theCUBE + NYSE Wired: AI for CFOs & AI Leaders in Silicon Valley | Caitlin Leksana, Fazeshift

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More from theCUBE + NYSE Wired: AI for CFOs & AI Leaders in Silicon Valley

Caitlin Leksana

Co-Founder & CEO

Fazeshift

play_circle_outline Exploring Cube and NYSE Wired: AI Innovators in Silicon Valley Implications for CFOs

play_circle_outline Exclusive Interview with Fazeshift CEO on AI Automating Accounts Receivable and Finance Tasks Benefits

play_circle_outline Discussion on undifferentiated heavy lifting and AI in business

play_circle_outline Customization and personalization in AI tasks for businesses

play_circle_outline CFOs role in AI adoption and business model transformation

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Caitlin Leksana, Fazeshift

Caitlin Leksana

Co-Founder & CEO Fazeshift

TheCube special presentation in Palo Alto studios with Caitlin Leksana, co-founder and CEO of Fazeshift, an AI startup focused on accounts receivable automation. Fazeshift helps companies automate manual tasks, allowing teams to focus on strategic decisions. They work with teams using NetSuite and Salesforce, connecting data and customizing AI workflows. The company has recently gone through Y Combinator and is announcing fundraising in January. Fazeshift's focus is on AR, aiming to become a multi-billion dollar company. The goal is to bring AI into old schoo... Read more

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>> Welcome back everyone to theCube special presentation here in our Palo Alto studios. I'm John Furrier, your host of theCube. This is part of our Cube and NYSE Wired community presentation. We've got our studio here in Palo Alto. Of course, we've got theCube East on the show floor of the NYSE, and we've got great content coming together, great community development, very, very fast. And of course the focus this week is AI innovators in Silicon Valley here in this subnet of our community, but also a focus on AI for CFOs as business transformation becomes a big part of their job. And of course it's going to impact decisions, architecture, budget, finance, revenue costs, all good stuff. So check that out. Caitlin Leksana here is the co-founder and CEO of Fazeshift, who's an innovative startup just hitting the scene. Love having founders on, Caitlin thanks for joining us.

Caitlin Leksana

>> Thanks for having me

>> Joining the community. Appreciate it.

Caitlin Leksana

>> Awesome.

>> So you guys just came out of YC '24, you and your partner, HBS buddies working together on projects. So you're in the classroom working on projects. Now you've got a startup. Love what you guys do because this is a thesis we're seeing in the CFO conversation around the operating system of business. The plumbing is money and linkages between investments that have been made, sunk cost and investments as well as dependencies and all that stuff. And platforms are also a big part of it. In the old school way, buy some IT, okay, ROI, TCO calculations, payback, serving the business, text the business now. You guys have a service that's kind of core to business as a managed service. We've seen Meteor and other companies really doing well, owning kind of a subsystem of the business model that just works and continues. Take a minute to explain Fazeshift, the origination. Obviously you got the Y Combinator, you went through that process. What are you guys offering? Talk about the business model.

Caitlin Leksana

>> Yeah, absolutely. Like you said, it's core of the fundamental operating system of a company. Basically we help companies get paid, we help move money. So Fazeshift is an AI agent for accounts receivable. Basically we help companies automate anything that's still manual about their accounts receivable process and help the company get paid. And then so we build customized AI workflows, we bring that into the office of the CFO, into finance teams, accounting teams, and we replace everything that's manual so those teams can focus on strategic decisions that actually move the business forward instead of doing the very manual tasks that kind of bog the organization down.

>> Yeah, I mean I remember my first time I interviewed Andy Jassy in 2014. He said undifferentiated heavy lifting that's become a mantra of, say cloud generation. Back then it was labor for tech, so they had that step function, 10x engineer. Now we're seeing the 10x business person come in with AI. So you guys are doing something that, I mean everyone knows accounts receivable, that's money. Cash flow, cash is king.

Caitlin Leksana

>> Exactly.

