Hello. Thank you for joining us this afternoon. I'm Cynthia Hiponia, Box's Vice President of Investor Relations, and just a big welcome to our friends here in person and those attending via the webcast. Of course, I have to talk about our forward-looking statements. They do involve risks, uncertainties, and assumptions, including statements regarding our growth and profitability, the impact and potential of AI and its impact on Box, and our ability to achieve our long-term financial targets. You can see the full list in our recently filed 10-K for the period ended January 31st, 2026, filed with the SEC. Also, quick mention that our financial measures are all in non-GAAP. You can find the reconciliations in an appendix to this deck, which will be published right after the event. Finally, we have a really great day scheduled for you.
Aaron's gonna kick it off with an overview of our product strategy. Ben and Diego will discuss our AI platform and products. We'll take a quick break and come back with Olivia and Jeff talking about our go-to-market and sales strategy. We're really thrilled to have Araya Solomon here with us from a very important partnership at Slalom, who's gonna be interviewed by Jeff. Then we're gonna wrap up the day with Dylan discussing on how all of these are drivers for our long-term financial model. With that, we'll be doing a live Q&A afterwards for both our in-person and our virtual attendees. With that, I'm gonna kick it over to Aaron Levie, our Co-Founder and CEO.
Cool. Thanks, Cynthia. Appreciate everybody making it out today. Good to see everybody. We're incredibly excited to walk through a bit about the Box Platform, where we are going as a company, and obviously, we sit at the really start of a transformational moment in what the future of work looks like in the enterprise. Our mission at Box is to power how the world works together. When we started the company, the idea was really just about how do we make it so people can share and access and collaborate around their files and their enterprise content from anywhere and be able to share with anyone. Then we realized that actually people wanted to also be able to share with various systems and access information from different applications.
It became really about how do people work with their content from anywhere and in the application. Now we have a new, sort of constituent that we think a lot about, which is how do you share and collaborate and work with agents. Our mission at Box is to power how the world works together with people, applications, and agents, across all of your most important enterprise information. We're incredibly proud to now do this, for organizations all around the world in every single industry and every single segment of the enterprise.
Organizations like Zurich Insurance, powering secure collaboration, Morgan Stanley powering secure document vaults, for clients in wealth management, the U.S. Air Force driving mission-critical operations using the Box Platform for securing critical content, retail brands like Marriott, again, enabling new transformational use cases for managing information, and many organizations all throughout the media and entertainment industry delivering incredible blockbuster films, by leveraging the Box Platform. We're used across some of the most regulated organizations, obviously government institutions, every other industry, that fundamentally is powered by and runs on enterprise content. We're trusted by over 120,000 organizations globally. We have this incredible purview into what is happening with content inside of the enterprise and what's happening with content inside of organizations.
This gives us a very clear sense of where work is going, how companies are using their information, and what the future holds for this. We're incredibly proud to be able to work with so many amazing organizations that are transforming how they work with their enterprise data. At Box, we have a clear, focused strategy for driving long-term profitable growth. We've broken this down into four key areas, and I'm gonna cover the first one. The first is really how do we go and attack a massive market by building the leading intelligent content management platform that transforms, again, how companies are working with their enterprise information. This market is comprised of a few different components. You have sort of the core of the market, which is enterprise content management.
This is sort of the traditional industry, where companies are spending, you know, in the kinda $10 billion range globally, on enterprise content management systems. This is things like traditional OpenText and document management platforms. We have a lot of the adjacent categories of how companies work with their information, contract lifecycle management, infrastructure, network file shares, all of that kind of infrastructure. That's another tens of billions of dollars in market size. We also have new markets that we are expanding into. Spaces where you wouldn't have traditionally seen spend go into software, it's more spend going into maybe outsourcing services or professional services, where we can now expand into being able to do things like document processing and automation with the power of AI agents.
What you're gonna see is actually the total spend that goes into technology will actually grow over time because agents will be doing a lot of the work, for those workflows, and that monetization will then go into the platforms that are powering those agents. We see this as a massive market expansion opportunity, when you combine the power of AI agents and enterprise content. That's the market we're going after, and I'm going to share a little bit more about what we are doing to attack the market. We're going to build the leading, intelligent content management platform. You're going to see a lot of critical capabilities, both at the platform level from Ben, as well as critical features that we are launching.
We're going to give you a little bit of a peek into some of the future of our platform, from Diego and some of the critical capabilities that will power how companies work with their enterprise content. Then Olivia and Jeff will talk about how we are actually going and bringing our full platform to market to our customers, by bringing the power of Enterprise Advanced, which takes many of our leading capabilities, for more sophisticated use cases and intelligent workflows and gets us into the hands of enterprises. We're also driving more of a partnership motion, over time. You're going to hear from one of our key partners and, how do we make sure we are delivering these services, to our customers through equally a consumption model.
We want to both balance out the seat growth motion of Enterprise Advanced as well as more consumption activities with our platform business model, and we'll share a little bit more about what that looks like as well. Finally, this all comes together with our long-term financial profile, where we are committed to double-digit growth as well as significant margin expansion over the coming years. Dylan will share a little bit about how that profile plays out. This is our overall strategy. This is what drives us internally. This is what we are focused on delivering on. We're going to break it out into a few parts to walk everyone through what this looks like over the next couple of years.
First, the way that we kind of think about this is if you step back and you say, "Well, what's happening in work, and how is this going to drive Box's strategy?" I think what's very clear that AI is transforming everything about how we work today. You can kind of see the journey that we've been on now for the past few years. It's amazing we're already kind of three years into this. First, you know, it started with that ChatGPT moment where you had an AI assistant that could answer any kind of question. This was really kind of the next generation of search and being able to find information and get questions answered.
That was obviously the kind of core foundational moment of the chat wave. We saw actually what AI really could become. What if you took an AI model and you gave it more of a task? It wasn't just answering a question from the model's knowledge or giving it a little bit of context. What if you gave it a task? It could do things like extract data from a document, or it could write some amount of code inside of a code base. This was sort of the next phase of AI agents, and it's kind of the period that we've been in for the past year or so.
You can kind of sense that there's a major change on the horizon, which is, well, what if you could have these AI agents that did tasks? What if they could be longer running? What if these agents could actually go off and maybe do the equivalent of a day or two or five days of work in a couple of hours? We have these tasks that we can go and kind of run on their own. What if you could deploy multiple of them at once? What if we could have agents in parallel be able to run across a workflow, and we could manage them, as they are going and doing that work? This idea of kind of agent swarms that can go off and execute work for us.
The first big use case is obviously in things like coding, where you have agent swarms that can do things like generate code, review the code, be able to process the code for security. That same style of work is now going to come for enterprise knowledge work, which is what if I could have the ability to deploy agents across a set of workflows in an organization that can go and help me automate my work? That could be an end user that's going and deploying those agents on their own, or that could be a pre-designed workflow that deploys those agents across a business process. You're going to see what that looks like, in Diego's section, especially around how do we go and deploy these agents at scale in an enterprise.
I think the mental model that we have at Box and the thing that kind of grounds us in where we are going as a platform is, well, what if you had every employee in an organization had the ability to have an analyst, a researcher, or a domain expert that worked 1,000 times faster than they did? What if you had that resource available to you, and it was relatively cost efficient to deploy that resource at any task that you wanted, and you could deploy as many of them as you wanted? Well, this would be a very different way of working. We start to imagine, well, we probably have an organization that has a hundred or a thousand times more of these agents than we have as people, and that begins to fundamentally transform what work looks like in an enterprise.
The first big way is obviously it will transform how we work as individuals, and we will begin to accelerate our knowledge work. This might mean things like, how could we review documents instantaneously? How do we get analysis on contracts that we're working on so we can figure out any risk that's inside of the business? How do we create presentations or proposals or RFPs based on the existing data that we already have? How do we write code automatically, and how do we get expert analysis into our information? These are the kind of, you know, examples that we're starting to see from within the Box customer base, and obviously that we're operating with internally and that we're seeing across the ecosystem. We can begin to accelerate knowledge work with the power of AI.
Now what happens when you again have those agent swarms and they're working across an organization and they get deployed in business processes and workflows, then you start to actually transform entire end-to-end processes. It's not just the knowledge worker going in and doing a prompt and having their work accelerated. It starts to look like process redesign and re-engineering of the actual workflow of the enterprise, for things like smoother contracting with clients or being able to rapidly accelerate client onboarding inside of an organization, taking that from maybe weeks to a matter of minutes or hours. How do you do personalized marketing that's much more targeted in the different segments or regions that your customers are in?
Can you accelerate product development because you can now get insights from across all of your product information, to be able to analyze that and answer questions faster, for what to build next? Or being able to reduce business risk because now instead of taking a sample of data that you're looking across, you can have agents look across all of your enterprise information, process that data for risk, and alert you to any kind of, you know, either the things that you should be looking at from a fraud or risk or compliance standpoint. These are the kind of use cases we're talking to customers about where they're saying, "What if we could have agents that, again, can kind of work 24/7?
They can run in parallel; we can deploy them at whatever we want, we can put them inside of a business process, and what kind of work can they do for us?" This fundamentally transforms what these processes look like inside of an organization. Now, this is the kind of conversation that we again have with our customers. We in the past day, you know, just being in New York, we've met with 20 customers and enterprises across financial services, and these are the types of use cases that we're talking about. How do you onboard into your bank much faster? How do you do a loan review process much more quickly? How do we do due diligence on a company much more rapidly?
How do we disseminate things like, research and information, to our clients or our employees much more quickly? It's fundamental sort of process reengineering that is now happening with the power of AI. When we talk to our customers, I think the thing that is sort of very starkly obvious is if a company is gonna transform with AI and AI agents, the big challenge is that those agents have to know everything about your business. You know, we're pretty clear on this at this point, but, you know, AI models are trained across generally public information, and so that same model is gonna work exactly the same way between, you know, every single firm that is leveraging that model. The question is, how do you get differentiated results from that model?
How does it actually work for you and for your organization? Well, then the model needs context about your organization, about your business. That context is living inside of things like your product specifications. It's living inside of your research information. It's living inside of your marketing assets. It's living inside of all of the information inside of your enterprise that makes your business unique. When you think about the context that an agent needs, it needs to know about your business. It needs to know about the practices, the decisions you've made, the implicit things that have gone on, the explicit decisions that were made. It needs all of that context. This is sort of the big challenge that enterprises face.
It's funny, when we have conversations with customers, it sort of on one hand is an AI conversation, and very quickly the other side is just it's a data conversation. Most companies don't just have an AI problem, they have a data problem, which is, how do I make sure my data environment is set up to get agents the right information, the right context, make sure that it's appropriate for what the agent should be able to look at? We have this massive context challenge in the enterprise that companies are facing. Where is all this context? Obviously, we're very focused on the idea that so much of that context is sitting inside of our enterprise content. When you think about, well, how does a company launch new products? Where is that context?
Well, it's inside of a lot of your critical product roadmap information and your product specifications. It's inside of your product design files. That's all unstructured data in the enterprise sitting in enterprise content. How does a company close the books? Well, that data is inside of your financial documents and your financial process information. How do you market to customers? That's in your marketing assets. It's in your marketing plan and strategies. How do you go and hire and onboard your employees? Well, that's gonna be inside of their, you know tax information, their resumes, their contracts, all of that data is enterprise content. How do you go and sell to new customers? Well, that's in your sales resources. It's in your sales playbooks. It's in your sales and marketing information. That is all enterprise content.
If you think about it, the critical business context that agents need is living inside of our enterprise content. What we are working on is this idea of how do you unlock the full power of AI by connecting enterprise content to agents? This is sort of going to be the big problem that enterprises face when they're going and deploying AI strategies. The exciting thing is that this is actually, you know, the biggest source of information in the enterprise that organizations are dealing with. It's not only the biggest data problem, it's actually the biggest upside when you think about what can agents do in an enterprise? Because about 90% of corporate data is unstructured data. The vast majority of that is enterprise content, and 10% of data inside of an enterprise is structured data.
The structured data, we've always been able to kind of analyze and query and summarize and calculate. We've been able to do that in databases obviously, really since day one, but all that unstructured data we've never been able to tap into. Companies are sitting on mounds of information that's unstructured. They collect this data, they create it, they share it, they store it, but they often never really are able to reuse it over and over again. They're never able to get the full value of the intelligence that's sitting inside of that enterprise content, because unless a user goes and pulls up a file or shares it or collaborates on it, that file is sort of just sitting there and not producing ongoing value for that enterprise. They do have to store it.
They have to retain it and manage it for records, but it's not able to go and generate more and more ongoing value. What if you took all that enterprise content and it became business context for agents? What could we now do with the power of agents inside the enterprise? These are the kind of use cases that we're talking to our customers about. Well, the first is you'd be able to instantly answer any question from your existing data. You know, imagine looking across all of your enterprise content and saying, you know, "Show me different risks for the new product that we're launching." It would need to look through product specifications and meeting notes. It would have to look through marketing assets.
It would have to look through your product plans, and you could instantly get these kinds of answers from your existing information. Again, Diego in a few minutes will share some examples of what we're doing with our Box agent to be able to power these types of use cases. Imagine if you took all of your research materials and you were in life sciences or you were in you know, some of the advanced manufacturing spaces. What if you could take all of that information and had an agent go off and say, "I want you to go and look for different, you know, trends inside of my data?" In this case, it's not just a human that would be looking through kinda one file at a time.
It's able to go through hundreds or thousands of files and process them, and go and generate results for you. Now you have a new level of scale of being able to work with enterprise information, to really be able to pull out the right insights, from that data. Imagine you're working with a client, and you wanna be able to go and collect the right investment, decisions and insights into what they should be looking at. You might take equity research. You might take their personal investment, decisions in the past and meeting notes that you've taken and unstructured data from those calls to be able to develop that plan. Well, again, all of that is unstructured.
Almost 100% of that data that will go into that decision and insight is gonna be unstructured data that you have to work with. Finally, what if you had a long-running agent that can do things like, "Hey, I wanna have a set of folders, and inside those folders are a bunch of M&A documents and financial information. I wanna have an agent go through that and generate the initial report on due diligence on a particular client or an M&A deal." All of that crunching of documents and spreadsheets and presentations, agents can now do that in again a matter of minutes or a matter of hours, where humans would have taken days, weeks, or months.
