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- [00:00]
A
Hello, I'm Ray Wright, Founder and CEO of Benchmarket and your host of the Metrics at Majorette Podcast. We talk to a wide variety of the top B2B SaaS and Cloud Thought Leaders, CEOs, executives, investors and people just like you to discuss the metrics and benchmarks they use to make metrics informed and benchmark validated decisions. Now onto today's show Foreign welcome to today's episode of the Metrics that Measure up podcast. Today I am joined by Albert Gozzi, the co founder and CEO of Aleph. We'll be covering four primary topics with Albert today. First, the vision behind founding Aleph, the evolution of AI in the FPA category. What is FPA's role in developing corporate strategy, tactical versus strategic function and fourth, the growth strategies for a company competing in a fairly crowded category. So Albert, with that, would you please take a moment to give a brief overview of your journey to becoming my guest here on the Metrics that Measure up podcast?
- [01:20]
B
Yeah, thanks Ray. Thanks for having me. Your program. Pleasure to be here. And yeah, a bit of my background. I'm originally from Argentina. That's the accent that you can hear. I started my career in finance, worked at Procter and Gamble. Then I worked in management consulting, working a lot with CFOs before becoming a COO and CFO myself. So first 10 years of my work life working different finance roles and I think that's a bit of what got the idea, where I got the idea for a laugh and, and how the company got started and I happy to share more and, and go deeper into all things finance and fpa.
- [02:01]
A
Okay Albert, you and Steve Ballmer had your career started at the same place at P and G. I don't know if you knew that.
- [02:09]
B
I know like let's see who ends up higher.
- [02:13]
A
Yeah, let's, let's see if the, the next 10 years unfold just as well for you. But hey, I have a question for you. So what was the motivation or that catalytic moment to found a, a company and a FPA platform? What was, what was the catalyst?
- [02:33]
B
Yeah, I think it goes back to, to that brief background that I gave you, Ray. I lived and breathed FP and A for the first 10 years of my career. And when I was a management consultant I was always, you know, pretty good at spreadsheets. I always joke that even today I can challenge most of the team at spreadsheets and still win. As our team gets bigger, you know, that gets a bit more challenging and I think I combined that, you know, love and passion for spreadsheets with also a technical mindset. So the way that I had solved solution problems with spreadsheets in the past was writing Visual Basic back in the days and later on Python scripts to make them better, more automated. And I think what I ended up with is something that worked pretty well, actually, and I was pretty proud of across many experiences and situations. It also felt very fragile and it felt that one typo in one of my scripts, you know, might collapse all of the, you know, the tower of cards that I was building. So. So I think, like, that was the seed for the idea for a laugh. And the motivation is, can I take all of these? That actually works pretty well. Can I productize it and can other people benefit from all of this without worrying that it breaks every week or every month? So it's like, yeah, solving the problem that I have in the way that I wish someone had solved it for me.
- [04:11]
A
Yeah. I still remember during budget season having those Excel spreadsheets with 12 to 15 different worksheets, links that were pretty precarious everywhere. And then let's throw in the macro that someone designed is like, oh, my God, it's a scary thing, huh?
- [04:30]
B
And the funny thing, Ray, is you might be talking about, you know, 1995, or you might be talking about 2025. I think, like, you know, that that statement isolated can apply to any time, you know, again, 30 years ago or last week, as many finance teams go into budgeting season.
- [04:49]
A
Yeah, well, that's the perfect topic to kind of pivot just a little bit. So 1995 versus 2025, what are some of the most common challenges you see the Office of Finance facing today, which are new or not new from 1995, and then specifically that FPMA leader, what are we asking him or her to do today? Or what challenges are they focusing on today that they really didn't see as much 20 years ago?
