How are large enterprises navigating the seismic shift to artificial intelligence? For many, the journey begins with managing the 90% of their data that is unstructured—documents, images, videos, and contracts. In this conversation, Nataraj sits down with Ben Kus, Chief Technology Officer at Box, to explore the real-world challenges and opportunities of becoming an AI-first company. Ben shares critical insights from Box’s own transformation, detailing how they leverage generative AI to unlock value from an exabyte of customer data. They discuss the evolution from specialized machine learning models to powerful general-purpose AI, the practicalities of managing AI costs, and the essential steps to ensure data security and customer trust. This discussion moves beyond the hype to provide a clear-eyed view of enterprise AI adoption, from initial use cases like RAG and data extraction to the future of complex, agentic systems that can perform deep research and automate sophisticated workflows.
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Nataraj: I was really excited because I work in unstructured data as well and I realize how important it is. But let’s set a little bit of context for the audience. In the storage industry, it’s a common phrase to use unstructured data. But it would be good to set the context of what is unstructured data and why Box is in the center of all things AI.
Ben Kus: It’s interesting. Oftentimes, if you say the word data to anyone, especially computer scientists or people who have come from programming backgrounds, you naturally think of structured data. We want to become more data-oriented; we need to use data. And it’s partially because there’s been a massive data revolution over the last 10 or 20 years. It used to be that my data was in a MySQL database somewhere. Then it became more tools available where you would use terms like data lake and data warehouse, more advanced analytics tools. You see companies like Databricks and Snowflake that become these very powerful platforms of structured data. That’s just naturally what you think of.
Now, the world of unstructured data, which I would define as data that’s not in a database and doesn’t have a schema to it—things like emails, messages, and webpages. In our world at Box, it’s the world of what we call content or files, the stuff that goes into documents, PowerPoints, markdown files, videos, or images. All of this is unstructured data. Interestingly, almost every company you talk to, in a business-to-business, enterprise-oriented thought process, 90% or more of their data is actually unstructured. At Box, we have 120,000 enterprise customers, we have over an exabyte of data, and this is what we’ve always lived by. You need to collaborate on it, you need to sync it to get it to different places.
But then generative AI comes around, and generative AI is born on unstructured data. So it naturally, immediately, every company I’ve ever talked to, if you ask why they’re interested in generative AI, one of their top three things they’ll say will be, ‘Well, I’ve got all this internal stuff in my company that is unstructured data, and I don’t think I’m taking advantage of it enough.’ It takes a million different forms, and it’s partly why it’s been hard to really automate or make specialized applications to deal with these types of data. But there’s this huge untapped potential in unstructured data. So for Box, with all of these new models coming out from all these great providers, it’s a gift to companies and to people who think the way that we do, which is how can you get more out of your unstructured data? Now AI can basically understand unstructured data. For the first time, you have this automated ability to have computers be able to understand, watch, read, and look at these things and then be able to not only generate new content for you but also to understand and help you with the content that you already have, which in many companies is massive—petabytes, hundreds of billions of these pieces of content that in some cases are the most critical stuff they have.
Nataraj: Unstructured data includes Box, Amazon S3 files, Azure has Blob, and any given enterprise has multiple places where they’re storing data. In terms of your strategy for building products, how much are you thinking about extending the Box ecosystem into all these surface areas versus building tools or products within the ecosystem? Talk a little bit about your strategic approach.
Ben Kus: If you go back to the analogy of where people store their structured data, it’s in many places for many different reasons. Similarly, there’s the very generically large term of unstructured data; you would store it and use it in many different ways. But for Box, one of our things we’re typically known for is to make it very easy to use, extend, secure, and be compliant for all of your data. For that, we typically would need to manage it. We have a million ways to sync data between repositories. We recently announced a big partnership with Snowflake where the structured data, the metadata about a file in Box, automatically syncs into Snowflake tables. That kind of thing is definitely part of what we think about.
