of you suggested that you think finance has an opportunity to lead the organization's thinking about AI. So love to get a little bit of more of your thoughts there. And we'll start with you, Jason. I mean, how does finance lead in this? We just talked about the CFO owning a bit more broadly, FP&A, different things. How do they help lead?
Guest: Jason Brisbane (39:22):
So one, we talked a bit and I do believe wholeheartedly the finance team in particular, FP&A should have direct ownership of understanding token costs. Things are moving fast. So for example, hey, now recently there's Fable. And every time there's a cool model, the organization, obviously there's going to be curiosity. You're going to hear these great things, "Hey, I want to use this." So I think the finance team should lead that and having an understanding. But then also I think this creates, when I think about FP&A, there was a cadence of when I could touch business partners. I think back to some of my times when we were doing headcount outlooks, week six, nine and 12. I think this creates an opportunity to increase the cadence of working with our business partners. If I was in the seat right now, if I'm owning token costs, I would want to be on top of my business partners when the new sexy model rolls out to say, "Hey, this model can do this and this." But then we're going back and revisit some of these projects.
(40:31):
So I think that creates the opportunity for the FP&A team to lead here. They should be the first ones that are talking about the new model, how to implement it, but then, "Hey, let's go back to these projects that we were looking at and again, attack it from the band."
Host: Paul Barnhurst (40:45):
Great. Thank you. Tanvi, your thoughts on this one?
Guest: Tanvi Lal (40:48):
I think Jason hit the nail on the head there. The only thing I would add is I think the FP&A and the finance team is uniquely positioned to guide different BUs on how to think about use cases where AI is a good fit versus which model you should and shouldn't be using. You don't need Fable for a content task. And even if you're doing, let's say you're creating a presentation, you can use Fable for the outline, but when they're like, "Do you want me to create the deck?" No, use a different model. You don't need Fable to create the deck. It's a reasoning model. It's there for the thinking and the partnering. It's not there for the execution. And when you just get a $200, $500 budget, you're not necessarily thinking about these things because you're like, "Whatever, it's my company. I can just go ask for more." But I think as you are allocating budgets, just explaining the thought process and the assumptions behind how you came up with this and what kind of model usage you're assuming, I think is a really strong way to influence how work gets done.
(41:47):
And to Jason's point, being on top of it when the new model comes out, it's like, "Hey, this is how you use this new model and this is what we're expecting in our budgets." And this is what we're applying consistently across the org. And yes, you're special, but no, you're not. I think that neutrality piece that you talked about, Paul, is really important here.
Host: Paul Barnhurst (42:04):
And I like how you both mentioned there's a real education opportunity there to help understand from a cost perspective. Like you said, Fable, basic content, basic tasks. Compared to Haiku, you might be spending 10 times as much. If I told you you could have a basic dinner, your drink for $5 or $50, the dinner, the drink, and they're roughly the same, everybody's going to take the five, but yet we often just use Fable 5.1 because it will be better. Not necessarily. There's a lot of that kind of thing. I mean, I find myself doing it sometimes. Oh, I'm just going to run probably didn't need that. But especially when you're on a subscription versus when you're paying for every single token call, you treat things differently. And businesses are generally, when you're buying API, you're paying for each token, which is different than just having a subscription.
Guest: Tanvi Lal (43:03):
And I think the issues that orgs have right now is there's no one centralized place of disseminating this information, personalizing it for the business and how you do processes and context like, oh, when you're creating this type of analysis or when you're shipping this type of product or when you're delivering this kind of service. It's so different within each business. So actually putting all of these basic concepts in practice for your business. There's no part of the org that does that right now. Every team's just figuring out on their own. Every person's just figuring it out on their own. But yeah, influencing individual behavior like this is difficult. You need to educate your business partners, your business partners need to educate their team, and that's how it successfully runs. I mean, right now we kind of have a cold start problem. Nobody owns it and nobody's telling the business how to do these things.
