... All right, awesome. Thanks everybody for being here for day one of the Citi TMT Conference. I'm Steve Enders, part of the software research team here. And with us today, we have the team from Box. We have Ben and Dylan. I want to thank you both so much for being here.
Thanks.
Maybe just to start, I think probably most people know Box. It's been around for, I think, public for almost ten years now. But maybe just talk about, you know, takeaways from the recent quarter, and also introduce yourself, especially Ben. I don't know how much, how often you're doing these kind of things, but-
Sure. So Ben Kus, Chief Technology Officer at Box, and I'm helping to lead some of our AI initiatives, yep.
Yep, Dylan Smith, Co-founder, CFO, and I would say in terms of just takeaways from the most recent quarter, really you know our excitement around the way that our newer AI capabilities are resonating with customers, that shows up in a lot of the financial results, as well as you know kind of the tone and tenor of the conversation we've been having. That's you know both with Enterprise Plus, and then you know also with Enterprise Advanced, which is our newest plan that we launched just back in January, and we're really pleased with the initial adoption, which is what drove a lot of the outperformance that we're seeing in various top-line metrics.
So, really excited about, you know, what's to come and the roadmap and the journey down that path. And we'll be sharing more on that in just about a week at BoxWorks, our annual customer conference.
All right. That's a great place to start off. I do want to start on the macro side first, and then, Ben, I swear we're going to get to a lot of AI and product stuff after that. But just in the deal environment, like, what are you seeing out there right now? You know, how are things maybe a little bit different this past quarter? 'Cause I think to your point, the metrics look pretty, they're pretty solid. But you know, what are you actually seeing? Maybe what are you seeing across verticals, geography, segments, all that?
Yeah. So we'd say, you know, we'd actually describe the environment as relatively stable versus this past quarter. So I think, you know, customers are, you know, certainly still making decisions and buying, and there's a lot of excitement about AI. But it remains, you know, a pretty cautious and somewhat challenging environment. Nothing too unique to, you know, verticals or geographies, other than there's probably less of a, you know, maybe freak out on the public sector side. Still, you know, a pretty challenging environment, but I think, you know, a quarter ago, we were much more in the thick of things, and there was even more uncertainty. So that has, you know, steady kind of settled down a bit.
But overall, I would say the most recent quarter's performance was more probably Box-specific factors and some of the newer products driving that, versus any real discernible change from a macroeconomic standpoint.
Okay. That's great to hear. I do want to talk about Suites. I think it's been a big point of focus for you all for a while, but just maybe, how are the use cases that you're seeing different from that perspective? How are you, I guess, leveraging AI in those kind of use cases as you expand that product set?
You want to go?
Yeah, so one of the things that we're noticing with customers is that many of them, when you ask them, like, how will AI actually provide value to your enterprise? One of the top things they'll talk about is their unstructured data. And we think a big reason for this is that we've had about, like, you know, 10 or 20 years of customers, like, going through and optimizing their structured data, getting new tools, getting new capabilities, new technologies. But then usually anything that was unstructured data would be something that required just usually intense, like, work from a person to understand, to create.
And with generative AI basically being born on this class of data, there's a lot of opportunities that customers are thinking about, which are things around: how can I better make use of some of my most valuable content? It's typically about 90% of what customers have is their unstructured content. And so then, in terms of the Box offerings, we had created this set of capabilities around our Enterprise Advanced Suites. The ability to do Box Apps, the ability to do more advanced workflows, the ability to do things like forms and document generation. And so when you add in the power of generative AI, in particular, around the idea of structuring unstructured data, suddenly you can start to do things that you couldn't really do before.
For instance, companies that have a lot of unstructured data for something like leases for commercial real estate, or if they're talking about their client files for customers who are doing, like, wealth management, or for companies that are like doing life sciences reports, and on and on. Every company has a set of very, very critical data they call unstructured data, and our Enterprise Advanced capabilities let them manage that, and then what AI does is then allows them to be able to marry their unstructured data, which is typically authoritative, with the idea of the structured data, lets them query it, lets them understand it better, lets them draw trends.
