Box, Inc. (BOX) Earnings Call Transcript & Summary

Good afternoon. I'm Cynthia Hiponia, Vice President of Box Investor Relations, and welcome to BoxWorks 2025 Investor Product Briefing. Since our comprehensive product strategy briefing that we provided at our Financial Analyst Day in March, our product leadership and pace of innovation has continued. I'm sure you've seen our press release today regarding our exciting new product announcements. Today, we're excited to have our CEO and Co-Founder, Aaron Levie; our Chief Product Officer, Diego Dugatkin; our Chief Technology Officer, Ben Kus; our Vice President, Agentic AI Workflows, Kelash Kumar; and our Vice President, AI Security Compliance, Manoj Asnani. The leadership team will provide an overview of today's exciting news and do a deeper dive on our intelligent content management strategy. Then we'll have a live Q&A session at the end of the presentation. Feel free to send questions to [email protected] or enter them into the text chat box. During this presentation, we'll not be providing any financial updates, nor will we be able to address any financial questions during the Q&A portion. Please note that this presentation may contain forward-looking statements that involve risks, uncertainties and assumptions. Further information on these risk factors that could affect our forward-looking statements we make in this presentation can be found in the documents that we file with the Securities and Exchange Commission. With that, I'll turn the session over to Aaron Levie, Box Co-Founder and CEO. Aaron?

Hello. I'm Aaron Levie, CEO and Co-Founder of Box, and welcome to our BoxWorks Investor Product briefing. Just this morning, we announced at our BoxWorks 2025 conference, some of the biggest product announcements we've ever had as a company. And this afternoon, we wanted to share some of those key highlights with all of you as investors and analysts. We'll also be opening up for questions after these announcements. At Box, our mission is to power how the world works together. And we couldn't be prouder of our ability to do that for over 115,000 customers globally. We get to see all of the transformations happening of small start-ups that are rapidly growing to some of the world's largest companies across every single industry. And what all of these companies have in common is that AI is beginning to transform everything about how we work. Just imagine, if every employee in an organization had access to an analyst or researcher or an expert in a particular domain like legal, finance or sales that could work 1,000x faster than any other person. Think about all the ways that we would be able to use these agents to drive more productivity and accelerate our teams or individual productivity or businesses. Now imagine if those agents could run in the background. They could be involved in any kind of background task. They could be run in parallel. You can kick them off and wake up in the morning and see what they've done. That's a future where we're going to see vastly more AI agents than even people inside of organizations. And this is going to change every single aspect of work. Think about the individual productivity that we've already seen from AI. We can instantly review documents. We can look at contracts and find risky clauses. We can create presentations. We know that we can write code automatically. We can get expert analysis on our data. But the big impact is when you deploy those agents at scale in an organization across your entire business, that's what we can use to deliver broad organizational efficiency. This means you can onboard clients faster. We can get personalized marketing in any segment that we're going after. We can accelerate product development to ship more products to customers, and we can reduce business risks and bring more automation to our supply chains. This means that companies can sell to more customers, get more product to market faster and be able to scale much more quickly. Now the big impact of AI and AI agents is really on our underlying workflows. If you think about it, we've long been able to automate the processes and the workflows that run on structured data. That's the kind of data that goes into a CRM system, goes into an ERP system or an HR system. This is the data that goes into a structured database. But what's amazing, that's only about 10% of the data that we work with as an organization. And it only represents a small fraction of our overall work. Think about all of the business processes that we have in a company. And then think about how many of those are much more dynamic and involve people sharing data with one another or onboarding data into a process or reviewing information to figure out what next step it goes to. That's the vast majority of work that we do in an enterprise. It's launching a marketing campaign. It's doing clinical research in life sciences. It's processing claims in an insurance workflow. It's onboarding and working with clients and reviewing their data. And what all of this work has in common is it deals with unstructured data. And in fact, that's 90% of the data that we have in an enterprise, and it's the vast majority of our workflows. AI now for the first time ever, lets us begin to tap into the value of all of that unstructured data, where we can now automate any workflow and get insights into that information at scale. We could never have done this before we had AI. So think about the kinds of workflows and processes that we can now transform. We can accelerate product development processes with the power of AI agents. We can use AI agents to surface insights and get new discoveries from clinical trials and medical breakthroughs. We can use AI agents to be able to accelerate account opening and client engagement so we can streamline what sometimes takes days or weeks inside of a bank and turn that into minutes. The challenge is that most enterprises can't yet tap into the full value of their unstructured data to actually get the benefits of this automation. If you think about it, where is most data today across a lot of organizations? It's in all of these data repositories on-prem, sometimes in the cloud, legacy systems or point tools, document management systems, collaboration technology, storage infrastructure. And these technologies have long been a problem for most organizations. And it's generally always been a major pain because it creates security risks. It means you're duplicating data, so you're spending more on technology. And you have workflows that really get broken because people have to hop between different systems. So it's always been a painful challenge for organizations, but now it's actually existential. Think about a world where you have 100x more AI agents that are roaming around and need access to information to make decisions or to automate workflows or to enable an employee to get access to the right answer or the right piece of data to do their job. Well, now imagine that fragmented IT architecture with a mix of legacy systems or point tools. Think about the kinds of challenges that will get created when you deploy an AI strategy in this environment. The first is that you'll have agents that just work with the wrong information. They'll be working with out-of-date data. Somebody won't have kept a copy up to date and an agent will go find it and use that for an answer. You'll have agents that leak information. So this is a scenario where you don't have permissions that are in sync between multiple systems or somebody has overexposure or access to data. And now an agent is actually exposing answers that it shouldn't to an employee. And then finally, if you have a mix of systems that don't play nicely with each other, maybe a model provider works differently in one environment than another, then agents can't actually work effectively across systems. You're not able to tap into the power of all the amazing breakthroughs happening in the AI space with your unstructured data. So as companies go AI-first more and more, their data architecture and in particular, unstructured data architecture, represents an existential challenge if they don't get this right. So enterprises need a platform that can connect content to AI securely and then integrate across all of their applications. And this is the intelligent content management platform from Box. At the center of our platform is content. This is all we think about. We have a single file system that manages data for the enterprise. There's not multiple fragmented environments. People don't have to think about personal storage repositories and then team or broad site repositories. It's a single platform that manages content across the entire life cycle. We then bring all of the relevant capabilities to that content. Of course, the storage, the sharing, the collaboration, the publishing, the e-signature and workflow automation. And then we have an AI platform layer that brings the full power of all of the leading AI models, the vector embeddings, the retrieval augmented generation and the ability to build agents on data, all in a single platform that makes this incredibly easy for any end user, IT admin or developer to tap into. And then we integrate all of this technology and the customers' data across their entire tech stack. This could be with products like IBM's watsonx Orchestrate. It could be inside of Salesforce with Agentforce. It could be in Slack or ServiceNow in their Agent Fabric, Microsoft Teams or any other product that a customer is working within. And more and more with our no-code app builder, customers can also build any kind of content-oriented application on top of the Box platform incredibly easily. So this is the intelligent content management platform from Box, a single place to store, manage, share and secure the most important unstructured data in the enterprise with an agentic layer on top that ensures that you don't have to move data around between lots of different systems, but instead, those platforms can talk to the Box APIs or agents to access the most important information from that customer. Now the companies that thrive in the AI-first era will be those that are able to take full advantage of their unstructured data and information at scale. And today, we're incredibly excited to have announced some of the biggest breakthroughs in our platform's history. So today, we are going to revolutionize how we work with our unstructured data and to share a bit more about our overall platform strategy and where we're going, I'm going to bring up Diego, Box's Chief Product Officer.

