AI Is Coming for the CFO Office | Stacks, Albert Malikov

0:00 Some of the systems that are in place are [music] not fit for business needs today. What is needed is quick ROI and real use cases [music] that you can solve immediately. And it was clear that monthly financial close was that workflow. The company called Nivoda, a trader of [music] diamonds with more than 13 entities, 130 bank accounts, they were spending more than 8 hours for journal entries. So, we cut down this time to less than 10 minutes. Reduced time for closing the books for 8 days.

0:31 Just imagine that you can have a [music] view into your financials and your data 8 days faster. Accountant will change as well, because there'll be tools allowing [music] to do a lot of work much easier. And what would really be an important >> [music] >> skill for finance person is Hi everyone. I'm Alex Schmidt and this is the Investment Memo, the show where founders reveal the stories behind their businesses and how they will shape the future of their industries. Today, I'm joined by Albert Malekov, founder and CEO of Stacks. Stacks is building an AI-first operating system for the office of the CFO, starting with accounting reconciliations and expanding toward a broader AI-native finance stack for the enterprise. Today, we'll talk about why the CFO function is ripe for disruption, how Stacks is approaching this problem differently than legacy vendors and point solutions, and what it takes to build [music] foundational infrastructure in a highly competitive market. Albert, welcome to the show.

1:29 Thanks Alex. Very excited to be here today. >> [music] >> You're announcing your Series A today, which is a big day for you, considering that you've only raised your seed around less than 12 months ago. Let's start with actually the outset, the market that you're building in. Why the CFO office is actually ripe for disruption. You know this because we've shared this with you. We've actually published a thought piece on the ERP super cycle and replacement layer and there we're talking about the fact that the core ledger of the ERP is in some sense is replaced but in other places even more augmented by solutions that really actually do the work versus just support the work. Maybe level set us in this conversation a little bit. What's happening in the office of this CFO today? There are three interesting trends colliding together.

2:30 One, there is a transition of the finance function in being more strategic and driving intelligence and driving the business decisions forward. So this is first one. The second one, so what we're seeing there's a huge shortage of CPAs, accountants in the space. The number of exams for CPAs in the last 10 years dropped by 40% and US alone seeing a shortage of accountants in the amount of 300,000 people at the moment.

3:03 This is the second trend and then the third one. So there's a huge data fragmentation that is happening in the space. As you mentioned with the unbundling the ERP itself, we're seeing a huge adoption of the point solution which makes data being a big problem in the space. All of these trends together with the technological shift of the AI making this space very interesting for innovation. So if I'm listening closely to you, what you're highlighting is basically there's massive labor shortage but at the same point data fragmentation. And so if we dive a little bit in the jobs to do, if you look at accounting, reconciliation, why is this all still so labor intensive especially when you're in a lack of labor? Yeah, absolutely. Maybe um just before going into the why, I'll explain a little bit uh what are the key workflows uh for the finance teams. Uh one of the core ones is a monthly financial close, which is um the core uh is job number one for um every finance team. And um it could be abstracted into um three main steps. First step number one is collecting the data and entering the data into the uh core um system of the record. Applying uh business logic and also uh accounting standards. Once it's done, once the data is in the main system of the record, um so uh the the teams need to perform so-called reconciliation process, which is in fact is just verifying the data using the um uh external um data sources. Once the data is uh verified, accurate, it's all about uh telling the story and telling the so what um on this uh on this data that is in the system. So, usually the company is doing this uh every month.

4:53 This is um best practice. If we think about this uh workflow conceptually, it's the uh data problem. If you look back uh in time, the businesses were different. The complexity of the different uh of the businesses were different. So, um uh they were very much uh manufacturing uh driven businesses where the systems of the record ERPs were uh very monolithic uh where uh most of the supply chain and other parts of the operational functions of the companies were um in one system.

5:23 So, you go to the dealership, buy a BMW, uh then system transactions trickling down to suppliers, eventually just comes back to the ERP, everything integrated in one place. Um and then uh while the companies were integrating the ERP, they were um using the integrators and putting the um uh automation with the if-else uh code logic in place. So, what happened in the past a couple of decades, businesses changed, businesses became uh global. >> More complex, more global. And um so it uh it changed uh the the core systems. Uh companies started implementing a lot of point solution, best-of-breed solution for the different parts of the supply chain, um for expenses, for payrolls, etc. And um so what we started seeing is that um there was this uh fragmentation of uh data.