And then when you put that into Box's Enterprise Advanced capabilities, you get this sort of new way to interact with, oftentimes in a very much more productive, much more efficient way. And so we're seeing a lot of customers who are doing things like, you know, loan origination or handling processing of medical documents, handling processing of, like, life science and materials. They're seeing efficiencies not only with just time spent, but also delivering better results to their clients. Processing those loans faster, you know, dealing with their customers more efficiently, and so on and so on.
Okay. I mean, it sounds like the use cases, I think that you've called out, especially on the earnings call the past couple of quarters, sound pretty different than maybe what we've seen historically from Box. Maybe what has... How are those use cases evolving? How are you kind of seeing the opportunity around some of the kind of the workflow automation/content capabilities kinda coming together and marrying that together?
... I think this is not only has the sort of Box product set evolved to be able to handle these and what we call our advanced suite, but then also this is, AI has now matured to the point where it can handle these kind of use cases better than before. So especially with some of the newer, more sophisticated models, the ones that have introduced the concept of thinking abilities, in addition to the idea of using the sort of agentic paradigms, like where you have AI agents not just do one thing for you, give you the answer quickly, but then also do more complex tasks, and you give an objective.
You're like, "I'm trying to accomplish this," and AI agents can, like, sort of work with you and work with other agents to make this happen. Like, these are the areas that people are starting to say-
That's right.
Like, these were previously une xplored, and so, again, because a lot of it revolves around unstructured data, then our customers are coming to us and saying, "Can you help me work on this internal process that we have to try to make sure to make this work better in different ways?", and so this is where the features that we've been building for many years are directly playing together with the new AI capabilities and sort of letting people start to get this, like, new technology disruption, but in a form that they can utilize without having to build it all themselves.
Uh-huh. I guess from a, you know, budgeting perspective or I guess the use cases you're going after, you know, how much of it is having to kind of coach the customer along and tell them that this is something that they, like, you know, need this? Or is it kind of a replacement of something that they were trying to do historically? Just what does that look like?
I think a lot of, like, the concept that AI is going to be very valuable to enterprises is something that every single customer that we talk to is very aware of, and in many cases, they're trying, some of them are trying to build it themselves. Some of them are looking at different platforms that can do this together. So many times when we're working with customers, they have an idea about the kind of thing that they want. Maybe they've been experimenting with it, maybe they've been building some things themselves. But many of them come and say, they don't want to be in a world where they have to maintain everything, that they have to make sure they handle...
Like, in the world of, unstructured data, there's just a ton of things you have to do to prepare the data to make it able to be, you know, do the embedding, so you can retrieve it using retrieval augmented generation and using vector databases, and all these underlying technical aspects that make it so that you can get really quality answers, what we call enterprise-grade security, in addition to just making everything work. So many of our customers are asking, "What is the platform of the future that will let me do these kind of capabilities that I can rely upon, not only today to do these use cases, but then also be able to keep up with the ongoing trends?" Like, if you just look across most...
The top model providers these days, I think there's been 15 models in the last 12 months that have been, like, arguably the most intelligent piece of software that has ever existed in the history of civilization. But most people have a hard time even naming them because things change seemingly every week.
Mm-hmm.
Being able to keep up with that so that they can just make choices as opposed to trying to, like, support all that, it becomes something that we need the support of these enterprise-grade platforms.
Okay, I know, I think Aaron has been very prolific. I think you've been pretty prolific out there, talking about the future of AI and what that looks like. I guess, what does that mean for Box? And I guess secondarily, you know, to your point that the model changes so quickly-
Yeah
... and, you know, there's something new that comes along that's better, how difficult is it, how easy is it to actually hot-swap models and figure out kind of the best model for a use case that you might be using?
Yeah, I mean, it's one of the challenges, is that, although there are a lot of very good models in the world, at any given time, some of them are better than others.
Mm-hmm.
Especially some of them will be better than others for the thing you want to do at that moment. And then you see with some of the. You know, it's almost rare for especially some of the bigger organizations to, like, offer you the full suite of models. Oftentimes, they have a preference in one way or another because maybe they provide it. So for Box, we support Gemini-based models, we support Anthropic Claude-based models, we support GPT models from OpenAI, and we support Llama-based models from Meta, in addition to some others. And so, our job is to make sure that we can not only provide them to customers, so they can pick and switch if they decide that one of them works better for the use case. No problem, it's an option in Box.