But in general for Box, it’s key that we offer so much AI, in many cases for free on top of the data you have, even though it’s quite expensive, because we want people to bring their data and get all the benefits of security, collaboration, and AI. But we don’t believe we’re going to be the only people in this AI and agentic ecosystem, which is why we partner with basically everyone. We believe there will be these major enterprise platforms that every company will be looking at. Our job is to give the best option for them for unstructured data and then integrate with everybody else so that you can have our AI agents working with other companies in addition to custom AI agents that you build yourself. Because we’re unstructured data and a lot of people need to use it, we integrate with other platforms, non-AI in addition to AI integrations that let other companies call into our AI capabilities to ask about data, do deep research, do data extraction, and so on.
Nataraj: Was there a moment within the company where you guys realized that this is a big shift? Box has been around for almost 20 years, starting in 2005. Was there an internal moment where you said, ‘Okay, this is really big for us?’
Ben Kus: Sure. If you look back five or six years for the term ML and unstructured data, you’ll find we had a lot of big announcements around how Box uses ML to structure your data. So taking unstructured data and structuring it is a big thing we’ve done for many years. We’ve always been trying to be on the bleeding edge of what’s available. But there was this challenge. Imagine a company with forms people are filling out, or documents, contracts, leases, research proposals, images—anything a company does day-to-day. If you were to have AI or ML help you, it would be training a model. You’d get a data science team together or buy a company. We would see that getting an ML model to handle contracts and structure them was too complicated. You’d need a model not just for contracts or leases, but for commercial leases in the UK in the last three years. You’d have a model for that, and it didn’t really work that well. You’d have to train and customize it a lot.
That was the nature of how it used to be. When Generative AI came out, we were watching the early days of GPT-2 style models, and it was okay. But somewhere around the time ChatGPT came out, with GPT-3.5 style models, you suddenly saw this amazing moment where a general-purpose model could actually start to outperform the specialized models. It could do things you never even would have bothered to try, like, ‘What is the risk assessment of this contract?’ or ‘Can you describe whether you think this image is production-ready for a catalog?’ You couldn’t even imagine the feature set you would give a traditional ML model. But Generative AI could kind of do it. As it got better, GPT-4 was this big, ‘Oh wow,’ moment where some of the challenges of the older models were being fixed. GPT-3.5 was the moment where we said, ‘Let’s just go back and retrofit everything about Box to be able to apply AI models on top of it,’ so you could do things like chatting through documents and extracting data. It was amazing how fast you could get things working and get them working better than you ever had before, even after spending a ton of engineering resources on trying to get something working. An hour and a half of using one of the new models actually gave you better performance. That was a big aha moment. And then of course you realize you’ve got 90% of the problem, and the last 10% is going to take all your time going forward. But since then, all of our efforts have been around preparing Box to be an AI-first platform. We often talk internally, ‘What if we were building Box tomorrow?’ It clearly would be an AI-first experience. So why don’t we do that? That’s just part of our mentality.
Nataraj: What are some of the earliest use cases that you launched at Box, and how has the enterprise customer adoption been? In enterprises, we often see the cycle of adoption is a little bit slower.
Ben Kus: Some of the first features we launched were around the idea that if you’re looking at a document, you need to have an AI next to you to help you chat with it. I’ve got a long document, a long contract, this proposal—help me understand it. It’s almost like an advanced find. That was a simple feature, but it was this new paradigm. And then we added the concept of RAG, not just for a single document but across documents. You can implement chunking, vector databases, and the ability to find the answer to your question, not just a document like in a search. I’ve got 100,000 documents here in my portal of product documentation. As a salesperson, I need to find the answer to this question. I ask it, and the AI will ‘read’ through all of it using RAG and provide the answer.