Thanks, Aaron. Hello all. I'm Diego Dugatkin, Chief Product Officer at Box. As Aaron shared, the potential for AI to transform enterprises' performance is huge, but it depends on their ability to use AI securely with their unstructured data and the right context. At Box, we have been addressing these issues in 3 keyways. First, making AI governance security and compliance seamless. Second, making all major models have the correct data and context so customers can choose the best model for them. Third, combining content workflow capabilities and AI to give our customers the best AI agents for content. Since our Financial Analyst Day in March, we have delivered on our road map with significant releases across our portfolio. We have added the latest AI models and empowered multi-doc query and new formats. We launched Box Archive and new security and governance enhancements. Box AI for Hubs has been enhanced as well as our core Hubs functionality and sign compliance. We have also worked across the AI ecosystem to ensure Box AI content capabilities are embedded through robust APIs and key partners. This has been a huge delivery from all the teams at Box. I'm especially proud of our Enhanced Extract Agent. This is critical for understanding large quantities of unstructured data and making it actionable. Box Extract works across multiple formats and at scale, and it's a foundational tool for unlocking new content processes. In April, we also released Box Archive. Effective archives are a key tool in regulated industries. Now with AI, good content hygiene is even more important where we need to ensure that only relevant data is accessible to improve accuracy and to avoid leaks. Finally, mission-critical business processes currently run across multiple systems. And so as these platforms develop agents with deep specialization, we foresee multi-agent workflows. This is why we have been enhancing our AI APIs with the integration of Box AI and key partners like Salesforce Agentforce, Microsoft and ServiceNow. Bringing this all together, the Box Intelligent content Management platform is designed to provide all the enterprise-grade capabilities for AI on unstructured content now and in the future. With today's announcement, you're about to see how this comes to life through new agents with deep interoperability, powerful agentic workflows and advanced security features. Now I'll hand it over to the product leads to take you through each one of these areas in more detail, starting with Ben Kus, Box's Chief Technology Officer. Ben?

Thanks, Diego. I'm Ben Kus. I'm Chief Technology Officer at Box, and I'm here today to talk about our AI platform. Now as Aaron mentioned, the potential for AI in an enterprise is huge, but AI needs enterprise context to be successful. Many of our customers tell us they don't actually have an AI problem, they have a data problem. And for customers who have tried to go and solve problems themselves, they've run into a series of challenges, keeping up with things like security and compliance concerns, preparing the data for AI, making sure that AI can find the data that it needs, making sure that AI is accurate and reliable and more. And while this is happening, AI continues to advance at a tremendous rate. In the last 12 months, there have been 15 new releases of AI models. Many of these models potentially were the greatest piece of software that's ever been released in the history of civilization. So you kind of want to take them seriously and make sure you incorporate their latest technologies. Meanwhile, attackers are getting more sophisticated at using AI and attacking AI, trying to get access to your data. And this is why you need to continue to be vigilant to make sure that AI doesn't turn into a data leakage challenge. This is why we built our Box AI platform. Our Box AI platform is built on the underlying aspect of the content management platform that our customers have loved for over 20 years. including unlimited storage, internal external collaboration, integration with workflow, metadata, all done very securely and more. And on top of that, we layer in the ability to do enterprise-grade AI, where our customers are control of the AI, it uses trusted models, making sure that everything that you do is safe and permission-aware. And then on top of that, we then layer in our AI content foundation. This is where we provide the idea of model flexibility, including using our OpenAI GPT models, our Gemini models, our Anthropic models, our Llama models and more. Adding to that, we have the ability to prepare the data using OCR, using vector embeddings so that the AI can find what it was looking for using our secure RAG mechanisms. And on that, we also add the idea of our new agentic platform. This is where you are able to have AI agents to help you work for you, including data extraction, making sure that AI can find insights, do research, help you then also accomplish these workflows and more. And all this together then powers not only our application for people who use Box every day, but then also our integrations for applications that use data inside of Box. And so that customers don't need to rebuild all these capabilities themselves, we allow customers to build on top of these using our APIs or MCP server so that they can utilize Box from custom applications. If we go one step deeper into what our AI agents do, they are powered by the same AI models, but they're also objective-driven. You give them custom instructions so they can perform the task that you're looking for, including the different tools that they need to accomplish their task and all underlying powered by Box context. And this context is critical because this is what then enables your agents to do the kind of specific work in your organization to accomplish the task that you need. At Box, we specialize in unstructured data agents so that you can get the most out of all of your unstructured data. But we also work in an AI agentic ecosystem where we're also able to use the power of different agents from other enterprise platforms so that you're able to get the best out of all agents inside of your organization. And we're proud to have released our Box MCP server. This standardizes the approach to being able to have AI agents reach in the Box to be able to use all these capabilities in a way that's super easy for custom applications, integrations and more. All these capabilities underpin our AI agents, our agentic workflows, security and more. And we're proud to be releasing our Box AI Foundation agents. These include things like our Extract Agent that helps you structure your unstructured data, our new search agent, our new research agent and more. And all of this is customizable inside of our Box AI Studio. You can build custom agents. You can now add knowledge so that the agents can reference material in addition to selecting your model, testing it in your secure environment. And with that, let me turn it over to Kelash, who will tell you more about Box Automation.