But we don't make them have to know everything. We're opinionated and we say, "What we found that this works well, so the default out of the box, you have to do no work to configure." Maybe you never heard the name Gemini, it doesn't matter. You can still get the functionality that you want, so we're part of our value as a unstructured data platform and as a company that caters to making everything straightforward is to say, "We'll make the decision for you if you don't in the best that we know, but then allow you to both swap it but also customize it to get the best for whatever your use case is.
Okay, that makes sense. I guess tying it maybe back to the model, to the financial side of the house, just how are you thinking about maybe where the AI kind of revenue base is today? How do you think about what that maybe looks like in the future? And I think you have a medium-term model out there, and what are you kind of embedding assumption-wise into that?
Yeah. So I think about it, you know, we don't really think about the kinda AI revenue, 'cause, you know, Box is really an AI-first company, and we have different levels of capability, but we include, you know, some pretty compelling AI functionality to all of our plans. As it relates to, you know, kind of the model change and impact, that's also pretty closely tied to AI. We certainly have our Enterprise Advanced plan, that's been in the market since January. Really pleased with the early momentum there. We expect that to be a driver, not just to kind of sustain and deliver the steady improvements in price per seat that we've been seeing-...
But, we're really encouraged and called this out at our most recent earnings call, that it's because the use cases that it's enabling, we're starting to see seat growth have a bigger impact and pick up a bit, which is what drove our net retention rate up a point quarter on quarter and got there to that, you know, year-end target earlier than we'd expected, just halfway through the year.
So ultimately, I think the impact of AI, it's really gonna show up in both through the impact on our net retention rate just giving customers more to buy, more use cases to make it, you know, compelling and applicable to a broader set of users in our enterprise customers organizations, as well as the different ways that we're monetizing our high-value AI capabilities separately. So there's this concept of AI units for these really high-value use cases on top of what customers would be paying to get into Enterprise Advanced, which is a 20%-40% uplift beyond Enterprise Plus. We're also monetizing through AI units.
So that is, you know, a variety of different ways that our customers would be using the platform, and we expect that to contribute from about 5% of our revenue today to 10% plus a few years from now. Those are kind of the biggest, you know, drivers we think about bridging the AI impact on our overall top-line growth.
Okay. All right, that's, I think that's pretty clear there. I do want to dig into the seat dynamic again, which, I mean, great to see it's growing. How much of that is from, you know, expanding use cases, capturing new users, versus maybe churn dynamics improving, or I think there's been some, you know, seat-based headwinds in the model. Like, is that beginning to stabilize? Just how do we think about those various factors that are starting to support that seat growth again?
Yeah, so really more about the use cases and the actual seat adds is what's been driving that. What we did see, you know, looking back a couple of years, was some you know seat pressures reductions as customers were going through, you know, a pretty jarring economic shift, and that that caused some of that, but you know that's actually stabilized and been stable for you know several quarters now, and so our churn rate has been stable as well, and the more recent trend has really been about adding back versus stabilizing any losses.
Okay. All right, that's very clear on that, on that side. Maybe going back to the product side and talking about AI a little bit more. You know, how are you thinking about the future roadmap in the product side of where it makes sense to kind of layer in more gen AI capabilities? Maybe what does the future of content management look like in an agentic world, and what is Box's, you know, Box's place there?
Yeah. Sometimes internally, we talk about the idea that if we were rebuilding Box today, then what would we do? And then, of course, the thing we would do would be to make it an AI-first company and platform, and that's how we are actually approaching everything we're doing, is to think of it in those terms. So one of the most important things for us is that not only do we have AI features that are helpful, but we also have an AI agentic layer on top of our platform to be able to provide all of the latest AI capabilities with the latest AI technology paradigm, so that we can then be able to handle almost anything that a customer might want to do with unstructured data, then we'll provide to them.