We'll show you some examples of how we are doing that, and Ben will share some demos in a couple minutes on this. You can see that all of that enterprise content becomes critical context for agents to be able to operate with. Agents really need that context to be able to make their decisions, and ultimately, this is going to accelerate the work that we're doing inside of the organization. Now, there's a catch, of course, which is that a lot of our technology is not set up for agents.
We have all of these enterprise systems that have been built out in the enterprise, especially around managing content, and they were not built for a world of AI agents needing to instantly access this data, get the right context retrieval, be able to process that data in a way that is agent-friendly. We were not built for this era of working with this information. Most when you go to a lot of organizations and you say, "Okay, well, where is your content? Where are your files in the enterprise?" You know, we see some version of this in a lot of organizations that we haven't worked with yet. You know, data might be in network file shares, document management systems, FTP sites. They might be in various storage repositories. They're in point solutions. Data is fragmented all across the organization.
Now, this has already been a challenge for enterprises, and this architecture actually has already led to much of our growth over the years, where companies say, "Okay, this is obviously a huge problem because users don't know where to access their information. Managing security across all of these fragmented systems is very hard, and I'm ultimately overpaying probably because there's a lot of redundancy here." This has already been the conversation we've had with customers over a decade, over the past, you know, decade plus, as we've gone deeper in the enterprise. The challenge now is this is an existential threat for organizations. The reason it's existential is imagine that architecture, and it wasn't just people going and having to find the right file to look for.
It wasn't just an end user that could take the time to peruse the right structures of how data was managed in those systems. Instead, it was an agent, and they had to get that task done in five seconds. They had five seconds to go find the right source material for answering an employee question, and they had to go across 20 different systems to do that. Now it's an existential challenge because when you have hundreds or thousands of times more agents running across those systems, now we have a real challenge that we have to face in the enterprise around how we manage our data. There's a number of major sort of issues with that type of architecture. The first is that agents will just often work with the wrong content.
Imagine trying to keep 10, 20, 30 different systems in sync with the right sort of sources of truth of information. You know, you very quickly have a bunch of drift of authoritative materials across that fragmented environment. Where's the latest contract for a particular client? Well, if you have five places to store that contract, you're probably gonna have two or three different versions floating out there that are wrong. Where's the latest marketing asset for that campaign? Again, same problem. You're gonna have multiple versions, multiple copies of that, and the agent, when it goes out to try and find that information, is just gonna get it wrong a bunch of the time. It's gonna go to one repository that has an out-of-date piece of information, but it's not gonna know that that wasn't the most relevant piece of content to go find.
Agents are gonna answer with the wrong information way too often in that kind of fragmented architecture. Now, that's already a problem because now you're gonna have end users that are just unsatisfied or dissatisfied, and they're just gonna be frustrated with the experience. We have a bigger problem, though, which is that agents will often then produce a result from information that maybe the user had access to at one point, but they shouldn't have anymore, which means now the agent's gonna be leaking information, right? These agents are very goal-oriented. They will happily go through and find everything you're looking for, even if it's stuff that you really shouldn't have anymore.
Again, when you have 20 or 30 different systems where content is being managed, all of a sudden now you have a major security risk, which is how do I possibly keep all of those systems up to date with the security and access controls that enterprise needs to be able to ensure that agents aren't pulling from out-of-date information or the wrong data that user shouldn't have access to? A huge security nightmare for companies to go face with. Then you have a very practical kind of IT deployment issue, which is we know that the agent space is moving incredibly quickly. Just in the past six months, right, we have probably introduced, you know, multiple new ways that we're even doing our engineering practices because of how fast the agent space is changing.
Now you go into organizations, and you say, "How are knowledge workers using agents?" There might be, again, two, three, five, 10 different systems where agents need access to information. Imagine that many-to-many problem of you have many agents that need access to data, but you have many systems they need access to. This just becomes a nightmare of interoperability. How do you really manage an easy way to get agents access to your critical corporate information? Interop becomes a huge challenge, especially with legacy systems that don't work well with these agentic platforms. Now it's an existential threat, and you can kind of feel it in the conversations that we're having with customers.
They're saying, "I need to make sure that my data state, the way that I manage my enterprise content is prepared for a world of agents." This is a much harder, much bigger, much more important, much more pressing problem than it was when it was just people going and accessing files. 'Cause you could just say, "Hey, you'll go click a couple menus and maybe get something that you're looking for." Agents, again, will sort of dramatically exceed the workloads that we're expecting. They'll go find the wrong information. They'll leak data. This becomes a huge challenge that we have to face. Enterprises need a platform that can connect content to AI in a secure fashion. This is obviously, you know, our core focus at Box and what we're building with intelligent content management from Box.
Now, the core of the platform really is, you know, it starts with our global infrastructure. We give our customers unlimited storage. We want to make it incredibly easy to pull content into Box. We think this is going to be a data gravity war, where, by having and ensuring more content is within Box, we make it easier and easier to build applications and have end users work with their content and have agents work with that content. So actually, infrastructure becomes incredibly important in this world. We then have a layer of data protection, security, and compliance, for things like threat detection, classification, document governance, retention management. All of the things that give us the permission to go and deploy AI and AI agents in the enterprise.
Without that layer, you're basically dead in the water when you go and talk to an organization. All of the investments that we've made in managing the data security and protection for our clients and our clients' content becomes even more important in a world of AI agents. Again, it's like having that same end user, but now there's 100 times more of them, which means there's vastly more security risk that you have to deal with. We have to be able to protect that data in a very robust fashion. We then have a layer of content services. This is things like managing files and folders and having metadata on that content, so you can describe that content with extra details.
Being able to collaborate on content seamlessly between users and now agents being able to automate workflows. We'll show you some examples of what that looks like in a few minutes. Having a robust search technology. What you're going to hear a lot about is really this idea of context retrieval. How do you get agents access to the right information? The search backbone becomes incredibly important. How do we build applications instantly on top of enterprise content that are really kind of purpose-built for the use case that a customer has? This again becomes another really important moment in our AI strategy. We have an AI platform. Our AI platform lets you do things like build customizable agents working with any AI model.
You've probably seen from us that, like, the second an AI model drops, we have a new eval for that model, and that model is available in our AI Studio. The reason for that is we have some of the strongest and again deepest AI evals for any content-related use case. We have very strong partnerships with all the leading labs, so we get early access to those models, so we can test them against our benchmarks and make sure that they're in the hands of customers right away. We're building out agent guardrails so we can ensure the agents are only doing the appropriate things that you want with your enterprise content.
We're building out an enterprise-grade agent harness so we can make sure that the agent is able to use the right tools, have the right compute operations on top of enterprise information. Then many other capabilities around, again, how do we secure these agents and make sure that they're only doing exactly the right thing, for our users. We then have a platform layer that ensures that we take every one of the capabilities that I just mentioned, and we enable those capabilities for certainly users, often through our end user interfaces, but also now agents and applications. You're gonna see this from us more and more, which is how do we bridge people and users, agents and apps to ensure that they all have access to the right information, they're working off that common set of data.
Every capability that I just talked about, especially in things like our content services and our threat detection and our data protection and compliance, this becomes even more relevant in a world of agents. Because again, if you have an agent that is generating a loan document or doing due diligence on a customer or generating equity research or looking through healthcare information, that agent, you're gonna wanna have the same controls and same governance of what that agent can do, what they looked at, the same auditability of that agent that you had for people. Again, you will not be able to deploy enterprise agents inside of a regulated organization if you don't have that data protection and compliance layer. A lot of that core investment in our platform becomes even more important in a world of agents.
Again, the power of the platform now is that we can securely connect with content, with people, agents, and apps. Now if you're in the Box agent and you ask a question, it can go and federate across the data within Box and ensure that you're getting exactly the right answer. Equally, our openness and interoperability means that you could do that same query inside of Claude or Claude Cowork or ChatGPT, and you'll be able to have that agent also pull from your enterprise information. You could set us up in a workflow with something like Salesforce Agentforce, and again, that same enterprise content will show up inside of that workflow where the agent is executing some tasks.
For us and our strategy, we're incredibly excited about how much innovation is happening at the agent layer and the application layer of agents because all of those agents need access to the same critical business information. Critically, that business information and content that they need access to has to have the same access controls, permissions, security, compliance that again, users had. We really, you know, prefer a world where there's as much innovation happening at the agent layer as possible because all of those agents need access to enterprise information, and enterprises don't want to have all of their different agents have completely different ways of accessing that data. It's just untenable any other way. We're driving really a strategy with our customers to take them on an AI transformation journey.
There's a few big components, and we're gonna be double-clicking into each of these from a product strategy standpoint in just a few minutes. The first is we want to help our customers accelerate their knowledge work. What if we could take every single task in the enterprise, especially ones that deal with enterprise content, what if we just dramatically accelerated them? Make them 5 times or 10 times or 20 times faster. Generating that RFP proposal, being able to do that due diligence of a company, being able to generate a sales presentation for a client you're working on. We want to dramatically accelerate knowledge work with enterprise content. The next big phase that we're working on, and we're seeing an incredible amount of demand from customers, is to be able to mine intelligence at scale from data.
This is again, very, very ripe in a space like financial services when you think about how much unstructured data, how many documents, how much enterprise content every enterprise is sitting on. This is true of the public sector, it's true in healthcare, it's true in life sciences, true in manufacturing, true in the technology industry, all of that unstructured data. What if you could have agents go and mine all of the critical structured data from those documents? Things like, again, the financial details of a client, the critical information inside of an invoice, or a bill, or inside of a lease agreement. All of that structured data now becomes critical insights for the enterprise, and it becomes the core source material from which you can automate a workflow.
We're seeing that companies, you know, again, are really, really driving this idea of how do we begin to mine the intelligence from our information. Then finally, when we both have that structured data and we have agents that can start to do much more, you know, kind of deeper judgment work for us inside of a business process, what if we could wire up those agents inside of a workflow, and now we can have a swarm of agents go and process information and begin to actually automate a workflow end to end? We can describe our workflow. It could be client onboarding, it could be due diligence, it could be an FDA drug trial process.
What if we could design that entire process inside of a workflow and have agents drop into that workflow at various points and people drop in at other points, and have agents be able to execute different decisions and tools within that workflow? Now we can begin to transform our processes with agents, and we can drive a new level of automation in the organization. This is where we are. This is sort of the ultimate point that we're taking customers to, where now you can again deploy swarms of agents across a business process, connect it into all of your applications, and really kind of re-engineer those processes from the ground up.
Again, all of this is built on a core platform of openness, interoperability that works with all of your agents, all of your applications, and with a level of security and compliance that is, again, unmatched in the industry. Again, Diego will share a bit more of what this looks like in practice with our product roadmap. The opportunity for Box is massive. I'll sort of bring this together from a commercialization point, and Olivia and Jeff will certainly be double-clicking on this in just a few minutes. We're taking many of the key capabilities that you're gonna see again in just a few minutes, and the ones I just mentioned around agents, data extraction, workflow automation, and application development. Those are really targeted for the Enterprise Advanced plan.
This allows us to drive price per seat up over time, but also allows us to get into new segments within our customers and bring in new logos. We now get to have completely new conversations with customers, with prospects rather, that we have not been able to break into in the past, because now we're talking about intelligent workflow automation, which is a completely new space that we can empower for our customers. It lets us expand within existing installed base accounts, where maybe we had the sales team previously using Box with something like Enterprise Plus, but all of a sudden we can get legal operations, finance, or other departments with something like Enterprise Advanced for workflow automation.
We have now hundreds and hundreds of these kinds of examples that are emerging, and obviously soon to be thousands and tens of thousands. Enterprise Advanced really driving both the price per seat motion up as well as seat expansion within accounts. For the non-human users, the non-seats, we have two modes of monetization that you'll see increasing over time. The first is AI unit consumption. The way to think about this is if you're an end user that's using Box AI for sort of daily knowledge work tasks, or you're integrating Box into something like Claude and you're working with that, we generally consider that well-monetized within the seat.
There are some highly extensive use cases where maybe you have an agent go off and run and it's doing, you know, hours of work doing due diligence in an M&A process, or you're doing at-scale data extraction from millions of documents and contracts, or you have an agent running in a repeated workflow, that will be monetized through AI unit consumption. We have sort of this direct correlation between these high-end agent tasks and AI units that we will go and monetize based on the volume of that work. For anything that maybe Box AI or our agents aren't powering, but it's another agent that's powering it, or we're embedded in an application, this is where we are monetizing through API calls.
Again, if you have a massive workload that's happening through Claude Code or Claude Cowork, or, maybe OpenAI Frontier, or you just have new applications that you're building, and you're sort of vibe coding apps built on the Box Platform, that will be an API call monetization as well. As we imagine a world of 100 or 1,000 times more AI agents than people, again, we expect to continue to monetize the end user seats through our typical seat model with more and more value added there. But all of those agents, all of that consumption, all of those new applications that are built, we have multiple ways to go monetize that to drive further growth in the future. We're actually incredibly excited about how many agents that we're seeing in the world.
Those agents are all gonna be generating, reading, processing, working with enterprise content, or they're gonna be building applications that need that same content. We're very, very excited about being able to power those entire processes within our customers' ecosystem. To share a bit more about our overall technology strategy and our platform, excited to bring up our CTO, Ben Kus.
Hello, I'm Ben Kus. I'm CTO of Box, and today I'm gonna be talking about our AI strategy and our AI technology. Really, we've been talking at Box for 18 months or so about agents, but things have changed significantly in the last six months or so. Previously, the AI models sometimes struggled when you gave them complex tasks. Sometimes, previously when a user would want to sit down and talk to an AI agent, they would only have so much time they would allow to let the agent come back, and they typically had this, like, one-shot style of responses from AI agents. AI agents often struggle to get access to your enterprise data. A lot of that has changed recently because of some of the new technology enhancements.
Specifically, the latest frontier models from Anthropic, from Gemini, from OpenAI. In addition to the idea of the way that you use these models to not just answer and not just look through and do sort of standard retrieval-augmented generation, but instead have the agents reason, have them think, have them do complex tasks and check their work in this sort of multi-step style of the new agents. In addition to being able to provide the agent more tools and more ability to go get access to data. These changes have really led to this new inflection point in which not only are people using these agents to sort of answer questions and to get access to some data, but also then now you're treating them more like coworkers.