6:13 And so a lot of this if-else logic uh moved into the most flexible and customizable tool that we have uh on the planet, which is Excel. Excel, yeah. Um so and what we having today is um a lot of this logic lives in Excel spreadsheets with a lot of uh labor on top of that to uh manipulate this data and uh eventually close books. I think what's interesting what you're highlighting there is that these businesses are becoming so much more complex, yet kind of the finance function is still very much running on Excel. When we spoke to a couple of, you know, potential customers in the industry while getting to know you better, uh we found it so crazy that some of these customers aren't even able to close month-end anymore. And uh that obviously gives you no visibility into your processes, no planning security. You don't even have to talk about a finance function that can be a value partner if you're not even able to deliver on the core premises of what you're set out to do. It's pretty insane to think about that um that um in the world where uh we have su- such a powerful uh tools, uh so powerful technology, uh a lot of companies cannot even uh have a full visibility uh of the past, uh not uh to speak about the um the future. Yeah. It It sounds like a perfect storm. Why hasn't kind of AI arrived to that function yet in a bigger fashion? It's actually interesting. We've been talking with you a little bit about that um as well. If we look at the um other industries where we see AI penetrating much faster like legal customer support, I think there are a couple of differences there. First is on the data side of the things and then second is on the context. The data structures of the finance teams are a lot more complex than in legal. First of all, in legal it's very text based.

8:05 And also very much consolidated in one place as opposed to finance where data is fragmented and it's also numerical data. That's one and then second is the context. The context allowing to teach this agentic systems so they can learn and become smarter. Again in the finance world, the context right now is very fragmented. It doesn't live anymore in the ERPs because teams are doing the work in multiple different systems including Excel, Slack, emails, and many many tools. So there's a fragmentation of the context not allowing to really sort of bring it together and learn from that. All of this makes the penetration of the automation much harder in in the finance world. But at the same time for for those who solve this fundamental problems in this space, there's incredible opportunity because the space is massive and the impact that you can make is huge. I guess it's a great segue to talk a little bit about the role of the ERPs in this whole new world that you're describing. ERPs used to be the core tool that CFOs were paying for. It used to be the tool that was the biggest spend that they were actually having. Everybody was talking about this is the single source of truth, the core ledger that you're having. What is the role of an ERP in in this new new world? As already mentioned, what we're seeing is is um a lot of context, a lot of work moved away from the ERPs today. But ERPs are still a very powerful source of the record within the companies. So, basically again, just a building a lot of automation um that data that is within the ERPs is incredibly important, but then the context that is on top of the data is incredibly important as well. So, in order to build a system that is truly making change and delivering ROI uh for the customers uh one has to bring together context uh where the people doing the work and the data together. You um mentioned this in one of our conversations as well. Um some service with the CIOs that um there's increased trend of uh finance teams thinking about upgrading their systems. And I think it's truly signaling that um some of the systems that are in place are not fit for uh business needs today.

10:35 But at the same time, um what we're hearing and seeing in the big enterprises, changing those systems is very difficult. As someone was saying is almost like doing a heart >> open heart surgery. on the heart surgery on the athlete who's running on the track. Um and in many ways so it's not needed. Um what is needed is a quick ROI and real use cases that you can solve immediately. I love that analogy. So, you're doing an open heart surgery and then immediately after jumping on a treadmill to run on the track. Yeah, it's really great. I think I think it's really encouraging for CFO functions that they have to have obviously a great kind of core infrastructure on the ERP layer, but that they can drive a lot of the efficiency gains and the insights that they really want to do by building kind of around that core with a value-adding layer such as Stacks, I guess, which which is a good segue in talking a little bit about why we're sitting here today.

11:31 And that is obviously you announcing your series A investment round. And what makes me happy about this announcement today is that this is not the spur of a moment that we're sitting here, but we've actually gotten to know each other over 1 and 1/2 years uh pretty well. Um, I very vividly remember our first day of having a walk in rainy gray Berlin in a in a spring day. Um, and then I very vividly also remember the moments that we spent outside of the office where we brought you to our kite trip that we took over hat. And there's a great picture of us in here that we can look later on as well. But but maybe kind of taking us a little bit on the journey of of Stacks. And I know, you know, that that company is is still relatively young, but you you kind of came from a very impressive background. You've built the core functionalities in in Uber's finance teams. You've helped Plaid to land in Europe. And you saw these problems in the broader finance stacks today. So, Albert, help our viewers understand what was the vision for Stacks at that moment when we met and how has that evolved into what Stacks is today? I still remember our walk in Berlin when we were talking about the the space.