- [05:19]
B
Yeah, I can tell you what they do versus what they were hired to do and what they would like to be doing. I do think that there's still a lot of the old ways, which again, was true in 1995 and is true today, of disparate data sources. The information that you want in order to show insights or make decisions, living across three, five, seven different systems. A lot of manual data wrangling, copy pasting from one place to another one into spreadsheets, into different systems. There's a lot of, know, error checking. You know, data is not clean and you need to do a lot of that manually. And you feel that you're spending 95% of the time on that, like preparation, 5% on the analysis, and you can. So I think that's the reality for 80% plus of finance teams today. What they were hired to do, what they would like to do, and some of what I think, you know, we can do as a company. But I think like the this is not to talk only about Aleph is freeing, you know, automating all of that, putting all of that in autopilot and make it so that you spend 1% of your time data wrangling, data collecting. You spend like 1% of your time reviewing all of the errors and anomalies that someone flagged for you automatically. And you spend 98% of the time doing the insight and the analysis, also using that to drive decisions. Right. And I think it's not about insights and numbers as an abstract concept. Those are only useful if they drive actual decisions. And I think that's what top finance teams do today. And I think that's what everyone would like to be doing. No one gets enjoyment out of, you know, correcting data errors or copy pasting.
- [07:23]
A
Okay, so transitioning from being a data wrangler to a true business analyst. And we're going to get into later in the podcast, becoming more of a strategic asset for the organization. Right, but this is 2025, Albert, so we know the answer of how to fix all this AI. AI fixes everything. But you did a LinkedIn post not too long ago and you talked a little bit about where AI is first going to be used and add some value to the FPA function, then how it's going to evolve over time. Would you mind sharing a little bit about how you see AI initially helping FP and A organizations and then how it's going to evolve?
- [08:06]
B
Yeah, for sure. One of the frameworks that I use is this idea of thinking about the the org chart in the office of the cfo, all the way from a finance intern to the cfo. And that's actually the way that we think about our roadmap. And when we think about that, we think. And it makes sense. That's how you've seen, you've heard probably this metaphor, Ray, of people talking about LLMs as PhD level analyst versus master level analysts. So I think that's the way that OpenAI Anthropic benchmark their models against. So I do think it makes a lot of sense to use that same mental model and that same analogy to think about AI in finance. And I think today where we are is we Are, you know, somewhere in between the finance intern and the finance analyst level. And, and like anyone that tries to forecast how fast that will climb will look like a fool. So I'm not going to try and do that today, nor ever. And I think things are moving very quickly, but that's where we are today. So I do think that what AI is very good at, I do think a lot of that data gathering, data cleaning, AI is pretty good at and things like specific problems that sound very niche. But every finance team has of I have my vendors and sometimes the vendor comes in as a left labs and sometimes it comes in slf. So I cannot analyze it properly because it's two different ones. We can solve that fully solved problem. No one should be doing that. AI is pretty good at doing backwards with the right guardrails. And I think there's a lot of thought that needs to go into that. AI is very good at backwards looking analysis. So we have now a variance analysis agent that automates. We have a lot of what we call an evaluation data set. There is like hundreds of hundreds of rows that have been explained by finance teams in the past that we compared our AI against. We are like 95, 96% in being able to successfully explain why a number changed in the past. And that's like today with GPT5, you kind of think even as the models get better, we'll get to 99% very quickly. So I think a lot of those are already being transformed by AI. I do think that what comes next and what's the frontier in the future is how can you go from explaining the past to actually predicting the future more intelligently. And I think what's hard here is that a lot of that is actually going to be a combination of LLMs with traditional machine learning techniques that have been around for 10, 15 years. And how you combine one and the other one is the key in order to make this a true something that actually impacts and something that actually makes a difference. So I feel like that's how we see it evolving. But there's always going to be a so what? Right? And I do think that that's the last stronghold of finance teams. And you know, yes, a lot of your time will be controlling the agent and steering, I use like the word steering a lot, the agent that does all of the other work for you. But I think going from the insights into action, you know, like making the decisions driving the organization towards from where it is to where it should be based on your analysis, I think like the best finance Teams and the more strategic ones going a little bit into what we're going to talk about next are driving the change. They're not just like recommending and ending their work with a, you know, PowerPoint or Google Slide.