These are agents now who can not just do an assistant-like task, but then can operate and do more complex things on behalf of your users. They can be triggered as workflows. Instead of thinking about an agent kind of answering something for you start to think about how you collaborate with an agent. We've seen this a lot across the industry with things like engineers working with agents to do coding tasks, where people will use things like Claude Code or Cursor to go send these agents on these more and more complex tasks.
We believe that this is the beginning of this paradigm where you're starting to see this across the board where you're the manager of agents, and it's starting to become part of how people all knowledge workers are starting to work. With the coding agents, you would typically have them collaborate with you on code, where they help you create and update and be able to sort of generate code with you that you can double-check. But for a typical knowledge worker, files is the way that they interact, and it's a way that agents naturally are beginning to not only read and to think with these style of files, but also the way the outputs are what the agent then sends back to the user to be able to continue working. Now, when you do this, you immediately have some significant challenges.
The idea of how do you give an agent a hundred thousand or a million or in some cases billions of files that many of these organizations have? How do you get agents to work across petabytes of data? As you're doing that, when you're taking this data and you're having agents work with it, how are you protecting this information? You can think about the things that you most want your agents to do are some of the most critical data in your organizations. HR information, financial information about your latest M&A, information about your latest projects and some of your, like, most important IP.
When your agents are working on this data, they need to get access to it, but then also their output that they are going to share back with you must be controlled. These kinds of challenges become the data challenges associated with how you work with these kinds of agents . For Box, we provide the capabilities for how you can work with these agents with your data in a way that is using the latest techniques from agents. Specifically, context retrieval is a major part of using agents, where you need the agent to be able to go find the latest information. You need to handle things like the fact that some file formats are not particularly friendly for agents to look through, doing things like conversion, markdown extraction, OCR.
You also need to make sure that you're providing the agent the right set of tools to go find your data. Things like not just the traditional lexical style search of keywords, but also things like doing vector embeddings, making sure you have a vector database so that you can search these in this hybrid search capability that's the newest technique for getting information to these agents. In addition to the idea that searching more and more, not just your text data, but also the other modalities, so images, audio, video.
You can imagine in the future, somebody says, "I wanna know what we talked about at FAD for our this year?" and you're able to not only be able to have the agent look through the words that I'm saying, but also what's on the screen behind me, and have the agent understand all of that, so they can come back and then analyze it to give you whatever information that you need. On top of that, you need to make sure that as you're working with these agents, you have a collaboration layer. What's how is it going to get access to what you want in addition to be able to share back the information?
In all of this, you need to make sure you're securing the data, but then also be aware of the AI-specific challenges, the agent-specific challenges for security and governance. Specifically, any type of data can come in from anywhere, it can be untrusted, and therefore you have to worry an awful lot about prompt injection, about people trying to go out of their way to trick these agents, in addition to things like making sure you have guardrails on the agents to make sure they can only do what you've authorized them to do. For Box, we provide not only the ability for you to have these kinds of controls for your users but also have the controls that are available for your agents.
From the global infrastructure to securing the interactions across all interactions, and then be able to have the metadata, the workflow, the applications, so that you can use the data after the agents are done with it, and then also just all of the capabilities around the AI platform so that you're able to have your agents securely doing work with all the capabilities we just discussed, and using this across users, across agents, and across other applications. Specifically, as we look into this, you can start to see that agents can do more complex things. Some examples of a couple different industries, and of course, these are illustrative examples because every industry and every customer have a different set of these kinds of very important tasks that many people in the organization routinely do.
For instance, let's say that you had. You're a manufacturing company, and you have suppliers, vendors, and you wanna know what's your risk if the world is changing of the laws and the different geopolitical states of how tariffs work. How are you going to look across tens of thousands of these different files to be able to do this? You can do this via agents. Many companies had to deal with RFPs and a salesperson, typically a really subject matter expert on your product, on the customers, and have them go through and answer a series of questions.
If you're able to then give these agents the ability to not just look through the product documentation, but also look at previous responses, and then to be able to then craft answers to these kind of questions. Into other sort of tasks where a knowledge worker is required to go through and be able to build some new content, something like if you want to update your budget for your company or for your department that you need to update every year. These are the kind of tasks, a few examples of them, that are very common for a typical knowledge worker. Let's see a couple examples of this. I'll show you a demo.
The first demo will be one which we're going to use a Box agent to do one of these tasks.
Here we have logistics, brokerage, warehousing, and trucking contracts stored in Box. When leadership asks about tariff exposure, teams often have to review dozens of agreements to understand where the risk sits. An AI agent can help analyze the documents and surface the key patterns for review. In Box AI, we'll first add our documents as a source. This way, our agent can securely access these contracts as enterprise context. We'll now ask Box AI to review the contracts, categorize tariff risk, flag higher-risk vendors, and prepare briefing for legal and procurement. We'll go ahead and submit this. Our agent is going to analyze the agreements and organize the findings into a structured report. It might take a few minutes for the agent to complete, so we'll jump ahead to the finished result. When it's complete, we can check out the report saved back to Box.
Our agent summarizes where tariff risk appears across contracts, highlights vendors that might need closer review, and also suggests areas the legal and procurement teams may want to address. This is AI agents in action, helping teams analyze enterprise content and surface critical risks and insights.
This is an example of seeing our Box AI Agents working on data inside of Box. Really enterprises have a lot of different ways that they can use AI. We'll show you another example here, this time using Claude Cowork to be able to work on the data inside of Box.
Agents are becoming coworkers that can execute real workflows across company data. Here's a set of finance documents stored in Box: last year's budget, headcount projections, strategy deck, and a meeting transcript. Instead of reviewing each file manually, an AI agent can work across them. Claude's connected directly to Box, so it can securely retrieve the right enterprise content. We're going to ask Claude to create a new 2026 budget using these documents. Claude reads the files, extracts the relevant information, and runs a workflow. This takes about five minutes for the agent to complete, so we'll jump ahead to the finished result. When it's done, we have the new 2026 budget created from the source documents. It also created this overview summarizing the key changes, which it uploaded back to Box. This is AI Agents in action, securely using enterprise content to complete real business workflows.
You can see from these different examples that you can use AI Agents from different vendors, including Anthropic, including Gemini, including OpenAI. One of the keys here is that when you need them to look through your data, when you need them to access and be able to find the right information and be able to then write back files to the user to collaborate with them, Box provides you these kind of capabilities that really prepares you for your next phase as an enterprise and how to use AI Agents. To tell you more about our product roadmap, I'd like to introduce Diego, who can tell us more.
All right. Thank you, Ben. I'm Diego Dugatkin, Chief Product Officer here at Box. I'm delighted to be here with you today. We have an amazing opportunity to really leverage what Aaron described as the change on how we work, and what Ben just described on how agents are going to use files to really accelerate what they can do. We talked about the specifics of how now this transformation that companies are going through from knowledge work to really the acceleration of complex workflows in the enterprise. Let's get started to how our customers are really going from first having knowledge work improved. The acceleration of knowledge work for all of us typically starts with, well, we access a file, we tend to read and get information, we need to do a further search.
We may want to also collect additional information to then expand the creation of a file and eventually create an output, distribute it, store it. Agents can help in each one of these steps. Each one of these steps, for example, in the case of a compliance questionnaire, it goes through each one of those where an agent can actually help individually. We came to the conclusion that in addition to the help in each one of them, it might be best to integrate all of them in a single agent that would have multiple skills, multiple abilities that can be all integrated in one environment. For that, we've created the Box AI Agents, which we're releasing very soon, and it's actually able to take each one of all of these tasks and help us throughout the process.
Now, the Box AI Agents allows us to also create a plan and go through each one of the steps in a very structured fashion but basically working through all the surfaces throughout the Box Platform. You can actually initiate the work with a Box AI Agents when you're doing a preview of a file, and then immediately resume it working on another part of the surface of the portfolio. You can work with the same Box AI Agents that kind of follows you throughout. Connected to what Aaron mentioned earlier, the future of work is human workers and agentic workers both having agency to really take action, but in an integrated way where the agent works with you and for you throughout any interaction with content.
Now, in addition to having one agent, you want to scale to then have multiple agents also working for you in a connected way with content where you can connect the content specifically to what the agent can do, for which we have Box AI Studio. It's already generally available, and you can customize the specifics of what the agent can do with the content that is most tailored for that. Now, once you have the agent that can help you in all its work, the next thing to look after is, well, how data is going to actually help scale this and mine the intelligence that is trapped in all of those documents that the agent is going to use for the delivery of the task.
Because in the enterprise there are all kinds of documents with mission-critical information, we also see this as an opportunity to really expand how we can actually get more use of AI through the platform. For example, a legal team might be going through thousands of commercial lease agreements, hundreds of pages long, and this type of information and the extraction you need to do from the intelligence trapped in the document may be very different from another part of the company that might be working with structured information that is trapped in handwritten form, stamps, images, and we want to have a system that actually works throughout each and all of these use cases. Another scenario is, imagine a loan processing system where the information is trapped in a way that the extraction needs to be done differently.
You want a platform that can work throughout all of them. Typically, point solutions that work with each one of these are forcing manual processes, create siloed information, result in security risks, and also don't render the ability that Aaron referred to earlier, where agents need to work across systems to really extract the correlation between the information perhaps trapped in different types of documents. How to solve this? Well, for this, we have Box Extract. Box Extract can extract information from any type of document, work throughout any department, and really provide the information that interacts with the user to, in some cases, also include human in the loop.
Once you have the ability to extract information, you have the ability to work with agents and to do that at scale, the next step is to say, "Well, how to create a workflow that solves problems that in industry sometimes also render the need for point solutions to really accelerate business?" Typically, enterprises have hundreds of content workflows throughout, and each one of them may require the integration of a point solution that could also be simplified. If we go back to the loan processing, you know, case, there are many steps where you could have the use of an agent assist in document verification, where you first check for completeness of the information. Then you could also have an agent that could assist in the evaluation and approval process, then the compilation of a response, and finally, the documentation and the archival.
Now, for the creation of each one of these steps that are effort-intensive, time-consuming, and error-prone, you want to have one system that creates the workflow and allows you to go through all of them at once. Now, each one of the agentic implementations can solve every one of the steps, but for this you want to have one environment that actually solves the automation of the whole process. For that, we have Box Automate. Box Automate that is going to be released very soon, it's going to be solving the workflows for teams and agents combined. It's going to also allow the customization of the agents on the fly and produce high scalability where we can also integrate third-party apps in the process. Let's take a look at the demo.
I'll start from a blank slate and drag in the first step, a form trigger. Next up, I'll add a document verification agent that ensures all the required information is in the submitted documents. Then there's a risk assessment agent that helps loan officers determine if the application meets our company's risk thresholds. Finally, I added some typical workflow actions like assigning tasks to my loan officers to complete the credit and finance reviews, along with steps for doc generation and e-signature. This is how easy it is to create workflows with Box Automate, and these workflows combine custom AI Agents with Box's powerful content collaboration features without requiring any coding or help from IT to set up. Now that I've built the workflow, let's dive into the risk review and approval step you saw before.
Earlier, I created this custom risk assessment agent to read through the loan application and submitted documents like bank statements, pay stubs, and use it to calculate key metrics like debt-to-income ratio. I supplied the agent with our company's risk evaluation guidelines document, which outlines the acceptable ranges for these metrics. Given all this information, I asked the agent to deliver a risk evaluation of low, medium, or high. Since this data ultimately determines the offer and terms of the loan, I built in a human-in-the-loop review to ensure that the agent's recommendation is double-checked by a loan officer before a final decision is made. Let's see what this review step looks like when a loan is being processed.
Ray, one of the loan officers on my team, just got a task from Box. A new loan application is ready for review. He clicks the task and sees the agent's risk evaluation as low, along with the key data points it calculated. He also has the complete application package and extracted data all in one view, so he can verify it as needed. The ratios look good, and so does the credit score. Having reviewed the application, Ray agrees with the agent's evaluation and approves it. As you can see, Box AI Agents and Box Automate have revolutionized my team's operations by handling the document processing, analysis, and assessment in accordance with our company's guidelines. This doesn't just happen once. Every time a loan application is submitted, my team of Box AI Agents is there to lend a helping hand.
Isn't this cool? This is coming up soon, and we are super excited because really connects the different parts of the platform in one integrated way. However, in the industry, we have tons of applications that are really spawning and creating a bloat of systems to really solve the different workflows. Typically, IT departments, CISOs, and CIOs are asking for the simplification to reduce the high cost, high risk, and high complexity that they create. There is good news on this. Many of these applications have many points in common. They have the same building blocks. They need to have security. They need to have governance. They need to have a workflow way to integrate them. They also are always requiring a simplified and easy-to-build user interface. For that, we have Box Apps.
Box Apps, which is currently available and is expanding its application and way of functioning to really have an agentic creation, can help you go from ideation, from the conception directly to deployment. It's super exciting because this agentic framework allows you to simplify the tech stack, to take applications that otherwise are bloating the complexity and the security risks of your current enterprise software and general environment, and also allow you to create applications that perhaps don't even exist today by creating applications that simplify the operation that users have into an agentic environment. To show you this in action, let's take a look at the next demo.
Box Apps puts purpose-built tools right where your content lives. Need something new? Just describe it. Here, we're asking Box AI Agent to build a contract management dashboard. No templates, no developers, no waiting. All powered by the Box Platform with the scale and security guarantee. In a matter of seconds, you have a complete working application. Visualize the data that's in your documents, monitor your business workflows, and run them all with an easy-to-use interface. Customize it to fit all your needs, like filtering, searching, and sorting. Just say the word and it rebuilds on the fly. Every component stays connected to your actual Box content. Go beyond dashboards. Set up actions and trigger automations in the same conversation. A weekly notification trigger? Done. No switching tools, no code. Describe it, refine it, automate it. Your next business app is just one conversation away.