12:44 At the time, uh what we knew is since I was a part of the Uber finance team working on as a product manager on automation side of the finance. So, it was clear that this is a massive space. It was also clear that the problem and the manual and mundane work in the space is very acute. And finally, we were living in this world of new technology emerging that giving a hope that we can completely rethink some of the solutions there grounds up. So, we had this longer term vision, but we didn't fully have a view on how we're going to get there and where when we're going to start. Yeah, in the case of the Uber, we were building almost 60 businesses at the same time because every region was a business and decision-making was very decentralized. So, we had GMs that had had to have had to do a lot of decisions and they needed data on their fingertip fingertips with a lot of this data coming from the financial data as well. At Uber, it was in a way solved by a very data-obsessed teams and would access access to the data. But, not every company can do that. And for us was really a North Star how we can really bring all this finance teams in the world where they have financial intelligence at the fingertips so that everyone in the company can make better decisions whether they invest in one product, another product, they invest in one region or another region.

14:16 But, to get there, one thing we realized that it's fundamentally data problem because data is segregate data is is everywhere and it's difficult to build intelligence without having a good data layer. But, you cannot build that data layer from day one. And so, we decided to go and talk to the customers, do research, we spoke to hundreds of customers across the world to really understand how can we get there? What would be the best wedge through bringing efficiency ROI to the companies as quickly as possible.

14:51 And it was clear that monthly financial close was that workflow that is very manual where more than 60% of labor force is is concentrated and where AI can make a huge difference. So, we decided to focus on this area. And interestingly, this area is also allowing us to get the view of the data more horizontally and really just get us closer to that vision. So, focusing on very immediate ROI, we decided to start uh with this uh workflows uh related to the journal entries, reconciliations, and reporting, and then work our way into um just making companies a lot more uh intelligence in their intelligent in their decision-making. What I found so impressive when I saw you embarking on that journey, and you mentioned that just very casually right now, is like you were obsessed about your customers.

15:43 You had literally done more than 100 customer interviews at that time and really figuring out what is that best wedge in And then at the same time, you were very much focused on still doing the hard pieces, which is building the underlying infrastructure and data platform for Stack. So, describe what does Stack look today like, and how do customers use that product? Yeah, absolutely. Um so, uh our product is structured on uh three fundamental layers. Um one layer um that I just mentioned, it's uh it's a data layer. We call it uh AI-ready uh parallel ledger. Second um layer in our system is deterministic tools. Mhm. Um it's machine learning tools such as transaction matching, uh transaction matching categorization, and other different um uh data analysis tools. So, finance is a space which uh is um uh very sensitive to the accuracy. So, that's why we're using this um deterministic tools where like we're doing the math.

16:42 And then uh the final >> right about that, actually, yeah. Absolutely. And then the third layer here is um our workflows. We call them agents. Uh the agents orchestrating the work uh the workflows. This is the interesting part because um uh uh what we've seen at Uber is that a lot of um workflows are um not linear. And you can automate 95% of um uh some of the um reconciliations, but then uh a lot of time is spent on that last mile.

17:14 And last mile uh is really um uh less deterministic uh depending on the exceptions, you might actually want to trigger different workflow. And that's where agents are very powerful because they learn um from the context. They learn seeing how accountants doing the exception handling and based on that learning they can start different workflows. So those are fundamentally three layers that allowing us to achieve a much better automation and also accuracy with our product.

17:49 >> I think Stacks will stand as a playbook example on how you actually use deterministic workflows together with generative AI tools to really bring value to a function where ultimately precision and reliability matters a lot. I'm I'm I'm curious to see and write that case study together one one day, yeah? Yeah, it's in this space fortunately or unfortunately, you cannot just throw data at LLM and hope for the best. Um so that fundamentally foundational layers on the data side and then on deterministic machine learning tools are incredibly important. And the way we build the this deterministic tools are very much AI first. They can be used by teams to complete the workflows in semi automated way or they can be used by agents that can do tool calling to execute workflows as well. So that's that system makes our platform unique because it's learning over the time and over the time we're pushing what's possible from automation to to the new levels. When I bring down your concepts of how you think about the product into real life examples, can you walk us through how customers are actually using the product and what impact that creates for that finance function. Yes, absolutely.

19:13 Uh there are three uh most important parts um of of our workflow of our product. The first one is I will call it um a journal entry, so like entering the data. And um so be in the world before uh take as an example payroll, um you um as a company using five different payroll providers, and um uh teams were pulling together this data um in Excel spreadsheets, and then um manipulating the data and creating a CSV file sending into the ERP system. So we fully automated um uh this workflow. Uh so now you can do that uh with a few clicks um in our system. So that's one.