For enterprises, they were scared, and some of them still are, about AI because it’s so different. Data security is critical. No matter the benefit of AI, if you’re going to leak data, no one’s going to use it. In many cases, for bigger organizations, the first AI they’re actually using on their production data is Box, partially because it’s very hard for them to trust AI companies. You need to trust the model, the person calling the model, and the person who has your data. Since Box is that whole stack for them, they were able to say, ‘I trust that your AI principles and approach will be secure.’ Then they’re able to start with some of the simple capabilities. One of the more exciting ones is data extraction, where you have contracts, project proposals, press releases. There’s an implicit structure to them. You want to see the fields, like who signed it, what time, what are the clauses. Then you can search and filter on that data. Enterprises look at that and say these are very practical benefits. They get through their AI governance committees, security screenings, and ensure nobody trains on their data. That’s the scariest thing to them. We have to go in and meet with the teams, explain every step, show them the architecture diagrams, and the audit reviews so they know their data is safe. That’s typically their number one concern.
Nataraj: I want to talk a little bit about the cost of leveraging AI. It has dramatically gone down. Are you seeing improvement in your margins by creating AI products? How is it directly impacting your profitability?
Ben Kus: This is a particularly hard problem. We’re a public company. We publish our gross margin, our expenses. It’s not practical for us to do something that would double our expenses. Nobody has $100 million laying around to apply to whatever cool ideas. At the same time, it’s very clear that if you’re too worried or stingy about your AI bills, you will lose to somebody who is just trying harder. There’s been a really nice byproduct of all the innovation in chips, models, and efficiency—they’re much cheaper than they used to be. Sam Altman said a few years ago that models would get dramatically cheaper, but you’re also going to find you’ll use them more and more, which will slightly offset that. That’s exactly what we found. We are doing way more tokens than we did previously, by orders of magnitude. However, we’re now utilizing the cheaper models, and they’re just offsetting.
When you get to agentic capabilities, like deep research on your data, that’s way different than RAG. RAG might use 20,000 tokens. But for deep research, you might go through many documents, 10,000 tokens at a time, maybe 50,000, 100,000, and then reprocess that. You might spend hundreds of thousands of tokens or more. That’s a massive exponential growth in your AI spend. But you get a great result. Deep research on your own data is revolutionary. The way we approach it is to give AI for free as much as possible because that’s what an AI-first platform would do. Sometimes, for very high-scale use on our platform, you can pay. But whenever possible, we’re going to eat the costs ourselves and handle that risk because that’s what you want out of your best products. Nobody wants to sit there and worry when they’re clicking on things that it’s going to cost them. So we try to protect ourselves with some resource-based pricing but also just say AI is part of the product. That’s our philosophy.
Nataraj: What do you think about pricing based on usage versus pricing based on outcomes? I’m assuming you’re following the regular per-seat, per-subscription model.
Ben Kus: Yep. We’ve been through every single possible flavor of this. I hope business schools are doing case studies on how everybody had to rethink technology pricing. At the end of the day, pricing a product isn’t just about the supply side cost; it’s about what people are willing to pay and how they’re willing to pay for it. When we originally launched our AI, we had seen some people who launched AI were charging too much and people weren’t ready for that. Then there was this massive trend of $20 a month for enterprise-style tools, and the adoption was terrible because nobody quite knew what to do with it. So we decided to offer it as free as part of our product, but we put a limit on it. If you did too much, it would stop.
But then enterprises would actually not turn it on because they were worried they would hit those limits and then everybody would be mad at them. The limit became an adoption barrier. So we got a lot of feedback from our customers and turned that off. There was no limit. Now, there’s the idea of abuse we could address. You can’t just buy a seat to Box and use the API to power another system. But for normal usage, we handle that risk. It’s incredibly expensive if you look at public cloud rates for transferring and storing data. We’re used to infrastructure expenses. So we decided we’re going to eat the cost of it as a way to deliver better services to our customers. That is our continuous philosophy.