Thanks, Ben. We've now seen the power of AI agents. Let's dive into how we use agents to extract context and information from content. There are so many different types of content in an organization. For example, a legal team might be dealing with agreements with over 100 pages of legalese. They'd want to know what terms and conditions are in there. Our marketing team needs to review all assets for a specific ad campaign to ensure that the look and feel and the color palette used is on brand or there might be scanned documents with handwritten annotations, tables, stamps or images that help confirm important shipments. This applies to different industries as well. For example, for someone in the lending business, accurately extracting customer information and account numbers from bank statements speeds up loan processing, driving higher loan volume and a better customer experience. Such documents have an enormous amount of critical information trapped in them. And accessing this information is the key to unlocking productivity at scale. Your mission-critical processes need highly accurate data extraction that's reliable and consistent. And to address that need, I'm excited to announce Box Extract. Box Extract simplifies the process of data extraction at scale and enables an enterprise to get their content AI-ready. Our customers have already been using our Extract Agents via our APIs. Now everyone in the org can set up and manage extraction processes through an easy-to-use interface. Box Extract enables enterprises to customize our extraction agents to maximize the accuracy on their specific content. Then they centrally manage these processes and deploy them at scale on a wide variety of content. And finally, Box Extract provides customers the AI tools to get reliable and consistent results. At its core, Box Extract uses agentic reasoning to understand the document and extract information. These agents are powered by our standard and enhanced agents that many of our customers already use. We then use advanced techniques to enable higher accuracy and reliability in the process. AI needs to be accurate to be helpful. This enables users to handle everything from extracting data points from simple text documents to inferencing information from long complex documents, all the way to multimodal assets like images. Let's take Box Extract in action.

For this demo, I'll be playing the role of a contract manager for a large retailer. Our team receives hundreds of thousands of vendor contracts for all the different brands that sell merchandise at our stores. We needed a smarter way to extract data from these contracts so that we can manage them effectively. That's where Box comes in. To get started, we'll create a new custom Extract Agent to automatically capture key data from our vendor contracts. A challenge with these contracts is that since they are created by other companies, each one can be quite different in formatting and content. Take the contract end date, for example. Some of our vendors clearly state an end date in their contracts, while others do not. Fortunately, I can solve this by giving Box Extract custom instructions that if end date isn't explicitly mentioned, calculate it by combining the start date with the contract duration and check the terms and conditions for any additional information. Since these vendor contracts can be long and complex to parse, I'm going to use the Enhanced Extract Agent because it leverages powerful models and can handle more advanced data extraction jobs. As the final step of the configuration, I'll specify which folders this Extract Agent will act on. Let's see the Extract Agent in action. I'm going to upload a new vendor contract into the intake folder. As you can see, the Box Extract agent that we just set up has already extracted the necessary information. Now that I've extracted data from my vendor contracts, I can use it to analyze them. That's where Box Apps comes in. Box Apps are customizable, require no coding and give my team a data-rich view into all our critical documents so we can make better decisions. Let's switch personas here. Now I'm a regional manager for this large retailer. It's my job to monitor vendor contracts, ensuring that my stores have the right inventory to meet customer demand. My IT team created a vendor contracts app, allowing me to easily manage all the contracts in my box account from one place and perfect timing because I've noticed that sales for home fitness gadgets have been declining in our California stores. I need to find all active contracts with vendors that supply our California stores with products in the home fitness category. Box Apps makes that so easy. My vendor app has a chart that categorizes contracts by status. So I click on this bar chart to filter to active contracts and sort from there. However, Box AI allows me to take it a step further. Instead of filtering, I can simply ask AI to show me vendors that ship home fitness items to California. Here we go. I found 15 contracts that fit that criteria. Now I'm wondering, do any of these contracts have terms that will allow me to cancel or renegotiate the order quantity? Rather than go through each contract one by one and read all the policy terms, I'll just ask my contract analysis AI agent of these contracts, which allow for cancellation or reduction in quantity. Our company created this custom agent to read and interpret vendor contract terms. The agent analyzes the contracts in this view and reports back that 3 out of the 15 have favorable terms, which will allow me to reduce the order. Right from here, I can securely share these contracts with the vendor manager who handles contract negotiation. As you've seen, Box Apps and Box Extract accelerate your business.