19:52 Second is um uh reconciliations, and um one good example there is a bank account uh reconciliations. Um especially for the customers with the um millions of the transactions, this could be a big Excel files with the VLOOKUP uh functions there that takes a lot of time. And then finally, like once you have all of the data there, um uh telling the story and understanding why the spend is 20% higher this month, uh understanding what actually drives that is something that uh requires a lot of work looking at the transactions. So with Stacks, we automated this process again with a few clicks, um you get the uh detailed transaction level explanation. Uh for your flux analysis.

20:34 >> for your variance and flux analysis, exactly. I think the good example uh here is um the company called Nivoda. Uh they are uh a trader of uh diamonds, uh incredibly international business uh with the more than 13 entities, 130 uh bank accounts. >> Wow. So when we started working with them, some of these processes were uh quite uh manual. What we were able to do together, um so they were spending um more than 8 hours uh for uh journal entries. So, we cut down this time to um uh less than 10 minutes uh together. They were spending a lot of time on uh reconciling this uh 130 um bank accounts with the thousands uh uh even more hundreds of thousands of transactions there.

21:25 95% of this work is now fully automated. And um so, we're also rolling out our uh Flags uh uh product with them as well. And we're already seeing a huge reduction of the time uh spent on this. All in all, all of this allowed um the Nevada team to reduce time for closing the books for 8 days. Just imagine that you can have a a view in term uh in your uh financials in your data 8 days uh faster. That has real uh economic impact uh for the business and despite the fact that you finally can spend time on value-creating tasks versus these annoying tasks. Um that's a that's a really great example. I want to switch gears a little bit um and talk about the people at Stacks and the culture that you're building. Um in fact, I'm I'm going to look at our memo and I'm going to highlight we've written in the memo um that we think you're an incredible product-driven team that strives for excellence. Uh and that also means you you ran without an engineering lead for for almost all of the time at at Stacks.

22:28 Um tell us a little bit about like what are you looking for people that are joining Stacks and uh what what makes your culture so special that someone like us is describing it as a very high-density talent organization? So, I do not recommend um running a team without engineering lead for >> [laughter] >> for as long as we did. But, we had uh an incredibly high um uh standard and bar for that person. So, we didn't want to compromise. I'm very glad that we have this person already um uh uh joining our team.

22:58 >> [snorts] >> Uh I think uh in many ways um the um the the talent density comes from experiencing this in the different environments. For me, that was again during my times of the Uber. And there were like a couple of things that really um amazed me at Uber. First of all, it's ambition. Every time when I thought that I cannot be surprised by the ambition of the company, someone would come and just put it to the next level. Second is um ownership.

23:30 Um everyone was an entrepreneur inside the company. And that's why what's what I'm seeing right now, a lot of people I work with, they're running they started the companies that are quite successful around the world. And then the final thing is craft. Um so having like a high standard of the craft for roles such as whether it's design, engineering, was was very important. And those things that really sort of after seeing once, you don't want to compromise anymore.

24:02 And for us specifically at Stacks, what we were looking in everyone who is joining, three main things. First is the motivation because working in the early stage company requires a certain level of uh uh drive drive and you know, you need to be in a way like a high achiever and just [clears throat] prove to the world that you want to do more. So we asking the questions and trying uh get the signals that um the person joining the team been showing this before in the previous career.

24:37 That's one. Second, already mentioned the craft. Incredibly important. So especially for the for the roles such as an engineering, design. Design is not just a shiny interfaces. It's like how do you think about UX, how you break down the complex workflows into experiences that delight customers. And finally, um it's uh analytical uh rigor and problem-solving. In early-stage company, uh we would like to have people who who love solving the problems. When they solve 10 problems, they're looking for another 100 problems to solve. It's a little bit like mathematicians that cannot live uh without any problems coming in their their way. I love the talent density at Stacks. Um a fun anecdote is also that you uh celebrate uh every new hire on LinkedIn, and somehow every new hire has an outdoor sports picture in that celebration. Is there a special story behind that? I'd I'd love it if it's, you know, our kite trip that inspired that but uh tell us what what's behind this. Yeah, so it's uh something that organically started. Well, first of all, what we wanted to do is tell a little bit uh to the outside world uh about our team uh outside of uh simply the work that we do. It uh just builds um the um uh the trust with our customers as well, just seeing that uh we have a diverse uh people joining our team with a uh with diverse experiences, backgrounds, and uh and the interest. Somehow, um many of us uh like the uh outdoor activities. And there's a little bit irony there because um most of the time we spend in the office talking to the customers in front of our laptops. Probably, it's a little bit of aspiration to spend more time uh doing outside of activities um at some point in the life. Okay, so the next uh when I'm applying to Stacks, I have to send you a picture on top of a mountain and then maybe I'm I'm going to make it through the recruiting Oh, we'll already have a fantastic picture together uh and uh uh just guiding together. I want to switch gears a little bit uh and talk about this new capital stack that you've raised. Congrats again, by the way. At Lightspeed, we think it's an very exciting time because you're fundamentally rethinking how some of the deepest workflows are being done. And so, it's time for exponential innovation instead of incremental innovation. Um and what you're doing here at Stacks is is pretty much that. Um and we're very thankful to be part of that journey to be be part of the broader team in this. You you have an incredible set of partners already on the journey.