Nataraj: Storage is a horizontal use case, but AI is also being used to build vertical-specific products, like Cursor for developers or Harvey for legal assistants. Have you evaluated creating specific products on top of Box for different verticals?
Ben Kus: This is a very fundamental question for any company: am I going to focus on a specific vertical and a problem, or am I going to focus generically across the board? At Box, one of our product principles is to focus on the horizontal IT use cases. Much of our value proposition is across the whole environment. Everybody in the company wants the security features, the compliance features, the sharing features. This is why we talk about it as content or files—everybody needs files. Some companies specialize and talk about contracts and clauses, or digital assets and marketing materials. This is a big question for any startup: go deep or go broad. If you go deep, you can make more targeted products. But your total market size is diminished. For us at Box, no one industry makes up more than 10% of our overall business. We have a giant market, but the more you specialize, the more you’re probably not going to solve a problem for somebody else.
The interesting part about AI is that it pulls you in two different directions at once. Some people will start to use AI to very specifically solve problems, like in life sciences or financial services. But at the same time, in some cases, a generic AI can actually solve what a historical specialized company used to do. In which case, people might go back to a generic solution so they don’t have a million point solutions. You always have to analyze how deep to go in an industry versus how much you can provide horizontally. AI reshuffles it.
Nataraj: You guys are one of the first companies to adopt being an AI-first company. What does that mean and how does it change how you operate?
Ben Kus: When we use the word ‘AI-first,’ we think about building a feature knowing the full abilities of AI. Search is an interesting example. The historic way you would build search is completely different from how you would build it in a world with an AI or agentic experience. Not just from a technology perspective with better vector embeddings, but also from the technique. People act differently when they go to a search box than when they are talking to an agent. Many people use ChatGPT or Gemini for internet searches, and what you type into Google versus your chat experience is different.
That’s an interesting moment for Box. If you think AI-first, you don’t just put an AI thing inside a search box. You rethink the search experience from the beginning. We announced our agentic search, or deep search, where you ask an AI, and it will not just go through a complicated search system, but it will look at the results and figure out whether those results match what you’re looking for. It goes well beyond RAG and into using intelligent agents to loop and figure out if they have the best answer or if they need to try again. Thinking that way, not just ‘I have a model, I want to use it,’ but ‘What can AI do for you?’, especially if you think agentically, becomes a different product process, a different engineering process, a different strategy process. You start to invest heavily in your AI platform layers and common AI interactions in your products, like an agentic experience or AX. If you’re going to be an AI-first company, you need to examine the fact that maybe AI will change the way you’ve done something traditional.
Nataraj: We went through RAGs, we went through copilots, and now we are seeing agents. How are you thinking about agents within Box? What is your definition of an agent?
Ben Kus: My definition of an AI agent, technically speaking—and Harrison from LangChain has a fun definition—is that an agent is something that decides when it’s done. Normally, you run code and it completes. But an AI agent needs to figure out when it’s done. That’s a good technical definition. I have a slightly more detailed engineering answer: an agent has an objective, instructions, an AI model (a brain), and tools it can decide to utilize with context to operate. I’m a fan of agents that can call on other agents, like a multi-agent system.
When I’m thinking about agents, I’m thinking about multiple agents cooperating. To me, the power of agents going forward is this idea that you can think about them as little state diagrams of intelligence that can loop and do more sophisticated things. This is a very different thought process for most engineers. You asked for an example. One is deep research. To do deep research in Box, you have to search, look at the results, get the files, make an outline, create the prose, and then critique it. That’s like 15 steps for these agents. We call that a deep research agent, but it has a multi-agent workflow to process that. I don’t know if you could have done deep research very well previously because there are too many paths to handle. It’s the kind of thing that works really well for an intelligent system like an agent to orchestrate.
Nataraj: Do you see any form factor for agents? In an enterprise product sense, how does that form factor play out?