- [12:10]
A
In fact, I think in the LinkedIn post what I really liked was that that strategic decision support will be where humans, finance executives, FPA leaders will hold the last line. It's that kind of final decision making. And I wanted to ask you about that because know using an 8020 rule. Right. And I'm not a CFO, but worked with a lot of finance organizations very quickly in FPA. I found like 80% of FPA organizations were a little more tactical. A lot of rearview looking analysis, a lot of model building. Right. But a lot of the input from the models. Yeah, some came from the historic, but a lot of it came from the sales leader or the marketing leader or the R and D leader. Right. So my question to you is, you know, where do you see FP and a fitting today in that tactical versus strategic bucket? And if you're somewhat aligned with my belief, how do we move FPA to truly have a seat at the executive table and be a strategic kind of business strategy influencer, if not decision maker?
- [13:18]
B
Yeah. So a bit of the way I see it on this, like tactical versus strategic spectrum is on one side. Like level one is just like doing the reporting, right. Like crunching the numbers, getting them there. Number two is doing more of the insights. You know, you not only do the numbers but you do the. So what number three starts to become more strategical, that it's more about like influencing. It's not only I give you the numbers, but I tell you and I influence what behaviors those numbers should give. And for me the ultimate level and what finance should be striving for is more like orchestrating. So I'm not only influencing, but I'm also like putting the, like setting up the operating cadence and setting up the way that we make decisions and that we change things moving forward. And I think what's tricky about this is that definitely most teams are stuck in just crunching the numbers or at most doing some insights on top. And what's hard is that you need to do that. Right. The only way that you can. What finance brings to the table is they understand the numbers, they own the numbers, they understand them, they understand the context, they understand how the numbers should be, you know, slice and dice. So all of that must happen and you cannot be strategical without having the tactical things fully there. So then it's about how can you put those on autopilot? How can you do it? Do it at the depth that you need to, but have that be again, 1 5% of the time that you're spending on the problem and you're spending 95% on. Do we need a meeting with the sales team to review these numbers on a weekly basis? How do we make sure that everyone in the go to market team is aligned with what the numbers say and make decisions on a day to day? What's the interaction between us and our go to market leadership? Who owns what? I do think that all of that. So what? And around like, you know, building that operating system for making decisions that way. I think like that's what finance need to unlock to be more strategic.
- [15:35]
A
Let me ask this question. So you know, hey, I'm thinking about moving from my manual spreadsheet process, a lot of manual integration to fragmented data sources. And now I'm going to implement a left, right or any kind of modern FPNA tool. Just frame it for me. Kind of a mental mindset how that FP&A and CFO can say, hey, this platform is going to allow me to become a more strategic business partner. How do they think about how the platform enables that? You understand the connection I'm trying to make between investing money in a new FP and a platform. But the real goal is not just to do things faster, it's to become a more strategic business partner.
- [16:18]
B
Yeah, for sure. But I do think that one of the ways that you become more strategic is by doing all of those things faster and spending time where again, humans can add value. So an example of that is let's take a very common month end. I want to partner with my go to market team and I want to do some forecast versus actuals on what's going on. I think the old way of doing that is it takes you until day 10 or 15 to get all of the data because your closed process takes a lot of time. There's a lot of manual steps and you set up the meeting on day 16 because you actually don't want to be reviewing the month 25 days into the next month. So then the last night before the meeting you're like, okay, what does the data tell me? And you're just doing one or two very shallow recommendations or very shallow insights. And again, it's not for lack of capacity to do it properly. It's about the lack of time. You compare that with what I think Aleph can enable is this idea of okay, now you have and maybe it's not the 99% version of the numbers because accounting needs to do other things, but you have a 90% version of the numbers on day three or day four. So one, you can bring that meeting to day eight, but two, you can spend those four days going back and forth and also using AI to do that into okay, I want to dig deeper. I want to not only look at the past, I want to model some future scenarios. And what happens if we invest more here and what happens if you we switch resources from here to there. What might that mean? I think again, that's the part where ChatGPT can help you think through those scenarios. But in the end humans will be great at that and they can add a lot of value if they are giving the time to do so. I think again, finance is not tactical for lack of skill to be strategic. It's because they don't have the the time to do it properly.