I also love this. The ability to really simplify the tech stack, going from ideation to creation in one environment is quite magical, and we already see customers benefiting from this. We have a professional services customer that saw 40% higher engagement by implementing with Box Extract and Box Apps, the ability to distribute the MSA agreements. We also saw a different customer that actually had all their assets for marketing distributed and sometimes duplicated. By implementing Box Extract and Box Apps, they were able to save $250,000 that, in a way, justifies and accelerates the deployment of Box across different departments. This is a great way to upsell and extend the use of Box, increasing the engagement and the use of AI units. It's quite an acceleration.
In addition to this, Aaron mentioned the importance of expanding the platform and also working with developers to really connect applications, data, and users. We are also investing in expanding our developer platform because we believe that developers across the portfolio of developers in the enterprise, ISVs, and partners are going to take the opportunity to use our APIs and to connect through the MCP server implementation, any agent working in industry with the content we keep in Box. That's because we've moved the platform to be the fastest to start, the safest to ship, and the easiest and simplest to scale to really get developers to use more of Box as they develop their own agentic frameworks. Now, for that, let's take a look at this demo.
We're going to use Claude and Box to run a simple M&A diligence workflow end to end. Under the hood, Claude connects to Box through the Box skill and the Box CLI. Let's give the agent one assignment. Set up the room, use a routing map in Box, pull in source material, run the diligence analysis, and write the outputs back to Box. From here, we're not going to step through the workflow. The agent takes the assignment and works through on its own. When it's done, the agent writes a diligence memo and legal assessment back into the deal room. Since each folder is mapped to a particular team, the outputs show up exactly where the right people are already working. In Box, the room is scaffolded with the expected diligence workstreams, and the routing map defines where things should go.
This means that specific folders are shared to specific collaborators, so the workflow follows that structure. Of course, the agent populates the room with public source diligence materials and uses those as a basis for analysis. You also see the same thing happening across each folder in the deal room. In the end, the finished diligence memo is written back to Box for the broader deal team, and the risk assessment is routed into legal review for the legal team. Stepping back for a second, you give the agent a high level objective, and it breaks that work into smaller steps, setting up the room, gathering documents, running the analysis, and writing the results back into Box. It works through that sequence independently, and the outputs land in the right place for the right teams inside the same Box workflows and permissions the team already uses.
Isn't this also very exciting? It allows to connect agents to really get them to work with content that lives in Box and create new applications. We're basically standardizing how to connect the ecosystem to the data in a centralized place. The use, for example, of our MCP Server that you can think of as a single bridge that connects external agents to the content that lives in Box, not only allows the connection and the use of the content, but also the governance of what agents are allowed to do what through that single choke point that allows the coordination and the secure access to information. The ability to expand this is also going to accelerate business because we are enabling then any agent in the ecosystem to consume units working through Box.
Now, in addition to using MCP Server, we continue to invest in something we've discussed here last year when we met, and we continue to always expand, which is the standard integrations across the industry. It is true that customers really want to simplify their tech stack. They want to have less applications in a more coordinated fashion. Still, whatever they're using, we want to make sure it is integrated with Box. In addition to the standard integrations, we continue to integrate with all the AI platforms. Aaron and Ben both mentioned that we are working with ChatGPT, working with Claude, with Google Cloud. We expanded Box AI and Copilot integrations, and we continue to do that, and we will.
The message here is that the neutrality we present in all the integrations needs to always allow the customer to choose whatever is the best implementation they want to run with. In all cases, this expansion of integrations continues to bring business to Box. In addition to this important integrations, none of this would happen, and this is an important message, if the companies using agentic frameworks would not trust that the data is secure. We will always put this as a cornerstone of our implementation. Protecting valuable data, protecting the content of our customers is paramount.
What we see in the industry is that these agentic releases are creating also new attacks, new vectors of risk, where there is prompt injection type of attacks that are creating risk, but also a more subtle type of sometimes drifting of the behavior of the agent. We need to be attentive to both the proactive attack and also the misbehavior of the agent that sometimes, intentionally or not, could create exfiltration or infiltration of information that needs to be monitored. For that, we've continued to expand the platform. You probably already remember Box Shield from all these years, but now we've expanded to Box Shield Pro with an add-on that also extends what you can do. Provides additional AI classification.
It provides the ability to protect against ransomware directly at the device, directly at the endpoint, where there might be a problem that the user is creating intentionally or not. At the point of entry, we can detect there's something that may be happening. Also do general AI threat analysis. Now, in addition to the extension of Box Shield Pro, we're also adding additional elements to AI security, where basically we can identify these prompt injection patterns. We can identify an agent misbehaving or not providing the right output, or that is accessing the wrong input, and then stop it right at that moment.
Now, in addition to security, another important part of the portfolio is that we have expanded the aspects of governance, and we're going to release soon the extension of AI governance, not only for human users, but also for agents. We can think of that now hybrid environment where we have human workers and agentic workers, all of them need to have a very deterministic type of governance control. The extension also of the monitoring and audit trail created to really track what the agent was able to do also from an accountability perspective. With that, I want to close with the important message that this is an extraordinary time for all of us at Box. We've been expanding the portfolio and extending through innovation.
Next, what we're going to hear is, after a brief break to get a little bit of coffee, Olivia is going to come and show us how we're going to basically use all of this innovation to continue to accelerate our business. Well, thank you very much.
Yes. All right. Diego, Jack. Yeah, let's do this. Okay, well, welcome everyone. We are super excited to be talking to you about the go-to-market strategy. I was here a year ago and talked about what we were going to launch into this past year, so I'm excited to talk to you about where we came out, and then also talk to you about our strategy for this fiscal 2027. All right. As mentioned before, if you could advance the slide or give me a clicker. Thanks. As mentioned before, we are accelerating our AI customer journey and really working with our customers to take them on these automated intelligent workflows. That presents both an opportunity to us, both from a SaaS perspective, but also monetizing from a consumption perspective as well.
Some from a business model that's also been very exciting, and I'll talk to that. Okay, let's take a look back on this past year. There were four key elements that we set out to accomplish. The first was actually awareness. What we saw was when we were out in the market talking about Box, we actually got so much excitement from the functionality that was announced at BoxWorks last year. Enterprise Advanced truly was a new way in which our customers and prospects could use the Box Platform. There wasn't the level of awareness that we wanted, and so we put a huge push into awareness last year, and that really paid off.
The second was making sure we brought new logos onto the platform, and this meant getting out and meeting with prospects and articulating what the value proposition was and how they could use it to run workflows, run metadata extraction, and get intelligence out of their content. Of course, we had a whole engagement cycle with our installed base, so we could ensure to further expand our footprint with those customers and then bring them on that upgrade cycle. We also talked last year about partners. We had DataBank on stage with us, and we knew that it was very important to be leaning into that partner ecosystem in order to scale, to drive that further reach, and also to bring more complex solutions to our customers and prospects. All right, awareness. We did a great job on awareness.
We actually increased our awareness by 4x year-over-year, and we did this by doing three things. The first was we actually put more marketing dollars into awareness, and we shifted that money to more top of funnel. The second was that we went beyond the CIO. We extended to the CISO, more of the ITDM, and we're having much broader conversations across the enterprise. The third was we went to market with our partners. We showed up in our partner events. We showed up in our partner announcements and our partner communications. At Dreamforce, at re:Invent, at SAP Sapphire, we were there at all of those moments, and we were on the stage talking about what Box could do within the context of that partner. That really helped our reach as well. We had a tremendously successful launch of Enterprise Advanced.
If you remember, we launched it on January 12th. We've had now 12 months selling this offering into the market. What we've been delighted to see is that we've able to land both that upgrade cycle that I was talking about, right, into the SMB segment, into the Mid-Market segment, and into the Enterprise segment, but also land net new logos. Now, the interesting part and really exciting part about that upgrade cycle is we were able to hold a 30%-40% price increase as we took that solution to our existing base. What that said to us is they really valued what we were bringing to the table, and they saw the ROI for their business.
For new logos, what we saw was that these customers were excited to get on our platform, were excited to build with Box, and that led to actually, as we exited the year, 10% of our revenue coming from Enterprise Advanced. Now, that motion with the installed base was very intentional and very purposeful. We did a couple of things here. The first was re-educate our customers. We had to go back to our existing base and educate them on all that Box could now do. It was almost a new chapter of Box, right? We could do intelligent workflows, we could do Box AI, we could do metadata extraction, and now they could work on the 90% of their data that previously before really had not been tapped with the level of insight and the level of execution that they could now work on.
The next was that we really leaned in and worked at the use case level. We rolled up our sleeves and we said, "Okay, what are the business problems you're trying to solve? And let's talk about how the Enterprise Advanced platform can help you solve those problems." We went through use case by use case and really built those out with our customers. Then finally, we really found that in a scaled motion for that long tail, we automated it, we made it more rigorous, and we are able to drive upgrade at a much more repeatable way than we had historically, and that led to even more success with that installed base. Let me walk you through an example of a customer, right? This is actually an HR services firm. They joined our platform on fiscal 2021.
They actually came onto our platform on the enterprise SKU. They expanded their own business, their employees expanded, and so we drove expansion off of that. In 2023, they actually continued to push forward for us as the content management platform, but then that led to the enterprise-wide rollout of Enterprise Plus, which was security and governance, and that drove even more monetization for us. Last year, they actually decided to choose us as their ECM modernization solution. Last year, they upgraded to Enterprise Advanced. Excitingly, at the beginning of this year, they purchased even more AI units, and that's our unit where we allow our customers to run additional workflows and be driving that intelligence through AI units. That's yet a separate purchase for us, and that's a consumption purchase.
Really, you see the build of this customer, and we see this across many customers in our customer base, and this is the journey we're taking our customers on. Of course, I mentioned the partnership. We set out to do two things. One is to partner with ECM partners, right? To make sure that their practices were putting Box into their solution set and really delivering that as the more modern ECM solve. You heard that from DataBank on stage last year. We saw a lot of traction there. Also to work with SI's AI transformation practices, right?
You'll hear from Slalom later today, and that's an interesting way in which we're working deeply with many of our players. Slalom is one of the ones we value deeply, to get a broader reach and take deeper and more complex solutions into our customers. As I mentioned, we were building this momentum with our customers. We are in our announcements. We were out there in the market talking with one voice. Let's talk about this year. We're actually deep into this year. We're already eight weeks into selling. It's been fun so far. Our strategy really is similar, but with some important tweaks. Of course, we're gonna keep driving that awareness. That's very important.
We had a lot of success last year, but we continue to do more because we want everyone to think about Box when it comes to how do you get value out of your unstructured data in an agentic world? Of course, we continue to work with our installed base. We're continuing to expand and upgrade them through the motions that I talked about before. Now we're also leaning into more of this motion of consumption, right? How do we work with you on a workflow? Yes, you're wall-to-wall with Box. What are the various levels in which you can really get insight but also drive entire automated workflows on the Box Platform? As I mentioned before, that pulls through AI units. Even more continuing to work with developers and partners to make sure that that platform really sings.
All of this is underpinned by, yes, of course, the horizontal solution, but we're putting it all in vertical terms. We've put investments into our vertical areas to make sure that when we're talking to our customers, we're having that conversation, and we're solving those pain points in the context of their industry. Now, as I mentioned, awareness is key for us, but we're intentionally doing this by expanding the personas that we reach out to. Yes, of course, the CIOs and the ITDMs, but also the developers. You heard from Diego, the developers at the enterprise, at our partners, and then also at third-party applications that are building on top of Box. The CISO has always been important to us. We will continue to lean in here, especially when dealing with agents. You heard from Aaron and from Ben.
This is absolutely critical and probably top of mind for everyone thinking about how to deploy agents today. Then LOBs, right? Last week, I was meeting with the head of HR at Broadcom. They use Box to onboard every employee. That happens to be actually a lot of people because they do a ton of M&A, so they do these at scale onboarding. We are thinking about how you solve the problem of the head of HR, right? Making sure that we're out there having those conversations with the LOB leaders as well. Now, this was the thing that just got me really excited.
At the end of last year, twelve months of working with customers, twelve months of our solutions engineers thinking through use cases, deploying workflows, and we did a pull from our systems, and we saw that our customers used Box for over 250 unique use cases within the enterprise. What this said to us was, "Wow, people are getting creative. That functionality is truly impactful. We're really solving customers' problems." The interesting thing is, in many cases, they didn't really have a good way to solve this previously, right? These use cases are pretty specific. Also, we see that we're looking across the breadth of the enterprise, right? We're working with an LOB, but we're also working with a CIO, and we're solving across all these problems.
You can think about a GXP-compliant process in a life sciences firm, but you can also see that we had a use case, you know, issuing certifications for construction companies. You also see we had situations where we're doing fraud investigations and carrying that content layer for, you know, an insurance firm. This was absolutely really exciting for us to see. Now, I would say that we also took a step back and said, "Hey, are there some patterns here?" Right? How do we help our customers go from zero to one in building out these workflows? Because we want to make sure they can get started quickly. We did put together these get started kits, and we're pumping these out.
It's really fun to see these come out, and this helps our sellers because it's a really easy motion for them to be talking to customers about, but it also helps our customers as well because they have a faster start deploying and getting to value. Now, I spoke last year about our business model and how it's evolving from seats, and of course, we've always had a platform business, but we really feel like this year we'll see high levels of monetization through that platform consumption. In fact, even at the beginning of this year, these are some customers that have already put in additional purchases just on the consumption part, right? So, what we call AI units. It will take like a Valmark, right? Here they are processing insurance claims.
That workflow uses up a number of AI units, so they continue to purchase those AI units. Mercer is an example where they're onboarding clients into their wealth management part of their business, and that is an ongoing workflow, and it continues to draw those AI units. Then interestingly, we're working to add additional workflows. A company might be wall-to-wall with us in terms of seats, but as we work with them to add these workflows, that then pulls through those additional consumption through the AI units. Partners. The partners continue to be absolutely paramount. Our SI partner system continues to be strong. We added TCS this year, which is exciting. It'll give us more global reach. We're leaning in deeply with Deloitte and many others of the GSIs, including Slalom.
Then we continue to go down that ECM monetization path with some of the more ECM boutique players like a DataBank, like an MSI. We also, as you heard from Diego, have a number of technology integrations. What this means is we actually go to market with many of these players, right? We go to market and show up together with Salesforce. We go to market together and show up with Guidewire. This provides even more reach because they're bringing us into their customers, and we're there jointly solving the business problems. The most exciting one is on the marketplace side that I wanted to touch on, which is the AWS Marketplace. We just joined that marketplace. They have invested deeply in us.