>> I mean this is where you guys shine. Talk about that undifferentiated heavy lifting because it's differentiated in the sense of want the cash to come in fast and all kinds of policies behind it. This workflow is pre-existing. Talk about what you guys solve. What's the use case specifically and where's that value extracted?

Caitlin Leksana

>> Yeah, I mean the problem that we see is even though... you know, AI was around probably 10 years ago, right? There was the original machine learning, there was NLP, but the question that we ask is why is there still teams of people doing things very manually in these ERP systems and these CRMs still via email and Excel? That's the problem that we're trying to solve is so much of this is still manual and it really bogs the organization down. So what we come in and do is we have an AI agent that plugs into their existing software system, sits as a layer on top, and we actually execute the tasks that humans are doing today. And I think that's the new technology that we haven't seen before that we're able to leverage and bring that into the company.

>> What are some of the incumbent or inherent preexisting systems that you guys work with? What are you seeing most?

Caitlin Leksana

>> Yeah, when we come into a company, it's usually a team of somewhere between 5 and 20 people operating on top of NetSuite. So they're logging into NetSuite, they're creating records, they're then looking at their email, they're then logging into Salesforce and updating records. And so it's these existing operating systems, to your point. But then there's humans, there's knowledge internally. And what we see in when we talk about money movement, there's AP and AR. The AP side of it has a lot of control and leverage in the relationship. And so they decide how the process works. The problem is on the AR side, they have to conform to the process of how their customers want to pay them. And that complexity, that customizability, that's never been solvable with software before until generative AI allows us to bring creativity, customizability into the process.

>> Yeah, you bring up a good point. I mean if you think about every company's different and so-

Caitlin Leksana

>> Entirely. Exactly....

>> AI is a personalization dream scenario.

Caitlin Leksana

>> Exactly.

>> And we've been saying on theCUBE many times, okay, search, killer app, no problem. Find what you're looking for. Hey, where's that file? Where's that record? What's going on? Maybe a prompt, get a little answer, get a little help there. But tasks is where action is, and this is where you guys are innovating, right? Talk about that piece because searching and making my job easier, reducing the steps it takes to do something, interface check, that's minimum table stakes. Tasks is the killer.

Caitlin Leksana

>> Totally. And you mentioned personalization. Consumers are very comfortable with personalization. We're bringing personalization and customization into the tasks that businesses do every day and that the teams are doing every day. I kind of think about it is I think there's a lot of talk and a lot of interest around how is the role of someone in finance or accounting or the CFO changing? The way we see it is the role of everybody in a company is moving from a doer to a reviewer. How are they reviewing that the tasks that the AI can actually do automatically for them now? So part of what we do is we can identify what's the action that needs to be taken and we actually take that action in their softwares for them, whether we're going to NetSuite, we're going into Salesforce, we're going into their existing systems and doing what the humans are doing. But I think what's really important and what if I was a CFO, I would be asking my vendors is how do you give me the ability to review that and edit it and approve it before you actually go and do it? Because what's unique about the CFO is you are touching finance data, you cannot be inaccurate with us. And so how do you give people a place to review that? And that's really what we focus on is yes, we automate those tasks, but how do we give people an interface to understand how the AI thinks and what data it's using to make decisions.

>> I was at a conference, I won't say the VC's name, and I was participating also as an attendee, and VCs are like always run into the fire, build the next Salesforce, build the next CRM, low code, no code, "I'll do it." A couple of people died, someone wins, they win. But it's Salesforce, NetSuite, they have the data. So everyone's like, "John, what do you think?" I'm like, well, I love the bravado. Go run into the fire, be in the arena or whatever metaphor they want to use. But at the end of the day, the role of a Salesforce or a NetSuite, it's going to be a function of their ability to have good data. And again, everyone who uses Salesforce knows that it's not the best user interface, but it's a system of record. AI is a dream. That's why Marc Benioff's rebranding Agentforce because he now can make all those piece parts work better together with AI, but they'll still be a major system. Not the answer.