Ben Kus: There’s the AI models concept, which is more of a developer concept. Then there’s the idea of an AI assistant, where you have something there to help you in context, but it’s typically one-shot. The term ‘agentic experience’ (AX) is very interesting in this form factor discussion. OpenAI, Anthropic, and Gemini do a great job of building valuable capabilities into their agentic experiences. You go to ChatGPT or your favorite tool, ask a question, and it just figures out, ‘I’m going to search the internet, I’m going to do deep research for you.’ This idea that you go in and ask a system to do something, and it can recognize your context, is critical. Context engineering is a critical aspect of agentic stuff going forward. This might be the new form factor.
At Box, when you’re on our main screen, what you want to do is very different than if you have a file open or if you’re looking at all your contracts or invoices. The hard engineering and product problem is to make agents that figure out what you might want at that point. We think about building an agent that handles a certain flow but first figures out what the user wants, and then does a search or queries the system or brings data together. That context engineering is critical. I believe context engineering is one of the more interesting areas developing, and it will be something that everybody will want to hire for soon.
Nataraj: Let’s touch upon productivity. How much productivity improvement are you seeing within your company? And there’s a group of people panicking that AI is going to destroy jobs, starting with developers.
Ben Kus: For productivity for our customers, we see people start to use AI a little bit skittishly, and then they use it more and more over time. Especially in enterprises, adoption starts slow, but then they start to add it in big chunks, and you see an acceleration of usage over time.
Internally, we have seen benefits from using assisted tools for our developers, like GitHub Copilot and Cursor. As the models and integrations have gotten better, they are helping us overall. We don’t think of it as, ‘We can save money and have fewer developers.’ Instead, we’re like, ‘If 25% of our code is written by AI, that’s 25% more we can do to deliver value to customers.’ We’re not constrained by a fixed amount of output we want from our developers; we want more. If tools help people become more productive, that’s wonderful.
Economically speaking, I’m not a believer in the lump of labor fallacy—that there’s only a fixed amount of things people want to do. I think it’s the opposite. If things get better and cheaper, you want more of it. We want more videos, content, marketing, and internal content because new avenues are now possible. Now, there’s an important aspect: if change happens too quickly, it can be very disruptive. I’m very sensitive to the plight of people in the middle of a disruption. But I see this as a tool to help companies do more. You need good people using AI to help them, as opposed to cutting whole areas.
Nataraj: Some CTOs have the opinion that they no longer need a lot of junior developers. I always thought this is actually much better for junior developers because if it was taking them three or four years to become senior, it will now take them one year. What’s your take?
Ben Kus: What you said is true. When you add a junior developer, you often expect a relatively small level of output compared to more senior people. But now, a person who’s really good at using the latest tools is actually quite productive, and that’s a big value. At Box, we have the most developers we’ve ever had, and we’re not only hiring senior people; we’re hiring across the whole spectrum. We just expect people to be able to use tools. Anecdotally, I see that people coming out of school now have always known AI-assisted coding, and they’re good at it compared to somebody who’s been around for a long time and might be resisting it. Also, in areas like context engineering, which is a slightly different form of coding, some of our most successful context engineers are relatively junior in terms of how long they’ve been out of school but really excel at that kind of thing.
Nataraj: An audience member asks: can you share a little bit about document parsing and how you’re extracting from those documents and what models or technologies you’re using behind the scenes?
Ben Kus: In this world of handling unstructured data, there’s a set of things you always need to do. You have all these different file types. The first thing is to get it to a usable format. Markdown is a great format. Sometimes you have scanned documents or different formats. There’s a big conversion as a first step. Many people talk about PDFs because of all the weird things that go into them. A PDF is not a good format for AI to figure out; it needs to be converted. So step one is to convert it to text with some limited style support like markdown. Then you typically go through and chunk it. You want to make a vector out of the most important section of data. You want it to contain a whole thought. You wouldn’t do it per sentence, but if you did it for giant pages, you’d end up with too many confused topics. So you want a vector to indicate what that area is about. Paragraphs work well at a high level, but then you need more advanced chunking strategies. Then you stick that into a vector database or put the text into your traditional search database.
