B (3:58)

Let's start with that step one that actually we know that a lot of reasons for failure of artificial intelligence is nothing to do with actually the artificial intelligence project. It's to do with where you start. And actually your data is where you should start. I was fascinated, not only listening to your podcast, the AI Explainer podcast, but also reading into the work that bots does audit your data architecture keeps coming up again and again. What do we mean by that?

A (4:28)

When we talk to many of our customers, we often hear them say they don't actually have an AI problem. Instead they have a data problem. And in particular, their data across their whole company with is often a mixture of what you would call unstructured data or things like files and emails and messages and so on, in addition to structured data, which is like stuff in databases. So both of these in many cases are not sort of centralized in platforms that then make it easy to use with AI. And this is one of the key challenges that companies have, is that they say, I want to use AI to help me automate this process. I wanted to use it to help my salespeople create better Presentations. I wanted to have my data analysts be able to analyze the databases and then relate it to the different other parts of this data in our company. But then they say, oh no, but like that data lives over here and this old system that we have, there's a legacy file server somewhere. And so then this actually gets in the way of them being able to use the AI. Not because AI models are not good enough, or not because AI agents couldn't do that process, but because they had no way to give AI agents access to this data. And this is a huge problem that in many ways is like a common problem across many enterprises. Historically, for all sorts of reasons, companies have always had to deal with this data silo problem. But it becomes more and more important as you start to have these new technologies. And remember, AI typically runs on GPUs in a cloud system somewhere. And so it requires this like access to your data, which then bring up some concerns. You have to make sure that's secure, it's compliant and so on. But then also you need to make sure that the AI has access to it in a way that is readily available for the AI models. And in some way, when you talk about AI agents, it helps to think of them as almost like a new employee in the company. And it's like imagine you bring in the smartest employee you'd have in the company and you say, I want you to start to work on something. And then they say, okay, where's that data? And you say, I don't know, I don't want to give you access to it, I can't give you access to it. In which case, no matter how intelligent these AI models are, or in this analogy, how smart the new person who came in is, they're going to struggle to deliver real business value if they don't have access to the data.

B (6:28)

And Ben, how do you go about auditing your data architecture?

A (6:33)

There's kind of two approaches to it and I think many companies are doing both at the same time from a top level, like what tools have you made available to your company? From like an essential IT level, it is useful to do a top down audit. So you say, where do I typically store my structured data? Is it in a tool like a snowflake or databricks and so on, or your employee data with something like workday? Those kind of systems are very important for you to be able to say, are we using a modern system that will be able to enable us to access the AI? One of the key new areas is this world of unstructured data. Right. Because this is kind of what generative AI models were born on. They do a really good job dealing with things like emails and documents and PowerPoint presentations, and they're able to understand all these things. And this is usually the area where there's a lot of potential for companies to get a lot of new value out of it, because this was never before possible before generative AI got good enough and before AI agents got good enough to actually help employees do their work faster. When you're looking at, like, auditing these, you basically say, what systems do I make available? And are these platforms that we have internally able to be used with AI? Either of those tools come with the ability to say, we do AI ourselves to help you, or they have access openly to another system that allows it to be accessed by AI agents. And you typically see that, like, some of the more modern platforms have the ability to do both of these, where they support things like AI agents themselves, but then their AI agents also work together, the MCP or ADA protocols, which are sort of a new emerging ecosystem of the way that these platforms work together. But then in addition to that, centralized in many cases. One of the key things is that when you're looking across your company and you get either employees who are looking to say, I wish this thing that we do internally was faster, or I wish it was automated, or I could use some help in this area with AI, then just looking at, okay, well, where is that data stored? So we talked to some customers, and some of the challenges that they are facing are things like people are sending in scanned documents when they're doing things like loan origination. And it used to require that people would have to look through it, transcribe things themselves, be able to, like, go through, understand and find the key aspects of whatever that person submitted. And these were the things that were taking a very long time and those kind of processes, AI can help with a lot.

B (8:44)

And, Ben, can that be a relatively straightforward process? Obviously, there is complexity embedded in lots of the companies that you work with, lots of the people that you partner with. Is it quite straightforward? Is there a playbook that companies can use? And can you just explain whether this is a simple exercise, this audit?

A (9:04)

I think what we hear from many of our customers is that it's conceptually quite simple, especially the sort of the centralized approach. You basically say, do I have a platform to store these different and critical data for my company, and if not get one, then there's plenty of great options out there. But at a high Level, the challenge has been that getting your data into a centralized system, any of these types of data, and enabling it for your employees has been a challenge. Not every company has done it yet and they've said, well, maybe I'm too busy or maybe it's like too complicated to make that work right now. And in these cases, then they've kind of postponed this idea of this sort of upgrading their platforms of their company. And I think the challenge is that for many companies is that they have to now go do that in a way that sometimes it changes behavior of something where people were used to the old way of doing things, or it brings up some new security or compliance concerns. So this is usually where the challenge is. There's like a change management aspect to it and that is usually where you start to see the first set of challenges. So the playbook is straightforward in terms of at a high level, you say, well, was to get a better systems, but the challenge would be making it work in your organization.