Caitlin Leksana

>> They're not going away. Salesforce is not going away, NetSuite is not going away. But as innovators in AI coming into companies that have these legacy systems that aren't going away, the question for us is how do we still bring value to those companies? And a lot of that is how do you surface data, use the data, give it to their teams, automate away the mundane tasks that honestly aren't very fun and allow them to actually create value for the company in other ways.

>> I think the renaissance of the domain expertise in enterprise, which has been a hard nut to crack in tech, because it's very siloed, a lot of different data and systems. You can't just throw AI at that because there's a lot of knobs and buttons you got to push and people who actually have been doing it for years.

Caitlin Leksana

>> You're absolutely right.

>> But they're end-to-end processes that are well-formed.

Caitlin Leksana

>> Absolutely right. And that's what we've seen is there are all these legacy systems, operating systems that are in place, but they don't talk to each other. And so that's where the humans come into play. Exactly what you're saying, is there are people that have been in the job for 30, 40 years that know where to look in Salesforce for this data, where to look in NetSuite, how to connect that together. They have this knowledge in their head and we come in and say, "We can connect all this data together for you." And that makes it even more valuable than having it siloed in a single system.

>> Well, I love the business model. Talk about the reaction to the comment that I was mentioning earlier about the operating system and how smart CFOs are looking at subsystems, as you probably learned this in HBS and I did when I was in business school back in the day, core, non-core, core competency, non-core competency. Cash is a core competency, it's lifeblood of a business, but I don't necessarily have to build and own the IP of the plumbing so to speak. It doesn't sound like it's commoditizing what you do, but you're a specialist. You can say, "I'm going to be reliable with the data, accurate." I mean Meteor has the same value proposition on usage-based pricing.

Caitlin Leksana

>> Yeah, absolutely. And they are the experts in usage-based pricing and that's why people bring them in. And we see that a lot is people come to us and say, "What are best practices for bringing AI into our accounts receivable process? You are the expert, you understand how to implement it." And I think that's a lot of the value prop we bring is understanding what our best practice is because everybody's really figuring it out, and building it internally takes resources that they could be using for something else.

>> We had a previous entrepreneur on who's doing out-school, it's an educational thing, it was a little bit different, but they talk about democratization of learning. I've been saying, I've been waiting to see this in your role, you can be in the machinery doing AR and be a hero because if you identify process improvement at the point of needle moving moment, that's where the human in the loop could actually provide real differentiation because they have the domain skills. So we've yet to see that kind of explosive, wow, this person in the line, so to speak, in the workflow, actually knocked it out of the park.

Caitlin Leksana

>> Right.

>> That's going to come. Have you seen any evidence of that emerging and examples of where someone could say, "Wow, if I just do this thing, I know this one thing, if I move this, that changes the game on the other side, that creates a kind of consequence, a good consequence."

Caitlin Leksana

>> Teams come to us, it's really interesting. Teams come to us and they say, "I know AI should be able to do XYZ. I know AI should be able to read contracts. I know AI should be able to match my payments. I know AI should be able to draft these emails for me or categorize my emails, what have you. I just don't quite know how to do it." And I think that's kind of to your point about democratization, it's democratization of AI, but then you need a specialist to come in who understands how to get all of your data connected and actually bring the AI on top of all of that data and actually flow through your systems, and then that actually unlocks the people on the team to find process improvements that they've never been able to implement before because they've been busy doing, not reviewing.

>> Yeah, they're so busy heads down grinding, grinding. They got to do it. Okay, so talk about the funding levels you guys are at. Okay, where are you guys at on the company? What stage are you at? Product shipping, customers? Take us through some of the specifics.

Caitlin Leksana

>> Yeah, absolutely. We're at exciting point in our company. So we were founded over a year ago. My co-founder and I went to Harvard Business School together. That's how we met and we started building startups together. We got into Y Combinator, went through that with Garry Tan was our partner there. He's just incredible. We did our big announcement during YC and got a ton of inbound just from that. So that was really proof that there's something here that's been missing in the market, our unique approach to AI is really exciting to people. So got a lot of customers from that. Just got out of that this past summer and we have an exciting announcement about some fundraising coming in early January. So you'll hear about that soon.