We'll provide it in a form where they have the ability to use it directly in our product, but then also power integrations so that that you could have your unstructured data stored in Box and use it in another system, and then also something they could build on top of it themselves. And so this is across the board from things like doing retrieval augmented generation securely based on your permissions on your data, so that you can find information, not just search for data, but find, ask a question, the AI will figure out the answer and tell it to you and tell you where that came from. In addition to things like extracting structured data from unstructured data, data extraction is one of the most important things that our customers are talking about.
They're thinking about all the benefits that they have when the data is in a database, in a set of tables that they can query, that they can run advanced analysis on, but then also tie that to the unstructured data. So the AI can go through and basically represent it in both ways so that you can get the best of both worlds there. And so having these capabilities where you have AI that continues to evolve to do this is very critical for us, and you'll see things that we've talked about previously, like our deep research ability, where you can actually give the Box a whole bunch of data and ask the AI to do, not just answer a question, but do deep research.
Prepare a very comprehensive report, like you would get from an analyst, like a somebody who spent, you know, days or weeks looking through this kind of information. These are the kind of capabilities we build in agent form that can do more and more over time, agents working together, agents working with agents from other platforms in an AI ecosystem. We believe this is going to be the future evolution of the way that these enterprise platforms work together.
Okay. I think we've heard a lot about, like, MCP and utilizing that as kind of the next kind of, I guess, evolution of this.
Yeah.
How do you think about what that enables, and maybe how does that kind of further evolve the opportunity for you all?
Yeah, I mean, MCP - the MCP protocol is just kind of amazing for this exact reason, which is that, previously, if you wanted to have two platforms work together, they had to, like, sit down and do a bunch of engineering work together, and that limits roughly, like. This is why some enterprise customers historically have been always wanting more integrations that work better together. Then with MCP, though, the actual hard work of what it takes to integrate two systems together starts to be more of asking the agents to figure it out based on these published protocols in their MCP servers.
So you get this big benefit of the enterprise platform, but then also customers who say, "I want to use these platforms," instead of it being, you know, find some team who are dedicated, who are really good at coding, who can do these things, they can start to say, I have, you know, their own, either internal or take one off the shelf of an MCP client, and you can say... "Can you do this?" And then the AI agent will look through all the things, the MCP service it has access to, and it can just do it for you.
And it's this really amazing moment where you're like, "I just accomplished something that used to actually take giant amounts of coding, maintenance, work, testing," and then the AI agents with this powerful protocol can figure out how to do these very advanced tasks. And that's something that, I mean, it's part of the reason the MCP is very.
Yeah
... everybody wants to talk about it, is because it really enables this integration across platforms in a much more, rapid and sophisticated way.
Okay. I know it's still early. I'm sure it's very baked into the product roadmap and how you're thinking about that.
Oh, yep.
Yeah. What does that look like? Yeah.
So, the way it works is you have either what they call local MCP servers or remote ones. We've offered both. We've offered all of our, like, all the functionality of Box baked into this MCP server the customer is asking for. Over time, we would see that you would have more and more functionality going into this, used in more and more ways. But the value of an MCP server is that once you make it available, people can just start to use it and find a lot of value from just the things that are out there right now.
Okay. And I guess from a monetization perspective, it's about driving consumption, driving that element of it?
Yep.
Okay. I'm going to pause there, see if there's any questions in the room. Otherwise, I have a lot more that we can dig into here. Okay, I know you have... I think your BoxWorks conference is next week. Is that right?
Yep.
From a product perspective, I don't want you to, you know, give everything away here, but, you know, how should we think about, you know, what that looks like or maybe what the high-level feature of Box looks like?
I think in many ways, you've seen that we've talked about over the last year, that a lot of the focus is continuing down this idea of the Enterprise Advanced, the features and capabilities that we've launched there. Things like Box Apps, things like our workflow system, things like our forms and our Doc Gen. These kind of next generation of the, not just content, but your most valuable content organization and how you manage that. And then using the capabilities of AI, both from, like, the capability we provide, in addition to customizable agents. And then for all of those, there's a very long roadmap associated with all those capabilities, and we'll be announcing new capabilities that are part of, sort of, across Box, all of these kind of areas.