Nataraj: Are you building your own proprietary tools for this, or are you using things like LangChain with Pinecone or other vector DBs?
Ben Kus: My philosophy and the philosophy of Box is that we love all the tools that everybody makes. If people are building the best tool out there—the best vector database, the best document chunker, the best agentic framework—we want to use it. I gave a speech recently at the LangChain conference about the benefits of something like LangGraph. When we started, we had built our own because this stuff wasn’t available at the time. But we are more than happy to go back and retrofit to some of the other systems. I’m very impressed at how good vector databases have gotten in the last few years. Why would we bother to rebuild the things that people are doing such a great job building, especially in the open-source community, or tools that we can buy? We’re big fans; we will replace stuff that we just built because something better is available. With AI, you kind of have to reevaluate every six months.
Nataraj: What about the models you’re using? In an enterprise, you want to adopt the latest and greatest, but you also want to be secure.
Ben Kus: We made a decision a long time ago not to build models, and I’m super happy we did that. Also, we are going to support all of the best models that are trustworthy. For us right now, we support OpenAI-based models, Anthropic’s Claude models, Llama-based models, and Gemini. We consider those to be some of the best models out there. Not only do we support them, but we support them on a trusted environment. This is critical for many enterprises. For example, AWS Bedrock is a very trustworthy environment to run the Claude or Llama models. IBM will support Llama models for you. These are trustworthy names from an enterprise perspective.
We utilize these trusted providers and trusted models, and then we pick which model works best for a given task. Gemini is great for data extraction. GPT-style models are great for chatting. They’re all pretty close these days, the leading models. But we let our customers switch as they want. If somebody says, ‘I really think this data extraction is best for Claude,’ we let them do it. We support all of the models, and one of our goals is to support them as they come out. This is very expensive and painful internally because how you properly prompt and context-engineer for Claude is different from Gemini, which is different from OpenAI models. But for enterprises, they often have preferences, and our job as an open platform is to handle those.
Nataraj: One final question. If you were building something now, are there any ideas that you would go and attack?
Ben Kus: It’s a very good question. There are a lot of startups out there doing really interesting things. One interesting idea is to look at areas where an old-school traditional software approach could be disrupted, but maybe it’s so old that people don’t really think it’s cool or interesting anymore. Finding something that is very valuable but not as in the news might be a good approach. Anything we’re talking about all the time will probably have so much competition that you might be behind.
But I will highlight one thing. If you see something like Cursor—nobody talked about Cursor a couple of years ago. They were up against Microsoft Copilot, one of the biggest companies in the world. An interesting thing is that with Cursor, you start to realize that even though people are using AI to solve a problem, there might be a better way. If you can make a really good product, even despite the VC advice that you’ll never make it in a ‘kill zone,’ you might have a chance. Often, that’s very good advice, but if you really believe you can do it better, it’s a dangerous path, but there are demonstrations of people who built a really good product. I believe those still have a chance in these crazy AI times to become large companies because they just solved the problem really well.
Nataraj: Because Cursor literally cloned VS Code. They thought the UI could be better on just that product and that’s the main differentiation.
Ben Kus: There are a lot of dynamics that go into any existing product. Sometimes a fresh look at it, even a problem that seems solved, can be helpful.
Nataraj: This was a great conversation, Ben. Thanks for coming on the show.
Ben Kus: Excellent. Well, thanks for having me on. It was a fun chat.
This conversation with Ben Kus highlights the practical, strategic thinking required for enterprises to successfully adopt AI. By focusing on security, embracing a multi-model approach, and rethinking core product experiences, companies can unlock the immense potential of their unstructured data.
→ If you enjoyed this conversation with Ben Kus, listen to the full episode here on Spotify, Apple, or YouTube.
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