Intuit Inc. (INTU) Earnings Call Transcript & Summary

Awesome. Well, thanks, everybody, for joining us this morning. I'm Steve Enders, part of the Citi software team, and I want to welcome everybody to day 1 of our Global TMT Conference. With us today for this session, we got Intuit. Alex, I want to thank you so much for being here and thank you for joining us..

Absolutely, it's a pleasure to be here.

Maybe just to start, I'm sure people know you from here, but just maybe give a little bit of your background and talk a little bit about what you do at Intuit.

Yes, absolutely. So hello everyone, Alex Balazs, Intuit's Chief Technology Officer. I joined Intuit unbelievably in 1999, October of 1999, so coming up on 26 years. I joined a desktop software company, and I was part of the team that was tasked with put small businesses online. So I was one of the engineers on the first version of QuickBooks Online that we released in late 1999, and it's been an unbelievable journey at Intuit since then from a front-line engineer who wrote a lot of code, drove a lot of the early innovations that we had online, product strategy, platform strategy, I spent 4 years in the small business division. So really understanding small businesses and how to actually solve their problems, especially as they moved online. I spent 10 years in a platform organization, working on some of the early capabilities around shared data, identity, developer tools, customer support tools, things like that. Spent 6 years in the TurboTax group as a Chief Architect for TurboTax and ran some of the engineering teams there, really great experience on the cyclical nature of tax and the urgency of tax prep and really working with the customers there; 5-plus years is Intuit's Chief Architect and the Head of Platform Engineering where I drove technology strategy and a lot of the shared services that are used across all of Intuit's products. And I've been the CTO for 2 years, and it's been a whirlwind 2 years and an amazing 2 years.

No, it's great. That's great to hear. I mean with 26 years of the company, I'm sure you've seen a lot of attrition, a lot of background there. Maybe you can just kind of talk about how the Intuit product, how the platform has developed over the -- your 26 years there? And then maybe we can use that to kind of jump start in some of the AI agentic conversation that's very top of mind right now.

Yes, absolutely. So one of the more remarkable things about the time that I spent at Intuit is how comfortable the company has been at disrupting itself and reinventing itself. And that's certainly something that connects back to Scott Cook, the founder, and I have the great privilege of still having regular one-on-ones with Scott. And we're so customer-focused. We're so focused on solving customer problems that we tend to not get over concentrated on a specific technology choice, a specific platform choice, decisions that were made a year ago, 5 years ago, 10 years ago. And so seeing this company that I joined where I was working on, like I said, some of the early online products and online services, and then there were small moments of, "Oh, what are we doing? Like why would small businesses want to go online? They're really happy with the desktop products". And so there was like this little bit of inertia that prevented us at the moment from moving to the next step. But then as a company, boom, we just kind of blast through that inertia. And that's what I've seen. We're constantly reinventing ourselves, and that's one of the reasons why it's been so amazing to be there for such a long time because as we've hit each of these kind of platform shifts from being desktop to online, online to SaaS, SaaS to platform, platform to data and AI and now truly becoming this AI-driven expert platform, what I've seen is we reinvent ourselves. And so that reinvention certainly manifests itself in terms of the choices that we make about our technology stack and how we build products and how we market our products, but also it connects to how we actually interact with our customers and monetize and commercialize what it is that we do. So it's going from "Hey, this is a really good way to do accounting" to this is the all-in-one platform where a business can interact with us and never have to leave. They can run their entire business. And it's not just about the back office, it's about every part of their business. From a consumer perspective, it's not just about doing your taxes, it's about credit to taxes to wealth. And you can basically use this as your all-in-one platform, and you never have to leave. And so when I think back 25 years ago, to some of the original things that we did to where we have evolved as a company to be this all-in-one platform, I think it's been a testament to, as I said, this reinvention and this idea that the classic book, the innovator's dilemma, and that we constantly kind of power through the innovators' dilemma.

That's great to hear. Maybe just talk about AI, very top of mind. I mean you've, I think, been working on building out your own AI capabilities within Intuit for almost a decade now. What have maybe been kind of the key learnings? And how does that maybe set up Intuit in the platform for the future as we think about the agentic age?