>> Okay, look, you got a big smirk there. You're hiding the ball there.

Caitlin Leksana

>> Can't reveal all my secrets.

>> So this is a funny round coming in. First of all, Garry's a great guy. I don't really know him personally, but I love the work he's done in San Francisco. He's been a big proponent of this democratization, certainly cares about what was going on in politics. So Garry, shout out to you and the folks who made that happen. Talk about the idea. So did you guys come up with this at HBS? Was it something that you were riffing on? Was it a classic huge market let's go after we can solve this? Was it a personal thing? How did this all... What's the origination story?

Caitlin Leksana

>> Yeah, it's kind of funny. So my co-founder and I, coming out of Harvard Business School, we were actually starting to build a crypto startup. That's where we originally started. My co-founder and CTO went to MIT for computer science, was in the US Navy, taught cybersecurity. So he has a cryptography background, so that's really where we started. But funny enough in crypto, everyone wanted to pay us in dollars, not in crypto. And so we had our own AR problem at our own crypto startup and decided to pivot away from that. Turned out this was a much bigger market and much bigger need and something that we deeply understood as founders. And I think that's something that most founders bring to their company is their unique understanding of the problem. And we felt the problem ourselves.

>> It's interesting, once you get in and you kind of navigate through and you realize, well, sometimes you have to zig and zag and then here you guys stumbled upon a huge market opportunity. You guys thinking about the go-to-market as the service layer on the frontend with the backend scale operating leverage. Obviously there's some folks that are going to need learnings. And by the way, 20 years ago when you did a VC, all platform, all scale, no services. Services prime the pump.

Caitlin Leksana

>> Totally.

>> And so with AI, you get operating leverage. So you're seeing services being cool now as a go-to-market mechanism because you have operating leverage, you don't have to foreclose the scale. Do you guys think about that at all?

Caitlin Leksana

>> Yeah, it's not just cool, it's a necessity, right? And there's two reasons for that. One reason is that a lot of people don't even understand the power of AI yet, and so they are looking to the experts and the innovators in this space to give them use case and examples and recommendations of what best practices are to bring AI into their workflows. So that's one. And the second is when you are touching these data layers, I mean you mentioned the Databricks layer, all of these companies have huge amounts of data, they need it all connected together. This problem gets very technical very quickly. And so we take somewhat of a Palantir approach where it's a very customizable system, we've built our system to basically plug and play into anyone's existing data sources, a ton of customizability on top of that. But part of what that means is we bring almost this forward deploy engineer approach where there is an engineer in sales conversations because it gets very technical very quickly when you're talking data and AI.

>> Yeah,. And there's also the hoops you got to jump through. It's like going to TSA, you want CLEAR and TSA Pre. Go right through to the enterprise versus standing in line. You just want to get through the knothole. Sometimes there's some hurdles there. Have you experienced some of those and how does that go? I use that example over the top just to kind of make a point, but we've all been through that, right? It's like IT or security by the way too. Secure data is a huge thing.

Caitlin Leksana

>> Absolutely. I think there's two hurdles that we've come in and one is it's the question that everyone should be asking is how do I review and audit what the AI is doing? And this is actually what I'd call a mini pivot within our company. We start out being like, "We can fully automate everything. Everyone's going to trust what AI is doing." And the reality is-

>> We got this.

Caitlin Leksana

>> Yeah, we got this. Most CFOs are like, "That's not a responsible implementation of AI. I need to see everything that the AI is deciding, how it's making the decision, what's data it's using, what's it recommending, and then actually be able to audit it." Right? At the end of the day we're talking about financial and accounting records here. So that's one thing that we've really pivoted and focused on. And then when it comes to implementation-

>> Oh, it's a nice value-add too on the product requirements. You're getting real-time working backwards-

Caitlin Leksana

>> Entirely. Entirely. And when it comes to AI, the ability to get feedback of what's working and what's not working, a lot of unique prompt engineering going on behind the scenes with our forward deployed engineers to customize it to our clients.