They're actually funding, you know, as they do some of the roles that we've put into our own teams in order to really push and go to market with AWS. That's exciting because obviously their reach is really broad, but also, they have really effective partnerships with other players on their marketplace, and we're optimistic about the traction we can get from that. Could you advance a slide, please? There we go. Oh, back one. Okay, I mentioned platform, right? We are actually also similarly, Diego's working hard on the product part of the platform. We as a go-to-market team take it very seriously that we want to be engaging those developers, right? One of the things that we have really found beneficial of engaging with those developers of the LLMs is that we can show up and commit to model neutrality with our customers.
This Switzerland approach is really valuable. Last night at dinner with a number of very large banks, one of the things that they said was the ability to switch models, to choose whether it's because I want the latest models to work on XYZ use case or because I don't want to be too committed to one single provider, that commitment to neutrality is really, really important to our customers. The second one is we need to make sure that those developers at the partner teams, at the customers, understand our functionality, and they're able to build on our platform. We're putting a huge effort into that, not only from a marketing perspective, but from our partner teams and our customer teams as well.
We've importantly created a space where developers can come in and try the Enterprise Advanced for free, right? Not only that, but they're able to play around with those AI units and run those consumption experiences. That's hopefully getting them to really play around with this functionality, and we're seeing great uptick there. Finally, we want to be working with the developers and our partners, right? We want our partners to be building solutions and delivering that complex value to our customer, and we're right there behind them, making sure that that's a success story, and that will help us scale even further. All right. To wrap, thank you very much for listening. It's great to have you all with us today. I could not be more excited about this year.
We see so much momentum as we're taking off and so much more still to do. One thing I'm doubly excited about is our new CRO. I'm gonna welcome Jeff Nuzum to the stage. He has joined us in Q4 and landed an amazing Q4. Thank you, Jeff.
Thank you. Thank you, Olivia, very much. Awesome. Well, good afternoon. I'm super excited to be here. I have been on board a full, total of two quarters, and it has been nothing but fast and furious. It's been an amazing ramp-up period. I just thought I would share based upon what Aaron shared, Diego, Ben, and now Olivia. I thought maybe the best thing I could do is just cherry-pick a few things and highlight what I'm seeing in the field and what I'm hearing from customers and how things are ultimately resonating instead of like repeating everything that Aaron and Olivia just covered. Maybe a little color from the field and a little color from a lot of customer interaction over the last six months may be helpful.
If I can get the slide clicker to work. One of the things I was most excited about when thinking about joining Box was the fact that our customer base is truly a very diverse and global customer base, across all different segments and across all different industries. For what I do for a living, that is a very exciting proposition because that's where I spend almost all of my time is out in the field with customers. You see it here. I'm sure you've seen a lot of these numbers. I think what is super exciting is, again, how the message is resonating across all of these different industries, and I've been testing it. I've really been testing it.
A lot of what Aaron's been talking about, a lot of what Olivia's been talking about from an execution perspective, as a newcomer, naturally, I want to go and really test drive these messages and see how they're landing in the field. When I speak with senior level executives, both from the IT perspective as well as line of business, I'm happy. I'm very encouraged by the fact that our unstructured data platform and our content plus AI platform message is really resonating. It's resonating really well. Invariably, these conversations go from kind of macro-level positioning to really kind of the main challenges that our customers and our prospective customers are trying to deal with.
Olivia just mentioned it, but model neutrality is absolutely on everyone's mind given the pace of innovation and how fast things are moving in the market. Box plays very well in a heterogeneous agentic environment, if you will. Aaron and I were running around town yesterday talking to major financial institutions, commercial real estate institutions or companies, and again, this question always comes up. They use different models for different use cases. They use different technologies for different agents for different workflows and processes they're trying to solve, and Box sits right in the middle of that. We play very well there. As far as the horizontal platform, Box applies to a lot of different industries, companies of all shapes and sizes, public sector, so it's very exciting.
However, one of the things, and Olivia just touched on it, that we're really excited about is where we see use cases that are repeatable at scale for industries. We're packaging those up and enabling customers to get started in a much more accelerated way, versus like just starting from scratch. That's a tremendous opportunity for us, and it really resonates with customers and prospective customers. I think the number one thing that it always comes back to, and Ben and Diego talked about this, is security. Security, compliance, making sure that all of these different agents are orchestrated in a way, all of these different models and AI platforms are orchestrated in a way that doesn't unduly expose the customer's environment. Box, that's been a core tenet of Box since its founding, and it's always.
The conversation always seems to revert back to that point. You've seen this slide a few times now, but for me, what this really means and what I try to communicate to all of our customers and prospective customers is the fact that we can meet them anywhere they are on their journey. We can meet them where they are. There are some customers you go in and some organizations you talk to where they're pretty early in the journey, and they're using agents for more sort of assistant and kind of basic search and basic efficiency and knowledge work. You see more sophisticated use cases that they're starting to use more advanced features. They're starting to use multiple different agents. They're starting to really lean into their strategy.
On the far end, you see a lot more advanced use cases. I thought I'd share a couple of examples of those. If you think about the business that Broadcom's in as an example, they've been a highly innovative Box customer for many years, but they're really continuing to push the platform. The number one thing Broadcom needs to do is safeguard their IP. When you think about the business they're in, you think about the secure collaboration required with their design partners, with their customers in terms of building next-generation chips, in terms of sharing highly sensitive IP. They rely on Box exclusively to do that, and they have for a number of years. There's no better sort of validation around the security and compliance and granular level permissioning that you see here from Broadcom.
It's absolutely phenomenal to see what they're doing and what their use case roadmap looks like moving forward. Hopefully nobody in this room ever needs to visit the ER. However, if you do, it's a high probability that a physician or clinician from US Acute Care Solutions is providing the care. They partner with 420 hospitals. They see 12 million patients a year. They deal with a massive amount of unstructured data, and they rely on Box for a number of really, really innovative use cases. What really caught my eye on this one, the number one use case they're using Enterprise Advanced for right now is more accurate claim coding, particularly around motor vehicle accidents and motor vehicle incidents.
It turns out motor vehicle reimbursements, so accidents caused by motor vehicles, pay significantly higher reimbursement than like a general medical claim. What they're using Box for in advanced features around extract and deeper analysis is to get more accurate on the coding, on that claim coding for motor vehicle accidents. That's just the first use case that they're pursuing. It's just amazing how these use cases continue to just roll out from a roadmap perspective. USAA, when you go to usaa.com, I'm a member, my family's a member. When you go to the member portal and you either initiate a claim or you inquire about a claim, that's all on the Box Platform.
All that unstructured data, all that content, all the adjuster information, the media that has been captured by the adjusters all lives on the Box Platform. What's super powerful about that is the fact that the member has no idea. They're just going and making sure that they can effectively either inquire about the status of their claim or initiate a claim, do so in a very efficient, effective way, ideally in a very sort of compressed timeframe, and Box powers that whole underlying data platform for USAA. You can imagine the scale of this. I did some digging on this one, and the utilization of the entire Box estate at USAA on a monthly basis is incredibly high. It's over like 92% on a monthly basis.
When you think about the total population of Box seats, users, in the member portal, it's incredible the stability of the platform and how the utilization numbers look on a monthly basis there. It's absolutely, massively scalable and secure. All of these observations and things are very exciting for me 'cause I can bet on them, and I can stand behind them when I go and I talk about our value proposition and use case roadmaps with our customers. Olivia touched on this. I think Aaron touched on this. We're not doing anything dramatically different for our go-to-market model this year. We're just building on what we feel like are in solid and core fundamentals that we already have in place. I mentioned solutions.
I mentioned the importance of continuing to speak to customers in their language by vertical or by industry, and we're continuing to do that. One thing I will tell you, and I'd reemphasize the importance of net new logo and new customer acquisition. It's not like we're just purely relying on our customer base, which I mentioned is expansive and global in nature, but I'm big on net new logo acquisition and I've got the teams understanding that's a very high priority for us moving forward because I think, again, just given the relevance to a very expansive use case roadmap, there's just amazing growth there for us. The ecosystem, again, super important. I'm very happy to see that we have a relationship with Slalom here at Box. Olivia also mentioned our AWS relationship.
I'm very happy to see that we inked our AWS partnership. I spent seven years at Google before coming here, competing with AWS, and I'm really looking forward to partnering with them now. We have a great opportunity to bring Araya Solomon, who's the head of capital markets at Slalom Consulting up to the stage, and he can provide a little bit more context on some of the comments that I've made as well as what he sees in financial services. Araya, it's great to have you here, and thank you so much for joining us. If we could just give him a quick round of applause and welcome. Thank you. It's great to see you.
Thanks.
Maybe just a little bit about your background, and then if you could frame up kind of as head of capital markets at Slalom, what you sort of see as some of the opportunities within financial services, as well as some of the challenges your customers are seeing.
Yeah. First, I'll just say thank you for having me.
Absolutely.
I'll just introduce and contextualize Slalom. Slalom is a 14,000 people company, consulting firm that focuses on business and technology. We operate in all major financial centers. On average our people are 10+ years of experience having worked in the market before. A little less about myself. I've spent my career basically implementing large technology change programs, predominantly in capital markets, and a third of it in Europe, a third of it in Asia, and a third of it in North America. To answer your question in terms of some of the key challenges that we are seeing, obviously, with the big macroeconomic events that is happening, a lot of our clients are focusing on the bottom line.
The opportunities to reduce costs are a big factor for a lot of our clients, especially in financial services. The idea of lowering costs through things like technology has sort of been done before post-2008. A lot of the arbitrage opportunities have gone away. However, a lot of our clients, with the advent of AI, are focusing on use cases to lower costs in IT, in business, and ops. In that space, they're experimenting in tons of different use cases. However, they're challenged, particularly in being able to, you know, being able to reduce, like, onboarding times by 75% or reduce identifying signals which are resulting from unstructured data, you know, fraud. Even managing public and private markets, et cetera.
One of the key, three key things that clients are struggling in order to make sure to unlock these opportunities on use cases are, one is around just general architecture, the lack of it, and being able to build out an architecture that's meaningful in order to unlock these, use cases. The second is around having cloud infrastructure, and the third, probably most important, is around managing data, and governing data and having the right infrastructure and technology in order to unlock all of that.
You talked about. Well, you and I have talked before about your Zero Legacy initiative, and can you talk a little bit more about that and then start to think about maybe communicate, share where Box fits into that? One of the things that I always hear when I talk to customers is they're always on a modernization journey, if you will, as much as they're on, you know, an AI journey. It's part and parcel to it. Your Zero Legacy initiative is interesting and talk about where Box may fit in that.
Yeah. Zero Legacy for us is in order to address one fundamental sort of idea, which is innovation at speed. I guess most of us know that, you know, in the context of like mainframe and legacy applications, and also data that's not well-governed or resides in the right framework, they're challenged by being able to quickly to rationalize and be able to build out solutions, et cetera. On the mainframe side is quite straightforward. It's about, you know, being able to support migration activities around that. The application side is mostly around consolidation of technology. On the data side, however, one of the things that we looked at is to do this ourselves, because it does drive a lot of revenues and opportunity for us.
However, the reality is that when we looked out into the marketplace, and this is where Box fits in, we understood there's a couple vendors out there that were supportive of that agenda. However, one stood out, and Box is one of them, which had the right architecture in terms of modern platform that's inherently like cloud native. It does have the security and compliance more core component, which is very important, especially in financial services, being SEC and FINRA compliant. When you start adding all these different components, we went with Box.
A lot of our clients require the need to be able to take the massive amount of unstructured data, especially with a shift from you know, equities and fixed income trading into alternatives where there's a lot more data to be consumed, whether it's like satellite telemetry data, whatever not, there is a need to be able to capture that. There's a need to capture internal data such as like emails as well as like information PDFs, documents, et cetera, to in order support onboarding. We felt that Box is one of the solutions that will provide us a quick opportunity to transition clients and be able to unlock a lot of these use cases.
That's where we've spent a lot of our time together, and working towards supporting our clients' agenda, which is really either to lower costs and in some cases to drive revenue.
One of the things that I'm really appreciating working with Slalom is it's you bring a lot of process knowledge, a lot of industry relevant process knowledge to these use cases in addition to just the technology experience. The other thing that I really appreciate is the fact that you really help us on the front end of the process as well. I mean, we really partner well in our go-to-market motions. Hopefully, the next time I come and talk to all of you, we can talk about some of the recent big wins we've had together in the financial services industry, some really compelling and exciting opportunities that we're working on jointly.
Before those opportunities even actually signed on the bottom line, we were doing a lot of work very collaboratively together early in the process to really establish a level of comfort with the customer that Box and Slalom were the right solution and the right value proposition and overall solution for them to move forward with and to bet on Box and to bet on Slalom. I think that those were a lot of the kinda some of examples we wanted to share. We're sensitive to time. We're sensitive to your time, so I wanted to thank you very much. Thank you, Araya, for joining us up here on stage.
Yep, thank you for having me.
Thanks very much. Appreciate that. Thank you. I think now I'm gonna hand it over to our CFO, Dylan. Come on up. Here's the clicker. Thank you.
Awesome. Thank you, Jeff. Thank you, Araya. Another round of applause. Really appreciate the partnership. Awesome. I'm Dylan Smith, Box's Co-Founder and CFO. I'm gonna close us out by discussing how everything you've heard so far today flows through to our financial model and why we're so confident in our strategy to generate double-digit top-line growth and significant margin expansion over the next several years. I'm gonna start with a recap of this past year, FY 2026, including how the initiatives we outlined a year ago are already having a positive impact on our underlying growth and on our customer economics. A couple of weeks ago, we reported our Q4 FY 2026 earnings results, which capped off a year in which we comfortably exceeded all metrics that we guided to from top to bottom.