Yes, absolutely. So I remember -- I can't remember the exact date, but it's like 2015, 2016, I was in the TurboTax group and it was the first time that, at least I'm aware that, we actually leveraged AI inside of our products. So one of the biggest drop-off points in TurboTax was someone doesn't know, should I take the standard deduction or should I itemize. As they go through the entire trouble of going through itemize, they do all the work and hours worth of work. And then they realize that standard deduction would have been better and they get upset. And they either leave or they go to a tax preparer, whatever the case may be. So obviously, we want to keep those customers and we said, "Boy, wouldn't it be great if we could predict if this customer could actually benefit more from being an itemized filer before they give us all their information". And that was the first time we had trained a model to say, "Hey, let's see if we can predict, is this person going to be an itemized filer or standard deduction". And to our surprise at the time, it was like 97%, 98% accuracy we could actually predict before they gave us all their tax info. And that was kind of this eye-opening moment for me and for us as an organization to really understand "Boy, the power of data". And certainly, the AI is amazing. And we have a lot of AI folks who are working at Intuit. We leverage a lot of amazing AI technology partners. But really, when I think about the lessons that we've learned about leveraging AI, creating impactful AI, the first part of it is where the data has to be there. And so with the large set of data that we have at Intuit, hundreds of thousands of attributes about every business, about 60,000 attributes for every consumer that's on our platform, we can actually leverage that data. And so public models are great. Public LLMs are great. And we use them and we'll continue to use them for, boy, the power of the data. The data is so important. And so the investments that we've made in data in terms of making sure our data is clean, and organized, our data services to make sure our data is available and we can get data both locally in our ecosystem and go get it anywhere where it is on the Internet and make it available to our customers. And then how you actually leverage that in your AI, critical, critical part of our AI strategy. The second thing that I would call out is, the AI is great, and the AI can solve many, many problems, but for the combination of AI plus what we call HI, human intelligence, is even more powerful. So for us, that manifests itself in our live services. So TurboTax Live, Assisted, QuickBooks Live, Assisted, where it's the combination of what can the AI do and what can an AI-enabled human do together to actually go from, "Boy, this is a really easy product where I can do it myself to just does all the work completely for me, and I don't have to do anything". So that AI plus the HI for us is something that we continue to lean into. And in fact, as we released our agentic capabilities, so our capabilities that leverage LLM to automate entire workflows, one of the really critical features that we built into it was that at any point in this agentic workflow, they can pass you off to a human being. So if the agent doesn't know what to do, the AI agent doesn't know what to do, pass you to a human. And then the human can pass you back to the AI agent. And along the way, we keep track of everything that's going on. So the human agent knows what the AI agent does and the AI agent knows what the human agent did. And so AI plus HI, to us is such a huge part of our strategy, and we're continuing to lean into both. And then the last thing I'll call out is, we're very particular as to how we set up our teams to actually leverage AI. We want to make sure that they have unbelievable building blocks that we can have very small teams who act like they're start-ups and they solve problems. So the challenge that any big enterprise has, any big company has is how do you take the power of the company that you are and take the capabilities of you as a big company but then act like you're a small company, act like you are a startup, be extremely agile, take risks. And so what we did is we specifically in the age of AI, we knew that we had to create a setup and that's what we call GenOS, or generative AI operating system. We had to create a setup where our developers and really, it's more than developer -- developer, product design, AI folks working together, how do they act like they're a start-up, yet leverage the power of Intuit. So creating these atomic services and capabilities to allow them to move fast to experiment, to launch experiments in a matter of days and weeks instead of months and years. And then collect all the metrics about it, see how it's performing, see how the customers are interacting with it. Are they engaging? Are they reengaging? Are the predictions accurate? All of those things. We really made sure that this -- what AWS refers to as the control plane. We made sure that we invested in this control plane so that our developers and our builders can iterate very quickly and solve for our customers. So the old days, 10 years ago of AI, it was about big investments in big projects and "Hey, let's plan road maps and let's roll this out". And now in the age of AI, it's about how fast can you move, how fast can you learn. And it's really one of the biggest things I've been pushing as CTO is you got -- we have to have this builder culture where we're building and not talking. We're not in meetings, not talking about process, how do you build, get things in front of customers, see what wins? And then when it wins, scale it and commercialize it.

That's great to hear. I'm going to dig into the portfolio a little bit more. But before we do that, I do want to ask about, I guess, the own internal initiatives, and I think you touched on it a little bit, but maybe how has all of the changes that you've made there, how is that beginning to, I guess, translate in terms of the efficiencies within the R&D or how is it translating into the pace of innovation and how much more quickly you can build products?