(43:46):
And I think, again, finance is very uniquely positioned to be that person and that voice in the business.
Host: Paul Barnhurst (43:51):
Yeah. And I like you said, the cold start problem, great book if anyone hasn't read it. So on networking, marketplaces, just don't do them is the advice. They're hard.
Guest: Tanvi Lal (44:02):
Take everything with a grain of salt. Yes. All
Host: Paul Barnhurst (44:05):
Right. So Jason, I'll throw this over to you. You talked about speaking in bands, but any other advice about communicating the uncertainty, the senior leadership
Guest: Jason Brisbane (44:16):
Have really begin to educate your business partners in regards to, I would say the top 80%, the assumptions that impact the top 80% of your forecast. I think now that's the cool thing of the tools that we have. We have the tools to be able to present that and own that and really get the business partners in thinking about that and having that in mind when we think about projects. I think in terms of when I mentioned thinking in bands, it's the assumptions that support that, but then also logging actuals when the quarter ends against that and pressure testing the assumptions that you went in with.
Host: Paul Barnhurst (45:00):
I think you hit the nail on the head. In fact, you did on the pressure testing those assumptions. So often I've seen people, you make assumptions, you do it and you just report. And if you're close, nobody goes back and looks. But if you look at the assumptions, you might've been close, but you were completely off on every single assumption and that might change behavior. And so I think that pressure testing is critical because it's easy to just look at a top line number and go, "Oh, we're close. Good enough." When what's going on underneath may be leading to problems or maybe very different than the top line number. And I saw you smiling there a little bit, Tom, but you've never seen that, right?
Guest: Tanvi Lal (45:39):
Never. I'd never gotten totally incorrect in my assumptions, but vaguely accurate at the top of that, you're like, "Eh, good enough."
Host: Paul Barnhurst (45:48):
Yeah. If any of us done forecasting, we've all been there. I think you'll laugh at us. I had one time where I had a bunch of businesses and for the month it was probably, we'll call it five million, $10 million, may have been even more, like $10 million. And I came within a thousand on the businesses forecast. It was like, "You're an expert at forecasting." No, no, no. I got lucky. If I could actually forecast that well, I'd be retired right now.
Guest: Tanvi Lal (46:13):
On a boat somewhere.
Host: Paul Barnhurst (46:14):
Exactly. I said I'd be in Vegas or I wouldn't be here. So it's really about those assumptions and planning. Then yes, there are areas you have to be accurate, but if anyone's being accurate with their token costs, they either really don't have any, they got really lucky, or hey, come talk to me. We can start a business. One of the three. All right, so just a couple more questions, then I want to move into our FP&A section. So how should a company think about the AI related costs? I think there's a lot of people trying to figure out what do I put in COGS, what I put in operating expenses? So from a P&L line item, love your perspective, one as an investor, how you like to think it. And then just in general, any advice you'd offer FP&A professionals as they're trying to figure this out?
(47:05):
I'll start with you, Tanvi, on this one, and I'll go to you, Jason.
Guest: Tanvi Lal (47:08):
Yeah, I think this is actually quite a hard one because before you can really... You are going to have to segment some of your AI costs should go into overhead R&D or what have you, and some of them should go into COGS. So customer usage of AI should go into COGS. If you're doing outcomes pricing, the outcomes that do and don't work, all of that should be in COGS. That would be my dream of how to see financials. But the reality is we are in early innings of developing infrastructure to do this because you can have your metering layer, but then you need a reasoning layer in between that's actually saying, "Hey, this section of the meter goes here, this section goes here. Actually, this was a successful outcome, this wasn't, but both of these go into COGS." And so I think we need that type of infrastructure and that type of regularity in what the LLM costs are going toward before we can do this.
(48:06):
But ideally you are splitting across those line items.