These strong results were really underpinned by a very strong start in the market, as you've heard about, from Enterprise Advanced, which already accounts for a full 10% of our revenue. That's what enabled us to accelerate RPO growth to 17%, million-dollar-plus customer count to 14%, and our net retention rate to 104%, with all three of those metrics improving for the second year in a row. Turning to revenue growth, as our business gained momentum, you can see that we were able to accelerate our revenue growth sequentially in constant currency throughout the entire year. This was really driven by the strong trends that we saw both in seat growth and in pricing improvements.
This trajectory sets the stage for us to improve our revenue growth rate this year in FY 2027 by 2 points, and as we guided to 10% revenue growth for the current year or 9% in constant currency. That demonstrates that we're firmly on the path to achieving sustainable double-digit growth. Now we'll drill down into billings and RPO, which better represent some of the business momentum that we've seen over this past year. We saw an acceleration in both metrics, and both were driven primarily by strong bookings, but also aided by a high volume of customers who elected to upgrade mid-contract, generally into Enterprise Advanced. Both of those factors, the bookings and the upgrade dynamics, really highlight the strong demand that we've seen for our newest capabilities.
RPO growth was further fueled by longer customer contract durations. Since as virtually all of our Enterprise Advanced customers sign up for three-year commitments with Box, that's taken our blended average contract value to about 22 months currently, which is a month and a half longer than it was a year ago. This is important for our business model, both because it provides greater revenue visibility and because it gives us more time to ensure that our customers can be successful identifying and then successfully rolling out some of the new use cases that Enterprise Advanced enables. We've been investing to fuel this growth while we continue to build on our top quartile operating margins.
In FY 2025, you can see those margins jumped up, as we really realized, the full benefits of a successful data center migration to run fully in the public cloud. This past year, we continued to benefit from infrastructure optimizations and at the same time continued to make progress, against our workforce location strategy and, driving overall cost discipline across the business. In addition to the strong top-line results that we shared a bit ago, we were also able to improve our underlying customer economics over this past year.
That's important because the more profitable our customer base becomes, the more of the incremental revenue that we derive going forward can be dropped to the bottom line. If you go from left to right, looking at some of the highlights on the economics front, gross margin saw continued expansion this past year to 81.5%. We now have a full 2/3 of our revenue attributable to Suites customers, and that's up 6 points year-over-year. Again, a greater rate of improvement than it was the year prior. That mix shift into Enterprise Plus, and increasingly Enterprise Advanced is also having an impact on our overall customer contract values with average customer annual recurring revenue or ARR up 8% year-over-year. It further enables stickier use cases.
This past year, our full churn rate remained at a best-in-class 3% annualized, and this past year, we achieved the strongest ever gross retention rate that we've seen as a public company. These numbers all represent our total customer base, and we're seeing even stronger momentum within our largest customers. Those large customers are shown here and are very important to the business because, collectively, they represent more than 2/3 of our total revenue. Already about 75% of our a hundred-thousand-plus customers have adopted deployed Suites, and roughly 90% of our million-dollar-plus customers have done the same. It's in these customers broadly where we also see the biggest opportunity for impact with Enterprise Advanced adoption over time.
You can see that all three categories of large customers grew at a faster clip than they did the year prior with the strongest momentum in our million-dollar-plus customers. These most successful customers or million-dollar-plus customers also really demonstrate the power of our land and expand business model, right? If you look at those 174 million-dollar customers that we have today, only a seventh of them started as Box customers paying at least $1 million , and the other six-sevenths got there over time and passed that threshold as they adopted more seats and more and more of our advanced capabilities over time.
Decomposing Suites penetration, you can see just the relative mix of Suites that then where Enterprise Advanced is starting to show up as well, where, as a reminder, we introduced Enterprise Advanced with about two weeks left in fiscal 2025. The majority of those sales have been this past year in FY 2026, and really benefited and pleased with that early momentum, taking all of the lessons and incorporating everything we've learned over Enterprise Plus rollout in terms of enablement and being able to really co-communicate the value of those broader solutions, which was even harder with Enterprise Advanced, because as we've discussed, it's you know, really driving customers to be able to use Box for an entirely new set of use cases.
In some cases, there's a different economic buyer, different workflows, different solutions that we're competing against, et cetera, and have been really, really pleased with how the team has driven the success in the market so far. It's early days, but very important, because of the outsized impact that we expect Enterprise Advanced adoption to have on our business in the coming years. That impact is already showing up. In addition to the 30%-40% pricing uplift that we've talked about in terms of the difference going from Enterprise Plus to Enterprise Advanced, last year, more than a third of Enterprise Advanced upgrades also included seat expansion.
It's that powerful combination of pricing improvements and seat growth that are key drivers of our net retention rate, which was up 2 points over the past year, 3 points over the past two years, and surpassed our initial target of 103% when we entered the year because of that success. Now I'm gonna walk through how we expect all of those business model drivers to evolve over time, as well as how we plan to further accelerate our business momentum through the go-to-market initiatives that Olivia and Jeff and others have been discussing earlier today.
Not surprisingly, expanding the capabilities and the market penetration of Enterprise Advanced will really be a key catalyst for our longer-term growth and underpin a lot of the initiatives that we have underway. We expect both pricing improvements and seat growth trends to have an even greater impact on our growth going forward as enterprise adoption ramps up and expect each of those to contribute and to grow by about 5% or 6% on average per year in the years to come. Then as we extend our product capabilities, as you've heard, we expect those new use cases to disrupt incumbent software spend and to drive platform consumption revenue above and beyond what's included in our Enterprise Advanced offering, monetized through AI units.
These capabilities also create a much larger opportunity for the partner ecosystem, particularly with SIs and key partners like Slalom.
Flipping over to seat growth or sorry, pricing improvements, we have seen consistent improvements for many, many years now, and we expect to see a slightly faster rate of improvement, with the majority of that coming from the impact of Enterprise Advanced upsells because of just how strong that pricing uplift tends to be. As we expand the capabilities of our products and expand the types of workflows and legacy enterprise content management use cases that we can address and disrupt over time, we expect that to be a further pricing catalyst as it's in those types of conversations where we see the strongest differentiation as well as the highest pricing power. With those new use cases also comes the opportunity for new seats.
As we're not yet wall-to-wall in the majority of our customers, seat expansion represents a significant opportunity for us going forward. Now with Box Extract generally available, with Box Automate rolling out in the coming months, Enterprise Advanced will be able to power even more use cases that help us address that seat expansion opportunity. Another seat growth vector that Jeff and Olivia talked about is bringing new customers onto Box as well, both in under-penetrated markets and by extending our reach into new customers and prospects through our partner ecosystem. Looking at, you know, high-level expectations for Enterprise Advanced going forward, you know, we're certainly not slowing down, we're just getting started.
Coming off of a fantastic year this past year, we expect Enterprise Advanced, which is shown on the top in dark blue, to double as a portion of our total revenue by the end of this year from 10%-20%, and within three to five years, to be a full 50% of our revenue. That matters so much just because of all the impacts that Enterprise Advanced has on our business model from top to bottom, starting with you know, being a key growth driver. Right?
If you think about, there's the 30%-40% pricing uplift that is kind of the baseline impact, and then when you add in the seat expansion opportunity, and dynamic that we're already seeing, as well as platform consumption, and the different ways that Enterprise Advanced you know creates incremental monetization opportunities, we expect to drive significant increases in our average deal size, as we continue to gain momentum in the market. Turning over to platform revenue, as we've talked about, and just showing some of the numbers behind that, last year platform revenue represented about 5% of our total business.
We expect that to grow to 6%, this year, and then at a roughly 30% CAGR in the following years, getting to become about 10% of our total revenue, in a three-to-five-year period. Right? That's, you know, again, we launched AI Units in FY 2026 as a keyway to kinda capitalize on this opportunity. There's a range of different ways that this can show up and really drive value for customers, either by automating different workflows and types of, you know, kind of manual content-centric work that was not automated before, as well as by displacing kinda legacy enterprise content management workflows.
That can be anything from manual contract reviews, RFP responses, employee onboarding, you know, loan applications that would normally take a lot of time internally, repetitive work or, you know, maybe you ship it off to a BPO firm or something like that, but either way, a lot of work and cost that we can, you know, completely automate for our customers. Then, there's another set of use cases in the legacy enterprise content management systems, which were not only not designed for end users, but they weren't architected for a world of AI either.
When you think about some of the different capabilities, what customers are looking to do with their content, there's significant limitations with what they might have in place today. Companies are increasingly recognizing how they manage their unstructured data is going to have a significant impact on their ability to capture the benefits of AI. Many of these customers rely on partners such as Slalom to help them modernize and to get from here to there. Right?
As you heard, in, you know, this recent conversation, we are seeing a much larger opportunity, you know, largely with partners and SIs than we ever have in the past, as well as a really exciting emerging marketplace opportunity because Box can now drive workloads for these partners in a way that we couldn't before, just given the nature of what we do and the different, you know, ways that you can monetize those services. We're already seeing signs of success across the partner ecosystem. If you think about our SI business book of business, that grew by about 40% this past year. Overall, when you exclude Japan, a little more than 20% of our business is influenced by partners.
For Enterprise Advanced, that's more like 40%, right? Because there's just much more opportunity for them to get involved. They have those strategic relationships, you know which customers are looking to to move over and can benefit from Enterprise Advanced. The importance and the impact of our partner ecosystem is only going to get larger and larger over time. When you combine the impact of these kind of incremental growth vectors with our strong underlying financial model and the business momentum we're already seeing, we are confident that we're on a clear path to achieve and sustain double-digit profitable growth in the years ahead.
Now I'm gonna dive into how we're thinking about our financial strategy, our capital allocation strategy, as we march toward our long-term target model, which positions us to generate significant shareholder value over a multi-year period. Starting with our path to improve revenue growth, we have several levers to drive that improvement. We've talked about many of them today. Going left to right, and maybe starting with upsells and seat expansion. As we discussed, we expect Enterprise Advanced to be a core driver of those improvements for both pricing uplifts and seat expansion, which would result in a net retention rate in the 105%-110% range, and an improvement to our overall growth rate of up to 3 points.
We expect to be able to deliver another point of incremental improvements through you know new customer acquisition that new logo dynamic that Olivia and Jeff were speaking to through both you know partners as well as deepening our penetration in emerging and under-penetrated markets. All those growth levers that we've talked about and high-value AI use cases that Enterprise Advanced enables in particular create tailwinds for our platform consumption business. That's where we see you know up to a couple of points of growth upside which is you know kind of the translation of that 30% CAGR and what it implies for that to become 10% of our business three to five years from now.
Putting it all together, given the momentum we're seeing, the early success of the growth investments that we've been making, we're increasingly confident in our ability to deliver double-digit growth. Given those opportunities and all that we've learned over the past year, we are targeting the mid to high end of that 10%-15% range of growth. We also expect to expand operating margin to 34%-37% over the next three to five years. We have a strong track record of delivering operating margin expansion at scale, and where we expect the future leverage to come from is generally an evolution and extension of a lot of the proven initiatives that are already well underway.
Starting, we talked about the infrastructure optimizations that we have been consistently driving for years and continue to do so even following that public cloud migration. On the workforce and location strategy, we expect that to deliver an additional up to three points of operating margin improvement. Today, we have about 20% of our total employee base in low-cost locations, nearly half of our engineering team, and we expect to continue driving those trends going forward to help us scale more efficiently. If you think about just the ways that we can leverage AI, that creates a huge opportunity to improve virtually every line item of our P&L. Right?
In some cases, that might show up as higher productivity, in others, it might show up as, you know, the efficiency that you get from automating certain manual work, deploying agents, and in some cases, shows up as hard dollar savings when you actually rip out, some existing spend, either with, you know, third-party services firms or, you know, existing, you know, software that that Box can now, you know, address itself or things like that. Just highlighting a few of these examples, and just briefly, you know, what some of those different things look like and how they show up.
On the go-to-market side, lot of different ways and really see this as a productivity driver, where the way that we are both creating content, optimizing campaigns, has led to really meaningful improvements in the return on investment of a lot of our demand gen dollars, and overall efficacy, which is leading to increased pipeline, higher win rates, and more targeted and successful expansion opportunities over this past year. If you turn to engineering, we've talked a lot about the, you know, kind of coding agents, which probably isn't, you know, too surprising, you know, given how quickly those took off within the market.
Not only does it extend, you know, across the software development life cycle, but it doesn't start and end with just engineering. There's also the security code reviews at the back end to make sure that, you know, we're not bottlenecking the business, that we can keep up with this increased innovation, remediate those vulnerabilities, and, you know, showing up in the workflow of developers across the business.
Finally, in addition to just really running our entire kind of internal service operation for our business partner organizations on Box Hubs, we've also seen really cool examples that really cover all three of those, you know, benefits I mentioned around AI, you know, through the contract lifecycle management solution we were able to build with Box with our newer capabilities, which went live just a couple of months ago and allowed us to rip out, you know, a few hundred thousand dollars of spend while also significantly improving the productivity, you know, and efficiency of that process. Now we'll turn to capital allocation, which is a key part, something we've been very focused on and disciplined about as a way to return capital to shareholders.
This strategy really begins with robust free cash flow generation. Right? When you think about the combination of double-digit top-line growth, consistent margin expansion, that's what leads to an expected free cash flow growth rate in the mid-teens over the next several years. That results in well over $1 billion of free cash flow that we expect to generate over the next few years, and really kind of underpinning the strategy. That'll be the primary focus of our capital allocation strategy.
At the same time, we will continue to opportunistically, you know, pursue ways and acquisitions to be able to accelerate our product innovation and roadmap. We are very focused on you know managing stock-based compensation as a percentage of revenue, our overall burn, and making sure that we're being very you know thoughtful about how we manage you know equity dilution over time. Providing a bit of detail on that, you can see how we expect this to evolve over the next several years, taking our stock-based compensation as a percentage of revenue down to the mid-teens, and significantly reducing our total shares outstanding, just as we did this past year.
That's really the combination of disciplined equity issuances, you know, really driving leverage through, you know, more metered headcount growth and our workforce location strategy, combined with a robust share repurchase program. Consistent with that strategy, today we are announcing a new share repurchase plan of $500 million, which we expect to utilize over the next 18 months. You know, this is really this expansion, which is much larger than we've traditionally done, really underscores our conviction in delivering against our growth and financial strategies and everything that we've been outlining today, as well as that we view our shares as undervalued. We've walked through the components of driving double-digit revenue growth.