Thanks, Julia. We are already seeing incredible outcomes at customers using our data extraction. For example, Valmark Financial is now able to extract over 250,000 data points from complex insurance policies for downstream processing. In comparison, the previous solution was able to pull out about 4,000 data points. That is a 60x increase in insights from their content. A leading investment firm was preparing for an impending audit and faced the daunting challenge of categorizing and identifying key information from the client-related documents. With Box Extract, they were able to process over 3.8 million pages in 1 weekend. And it's not only our customers who are loving Box Extract. I'm thrilled to announce that DataBank, a leading provider of process automation and data solutions that processes over 1 billion records a year, will be using Box Extract to power their new paper-to-digital scanning technology. Let's switch gears to enterprise workflows. What happens when you want to use the extracted information to make decisions or power a process? What if you want to use agents as part of these workflows? We are now in the era of agentic workflows where agents and teams need to work together. That's why it comes as no surprise that 1,500 IT leaders chose the redesign of workflows as the #1 contributor to their ability to see revenue impact from the use of AI. These leaders and enterprises as a whole are fundamentally rethinking how work gets done. When it comes to content-based processes like onboarding, case management, contracts, asset management and many more, AI has the potential to make the whole process intelligent, faster and more efficient. And to realize that vision, enterprises need a way to design and execute these agentic workflows that bring together its content, AI and people. And that is exactly why I'm incredibly excited to announce Box Automate. Box Automate is an agentic workflow automation built natively in Box. It automates content-based workflows across your teams and AI agents. Automate enables you to orchestrate work across agents and teams with an easy-to-use drag-and-drop builder. With the ability to customize agents for workflows, you are able to provide critical context to agents and ensure that you get consistent, reliable outcomes every time. And Box Automate enables you to extend your workflows wherever your work is happening, whether that is within Box or your other applications. And to translate this process efficiency into business outcomes, we launched Box Apps earlier this year. Box Apps enable you to build intelligent no-code apps so you can manage your processes in one place. These apps bring together all elements of a business, content, metadata, workflows, users and now agents, all with the security and compliance of Box. Here's an app that our customers' marketing team uses to manage their digital assets and provide up-to-date images to their field. These assets are curated and tagged with AI. They go through a publishing workflow and are available to everyone in the company with powerful search and retrieval capabilities. The company not only saved on buying a point solution, they now have a fully integrated way to manage their digital assets, all on Box. Here's the property management app that a real estate company built to manage all their leases across the U.S. They extract critical information from their leases, route them for approvals and provide a centralized place to search and find this content. This enabled them to retire a dedicated contract management system, which was creating security and compliance issues as a separate content silo. Let's see automate and apps in action.

I'll be playing the role of a manager who oversees a team of loan officers. My team spends most of their time reviewing loan applications and all the supporting documentation to determine which ones to approve. But I didn't hire them to read through mountains of paperwork. I hire them to make smart decisions. I know AI agents can help. Let's see how I can use Box Automate to optimize this entire process from application to approval. I'll start from a blank slate and drag in the first step, a form trigger. Next up, I'll add in some AI agents to automate work previously done entirely by my loan officers. We've got the Extract Agent that pulls data from the loan application and verifies that all the required information is there. There's a risk assessment AI agent that helps loan officers determine if the application meets our company's risk thresholds. Finally, I added some Box 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. Also, 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 ask 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 through 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 Automate have revolutionized my team's operations by handling the document processing, analysis and assessment in accordance with our company's guidelines. And this doesn't just happen once. Every time a loan application is submitted, my team of AI agents is there to lend a helping hand.

Thank you. Next, I'd like to welcome Manoj.

Thank you, Kelash. As you've all been hearing, Box is integrating AI throughout the entire content life cycle. And as many of you could guess, this also includes security and governance. At Box, we work hard to remain the most trusted platform for creating and collaborating on your most critical content. With our products such as Box Shield and Box Governance, we've delivered security solutions across the content life cycle from our powerful malware and anomaly detection tools to classification labels driving access controls and retention policies with the new features like content recovery and Box Archive. But as we know, security is a moving target. The scale and volume of content continue to grow at a rapid pace. This is further complicated by the threats getting more complex and sophisticated. AI agents offer organizations a chance to scale their efforts beyond anything we've ever seen before. It can empower security teams to extend their reach and knowledge and supercharge their capacity to secure their organization's content. AI can now help with a deeper and nuanced understanding of our content, identify its level of sensitivity and security needs and have intelligence and automation to secure it appropriately. It can also help SecOps teams not just see and understand threats, but also help them react quickly and multiply their own capabilities to keep ahead. As you heard, we're using AI to obtain deeper insight into content and use it to enhance productivity. Now we'll be leveraging AI to better secure your organizations and empower your security teams. With that in mind, I'm thrilled to announce Box Shield Pro, a suite of our new agentic security solutions built to help organizations securely navigate the AI-powered world. Shield Pro applies deep AI insight towards better securing content, detecting threats and optimizing security operations. It significantly increases the security team's reach and coverage for classifying content with a classification agent, helps them with analyzing and responding to alerts in an efficient manner with a threat analysis agent and detects and protects organizations from ransomware activity. Box Shield Pro is an active beta right now and will be available as an easy upgrade to Box Shield. Let's first talk about the classification agent and how it's delivering step function improvement and increasing the reach and understanding and securing of the content. To have the best content protection capabilities, you want to be able to understand what the content is about and its sensitivity to the organization. Let's take an example of a technical architecture document. It contains deep technical IP for the company, but not necessarily anything that is obviously sensitive such as PII. Using traditional solutions, distinguishing this important and sensitive technical document from something public-facing like a data sheet would be a difficult and manual process. That is just one example of content that is not straightforward to classify based on rules. Organizations need a solution that takes a context-driven approach, determining the sensitivity of each piece of content and thus classifying all the content that enterprises need to secure. And our classification agent does exactly that. The classification labels intelligently applied by the classification agent carry with them a variety of security and governance controls such as smart access controls, which can limit who can do what with the content, retention and disposition policies that manage how content is governed and watermarking to help distinguish sensitivity to the end users. Our classification agent represents Box solution to complex content security challenges, starting from customized prompts and applying security across the content of the entire organization. Now I've told you a lot about what this agent can do, but let's take a look at it in action. Over to you, Julia.