>> Yeah. One of the things I'd like to ask you is that let's just say that you have a spectrum of customers because I think this will work at any start-up right out of the gate. Because remember during the cloud revolution, all the customers were developers and they became huge companies, Airbnb, Dropbox. So I'm sure startups when they hit escape velocity series B or series A, they might have Salesforce or spreadsheets. And then you have higher end like a big company that might be somewhat regulated or they have more systems, more of a data problem. Take me through the engagement. Say I'm sold on it, you had me at hello, I want in, I want my cash flow coming in fast, I want predictability, I want reliability, but I'm on spreadsheets and I'm on Salesforce. What's the use case for that one?

Caitlin Leksana

>> So what we initially do with every engagement is the proof is in the pudding. And so we ask people, "Give us some of your data, let's sign an NDA, give us some of your data, we'll run it through our models, we'll show it in our platform and you get to see and engage with it." Because I think a lot of people, they get sold on the idea, they love the idea, but then it comes down to how can I understand within my workflow how this is going to help? How do I see AI working in practice? I get it in theory, but how do I see it in practice? So that's kind of to your point about engagements, we always ask people for their data, we actually run it through our system, show them how they're going to use it, and then we need to talk with their IT departments and their security departments and actually get it implemented in there.

>> So it's not so much their maturity, you have the same kind of process depending upon where they are. So you can talk mid-market all the way up to large enterprises.

Caitlin Leksana

>> Yep, exactly.

>> Talk about the entrepreneurial zeal around, because I could see many things popping out where you have to say no to. I could see you guys saying, "Oh wow, I could actually get you some credit on that." So say, my client's paying 90 days, that's a problem which many people are having these days. It's like, okay, pay 90 days. So is there other derivative areas, adjacencies you're targeting or saying no to? What's the focus?

Caitlin Leksana

>> Yeah. Accounts receivable is a big enough market alone. If we just win an accounts receivable, we will be a multi-billion dollar company. What you see on the accounts payable side with Coupa, Bill.com, Ariba, now Ramp, they are multi $5 billion plus companies and that doesn't exist on the AR side because what I talked about earlier where they don't have the leverage, the customizability has never been possible. So AR alone is a big enough problem that we want to solve to be multi-billion dollar. The question is how do we get to $10 billion plus? That's where kind of the adjacencies come into play. It's always on our mind.

>> Are there surprises so far in some of the early engagements where it's like you discover, "Wow, we didn't invoice this or that was gone."

Caitlin Leksana

>> The amount of mistakes is insane, and people know it. The controllers, the CFOs, they know that this is happening. They know that there are mistakes in the data. People are not getting billed accurately. Payments are not getting applied correctly. And at the end of the day, these are customer facing problems that affect their relationship with our customers. So the sales team usually gets involved. So we typically find-

>> That's not productive.

Caitlin Leksana

>> It's not productive. I mean sometimes it's the CRO driving the sale, because they're like, "How do I get my sales team out this Process?" So sometimes we're talking to CFOs, sometimes we're talking to CRO.

>> I mean you're really cleaning house on the front end of the business, that if you do that work upfront, because it's a system now, you're plugged into a bigger operating system, the downstream consequences of mistakes on the frontend really are amplified kind of downstream. Because people are then, "Oh, we're good here." And then, "Wait a minute, there's big problems over here."

Caitlin Leksana

>> Right. And I think as an entrepreneur or anyone trying to move into this space, the question that I'd urge them to ask is what is still incredibly manual? Even if it's old school, accounts receivable has been out since businesses and cash has flowed, but how do we take this old school process, bring AI into it, become a system of record and improve the processes and bring real value to the company?

>> And AR also wants to talk to AP too. I mean AP and AR, they are like together.

Caitlin Leksana

>> Oh yeah, we're working on that too.