Just to kinda recap and go through the full P&L top to bottom and how we expect that to evolve in the coming years. You know, as mentioned, we have a bias toward and are very focused on delivering a growth rate at the mid- to high end of that 10%-15% range. Again, that's consistent with the net retention rate in the 105%-110% range. On the gross margin side, we're gonna continue to optimize the way that we deliver service to our customers. Our target also contemplates being able to give more capabilities and deliver more of these high-value use cases to our customers while still maintaining a very strong gross margin profile.
Next, on the sales and marketing side, this is where we expect to deliver 2-3 points of incremental leverage in the coming years. We're gonna continue to invest in our go-to-market initiatives, especially because they've been working. We have a significant expansion opportunity ahead that we've talked about. But at the same time, both through AI and as some of these investments, you know, start to achieve scale, if you think about the leverage we can achieve, you know, through the partner ecosystem and with larger deals, we still are confident in being able to deliver efficiencies over time.
You can think about this category of our P&L as really the one that's, you know, most closely correlated with where we are, you know, kinda ultimately tracking on the revenue growth side, right? If we are at the higher end of that range, means these investments are probably working. We're gonna wanna lean in, pour more fuel on the fire. Conversely, at the lower end, you would expect to see higher levels of profitability. Next on the R&D side, that's where we expect to deliver 3-4 points of leverage in the coming years, which, you know, contemplates even incremental efficiency versus what we delivered or versus what we shared a year ago.
That's because, you know, just given the outsized impact and opportunity that we're seeing, you know, now with a little bit more data and time, you know, to roll out AI, is what gives us that confidence. Similarly, on the G&A side, expect that to come down by about a point to 7% of revenue, which is, you know, at the low end of the 7%-8% range we gave a year ago, for similar reasons as with R&D. Then putting it all together, that results in a roughly 10-point improvement in our revenue growth plus free cash flow margin expectation, for that time period, you know, going from the high 30s into the 45%-50% range by then.
I know we've thrown a lot of numbers at you today, but here are some of the key takeaways from all this. First, our strategy is working, right? We've been in a dynamic market environment, but the success that we're seeing in driving with Enterprise Advanced is, you know, delivering both strong underlying business momentum, and you know, higher growth rates and seeing our customer economics moving in the right direction. We have massive runway for further improvement. As we expand the capabilities of our Intelligent Content Management platform and as we execute against all the growth initiatives that we've been outlining today, we're confident that this will further fuel our accelerating growth rates, and the strength of our financial model going forward. We are well-positioned to create significant shareholder value.
Not only are we uniquely positioned as a beneficiary of AI because of the nature of what we do and how critical and impactful unstructured data is, but we also are doing a lot of things to improve our financial profile, have already been executing consistently an effective capital allocation strategy that kinda layering all those things together gives us a lot of confidence that we're gonna be able to generate significant value for our shareholders over a multi-year period. With that, and before we open it up for Q&A, I'm gonna turn it back over to Aaron briefly for closing comments.
All right. Thank you, Dylan. Let me grab that. Thank you. I think everybody has a sense now of the comprehensive strategy we laid out, and Dylan bringing us home on the financial front. I think the things that we just wanna reinforce, and then we're gonna open it up for Q&A in just a couple minutes. We're going after a massive market. This market is far bigger than what we initially even set out over the past, you know, maybe five-plus years as we really went to go after disrupting the traditional document and content management markets.
This expansion of agents being able to do work on content, and being able to start to generate more revenue that, again, previously would not have been software revenue, it wouldn't have been in the TAM of the document management or enterprise content management space. We're just seeing that continue to show up day in and day out at this point. That will show up in Enterprise Advanced expansion, it'll show up in AI units, and it'll show up in that API consumption. The market is getting bigger and bigger. We're opening up conversations across lines of business that Olivia mentioned, really into, again, new universes within the customer base. We're building the leading platform to power this entire revolution within our customers' environments.
When you just think about if you kind of put yourselves in the shoes of an enterprise, they're looking to deploy an agent strategy. Every single day, there's a new onslaught of a new technology, again, you know, from an AI system or an AI vendor. What's really critical is to be able to have a neutral platform that connects to all that innovation, that ensures that you can securely get access to your content in those agents, that you can do things like, extract, you know, mission-critical intelligence from those documents, be able to automate workflows with swarms of agents, and be able to accelerate that knowledge work. That's what our platform is building out.
As you heard from Jeff and Olivia, really, really focused on how do we now do this massive super cycle of upgrades, moving customers from Enterprise Plus or even other plans into Enterprise Advanced. Getting the customer base into Enterprise Advanced, we've seen empirically what that looks like in terms of price per seat improvement, but we're also seeing what that means in terms of expanding the seat population that we can go and serve. We're gonna do that with partners. We heard that great conversation earlier, where we're going to market with partners. That is getting us into both new conversations and new workloads in the organization, and driving much more transformational customer experiences.
You're gonna see this nice balance of obviously more seat revenue because there's so much within Enterprise Advanced, and that seat population will expand, but also new consumption and platform-oriented business models. That brings us to the holistic business model. We have a focus on driving double-digit growth. Our bias toward is toward that mid to high end of that range, with a focus on margin expansion as well. That combination delivering top-tier financial profile. This is the strategy that we're executing. I think if you look at FY 2026, holistically, this represents the momentum that we're seeing, the tailwinds that we're seeing in the market.
More and more agents for us, again, means more workloads on content, more use of content, more use of content and strategic workflows, and we're gonna go power all of that. With that, thank you so much for coming out here. We're gonna take a two-minute break and then come back for Q&A.
Great. It's good to be back. Thank you. For those in the audience, Autumn, you can raise your hand. She will be giving the mics to those in the audience. For those of you on our webcast, please email ir@box.com. You can put the question in the console on your webcast or also email me at cynthia@box.com. Why don't we go ahead and kick it off with the audience, and Aaron, I'll let you moderate.
Go for it.
Great. Josh Baer with Morgan Stanley. Thank you very much for the presentation. Couple questions on the consumption piece. One, I was hoping you could unpack some of the methodology that you're using to get to a 30% CAGR in the platform revenue, what types of adoption, usage assumptions are backing that growth rate. And then as a follow-up, in Olivia's presentation, there was, I think it was the HR customer and the ARR journey example, and it looked like ARR doubled for them roughly, after they turned on the AI units and the consumption. And I was hoping for more color on that specific use case. What are they doing with Box, and is that, you know, do you think that that could be common as far as the type of uplift?
Great. Maybe Dylan, if you want to take the first and then Olivia.
Thank you.
This is on. Great. Cool. Sure. On the platform consumption side, really looking at, you know, coming down to the unit side of things and how we expect to price those, we already have a pretty good sense, and especially with a lot of the long-term contracts for, you know, what our cost structure is gonna be like is the basis for it. I think where there is, you know, probably more of the question with what evolves over time is just how the number of customers, 'cause what we are seeing is, almost to your follow-up question, tends to be driven, and we see a lot of demand that is concentrated right now in a lot of these high volume really high value use cases.
Because for a lot of the basic use cases, we give allotments and wanna support those within Enterprise Advanced. It really is being driven by, you know, those power users, but ultimately comes down to that, you know, kinda consumption-based model, you know, bottoms-up build based on a lot of the trends that we're seeing and how we're thinking about, you know, pricing and driving that versus what's out there. That's, you know, what kinda gets you to the ARR, you know, that we expect to see over time. The, you know, kinda growth rate and percentage of revenue and everything is just kinda math from there.
Right. To add on to that, we see those use cases stacking within the enterprise. We're looking at what we've already seen happening and then extrapolating out from there. It's really exciting, especially when it comes to an example like the one you gave, right? That's a clear example of where the consumption is meaningfully adding to the overall monetization of that customer. You asked specifically about the use case, so it's a staffing company. Think large professional services, but specifically for HR folks that are put within these different companies. Previously, they had been looking at resumes manually, right?
There's actually not a really good way before you had metadata extraction to be reviewing incoming resumes and then placing the person based on their skill set and what the other company is looking for based on what's showing up in the resume. Basically, they automated all of that, and that became the workflow, and the metadata extraction is what's driving those AI units and the consumption that you saw in that graph.
Just to underscore my kinda closing point, but hearing it right next to an example, I think you know makes it more real. That was revenue that would just never have been content management revenue. It's a totally new monetization model for our kind of platform because, again, you're going after, in some cases, something where maybe the customer wasn't even paying for before for anybody to do. It was just manual work, kinda slowed down the process. In some cases, we have seen you know maybe some BPO offering get pulled into Box and from a spend standpoint. But there's a lot of revenue that will be just totally net new to software that is just an agent is now doing work on content in a workflow.
The thing that is you know somewhat important to kinda think about is our use cases will tend to be some of the heaviest sort of token consumptive type use cases because you're processing documents which might have, you know, tens or hundreds of pages of information per document. Imagine a client that has, you know, hundreds of thousands of loan agreements or contracts or clients that they onboard, all of that unstructured information that has to be reviewed and understood, an agent has to go process that. That's a very high volume type use case that we can go and monetize.
Hi, Matt Bullock from Bank of America. Thanks for doing this. Aaron, I wanted to ask you about the concept of agent swarms that you started the session off with. By the way, we might have to come up with a less menacing name for that.
Fortunately, we didn't brand that, so
There's been this concept that, you know, AI advancements have been progressing so rapidly that, you know, the degree to which enterprises can actually bring those advancements to bear has been lagging.
Yeah.
I wanted to ask you about what you're seeing, customers and enterprises deploying today, how far that lag is now that we have these advances in reasoning and ability to do multi-step workflows. How quickly, essentially, can Box and enterprises at large bring these agent swarms to bear beyond coding use cases?
Yeah. Maybe I'll provide some kind of rough kind of color, and then maybe Olivia, Jeff, anybody else wants to chime in based on recent customer conversations what we're seeing. I think that, you know, one of the tricky things is that we've seen coding take off massively with agents, and I think everybody sort of wants to hope that jump just jumps over to the rest of knowledge work, and the same kind of thing happens. Realistically, it's gonna take a bit longer than maybe some realize from the lab side, at least. I think anybody in the real world kinda sees, you know, our workflows are kinda messy. There's context coming in from lots of different places.
You know, coding as an example is sort of like the best case scenario 'cause it's effectively all text in, it's all text out. You know, you don't have other sources of information that you're having to really kinda deal with. The engineers are obviously really good at adopting new tools. Most of them are pretty well connected online. You jump to the real world where you have a loan processor or a banker doing due diligence or a law firm, and somehow you have to bridge, how do you get the model's context? How do you make sure the right information gets into the agent? How do you ensure that the tools, you know, make it really easy to adopt this?
How do you make sure that the security governance compliance of the things that the agent is reading or generating, you know, kind of work within the confines of that customer and their policies? That's the big opportunity. We're gonna be one of the companies that acts as one of those bridge companies into the real world. The innovation that you see happening in the labs, we're gonna make it so our customers can take advantage of all of that within a safe, secure environment that they can trust, and they can trust with their data, and give them choice with all of this innovation that's happening on top. I think it's gonna take a.
You know, we're gonna have to be somewhat patient with that diffusion of the technology into the rest of work, but it's gonna be. I think, you know, we're obviously betting it's gonna be platforms like Box that make that diffusion possible because you will need on the other end a platform that can go to a customer and say, "We, you know, are FINRA compliant. We manage all of your, you know, financial records. We're HIPAA compliant. We manage your healthcare documents. We're FedRAMP compliant. We manage your mission-critical government data." Now you can have agents work within that environment, work with the same security controls, the same access controls on that information.
I think we act as one of those catalysts that can bridge all the innovation, you're seeing happening in, you know, Silicon Valley with AI labs, with the real-world data environments that companies are dealing with. I think we're early. The use cases that you heard on stage today all represent either thing that we're already seeing kinda take off, like data extraction at scale. That's a big one. What you saw with Diego showing Box Automate and Box AI Agents, that is gonna be a very big deal because once you can describe your workflow, then you can deploy many agents within it. That gets you the thing that is sort of agent swarms within your business process. Again, you need that scaffolding. Without the scaffolding, you can't just have these agents run wild to go and actually execute your workflows.
That's where things like Box Automate, Box Extract, Box Apps comes into play. I think you'll see us as one of those natural platforms that make it possible for enterprises to adopt this. You know, folks, feel free to add.
That is the one thing that we hear from customers, which is, too many agents give them anxiety from a security perspective, right? What they wanna hear from Box, which is why they gravitate towards Box, is, "Okay, we get the security from Box." Actually, they would prefer it if these other agents from these other places are talking to the Box agent because then they know that their content is protected. That's more where they orient towards. I would say in a more loving way about agents, we have 90 Box agents, you know, 'cause we're doing living Box on Box all day long, and those are incredibly helpful, right? Obviously, we have the benefit of running agents on top of our own platform, but those agents are executing different tasks across the organization.
You saw it in the slide that Dylan presented, and they add tremendous value. That's a situation where an agent swarm is a wonderful thing, and we're deploying them daily and hourly, and we get so much value out of it.
I have another point that I think you've heard from Box every year about the neutrality that we present. But for these swarms of agents where there is no platform that creates agents for everybody in the universe, there are many types of agents. The heterogeneity of these agents that need to work with content requires having a choke point of control of governance. There are very few environments that can provide that. We have the fortunate scenario where security and governance can actually allow all of these agents to operate with content. I think the deployment of these agents from different vendors would depend also on a good control environment for them to run. We provide that.
Great. We'll take the next question from one of our virtual attendees, an investor. This is for Olivia and Jeff. How are customers reacting to all of this news flow that will come out, it's like on a weekly or daily basis, from OpenAI or from Anthropic? How are they digesting these announcements? What are your conversations with customers? Are they as reactive as the market seems to be?
Certain parts of the organization are consuming it as fast as they can. This is largely the development community. They're keenly aware of what innovations are coming. The rest of the business is, you know, has to run the business, right? There's no way they can consume.
There's no way they can consume it as fast as it's being released. Nor should they necessarily, because it's gotta be tested. It's gotta go through their own internal vetting, if you will, of this functionality. There's kind of a run the business part of this equation, and then there's like fast and furious innovation that comes at the teams within the organizations that are interacting with the models every day.