B (10:10)

Are there, Ben, for you any red flags when you maybe are working with a partner in this first stage, which as you say, is sort of vital foundational principle where you would say you're not ready yet to move?

A (10:27)

Certainly the aspect of making sure that you have at least one interesting target use case that you want to start with. And in addition to what we talked about before, which having your data available in a safe and secure here way and usually just simple use cases is a great start, both from the person's perspective, like the people in your company as they get used to it, in addition to the idea of trying to have a measurable value benefit there. So sometimes when people hear about like, oh, AI is going to revolutionize things, they shoot very high. They say, I'm going to pick the most complex, the most valuable thing in my organization and I'm going to change it all and we're going to have it done soon. Usually like with any project, AI or not, like that's always a challenge is to do the most complex things. So what we've seen for many organizations and with many of our customers is that if they start with AI on relatively straightforward things, they get their people ready to use it. So have big ambitions, but start small is definitely one of the things that is typically a focus area for people that we see who are more successful in their projects.

B (11:23)

We'll go into that start small part of our discussion just in a moment, but I just wanted to finish off this sort of first foundational chapter of a company or an organization's Journey into successful use of AI.

A (11:36)

Yeah.

B (11:36)

This idea of the single source of truth.

A (11:39)

Yeah.

B (11:40)

That you have to have this sort of foundational sot. Just talk me through how an organization gets itself to a single source of truth.

A (11:49)

You start to look at what are the platforms of the data that you care about the most. And I'll sort of point to a few of, like, the kind of common areas, structured data, unstructured data, CRM data, HR data. Your first question is, where do we store that data? And then it is that data readily available to AI? Starting there is usually the first question, and sometimes it's actually even hard to answer. Oftentimes many companies have mini versions of these things. Usually you don't need to say, okay, well, step one is to get rid of all old systems and move everything, but instead say, let's focus on the things that are sort of the most important, especially if you have an idea so that you could then use that as your test case. And that often means moving to data out of old systems into new systems. And then that is one of the key first steps.

B (12:32)

So you get to this, at least in part of your business, this single source of truth. So you can start now. You've touched on governance and access control. There's this great phrase, you've used it at Bolt, so you use it in your podcast. I've also heard it elsewhere. AI doesn't keep secrets.

A (12:49)

Yes.

B (12:49)

If you ask AI something, it will tell you everything it knows about that thing, whether or not you should have access to that data or not.

A (12:56)

Yes.

B (12:57)

Now, Ben, how on earth do we ensure, as an organization that wants to be responsible, as we do in these spaces, that the governance structures are put in place so that inappropriately, data is not being shared between different teams that should not be seeing material that should be protected. But we are still allowing data search in unstructured places, particularly where this is a particular problem, to be fluid, efficient, and actually improving the performance for the company, for its customers.

A (13:34)

Yes. The challenge that you bring up is definitely something that is kind of for people who start down the AI journey, like a common way. They're like, okay, I'm going to take, you know, data. Oftentimes it's like a bunch of unstructured data. And then they say, if I just make it available to AI, then it can start to have all these great benefits. It uses things like retrieval. Augmented generation is one of these terms where the AI can kind of read a bunch of data, pull out the key things and answer questions, and start to do work. For you on this data. And so this is like the kind of the new, an amazing thing that AI offers. But then your very first challenge is what you said, which is, what is that AI going to tell somebody? In any organization, almost no two people have access to the same data. And so this is the key is that if you give AI access to all of this data and you're not careful about it, the AI will happily look through it all and tell you whatever it knows. And the AI is designed to be very helpful and it has no sort of sense of your permission structure by itself. So it's absolutely critical that you do not give AI access to what that person doesn't have access to. AI as a system should have access to a lot of things, but when that person is interacting with it, you have to be very aware of their role, their access controls and everything about that user. And so that it only looks up things that they have access to. Because you can imagine if you're a manager or an hr, looking through employee salaries like it's very different from somebody else in the org who says show me all everybody's salaries like. And so that kind of control typically is built into these underlying platforms that we talked about. The idea of user based access controls, the idea of role based access controls. And this is usually why one of the recommendations for most organizations is to say don't try to start over with your own custom built platform to then store all your data of all these types to then apply AI on top of it. Instead, like many of these organizations have provided AI capabilities. And then if you're going to build something custom or if you're going to, if you're going to be able to create these sort of internal automations and systems you can build, basically piece together using all your current systems, the ability to have AI reach into those, but with a very strong awareness of those access controls. And this is usually like one of the first things after you get the data consolidated where you have to solve this.

B (15:47)

We've said, Ben, start small. Don't try and boil the ocean at the start. You know, maybe picking a bottleneck, something that, you know is where you're, you know, maybe losing some time or losing some customer service. Yeah, Ben, can you take us through a, why start small? Just remind us again about that. But B, is there a case that comes to mind where you've supported an organization and you've been able to explain what small looks like?