>> Left hand, right hand. Caitlin, so we are doing the AI focus for CFOs. I'd love to get your perspective. Obviously you guys have a great view where you're at obviously and credentialed up big time. So as you look at CFOs that are watching, they're thinking about, they're under a lot of pressure. They got cybersecurity, you mentioned you guys have a background in cyber, and you probably know, it's like damn, the job's heavy on that side, insurance. But now the business model transformations are happening. What do you see and what's your vision for CFOs out there who were really open-minded to learning and progressing towards a playbook or some guardrails or at least getting their framework around how to handle the inbound tsunami of AI? Because they're getting told down from the board level, "Infuse AI for competitive advantage and revenue growth and cost optimization." But then you got the bottoms-up developers who are writing the apps, they're involved. So there's a lot of a nexus of those forces happening and CFOs are now tossed in the middle of it because they're, I think soon will be driving a lot of the business transformation questions and policy.

Caitlin Leksana

>> Yeah, this is absolutely what I've seen. I see it now as an entrepreneur. I saw it back when I was at BCG advising CFOs in Fortune 1000s. And they, I think this unknown fact that CFOs sit in the center of a lot of the decision-making. They sit in between these departments, they have a lot of visibility, they have a lot of say. And I think kind of something you're bringing to fruition is what's a framework that they should use as they think about adopting AI? And I think that they have this purview, and most CFOs tend to be very business minded, very numbers driven. And I think at the end of the day it comes down to numbers. And that's part of what we do with getting them to try out the product before they buy or even the buy versus build decision. The CFOs are best situated to be making these decisions as they bring AI into the company.

>> Exactly. I always say revenue models is one piece of the business architecture, business model. If it's technology value, the sales teams, they're stakeholders in a system.

Caitlin Leksana

>> Right. And I think something that we are trying to unlock for CFOs is as they are changing their understanding of their own business model, their operations need to conform to that. They can get more creative with their business model, different product lines, more creativity about who they go after, but their operations need to support that. And that's what we see as people who are like, "We have very complex operations already, but as we scale that will only get more complex as our business models change and we try to adapt."

>> And you have to make it easier. That's the minimum table stakes for you guys.

Caitlin Leksana

>> That's our goal.

>> All right, what's next? You got some news coming, so probably funding, kind of tease that out a little bit. Hiring, put a plug in for Fazeshift. What's the culture like? What's the north star? What are you guys looking to do?

Caitlin Leksana

>> Yeah, absolutely. So we just grew our team. So we've got four incredible engineers with us so far. We're a very product engineering heavy org, very focused on building the best product out there and giving that to finance and accounting teams. So growing product, fundraising and then growing our customer base. We've gotten great feedback from our customers so far and just looking to grow to more in the future.

>> How's the journey been for you and your co-founder so far? Obviously YC, awesome group, great alumni.

Caitlin Leksana

>> Yes.

>> I think it's over 10 years now at least I can think of. I don't know what year they're on, but it's been quite the institution.

Caitlin Leksana

>> Yeah, the combination of the Y Combinator community and the Harvard Business School community. I mean it's part of the reason that my co-founder and I decided to go to both is it comes down to the people in the community and they've just been so helpful. A lot of our customers are Y Combinator alums, unicorns that have come out of Y Combinator or Harvard Business School alums as well.

>> It's a great network.

Caitlin Leksana

>> It's a great network.

>> Well, thanks for coming in and joining our mission here. Appreciate it.

Caitlin Leksana

>> Thanks for having me.

>> Thanks for contributing.

Caitlin Leksana

>> It was fun.

>> This is theCUBE plus the NYC Wired community. This is our job, opensource content, putting it out there. As the world's changing the business model transformation is happening. Obviously we cover the tech as well as now CFOs aspect of it. And we've seen it in cybersecurity, seeing it in the data value, which is going to drive the revenue model. And Caitlin and team are targeting the money, get those AR departments all streamlined and get the cash flowing in. All right, more coverage here after this short break.