27:07 How did you go about choosing your partners for this? For us, it really came down to the three things. First, it was important for us to have a partner that is that has similar conviction in this space as we are. It's a long-term decision and um uh through our conversations and interactions with the with the firm, we we saw that conviction. That's first. Second, we believe that this wave of innovation is a very technology-driven. It's not just business model innovation. Uh a lot of um changes in the space happening deeply through the technology.

27:48 Uh as a part of uh getting to know the firm, I will met more than 10 partners across the world. And every conversation um made us believe that uh every partner is deeply technologist >> [clears throat] >> um at the firm. And for us is very important because that's how we think about the space as well. And whenever uh got so deep into the conversation with with other um firms out there. And then the finally, it's very much building uh and having the trust with the people that you work closely with. And uh we uh known each other for um more than 2 years for now and had a lot of um uh touch points and conversation about the space that really build this uh uh trust. And um I'm very excited to have uh you and Lightspeed uh on the board in in this journey of building um generational company. Excitement is equal and we're thank very thankful to be part of it, too. What does this new capital stack allow you to do now? One thing that I'm uh very excited about is accelerating the product development.

28:51 So, we have a a road map that's spanning 12 even more months, and we cannot wait to bring some of the products a lot faster to the market. We're living in incredible time when technology changing so fast, and the speed of bringing products to the market is super important. So, most of the investments will go into accelerating the speed, and many of our products also becoming more powerful as customers using them more. So, we we're investing in the distribution to bringing more bringing this ROI this innovation to more companies out there. Yeah. I love that you're so ambitious. You wouldn't have needed the money right now. You actually had a lot of the money that you've raised still on your balance sheet, but I think you're doing this now really enables you to kind of really bring those products to the market that all these customers are actually pulling out of you, and so I'm excited that we are hopefully transforming this finance function even even even quicker now.

29:54 Tell us what what does success mean for you at at Stacks? How does the finance function in in 5 years look like if you succeeded? >> One conversation that stood out to me when we were doing research even before starting the company, a controller at a global company told us that their team spending 80% of the time doing different data manipulations, and only 20% of the time looking into the data and looking into the insights.

30:25 So, when we were thinking about the future and success, for us it's just flipping that equation. Really just giving time back for the teams to focus on what really drives the business forward. What does that mean in terms of how the teams might look like? If we look at the enterprise company with 10,000 employees, usually you would have 200 around 200 finance professionals there with 50 to 60% involved in accounting workflows that are incredibly manual and then the rest of the team is focusing on multiple other functions including FP&A.

31:05 I think in the future the the composition of the teams will change >> Mhm. and then the teams becoming more strategic and a lot of this manual work will be automated and team members will be spending their time on value added work that the work that drives the business forward but also that would mean that the skill gaps and segregation that we seeing right now FP&A person is very different to accountant will change as well because [clears throat] there'll be tools allowing to do a lot of work much easier and what would really be an important skill for finance person is really intuition around numbers intuition around how to use this numbers to to drive the business forward. So I'm very excited about this future because I think we living in this transformational time. Yeah, I think there couldn't be any better closing words.

32:04 I'm very thankful that we going on this journey together. Thank you very much for being here today Albert and excited for the partnership to come and how we are transforming the CFO suite and by accident building one of the biggest and best businesses out there over time. Thank you for being on the show. It was great having you. I think we should shake hands that you going to sponsor the next kite trip when you build a 10 billion dollar company. Thank you for being here. Thank you guys.

32:28 Thanks for having me. >> [music] [music]