The good news is because we are constantly up to date, if not the first one out, often in the announcement of these new releases, that the customers just are confident that they honestly just don't need to think about it, right? It's there, it's in the dropdown menu. You can choose the latest and greatest and run with it. That's almost like the guarantee we provide.
Sorry, I know my name wasn't mentioned in that list, but I think the thing I would just add is this is presenting the real kind of existential risk of having your data in either a fragmented environment or not in a platform that is open and neutral. You can see, given the rate of change that's happening in the agent space, if you have somehow ended up with the wrong data architecture, you don't get to take advantage of all of those tailwinds. We've seen situations where, again, you lock your data in one particular platform architecture, and all of a sudden that content is not gonna be available to you in a variety of, you know, systems.
If you go into the Claude connector list and you can instantly see the content platforms that will work and the content platforms that won't work. There're only a couple content platforms that are gonna work with Claude, for instance. Then you go and you look at the ChatGPT list, and then you go and you look at the Salesforce Agentforce list. Very quickly, you're gonna end up with actually only one company that will work with all of the different systems that you wanna be able to work with. That company's obviously Box, and it's not gonna be a but it's certainly not gonna be a. I wasn't gonna leave you hanging there. Yeah. We're working on getting in that list. Okay.
But it's definitely not gonna be your legacy content management and infrastructure system. That's for sure. Architecturally, it's just a nightmare. That's gonna cause customers to say, "Okay, I need my content in the cloud. It's gotta be AI ready. It's gotta work with whatever the announcement was from Anthropic or OpenAI," then that's the platform that we're building.
Great. Lucky Schreiner with D.A. Davidson. With the emphasis on longer running tasks in the upcoming year, do you expect token costs to continue to decline from model providers longer term? Or as they potentially try to start monetizing their products more, is the expectation that those costs could be passed on to the customer?
Let's put Ben on the spot.
Yeah. One of the trends that we see is definitely more token usage over time because the longer running tasks they naturally use more tokens as in the more complexity, you know you think more, it often checks its work and so on. A couple things drive in the opposite direction though, including that the models are getting smarter, and so therefore the things that used to require the exceptionally good model, like the Opus style model, might be now more capable for like the Haiku model or Gemini, like Flash versus Pro, or our GPT Mini versus the more sophisticated models.
Over time, we are able to, say, utilize some of the, still very good models, but maybe some of the cheaper models as you see the cost prices go down. Additionally, we, both, the way that we do it in addition to the industry evolving, doing things like more caching of the tokens as you go through the whole thing, and being able to use sort of different models and for different kind of focused areas. All of these are sort of the optimizations that go into how to, keep costs under control even while you're using more and more tokens to get this kind of value.
Hey, Seth Gilbert with UBS. Thanks for the questions. I guess my first question is on the AI unit consumption, and do you envision all these customers will be Enterprise Advanced customers? Then, maybe as a quick follow-up to that, like how should we think about these power users today? Is it like, you know, maybe 1%, 5%, 10%? What could that look like in the fu ture? Thank you.
Yeah. I mean, I can just give you a rough framing if anybody wants to then chime in. We, you know, we wanna make the AI unit available to really anybody building on the platform.
If you're using kind of APIs into our platform for, you know, certain use cases, and you have a high volume workload, you know, we wanna make sure that's available to you. I think right now it's gonna, you know, generally lean more toward Enterprise Advanced use cases, just because those are the customers buying in the most. I think you'll see that. I think that also is correlated with why you're seeing maybe the power user adoption right now and why it's a bit of a power law in terms of the volume. I would expect that to get more diffused over time, and you'll just see, you know, workloads with many more of these use cases.
I would say that your question about the upgrade cycle, yes, we continue to see that. In fact, you know, that's kind of like the waves that our sales teams are moving in, right? Of course, we have the natural moment of renewal, but then also early renewals coming in as customers are getting really excited about that functionality.
Got it. Very helpful. Maybe as a follow-up, appreciate the bridge from 9% constant currency revenue today to 10%-15% longer term. At least before this Analyst Day, I don't know if the street was quite there at the 10% yet. Would love to know which, you know, could be a very good setup. Would love to know what you think is being mismodeled, misunderstood, or maybe sources of upside that the street's not thinking about. Thank you.
Yeah. I mean, I would say that,
It's hard to say exactly 'cause I know not a lot of models aren't necessarily structured, you know, that way with those discrete drivers. I would say that probably given the recent trends, when we talk to investors, I think there's much more of an understanding and appreciation for the pricing dynamics, just given the track record. It's pretty intuitive that as you're building these, you know, and rolling out Enterprise Advanced, we're seeing this uplift. We're seeing a greater than we had expected uplift, and overall momentum is strong. I think that is one that we're probably getting credit for. There is, I think, maybe a lack of appreciation for how different our seat growth dynamic is relative to many companies. There are obviously a lot of bear narratives about what happens to seat-based models.
I think with Box, because we have the kinda countervailing force of all of these extremely high-value use cases that will actually drive seats, and because, you know, we are more insulated from where there might be pockets of seat-based pressure because we don't yet, you know, we're not sold wall to wall in, many of our larger customers even, I think that dynamic is maybe underappreciated. You know, hopefully by sharing the data and some of the customers, there's a little bit of that. Then I think probably one that is just not maybe understood and probably baked into many models, be it, which it's early, is on the platform side.
I think why Box has a right to win in that space, I think folks are increasingly appreciating, you know, everything that, you know, the team was just talking about for the last 10 minutes. It's just a pretty new part, component of our model and most companies' models. I think that one is one that I don't know that people are underwriting as much would be my sense.
Okay, great. Steve Enders from Citi. I actually wanna follow- up on that last point, and then I guess have a follow-up on the model as well. I guess I wanna understand how you think about the Box agent differentiation versus other parts in the market, and everyone rolling out, you know, more advanced capabilities within their own LLM. Just how do you think about what's the Box right to win, where it makes sense, versus maybe some of the other model vendors?
Yeah. Maybe I'll do broad brushstrokes, and then maybe Diego and Ben, if you wanna jump in as well. I would say anything that deals with content, and then certainly especially the content within Box, you know, we will have the best agent to be able to solve content-related problems. If it's any kind of document processing, I need to look through, you know, 100 due diligence files or 1,000 contracts for a workflow, we will build the best agent for any of those kinds of processes. Now, it'll be powered by one of the leading models. It'll take all of the intelligence that you are now getting in something like Opus or getting in something like GPT- 5.4 or Gemini 3, you know, Pro.
That will be the kind of, you know, engine, you know, inside of our harness. But because of our tool use, because of our ability to understand our environment, because of the way that we've, you know, can organize content and give that context to the agent, we will handle those use cases just, yeah, again, kind of in an unparalleled way. That being said, strategically for us, we want, you know, agents to work with our data from any platform. And that could be either an external agent calling the Box agent to do work and then send back a result, or it could be using our APIs and our search service. Obviously, we'll monetize those differently depending on the use case.
Within the Box environment, that agent will be really good at doing content use cases. We believe that there's just a substantial amount of content-driven workflows where it makes sense to have a purpose-built agent that is super focused on content workflows. Again, we work in a very complementary way with every other agent platform out there and you know, maybe in a little bit of a different way than maybe some software providers. We kind of welcome all of the agents to leverage Box data and to kinda work with them. Maybe we'll monetize it in different ways, depending on the usage level and you know what the volume looks like. We want Box to be able to be connected to every agentic system out there.
I'll add quickly, like many of the customers we're working with right now, they'll maybe they're testing the AI capabilities in different systems, but they always ask us, "Can I please try to get this to work inside of Box using your agents? Because the data's there. We know Box is secure. We don't wanna add more security complexity by moving it out." For us, that's one of the dominant things is that getting our AI to work on the data inside of Box will just make customers less things to worry about from a security perspective.
One last point on this. The effectiveness of an agent does depend on the LLM. We work with all of them. We are at the forefront. We release at the same time as all the leading LLMs, but it's not sufficient. You also need to have the proper context, and that's where we have a major difference. The LLM alone may answer general questions, but specific to the enterprise, that needs to operate very close and very much on the content itself. That content gravity makes a huge difference. In addition to the point of having the latest and greatest, we have the proper governance and identity controls that gives a winning hand.
Okay, I lied. I'm gonna end with this also. 'Cause I think it's a really key point. Thank you Diego and Ben for getting me here to answer. You know, if you do things like, okay, I wanna have, you know, an agent generate code or for, you know, something like that, oftentimes you can get kinda like 90% of the value of just the model's intelligence for generating that code. It needs context about your code base. It needs to make sure that it understands your specifications, et cetera. That's kind of a low-scale data problem. Think about an enterprise environment where maybe you have a petabyte of content, and you need an agent to go and find the right thing to do, you know, work and execute off of.
You know, it's gonna be a gravitational force where the agent will wanna go, where it's easiest to access that information. Basically one of the largest repositories of enterprise content in the world, it makes a lot of sense for a lot of these use cases where I just wanna point an agent at the place that has all of the contracts. That's already gonna be in something like Box. That puts us in a really strong position for our native agent, as well as in a very complementary way with other agents.
Sorry, just on the model real quick. You know, just any way to kind of bridge the gap between here at 9% growth and the 10%-15%, and also just how do you think about the margin trajectory from here to the long-term model?
Bridging the gap between nine to-
Just the timeline. Just how do you think?
Oh.
about the timeline for, like, what the next three to five years looks like until we get to that three-to-five-year mark?
Yeah. I would say, I mean, you know, probably a lot of the dynamic is gonna come down to that interplay between growth rate improvements and margin expansion, really driven by the sales and marketing side, as discussed. You could think about, you know, kind of beyond this year, where we've kind of given guidance for that combined outcome to be fairly steady in terms of the improvement. You know, 200 basis points across, you know, a combination of growth and margin expansion. In any given year and what that actual mix shift looks like is probably gonna be a function of just how effectively these some of these growth investments are playing out and showing up on the top line.
Oh, am I next? Sorry. Brian Peterson from Raymond James. I love the stat on the 250 specific use cases you have for Enterprise Advanced. I'd love to understand, how many of those are kind of net new use cases for something they didn't have today versus something that you're replacing? And as you think about that going to 50%, are you enabling a lot of that white space, or is that something where you may be a more efficient way of displacing, like, an existing process?
That's a really good question. I think our SCs would kill us if we ask them to go re-inventory everything and answer that question. I'll tell you directionally what I think, and Jeff should chime in. For the most part, it's white space, right? Because they're able to solve problems that they really didn't have a way to do it hadn't occurred to them to do it, right? You know, like the resume review example I gave, it was just people reviewing resumes, right? That's what's been really exciting for us because once you pay for the platform, which obviously these customers have already done, then they see that 30% price uplift is pretty inconsequential when they stack up these use cases.
Now, we definitely have situations where it's a takeout, right? Like, we fully replace someone's ECM, right? Or we fully replace someone's DAM, or we fully replace someone's CLM. But it's that and we're filling in with all these white spaces. Jeff, you're out there. What would you say?
I'm glad you threw in the and because there's a ton of legacy out there still. There's a lot of homegrown, there's a lot of tech debt, if you will, around some of these use cases that with Box as a destination platform, there's a lot of consolidation and modernization happening in addition to net new use cases as well. It's definitely an and.
Great, everyone. I think that was it, and I will turn it back to Aaron. Oh, apologies.
Hi. How you doing? Rafi from Emmett Partners. Thanks for doing this again. Always enjoy being here. The question I wanted to ask, it's my second year here, so looking forward to the day when this room is just you know as a shareholder you want to come when the room is kinda empty, and then one day it gets you know blows into a big stage. Hopefully we make it there one day. When you see something like the Microsoft Copilot product come out, I guess I don't know it was a couple weeks ago. I think, I'm on the buy side, and I pitch Box to most people, and a lot of the conversations come back to Microsoft as the problem, that they'll eventually bundle.
You know, the history kinda repeats itself in a lot of ways. Why won't that happen with AI when you see things like Claude Cowork and eventually they're a little behind you, but eventually they catch up and eventually cause some pressure that ultimately hurts your long-term model?
Yeah. I've I mean, totally fair question, and we think about this a lot. Now first of all, we plug into Copilot from Microsoft as we would any other platform, just as we plugged into Microsoft Teams and other systems. I think there's sort of two ways to parse this. One is, why would a customer still use the Box Platform? In which case, almost none of the value proposition has changed because we often have better data security, better data governance.
We have a single file system as opposed to Microsoft sort of says, "Some of the data's in OneDrive, some of the data's in SharePoint, some of the data's in Azure." When you go into the real world and you say, "Hey, when you wanna go share content with your colleagues, how are you doing it?" It's a, you know kind of very messy environment with Microsoft that leads a lot of customers to come to Box. I think actually like the core value proposition is sort of no different for Box in a world of let's say Microsoft having more of the AI and we would just plug into that as another surface for doing AI work.
I would say, though, as a slight nuance, though, on the margin, I would say over the past 18 months, we're hearing customers move the other direction for a lot of their AI systems. We're hearing a lot more of OpenAI in the enterprise. Certainly, things like Claude Code and then Claude Cowork directly natively, I think is, you know, increasingly taking off. I think we're gonna be in a much more hybrid world at the agent layer within the enterprise than probably what Microsoft is used to in kind of prior eras of the IT architecture. Again, that's obviously great for us because we're very neutral to all those different platforms. I, you know, we kinda look at it like the I mean, like, the announcement was sort of expected.
You know, they had already done the Anthropic investment and partnership. Again, to us, it's just another thing that you're gonna say, "I need Box as one of the data sources to plug into." I think ultimately what we're seeing really in the market is customers are actually moving to a pretty dynamic model with other vendors than just the Microsoft stack. Cool. All right. I'm gonna close things up. Appreciate everybody taking the time today again. You're gonna see a lot of the products, you know, coming out. We, you know, Diego kind of walked through a bunch of innovation that you will see starting to roll out to customers over the coming kinda quarter plus.
We're very excited about the product roadmap that we're building. We're very excited about the customer expansion motion that we're seeing, and looking forward to, again, keeping everybody updated on all of the, you know, performance that we're driving. Thank you.