Host: Paul Barnhurst (48:10):
Got it. Yeah. And I mean, I think all of us would probably agree in an ideal world, we want to see it nicely bucketed out. Reality, like you said, is never as clean as what we - I
Guest: Tanvi Lal (48:19):
Mean, a girl can dream with her financial statements. You
Host: Paul Barnhurst (48:22):
Can. I dream what my bank account will grow overnight. It rarely does, but I keep dreaming. Jason, your thoughts on this?
Guest: Jason Brisbane (48:31):
Yeah, I would continue on the dream, but then from an FP&A kind of ongoing, we're measuring ourselves, we're trying to see if we're improving, that creates an opportunity to put some metrics around our ability to improve. If I'm thinking about it, hey, after a quarter ends, we're looking back, "Hey, what's our forecast bias? What is the forecast accuracy level? What's the persistence?" If we can break those items out, it gives us a better frame of measurement, and then we can isolate on particular items to improve on. Because again, it's interesting when you look at this stuff, if we're looking at a project, we're looking at the ratio between token costs being less than the orchestration costs. So if we can isolate that ideally perfectly in the dream, that's going to give us some opportunities to, I guess, measure and score ourselves after and make sure we're calibrating correctly.
Host: Paul Barnhurst (49:29):
Makes a lot of sense. So last question on AI, then we're going to move into the FP&A section. So last thoughts, is there a framework that you would recommend people use? Any kind of last minute advice for the FP&A professional as they try to figure out these token costs moving forward? So we'll start with you, Jason. Any kind of last thoughts, frameworks, anything you want to share?
Guest: Jason Brisbane (49:51):
I wouldn't share a framework, but I would say this is a time for the FP&A team to really sharpen their, I guess, research tool, really grow their research muscle, really grow your communication abilities with your business partners, your business partners. Hey, if it's marketing, hey, if it's HR, they're going to be using different HR, they're going to be using different AI tools. So having an understanding, keeping the lines of communication open in terms of what they're using, what projects they're using it for, and then begin to understand the cost behind those. So I would say one, sharpen the research muscle and really open up the lines of communication businesses and organizations you're supporting.
Host: Paul Barnhurst (50:39):
Makes a lot of sense. Tanvi, what's your thoughts?
Guest: Tanvi Lal (50:42):
Plus one to everything Jason said, the only other piece I add is to just be open and transparent about, "Hey, we're all figuring this out and this is our first pass at it. Let's iterate on it together because nobody really has this figured out from public companies, unless they're already a usage-based company down to fancy Andreessen, Sequoia-backed startups, even all the investors I talked to are all figuring it out. So I really think having that mindset and collaborating with your business partners to figure it out is the best move.
Host: Paul Barnhurst (51:15):
Yeah, collaboration is huge. We are all figuring this out. All right, so I'm going to ask each of you a question here. We'll go in order, and there's just two questions on the FP&A section today. So just looking for a quick short answer, you can add maybe a couple sentences elaborating, but just real quick, if I was to ask you, Jason, today, what is the number one technical skill you believe FP&A professionals need to master? What is
Guest: Jason Brisbane (51:41):
It? Research. Research in terms of cost, but then also I think, and this may be move outside of research, really understand the businesses and the organizations you're supporting, understand what they're doing. And then I think that kind of drives you to AI technologies that they're using. And I think you have to own those two things in this era.
Host: Paul Barnhurst (52:08):
Okay. What's your thoughts on that, Tanvi? What would you say is that skill they need to master technical skill?
Guest: Tanvi Lal (52:14):
At the risk of the old generic, I really do think it's like building your own AI tooling. You can do so much more to connect systems and spreadsheets, everything else under the sun to get the outcomes you need. So how can you make your work faster, better, more accurate, smoother using AI to either create a dashboard or a website or own little tool, what's so great about Claude Code and Codex, etc., is that you can really build your own little mini products for yourself. So being able to do that and being able to bring that to teams you go I think is the most important
Host: Paul Barnhurst (52:45):
Thing. Alrighty. What about soft skill, Jason? What would you say for that?