Okay.
These are the ongoing set of both now with these announcements and going forward, the kinds of things that we're committed to do, which is to provide to you all everything that an enterprise customer would need to be able to do AI on their unstructured data.
Okay, that makes sense. Maybe shifting gears in terms of, you know, the internal use of AI at Box. Just how are you all leveraging AI internally? I think you've made a big pivot towards being an AI-first company and trying to do that. But what does that actually mean from a day-to-day perspective, and what are kind of the core use cases that you've seen or you've been utilizing?
I can talk from the engineering side. It's, of course, one of the early great use cases of AI, just in general, has been the ability for it to code. And so one of the things that we do is we look to utilize, wherever possible, the coding assistant tools or the coding agents that can help you generate code ongoing. And in addition to not just generating the code, but then also the other aspects of the coding ecosystem. And for us, this has been a big great success because... and the majority of our engineers utilize this in some form. So we use multiple tools, and we're always experimenting with the latest.
But we see productivity benefits, which then, for us, turns directly into providing more capabilities for our customers, being able to accelerate some of the roadmap items, so we definitely see that faster time to create product is. We want more. We want it to be even faster. We even want to hire more people to even make it even faster, so that we're able to then continue down this path of being able to deliver more of this, like, long roadmap that us and companies like us have.
Sure.
Yeah, and then I would say, I mean, it's really showing up across the company, and just to get a sense of, you know, the type of, you know, excitement and commitment we have to it, around AI, you know, versus we are making it mandatory. Everyone at Box is going to be AI certified, just on the basics, how to use it, a lot of things like that. Just like we have, you know, security and data privacy training, this will be the second, you know, mandatory thing. Just 'cause it's that important that everyone deeply understands, you know, AI, how to use it, Box's capabilities, et cetera, and we recently, heck, what was it, two weeks ago, something like that?
A couple weeks ago, had our hackathon and can speak from a, you know, the person responsible for our G&A organization, we had dozens of submissions and people participating, which is-
Wow
... you know, not usual for a bunch of, you know, finance people and, you know, HR people to be, like, actually, you know, developing these applications and then pushing the limits of what Box can do. Which I think speaks to both the accessibility, as well as just the excitement and the groundswell that we're seeing, and so implementing a lot of those things. And then, the way it just shows up in other use cases, I mean, Ben spoke to the coding side, but, you know, we have a data insights portal that everyone in our sales organization is looking at to say, "Okay, which of my customers might be the best candidates for, you know, this feature?" Or, you know, "Who... Basically, who should I be spending, where should I be spending my time?
Mm-hmm.
And so they go there, and based on all the different, you know, analytics that we have, as well as running, you know, kind of some of these pre-built queries that we've come up with, that really informs, you know, the best way to be spending, you know, their time and energy. And then, you know, we also kind of structure all the information as a service organization in Box Hubs, just to make it that much easier for all employees to get access to information without necessarily having to, you know, write an email to, you know, our payroll alias or, you know, business operations or whatever else. And so, you know, really changing in a bunch of different ways.
Those are just some examples, of the way that we're using it on a daily basis.
Okay. No, that's great to hear. Any way to think about maybe the efficiencies or, like, the margin gains that you're seeing from utilizing this? Or are you kind of reinvesting those savings back into the business and in trying to, I guess, push faster on the growth side?
Yeah. So I'd say, you know, in the, you know, kind of current course for the foreseeable future, really more the latter, where we are seeing some real gains, especially in certain parts of the organization, but really using it just to say, "Hey, how do we ship, you know, more products faster, you know, get more done," you know, keep this team that, you know, can now scale really effectively with the organization, and just get more done, provide a deeper level of insights, has been the approach, you know, currently, and I think as these things build, you know, that may evolve, but right now, really just focused because of the massive opportunity in front of us.
You know, we're much more focused on, you know, kind of reinvesting those savings, those efficiencies, to get the message, get more products in front of more customers faster.
Okay. Maybe going back to Enterprise Advanced, and I think you said number of deals doubled or roughly doubled quarter over quarter. I guess, what's the... What use cases have you been seeing? Maybe what's been surprising from the way customers are utilizing Box versus maybe what you were kind of expecting from it?