- [18:39]
A
Yeah, I almost think of the ultimate FPA platform as a continuous business planning platform that it's capturing as near real time inputs as possible and it's refactoring or reforecasting. Hey, based upon what happened through the 15th or the 20th or the 30th compared to previous periods or previous months, hey, here's a couple potential outcomes. Do you think that's going to be the domain of FP and A over time or is that going to be more of a dedicated forecasting type platform?
- [19:13]
B
No, I think that's 100% FP and A and we think a lot about, you know, what are the elements of what should be the elements of Aleph to take finance teams to these. And I think it's a lot of that. It's like this idea of Aleph knows your company very intimately, intuits with all the data, but also learns over time. Alex is always on is one that we think a lot, quite a bit, which is what you were saying is, you know, it shouldn't be there only when you need it. It should be there proactively flagging and like guiding you to, to where you want to be. I think this idea of helping you communicate on the stories and like they say we have this phrase that they like quite a bit that is turning data into stories. And I think people are not influenced by data, they are influenced by stories. And the way that you tell those stories I think is key in again going from just providing an insight to influencing decisions. And I do think that FPNA is at the center of all of that. FPNA is the central brain of an organization that knows all the data and can use the number to drive a lot of action. So. So I'm pretty excited and I think we're at the time now where there's a lot of plumbing and there's a lot of piping data and there's a lot that needs to happen in order to get there. And I'm personally excited that I think we are in a pretty good place with all of that more basic piping that is required and we're starting to think much more about some of those things that are again, more strategic, more, more exciting.
- [21:00]
A
Well, let's do one last pivot and that is I've been following the FPA category pretty closely over the last three to four years as we do benchmarking, etc. And at last count, you know, there's close to 150 different FP&A solutions that I can find that includes the ones that are integrated into the larger ERPs. So I know you've had some great success and maybe even some news and very recently, but what are you doing as the leader of a left to really differentiate and stand out from what could be viewed as a crowded and noisy category?
- [21:42]
B
Yeah, I think in the end it goes back to like a few fundamentals that I think were, you know, were true five years ago and keep being true today and I think will in the future. I think for us, the idea of meeting people, meeting finance teams where they are is very important. And I think that was a flag that we were waving since day one of finance people lab spreadsheets. Any tool that is trying to fully have them migrate from spreadsheets into somewhere else is naive and it's facing an uphill battle. Does it mean that everything is spreadsheets? No, there's a lot of processes where spreadsheets are not the best medium. But it's a great prototyping tool, gives a great deal of flexibility and I don't think they're going away. So meeting people where they are is very important. The idea of very fast time to value and taking minutes or hours to set up, not weeks or months. When I tell you it sounds obvious and it sounds like why would I prefer something that takes months instead of something that takes hours? But you said 150 tools that you're tracking, probably 149 are slower than Aleph in how to set up. And I think that again, it's not rocket science how to do that. It's about really Systematically understanding where the things are, taking time, analyzing them, finding a way to make them faster or removing them from the critical path and iterating. So it's like it's a long, it's not again it's not rocket science. It's a lot of work and effort that the team has been doing in order to be able to claim things like, you know, you're up and running with your own data within, you know, 15, 20 minutes of onboarding into into Aleph. And I think the third one for us is to you know, embrace AI and embracing the right way. And, and I never want to, I want to talk about the left, not about competitors but for me it's more the, what I have seen the market most about the AI is things that look very nice, present very nice in a demo and then when you go and talk to customers, very few people are rarely using. So for us I think taking a bit more of like the long road, going very, very deep into features that can really make a difference and like we think can really have good adoption and focusing on fewer things but doing them better I think has been a massive differentiation over the last year, year and a half.