Today, we'll take a look at how a manufacturing company called iniTECH leverages the classification agent to enhance their security posture. iniTECH designs tech products and uses their Box account to store sensitive information about their widgets. A product manager is about to upload documents into Box related to their product, Atlas. Some of these documents are technical papers explaining the inner workings of the Atlas cutting edge design and some of these documents are technical papers explaining the inner workings of the Atlas cutting edge design and some of these documents are just how-to guides that can be distributed publicly. Can you tell which is which? Whoever created these files did not use helpful file names. How can iniTECH ensure that the sensitive technical papers aren't leaked externally? Fortunately, iniTECH uses Box Shield classifications and access policies to protect sensitive content. However, they can't always rely on end users to add the proper security classifications to content. And since technical papers don't always contain the same keywords, they're difficult to automatically find and classify. This is a challenging nuance for most classification tools to understand. But with the Classification Agent, the security admin can write classification policies in natural language, describing exactly what types of documents the tool needs to classify. AI classification is perfect for this sort of challenge because it relies on more than keywords and specific identifiers. It inspects a context of the document based on custom criteria to determine the correct classification. The security admin at iniTECH wants technical papers to be classified with the confidential label. So they type out that documents classified as confidential should include engineering designs and technical documents, but these product designs aren't the only documents that iniTECH considers confidential. They also don't want to leave documents with PII, financial data or information about mergers and acquisitions. The security admin is able to define all of these prompts and test the behavior before enabling them. Simply by defining what confidential means to them, iniTECH is able to monitor a large surface area of their organization's unstructured data, more effectively labeling and protecting it from security threats. With the new policy in place, now when the end user uploads these documents into Box, the technical papers are automatically detected and labeled as confidential. Importantly, Box captures audit details, including the rationale for the classification decision and when the classification action happened. In this case, the technical paper was given the confidential label, meaning the document now carries all the associated security controls. This file can't be shared externally or downloaded. As you can see, the classification agent intelligently finds and classifies confidential information across your Box account. Instead of solely relying on the presence of keywords or identifiers, the classification agent uses custom instructions to protect sensitive data based on real context, allowing companies to protect what matters most without slowing the business down.

Thanks, Julia. That was amazing. And our customers can't wait. Customers such as the County of Santa Barbara, Office of Public Defender, sees significant value in deploying these agents. And our early beta customers agree as well. They have seen a 10x increase in the amount and the kinds of documents they can classify and, therefore, secure. Let's also touch on how we're using AI to make security operations teams more effective with the threat analysis agent. As content increases at scale, the volume of threats security teams need to handle increases as well. On average, they deal with 1.3 million threats every year. Processing threats requires investigation and analysis to fully understand why the events were flagged and what action needs to be taken. That can represent a huge amount of time, leaving security teams with forced to make hard trade-offs. And we're addressing this challenge with our new threat analysis agent. Threat analysis agent takes large, detail-filled threat alerts that can be difficult to pass and refines them down into simple straightforward summaries using Box AI. By distilling these alerts to the crucial information and context using easy-to-parse language, security operations teams can accelerate remediation and better communicate on threat events to the rest of the organization. From our new security agents helping classify content, accelerate security operations and keeping ahead of threats to securing, governing and providing visibility into new type of user that is agents, Box is committed to helping organizations keep their most critical content secure. That's it for me. Let me hand it back to Aaron now, who's going to wrap it up.

Thanks, Manoj, and thank you to the entire product team for all of this amazing innovation. Hopefully, you have a clear sense of how these product capabilities come together to fundamentally transform how companies work with their most important unstructured information and with agents right at the center of all of that. Now to deliver this innovation to customers, we are continuing to double down on our Enterprise Advanced plan. The all-new AI agent-powered Box Extract will be in Enterprise Advanced. Our agentic workflow automation capabilities will show up in Enterprise Advanced. Our new enhancements to our custom AI agent builder or new advancements to our no-code application development will all be in Enterprise Advanced. This will help us continue to drive more upgrades and more momentum and acceleration of our most powerful plan yet. We also announced Box Shield Pro, which includes multiple capabilities to help customers take advantage of AI to help protect and secure their most important data in Box. And this will be available as a separate stand-alone SKU for those customers. So a ton of innovation now available in the hands of our customers with individual rollouts for these products over the coming quarters. And we're incredibly excited to continue to transform how our customers work with their enterprise content. And with that, looking forward to opening up for questions to all investors and analysts. Thank you.

You think about the pace of adoption for AI solutions and AI agents on the Box platform, can you talk about the delineation you are seeing for adoption by customer size? Is success coming from larger customers? And also, any specific industries you'd call out seeing the highest level of adoption for AI on the platform?