Yeah. So one of the things that we've been surprised about was how quickly they've gone from the world of saying, "I wonder if this is possible," to actually trying it out, using it, putting it into production and this is typically an area where you'd see, like, customers, like, didn't know if it was possible beforehand. So certain things, like even ideas like we have a company we work with whose they do loan origination, and so they had this process that was complicated, where they had to have the loan people verifying all of the documents to figure out whether or not they were valid, so they could then approve the loan.
It turned out this was the number one thing that slowed down the loan processing.
Mm
... in this example. And so, but they were skeptical because they had tried in the past to do things like be able to automate it, because every time, you know, you need, like, a utility bill or if you need, like, all these different things, like, they came in different formats, things changed, the scan was different, it was backwards, it was upside down. And so when they were able to just apply the simplest of, like, data extraction to that area, immediately then the AI could come back and say, like, "You know, this is the data that can be verified in real time," and by either automatically or by the loan originator.
And they could either tell them, "Hey, this is not valid, it's too old," or whatever. Or they'll be able to just have it then be ready to be approved. And so stuff like that across the different industries, across the different use cases, across the different businesses, these were all the things that people were saying, like, "This is like a whole new era of me being able to apply this idea of structured and unstructured data," and then using it to then perform, like, an efficiency. Oftentimes, it ends up with their customers are happier now because things went faster. And so this is...
Seeing a company do this, and then immediately start to replicate that over and over to different parts of their business, that has been something that we were building towards, but we were also surprised, in some cases, by how quickly they were able to, like, fully get that all working, and historically, this was something that would take a long time, but then this was something that they were able to get working fairly quickly.
Okay. That's interesting. I guess as you think about some of those use cases, I mean, it seems like it's either displacing something different than maybe what you were seeing in the past-
Yeah
... and probably too expensive for you.
Yeah.
How do you think about, you know, what is completely new greenfield for you versus-
Yeah. I talked to a customer who had. I think they had a million contracts, and then somebody had to pose them a question, which was like: which of these contracts had a clause? And then, and so we have ability to do that kind of stuff in Box, where it's a by-product of our AI and data extraction. And so we were talking to them, and then, and we're like: "Well, how did you do this before?" And they're like: "Well, before I would've had to hire an army of paralegals to come and go to do this." And we're like: "Oh, what's the ROI?" They're like: "Well, I never would've done that. It would be way too expensive. We never would've contemplated even doing that.
We would've had to do some other technique to guess the risk," and so in this case, they were able to not... They were able to get a fundamentally new approach to this problem, so it was just novel to them.
Yeah.
It was a technology disruption in this particular area of this thing, or we have another customer who was going through, and they were looking through these medical charts to see if there was a certain procedures there. They were all very valuable for them to get completely accurate, and then they're having AI double-check it to catch things that maybe somebody had missed along the way, was extremely valuable to them, and that was the kind of thing that was a net new way that they approached this particular kind of challenge, as opposed to before they... It wouldn't have made sense for them to, like, there was no technology available for them to automate that kind of thing.
Sure. I think one of the questions we tend to get from investors is just around, I guess, the competitive landscape, and I guess, what kind of gives Box the right to win for, you know, capturing some of these, you know, agentic use cases? Just how do you think about that, where it makes sense for you all to kind of own that versus, you know, maybe... Or I guess, maybe what are investors kind of getting wrong about that perception?
... I think one of the ongoing questions and challenges is that, at some point, like, AI is such a powerful technology, that some people might say, like, "I wonder if there's going to be one AI agent or one AI technology that, like, kind of rules them all from the perspective of just can do everything." But from our view, in practice, it's actually quite hard to make sure that you're doing safe and secure and compliant and high accuracy AI on something like unstructured data. And it's different for your ECM data, and it's different for your structured data, and it's different from your HR data. And so when we spend an awful lot of time making this not only work, but work well.