- [24:33]
A
Wow. Patience from an early stage company founder, that's pretty good. But I do agree that if you release an AI enabled product very quickly and the customer just doesn't see the value, you've probably done yourself more damage long term than good. Right?
- [24:49]
B
Yeah. And, and it's hard. You know, it's a, it's a new LLMs are not great at dealing with numbers. There's a lot of things I need to do around it. We Talk with the OpenAI and anthropic teams all the time. We're in touch with and we're like, I think we're pushing the, the limits in many ways and, and, and it's, it's not easy and some of those things take much longer than as a very anxious person and as a very anxious CEO, they take longer than I would like to. But at the same time I do think that it's the right path and I think it's, you know, it will pay off to be patient.
- [25:28]
A
Well, you opened up a view into that anxious co component of Albert Gozi. Let me ask you this question. Give the audience a chance to know you a little bit better. Is there a CEO or a company that you think is a must follow for other CEOs heading into 2026?
- [25:45]
B
There's a lot that might be in the news or that you might talk about. So I'M going to give you more of probably less known there's a fellow company where customers of as well called Pylon that is a customer support tool and I really like following their CEO Marty on LinkedIn and I think it resonates a lot with the communication style. That resonates with me personally of you know it's there's a lot of LinkedIn of you know feels very polished and feels you're only communicating the right things. I think he's very transparent, he's very direct and I think I have a lot of trust in that company going well. There are fellow Bain Capital Ventures portfolio company as well and I think having a glimpse into the journey from the early days and a very transparent view into it has been great.
- [26:42]
A
Okay, second question. Is there a tool that you think every B2B SaaS company should be using not your own?
- [26:50]
B
I do think that I'm a big fan of linear for anything engineering and product. I do think their success speaks for themselves. So I'm not here to add too much but I do think that that's something that is important for us and I think using world class tools in our stack helps our team see the bar for what software should look like. And I think it's very easy if you use tools that don't pay attention to design, pay attention to user experience that ends up making its way to your product and if you use tools that give you that delight then you will make your product more delightful. So so I think I'm a big believer in, you know, the impact that good design can have, good user experience and I we do it for a laugh but we also look for it in the in the tools that we use in our stack.
- [27:48]
A
Gotcha. And last question for those early career professionals out there listening to this podcast, what advice do you give them right now early in their career knowing that they want to become a founder like you someday?
- [28:02]
B
I think it's there's nothing more powerful than solving your own problems and I think you know when these are something that YC we did YC early on and it's something that you know I always had in mind and once you reinforced it is when you're the user, when you're solving your own problems you need to go talk to a hundred people about how to solve the problems. You can use your intuition, you can use what you accumulated over the years in order to make the best product decisions. And I do believe that like building great products there's a lot of division and the direction but there's a lot. That is the thousands of decisions that you make every month and every quarter that like, really make the difference. And if you're your own user and if you can both be the product person and, and like empathize very deeply, that compounds infinitely over the long term.
- [29:04]
A
Well, Albert Gozzi, co founder and CEO of Aleph, your words are going to be greatly valued and in fact, they're valued by investors. So congratulations on your recent Series B that you raised with Coastal Adventures in the lead. And I think you also, as you said at Bain, Picus and even Y Combinator participates. Congratulations.
- [29:26]
B
Yeah, thanks so much, Ray. And thanks for having me.
- [29:29]
A
And to our listening audience, if you're enjoying the amazing guests we have, like Albert and the content that they're sharing, you're going to mean the world to us. Go ahead and subscribe to the Metrics of Majorette podcast on your favorite podcasting app. Go ahead and click that five star rating. It makes me feel good, but it also makes our guests feel good. Thank you everyone. And thank you, Albert. Thank you for listening to this episode. The metrics at Measureup Podcast is brought to you by BenchmarkIT, which enables SaaS, companies and executives just like you with benchmarking research, events, media and the largest benchmarking index in the industry to make better metrics informed and benchmark validated decisions leading to more efficient revenue growth and increased enterprise value. To learn more, visit benchmarket. That's benchmarket with an IT AI. Benchmarket AI.