Yes, absolutely. So our initiatives around productivity actually started about 10 years ago, where we actually -- so step 1 of being more productive is to measure how productive you actually are. So we have a lot of automated metrics gathering that basically figures out how long does a certain task take for a member of the workforce. Certainly, we've invested more in areas -- larger areas of the workforce, so our engineering and development community, customer success. And so for each of these workflows that they do, we know how long it takes. And so step 1 was to say, from a productivity perspective, what can we put into place to actually grow productivity. So we -- in the tech organization, we talk about is development productivity. And in the past 6 years, we've improved our productivity 8x. So we can actually have 8x more throughput from an engineering perspective now than we did 5 years ago. Probably about 5x of that was just standard development practices. And the last couple of growth areas of productivity began in terms of our investments in AI. And when you think about AI-based productivity, everyone tends to go to the engineering first because we create the tools and so we came to aim it back at ourselves first and I was actually chatting with Dario, the CEO of Anthropic, two weeks ago, and he was talking about how development productivity so much of it can be driven by these AI tools and our early experience in terms of productivity for our engineers is when using these tools well, they can -- their productivity goes up by as much as 40% for coding activities. So -- and that's early. That's based on where the tools are today. And so we're constantly pushing on productivity, but it can't just be about the engineers. For us, it's about the entire workforce. And so we actually have a program where every single functional role in the company, we have a team who is dedicated to say how will AI revolutionize how this person works. So you have engineers, customer success, the most obvious ones, finance, HR, marketing, and in each of these roles, we are now going through the same process and saying, how long does it take to actually execute certain tasks and how do we make these tasks more efficient? And I'm actually very excited that in two weeks at our Investor Day, up on stage, I'm actually going to be demoing a couple of these capabilities, these tools that we've built that are completely changing, for example, how marketers work, completely changing how customer success works, how salespeople work and having it be all AI-driven. So big, big area of focus for us. And the great thing about, I think, the way that we're approaching this productivity is we're pushing really hard on productivity. And what that does is it gives us a lever. So with that lever, we can choose, "well, what do we want to do with that productivity". So far with engineering, for example, we poured it back into engineering, and we said, let's do more. Let's create more product, let's create more services for our customers. But we can make those choices independently. We can choose to say, "Hey, is this a place where we're going to take the productivity and do more with the same number of people or is this a place where we actually recoup some of that productivity and invest it somewhere else". So it's a big, big area of focus for us.

That's great to hear. And I guess a good plug for the Investor Day in 2 weeks on Thursday. Maybe it's a good time to start talking about how you're building AI into the product. And I think you mentioned you're thinking about the personas of customers and users. How does that kind of inform how you build out the range of products for your end customers?

Yes. So I think in order to answer that, I think I have to first talk about what's the wrong way to solve the problem. Because I think across the industry, there's been a lot of examples of the wrong way to solve the problem. So the wrong way to solve the problem is to say, "I'm going to take a specific feature and just turn it into AI. And when you do that, it tends to not resonate well with the people that you actually did the work for. So they'll say, "you moved my cheese or you, I don't understand this. I don't want -- I'm not here for AI, I'm here to manage the finances for my business. I'm here to do my taxes. Why are you showing me AI?" And so we completely changed our approach about a year ago, where we said, what are end-to-end customer workflows and that the customers themselves, they don't want to do. And if you could take this entire end-to-end customer workflow and actually automate the entire thing, then all the customer knows that it was done for them. And so how do you approach customer workflows. And so when I sit down with various teams and in fact, I was just here at New York office yesterday and I was meeting with some of the engineering teams. And they were asking me like, how should they approach integrating AI into our products. And what I told them was, don't think of it as you're creating smarter software. Think of it as if a human did that for that person, how would they solve the problem? Because that is a much better way to leverage AI. The way you leverage AI is you said, if you delegate this to a human, how would the human solve the problem. So the very specific example is if you're inside of QuickBooks and you're a lawn care business, and you go on a job and you are quoting some big commercial lawn care project to do all the lawn maintenance for some big office park. And the -- you write a quote, you write an estimate. So under normal circumstances, what would happen is you would take that estimate, end of the day, you'd go back to the office. You'd probably give it to someone, that person would transpose it and put it into an e-mail. That e-mail will be sent out to the customer. The customer would decide, "okay, do I actually want to take this estimate", they send it back and say, "Okay, I'll do it". Then another person takes that and puts it into the accounting system and creates an invoice, and they send the invoice and then a payment comes in or actually payment probably doesn't come in, they forget to pay it. So you have to send a reminder, right? These are all human activities. So everything I just described, our agents do automatically on our platform. So you create an estimate, you can write it onto a piece of paper, you take a picture of it. That estimate automatically goes into our platform. The estimate is sent out to the customer. The customer digitally can accept it. It comes back to our platform. It automatically sends the invoice. They approve the invoice. If the time goes by and they didn't actually pay the invoice, we'll automatically send the reminder. But along the way, the business owner has the ability to control various aspects of it, so they can control the voice and tone of the messages. They can control the frequency, but the work is done for them. And so that is an amazing use of AI, right? Because now instead of that business owner, not to mention the fact that one of the things that we've learned is that I believe it's 80% of the time, the first quote that goes for a business owner, the first quote wins. So now imagine you go back to the office and you send that quote that estimate late in the night. And by then 2 or 3 other people have sent estimates. You just lost that job. So when our customers see this, they truly understand the power of AI, but they don't see it as AI. They see it as you're doing the work for me, so that I don't have to do it. I'm more efficient. I win more work. I make more money. And then, therefore, with that value proposition, I'm willing to pay you more money.

That's interesting. I guess with that kind of perspective, when we think about what the Intuit product portfolio maybe looks like a few years from now, with agenetic experience is maturing with just generative AI product set becoming more powerful. Just what does that look like? And maybe it helps to kind of frame what is possible today versus a few years ago and maybe how that begins to progress even further?