Sure. I can maybe take that and then, Diego, if you're seeing additional trends you want to mention. But overall, I think we're seeing broad-based adoption across various customer segment sizes. You have a dynamic, obviously, by volume of customers. It's more weighted towards the smaller companies just because there's more of those within the customer base. But we are seeing some fairly transformational use cases in larger enterprises as well. We have customers that are doing everything from wanting to do data extraction across large workloads of their content, customers building custom agents that are going to help them with things like data generation or answering questions on particular knowledge bases within the company. And that's happening in companies of all sizes. So we're seeing great traction across really most industries at this point with, I think, extra emphasis on things like public sector, financial services, life sciences, areas where we already have natural differentiation because we can offer customers a more compliant, secure AI experience on their unstructured data.

Only a couple of things to add. So we see also a lot of traction in Japan, but it's a global trend. And we also see a lot of metadata extraction in combination with AI for very interesting workflows that basically combine the 2.

George Kurosawa from Citi. Thanks for putting this event together. I wanted to ask about the AI world that you see coming here and particularly where you see Box's role in terms of what types of use cases do you feel it makes sense for Box agents to handle within the Box ecosystem versus third-party agents doing the work outside of Box, but maybe leveraging Box data.

Yes. Maybe I'll frame it up, and then I think maybe Ben, from a platform standpoint, can build on this. But one of the things that we've seen is how important context is for AI agents. And this is obviously a core theme of our keynote today, the broader keynote, which is to get agents to really execute on a wide variety of knowledge worker tasks, an M&A deal review, an insurance claim process, a contract life cycle management process. The context that those agents have around that workflow is basically are the determining factor as to whether that agent can effectively automate that workflow or not. And where most of that context is actually going to come from is from the customers' unstructured data. So by virtue of how powerful your unstructured data has become in a world of AI agents, we actually think we're the natural place for a large set of agents to get created for many core knowledge worker tasks. For instance, if you had your sales team inside of an organization want to instantly get feedback on a deal that they're doing based on all of the prior sales materials and training information that you had as a company, well, Box is a very natural place where that data might already be living and then you would build a custom agent in the Box AI Studio to let you go do that. Similarly, if you had an AI agent that you wanted to create to review contracts coming in for all of your critical legal clauses and then be able to extract data and maybe automate a part of that workflow, every one of those -- the underlying context for that process would be something that Box could provide effectively out of the box. So we actually think that our role increases in criticality within the enterprise stack when you think about what an agentic world might look like. And then what we want to do is continue to then federate our agents into all of the other systems that customers are working in. For some reasons -- for some situations, that would be because the user interface is actually where maybe the employee is living, like if you're in Salesforce as a sales rep, you're going to want to talk to your sales unstructured information that might be living in Box, but through a Salesforce interface, and we're totally comfortable with that. But that's kind of the architecture that we're seeing play out in a lot of the agent use cases. Ben, I don't know if you want to share what you're seeing from customers or the platform overall?

Yes. I think whenever you have AI agents that need to do specialized work for you, it turns out that like there's a lot of work for every platform of different types, HR systems, CRM systems, structured data systems, unstructured data systems. You need to get your AI agents to specialize in doing that and doing it really well. And then -- so we start to see that all of our partners start to develop their own agents. And then so as part of an AI ecosystem, we work with them. And so we'd have our agents talking to the Salesforce agents or talking to agents from these other systems. And that is, we believe, the emerging way by which these enterprise platforms are talking to each other. So then our role is to provide the AI capabilities on unstructured data, but also just provide specialized AI agents that can then be called the MCP, via A2A, via API for our customers and our partners to call.

That's great. Maybe one more, if I may. You showed right at the end of your prepared materials that it seems like most of the new innovations you're announcing are included in Enterprise Advanced. -- from a pricing and monetization perspective, does that imply maybe the realized pricing on Enterprise Advanced adoption may be a little higher than what you had previously talked about? Or is this more of a carrot to speed up upgrades?

Yes. I think we would still stick to that kind of 20% to 40% uplift framing from Enterprise Plus, and we've continued to see that from the first 2 quarters, where the plan has been available. We want to just keep doubling down on the momentum of Enterprise Advanced. We're starting to build up more of the kind of steam for our sales force, really in every customer conversation, highlighting the capabilities of Enterprise Advanced. So we want to keep all of the focus on that. And we're just, I think, at the very beginning of an upgrade cycle that we think will have a lot of legs to it. And we -- obviously, the more value that we can create there, hopefully, on the upper end of that 20% to 40% range is what gets realized. But yes, there's a lot of excitement for the plan right now.

Lucas Cerisola, Morgan Stanley, here on behalf of Josh Baer. I wanted to talk about the outlook for seat growth as Box AI becomes more popular amongst your customers. On one hand, you have the expanded use cases being a tailwind for more usage across different divisions, et cetera. And then on the other hand, the efficiencies that people may see could lead to fewer seats in-house and maybe the dynamic there? And then to follow up, which verticals do you see having more traction with Box AI products? Is there more people looking for growth or efficiency?