And so we see that there's, like, a focus aspect of making sure that you not only have AI applied to these enterprise-grade platforms, but then also doing all the other things that we've done for twenty years, the things that people love about Box: making it secure, making it compliant, making sure that it has internal external collaboration, making sure that it has all of the full services suite. So for us, we see it as the AI can enhance the idea of what it means to be a good enterprise platform. And then that's the key focus of many customers, because they don't want to build all the stuff themselves. They don't want to have to invest in all the things it takes to make this work well.
Similarly, if there's an element of trust and focus that goes into this. This is what our customers will tell us: "I can't. I really want to make sure that all of the things that I use internally are enterprise-grade, and then also are. I can bet on for the future as being able to keep up with the rapid pace of change.
Want to add anything?
Nope.
Killed it. Yeah, great answer.
I got nothing to add.
Yeah. I do want to ask on, you have a new CRO coming in, Jeff Newsom. I guess, what will he be focusing on? Maybe how is he going to be different or targeting things differently than how Mark was before this?
Yeah, so, really coming in to just scale and I think double down on a lot of the, you know, parts of the strategy that have been working, that we've called out as really strategic investment areas. And when you think about the background, he has just, you know, team at massive, you know, larger scale, deep on the platform side, deep experience with enterprise customers. You know, really perfect fit for the next chapter. So not necessarily to, you know, kind of change anything about the strategy that we feel really good about, although obviously, he's a very senior executive who's going to come in, you know, just this week, and start, you know, kind of diagnosing, seeing where he can add the most value.
And more about just, you know, how do we take everything that we've laid out and kind of supercharge it? So really excited to have him on board and, you know, deeply appreciative to Mark for everything that he did getting us up to this point.
Yeah. We only got a couple of minutes left here. I want to do one last scan of the room, see if there's any questions. I do want to ask on the partner strategy. It seems like that's been a bigger point of focus kind of moving forward. How do you view that, and maybe how do some of the AI capabilities kind of fit into that strategy?
Yeah, so I would say that, our... Ultimately, I think we have a much bigger opportunity now to work with partners, SIs in particular, not because of, like, any, you know, stronger will that we have, but more because, now they actually want to work with Box, right?
Mm.
I think because they are seeing, if you think about the types of, you know, kind of client engagements they have, the types of conversations they're having, many of them, the existing ECM businesses, Enterprise Content Management businesses and portfolios they have, I think the writing is kind of on the wall. They're hearing day in, day out from their clients that they need to rethink these things, how important AI is, and so they're looking for opportunities and partners who can really help their clients transform the way they work with content, and that's where Box comes in. So AI is absolutely fundamental, and the reason that we see such a big opportunity, and that we're now, you know, kind of seeing this sort of demand from these partners.
And it's really just, you know, to us, is about why it's so important. And I know Ben did a really good job speaking to some of the use cases that are jumping out.
Sure.
That's absolutely critical to make these partnerships successful, to be able to develop those repeatable playbooks, use cases, and then work with partners to implement them, so they can scale their practices and just get that message, that technology in front of as many, you know, kind of new customers as possible, is a huge opportunity for us.
I know we're almost near time here, but I do want to ask on just the ECM legacy landscape. A lot of vendors out there that I don't know how innovative they've been, but how do you kind of see that opportunity to go take share? And maybe kind of where are we in terms of those potential opportunities coming up?
Yeah, so a much bigger opportunity, again, I think for similar reasons.
Yeah.
I mean, basically the exact same dynamic of the inertia of, "Yeah, this is probably not the system I'm going to be on." You know, "We've been with them for 25 years. They haven't innovated in the last 20 of those years." You know, is now I think AI is just a fundamental... It's pretty black and white. Like, if it's sitting in an on-premises system or you have fragmented siloed data, you just cannot get the types of insights and value out of AI that you can with Box. So, you know, it's a much bigger opportunity for us. Still pretty early days. We are starting to win some of those deals, but it's still the minority of the sales cycles that we're in.
We expect that to kind of continually evolve over time and for that to be a bigger and bigger part of our business just given the roadmap and even the capabilities today.
Awesome. That's great to hear. I think we're at time, so Ben, Dylan, I thank you both for being here, and I thank everybody in the room for being here as well. So thanks so much.
Thank you.