Guest: Jason Brisbane (52:50):
Soft skill, getting comfortable and being comfortable, being uncomfortable. I think that's the skill you have to build in this era. As Tanvi mentioned, this is new. We're all figuring it out together. So I think you have to be comfortable having those uncomfortable conversations that we don't know, but we're going to do our best to present a range and manage within that. And I think that creates a shift within the organization where, again, it goes back to what I said about variance as learning. We have to train our brains to not look at variance as a bad thing. If we're off, it's not this bad thing that we're trying to explain and move it away. And so I think FP&A in regards to, hey, leading the AI transformation, I think FP&A has to lead the shift in terms of way organizations have to think now.
Host: Paul Barnhurst (53:42):
Yeah. Okay. Makes a lot of sense. What about you, Tanvi? What would be your thoughts on soft skill?
Guest: Tanvi Lal (53:46):
I think coaching and advisory is probably the biggest soft skill that I see FP&A needing, potentially just becoming more and more machine critical to their role.
Host: Paul Barnhurst (53:55):
Perfect. All right, so I'm going to ask you each one get to know you question and then we'll let you go. So we're going to start here with you, Jason. If you could have dinner with one person in the world, dead or alive, who would you take to dinner and why?
Guest: Jason Brisbane (54:09):
Yes, good question. Dolly Parton. I think there is so much to learn from Dolly in regards to staying power and branding.
Host: Paul Barnhurst (54:18):
Yeah, no, she was loved by everybody and everybody knew who she was. I don't think I ever had anyone, they may not have liked her music per se, but I don't think there was anyone who didn't like her as a person. She had an amazing brand and personality, so that's a fun one. All right. So if I was to ask you, Tanvi, what is something, maybe a hobby you have or something you do, something that if our audience learned about you think they'd be surprised that they might not expect?
Guest: Tanvi Lal (54:44):
Well, I wish I had interesting hobbies inside of the normal dates, but I think something just like here, reading, walking, playing with dog. Yeah. But I think one thing that's a little unique is I actually, I went to boarding school for about five years in high school, so eighth through high school. That was a little bit of a different experience and it was a really strict school, so a very different high school than I think most others have had.
Host: Paul Barnhurst (55:05):
Yeah, I definitely have not had the boarding experience, so that would be, I don't know how I would've handled that and I'm glad I don't point out at this point.
Guest: Tanvi Lal (55:13):
I give it, then I take it. I have an early riser now and
Host: Paul Barnhurst (55:18):
Yeah. Yeah, no, I'm sure there's some great, like I said, like with anything, there's always things to learn, good things that come from it. All right, well thank you both for joining me. If our audience wants to get in touch with you, they can both reach out to you on LinkedIn. We'll put your information in the show notes. Jason, they can go to FinHelm, your website if they want to learn more about that. Tanvi, you have your budget tracker. And so thanks again for joining me, for sharing some of your thoughts today. I think this is a topic, a subject we're all trying to figure out. There isn't a perfect answer out there and rarely is there, but as time goes on, we'll kind of all figure it out together. So hopefully you learned a few things from today. I know I did, and thank you both for joining me.
(56:01):
I really appreciate it. Thank
Guest: Tanvi Lal (56:03):
You. Thanks so much for having us. Inspired to be on.
Host: Paul Barnhurst (56:06):
That's it for today's episode of FP&A Unlocked. If you enjoy FP&A Unlocked, please take a moment to leave a five-star rating and review. It's the best way to support the FP&A guy and help more FP&A professionals discover the show. Remember, you can earn CPE credit for this episode by visiting earmarkcpe.com, downloading the app and completing the quiz. If you need continuing education credits for the FPAC certification, complete the quiz and reach out to me directly. Thanks for listening. I'm Paul Barnhurst, the FP&A guy, and I'll see you next time.