Yes. So I think we're -- so we're certainly focused on both, actually, really kind of 3 dimensions. So price per seat increases because of Enterprise Advanced, seat expansion because of the use cases that we can now kind of increase the value proposition of Box for and then a consumption dimension, which is this consumption of AI units for any high-volume AI task. So as an example, you actually saw a mix of all 3 of those today with BoxWorks. Obviously, the Enterprise Advanced plan brings in our workflow, agentic workflow automation capabilities and data extraction capabilities and Box Apps. So that would be the price per seat increase. The seat expansion, you saw a lot of use cases, especially in KK's presentation or Kelash's presentation, which were lots of lines of business, the marketing team, the sales team, the legal team could all expand their use cases with Box because of the combination of automation and apps. So that gives you the seat expansion. And then with things like data extraction agents or building custom agents that you're going to run over and over again in a workflow automation process, that deals within consumption of AI units. So you saw a mix of basically 3 vectors in which we can expand revenue over time. We're not that worried about the sort of seat compression dynamic that I think is maybe out there in the conversation simply because we are in the tens of millions of seat scale as a platform, and we're really -- the potential market for Box is hundreds of millions of knowledge workers, billion knowledge workers kind of scale demographic. So I don't think that a seat compression in one area of a business or other would really kind of impact the overall seat dynamics in our business. And in fact, if anything, what's happening is by virtue of AI agents, we're actually seeing the expansion in seat areas that we wouldn't have sold into before because now all of a sudden in a business, again, the legal team might now have a use for box because we can have agents that read their documents and extract metadata from contracts, which then opens up actually more use cases. So I think for the -- any reasonable future that we could possibly model or think about, we would be expanding on all 3 of those dimensions as a result of AI and the benefits that we're bringing our customers. And then on your question of kind of where we're seeing the potential and demand, I think it's happening both for kind of companies that want to drive more growth opportunities or more acceleration of their business and companies that want to drive more efficiency. We'll have scenarios where customers say, "Hey, I used to spend a couple of million dollars on this type of business process. And with Box AI, I could imagine saving a meaningful chunk of that." And so we're seeing kind of every variant right now of customers finding ROI potential within Box AI.

The next question is from Lucky Schreiner at D.A. Davidson. Do you think adoption of more AI agents will be a catalyst for adoption of Box Shield Pro? Or is the reverse true? And when customer adoption of Box Shield with security functionality built in, are they more willing to adopt and trust AI agents?

Yes. Maybe just like the meta-answer would be, we continue to have this kind of core brand value proposition, which is we should be the most secure and governed place where you can manage your unstructured data. And so agents just give us another way that we can add to the data security of our customers, because when you have lots of data that you're working with as an organization, you want to be able to understand what's in that data. And the more that we know what's inside the content, we can help you better secure it because we have a bunch of security controls that can get triggered by data classification. So AI agents now offer another way that we can go and classify that data. And by virtue of them being more secure and being a better place to then put your content to keep it secure, we get more content and the more content you have, then the more you actually have a need to automate workflows around that content and use other AI use cases. So it creates a really nice kind of virtual flywheel, and that's why -- virtual cycle, and that's why we're continuing to invest in Box Shield and our other data security products.

Shield Pro would continue to increase the content gravity that Aaron is referring to, and we believe in it. So more of the latter in the way the question was phrased, I think it's going to help bring more traction to the rest of the platform, to everything else we offer.

Great. As a follow-up, what do you think is holding customers back from adopting AI today more broadly?

Many of the customers we talk to have gone through phases where they need to make sure that the AI is safe and secure. It meets their compliance standards, it meets their security standards. And for large organizations, because they want to use AI on some of their most critical data, they need to naturally go through and verify these things. I think that many organizations have been making their way through this process and are starting to adopt more, especially lately. And then in some cases, when you're talking about AI agents and they're coming up with new use cases, they have some more sort of natural diligence to do to make sure that they work well. But we see in almost all companies -- companies of all sizes across industries, there's a definite trend towards getting through all of those natural diligence items as they start to adopt more.

And I think what we offer by virtue of us being kind of an applied use case on AI is like very easy out-of-the-box use cases. And this is why you're seeing so much adoption with things like metadata extraction is just every company on the planet has unstructured data that they've always wanted to be able to pull out the structured data from that content. They've never been able to do that at scale. And it's an instant out-of-the-box use case that then lets them go and automate downstream processes. So we offer customers a lot of relatively low barrier, high upside, high ROI use cases with AI, and that's why we're seeing more success, I think, than maybe the typical software company with the power of AI.

And then that also then leads that the next time they have a new use case, be it data extraction, be it to do retrieval augmented generation across a bunch of information in the hub or all the different agent kind of capabilities inside of workflows, they don't have to redo the certification cycles because they've already approved the fact that Box does secure and compliant AI on your data, which is one of the natural benefits of a platform.

So as a follow-up, where do they decide to start with Box AI functionality? What is the typical use case? And how is Box helping them through that process?

Yes. I mean, usually, it starts with what we've actually added as kind of foundational capabilities within Box. So Box AI out of -- again, kind of by default, lets you talk to any of your data within Box, so you can go to a financial asset or a contract or a marketing asset and just interact with it and bring the expertise from the model to bear with that content. With Box Hubs, you can take a collection of data, hundreds or thousands of marketing files or sales files or HR materials and then let anyone interact with that data as a knowledge base that's intelligent by design. So these are kind of great instant use cases where every employee kind of knows exactly how to get value from those. That usually then encourages companies, maybe the AI team, maybe somebody in IT, maybe somebody in operations to say, "Oh, well, what if we could actually customize that or create an agent that would let us do this in a much more repeatable way." And that's where a lot of the capabilities within Enterprise Advanced come into play. So our AI Studio, lets you go create custom agents, our new Box Extract lets you do data extraction at scale. And then I think extremely importantly, and I want to underscore this capability, Box Automate then lets you start to deploy these AI agents in workflows in a repeatable fashion. So Box Automate will provide the underlying guardrails and the underlying kind of pathway for agents to then go get involved in more and more mission-critical work that you want to deploy at scale and you want that work to happen as efficiently and in as repeatable of a manner as possible. And so we had to build a next-generation workflow automation system to go and be able to power that, and it's agentic by design.

George Kurosawa from Citi again. When I think Box and workflow automation, my mind goes to Relay. Maybe if you could talk about the product differences between Relay and what you've just announced with automate.

Yes. So Relay was obviously our first foray into workflows. And Relay is a little bit like an if this than that type structure. So you say a file comes into this folder, I want to move it to this person, add a task to it. So it's simple by design, highly powerful because you could do that tens of thousands or millions of times. As we saw agents enter the scene more and more over the past couple of years, we've had some visibility into this. We started to say, well, what will the future of designing workflows look like? And how will you have an interplay between people in the business process and agents? And we had to kind of do a reimagination of what workflow would look like within the Box platform. So this gave us an opportunity to kind of start on a path of building really the next generation of workflow automation within Box. And so what Box Automate is that it's a full business process builder. You have a drag-and-drop builder of your workflow. It can be as complex or as simple of a process as any customer would like. And then you can choose whether either there's system events that occur, people are involved in those system events or if agents then get dropped into parts of that workflow. So again, we got to build this with agents right front and center on day 1 of building out this set of capabilities. And this really kind of takes our workflow system into a really an AI-first era of work. And then what we'll do is we'll evolve the Relay use cases and customer base into automate over time as we're building on the next set of capabilities there.

Just a follow-up for me. Lucas Cerisola, Morgan Stanley. So more and more, we're hearing customers -- or sorry, enterprises talk about AI lowering the barriers to service different types of customers. And I would be curious to know how your different innovations are allowing you to service new customers that you wouldn't have been able to service before.

Yes. So I think maybe at a high level, I'll share a quick thought and then Diego, if you want to add to this. But most of the way that we think about it is our use cases expand dramatically because we've often been constrained by the customers' human resources, human capabilities on the other end for the kind of use cases that they have for Box. So if we go to a company and we want to power invoice processing for them or HR onboarding for them or contract life cycle management for them, we are dependent on how many people do they have in those functions to go and review documents, to review data, to move documents through that workflow. And so by definition, there's a kind of a TAM constraint to some extent as to how many customers have that sophistication, how much -- have the teams to go and do that, do all the lines of business, have the talent for those types of workflows. AI agents effectively are bringing that talent to our customers. So all of a sudden, now we can go to a business of any size or go to a department that maybe previously wasn't able to automate something or throw human kind of talent at a particular problem. And with the power of AI agents, we can actually go in and automate that workflow. You saw an example of this on stage today with Box Apps of the kind of full breadth of apps that we can begin to enable for our customers. You saw an example of contract life cycle management. You saw an example of sort of a wealth onboarding type life cycle. We talked about things like insurance processing. So these are all new applications and categories that we can be entering because agents will bring along the work that traditionally our customers would have had to do themselves, which again, kind of narrowly constrains the market. And this is why I'm fundamentally convinced that the TAM of software is going to go up dramatically because it's no longer a function of the software spend of the company. It will be more compared with the people spend of a company, and it will be a ratio of people spend as opposed to the sort of typical finite limit of what you would put into IT OpEx and so it's a totally different world once you have AI agents that can do useful work for that organization, and that's going to expand the number of use cases and opportunities that we can go after.

Aaron covered it very well. The only couple of things I think important to add, big companies may have so many applications that they want to simplify on. So the bigger the company, the more applications in that long tail that we can actually help resolve with a combination of an agentic workflow and the many capabilities we integrate with. And smaller companies have small departments that actually cannot handle too many of them. So the smaller companies want to work with a vendor that actually solves the problem for them. The bigger companies want to remove 200 of the 300 applications they have. So we have actually an amazing opportunity in front of us because across the whole market, small or big, companies need a way to solve the challenge. And we apply to all of them. So these agent workflows that we solve for are basically applicable to companies of any size. It's not a matter of the spend only, there is a type of headache that we solve regardless of company size.

Yes. Actually, and if I can build on that because that's a very key point. Small companies have the exact same problems as large companies, but they have way fewer resources to solve them. So to exactly Diego's point, we can now bring more use cases. So I answered the question more from the lens of the labor side. But if you just think about even the software use cases, small companies typically weren't in the market for having a contract system or a full digital asset management system because they just didn't have the scale where their processes kind of require that and they didn't have the people to go implement them even if they did. And so we can make those types of use cases incredibly simple out of the box and then expand the number of workflows that we're powering in those organizations and then bring it all into one single platform. So that's where we can get a lot of leverage. And I called this out, I think, in the last earnings call, where we had deals in Q2 that probably would have been 1/3 to even less the size previously before Enterprise Advanced, so not just the 20% to 40% uplift, but a fraction of that because we could then power a use case that, that small business would not have been able to purchase before because just like the functionality wasn't something they would have been able to implement. And so we had 6-figure deals in the small business segment because they would now automate business processes that, again, they would not have been able to buy from Box in a prior era. So this is why you're going to see, I think, more and more TAM expansion based on this combination of agents, automation and apps all coming together.

I think that's a great note to end on, and I'll just give it back to you, Aaron, for any closing remarks.

Sure. Yes. Thanks, everybody, for attending in person or virtually today. We had some amazing announcements. We've been extremely hard at work, obviously, over the past couple of years, but certainly over the past year to really kind of chart the next era of our platform. And I would just emphasize how critical of a moment we're at right now where for the first time ever, companies can actually begin to bring automation to the workflows that deal with their unstructured data -- and that's 90% of the corporate information that we work with. So if you just think about now the total scope of what's possible to automate or get insights from, it's massive, and we're incredibly excited to be able to bring that innovation to our customers and work hand-in-hand with our customers to go drive that. So thanks for attending and obviously, continue to reach out if you have any questions or thoughts.