A (16:15)

Yeah. So I was talking to a customer recently and they do a lot of working with clients on their portfolios of their client data, all the different accounts the clients have. And they wanted to get to the point where they had AI helping their advisors to basically give really good financial recommendations at a holistic level to their clients. And they thought AI could help prepare reports, be able to bring the latest information to their clients. And they said, okay, so they have this big goal, and I'm always a fan of big aspirations, but then they're like, okay, so how can we then do this? And then you start to look through it, you start to realize that there was really no way for even people in the organization to very quickly understand what the clients were doing. The client had submitted all this data, right? But it required the advisor actually reading all the data to then give them this recommendation. So in the star small world, they said, well, why don't we start start by taking that information and then putting it into a standard report format. And it turned out that the hard part was actually starting with was to be able to understand this client's situation given this variety of data. And so the first step that they did to sort of start small was to say, can we have AI help us understand this data first to be able to put it into a format so that we can then draw these conclusions from it. So this idea of data extraction is usually a problem that many organizations have. And this is something where oftentimes starting there is a great step. So in this organization, depending on who you talk to, they would say, wait a minute, I thought our big goal was to get this thing available for our clients. But it turned out that most of the time and most of the challenges were actually at a step much lower. And along the way they gained a ton of experience. Right as they're starting to work through this, they're starting to realize what the AI's limitations. They're starting to realize a can do things that they didn't really quite realize is possible. We see people who approach it this way. They're starting to have more successful projects than just the normal do something big and do it quickly kind of approach.

B (18:04)

Listening to you, Ben, I'm really struck. I'm leaning back into the idea that culture also really matters when you're considering how to launch successful projects of any description, to be honest. But entrepreneurial projects where AI will be in the lead, how do you value a pilot? Is it three months? Is it six months? Is it a year? Obviously there'll be lots and lots of different projects or different timescales for different projects, but what are you looking for, for. And how do you, on your side Box, help people understand whether they've actually got something. You don't want to keep sort of flogging a dying horse and saying, I think this is going to work, I think it's going to work. And I think a lot of businesses start open ended, a little vague, maybe haven't done the foundational work that we've been talking about and then don't know when to stop.

A (18:57)

Yeah, I think this is a common challenge with these AI projects that often have a lot of attention and they have a lot of emphasis behind them. So a couple things I think, hopefully just in general best practices, but in particular for AI, the idea of being able to go in and say that you have very strong exit criteria and something that you believe to be achievable and then you have a directly responsible person who is able to do that. So what we've seen works quite well is in our organization at Box and in similar with companies, we've seen like we did this approach where we said, okay, we want to give access to some of our new AI capabilities to people internally and then we wanted to then do like a hackathon to kind of see what's possible in sort of a proof of concept sort of approach. What we learned in this process was actually some of the people who were closer to the problems, some of the people who were sort of at the manager level in different departments in procurement and finance and HR and other areas, they got very familiar with the way the AI worked, they became the champion of it, they became familiar with the details. And so interestingly, we quickly switched to the idea of instead of saying we're going to like, you know, drive this big efficiency number over here, we said, actually we know that if you kind of think about it a little bit differently from some of the sort of the proof of concept work is that automating this, this internal audit step or this procurement step or things like that from the person who's responsible for it, who's driving that project, who has become familiar with the details and who knows very well what it means to be successful or not. Let's have that be the initial goal with the initial set of exit criteria and that person is very clear on it. They're like, today I spend 10 hours doing this thing, my team hates it. This is the work that nobody wants to do. It often is delayed and I'm going to have AI start to do that. So then take it down for 10 hours to 20 minutes and in that case that became then something that that person was responsible for. They had very clear exit criteria which was to get it working at a level of quality that they could then use. And then that became the way that they approached that project. And then that that was critical compared to, let's say that we had done the other way that we thought was the right way, which was like seeing this metric at a high level that we use as a company internal metric that needs to move in the short term. Because in reality what would happen is like some of the metrics that are high level, you don't see their change for a while. So it's almost like it requires 10 of these projects to go through before you see some of bigger benefit. But I'd say in general this is the key, is to make sure that you have directly responsible people who have clear exit criteria and a clear goal that it will actually matter to them. And then those add up very quickly in our organization to becoming more and more useful, especially as we're talking about like internal productivity style of getting more business work done internally.

B (21:26)

Ben, let's move on to step four, which we've described as pushing beyond chat. Such was the sort of consumer revolution of ChatGPT when it first became really publicly available. And suddenly the general public thought, oh my goodness, there's this thing called artificial intelligence, which of course had been running for decades in many various forms. And this was simply one part of the artificial intelligence products suite. But nevertheless, everyone became hyper focused on chat functionality, on bots, on, okay, how can I speak to my customers, whoever they may be, via the chat function? That doesn't mean that is not important, by the way. That could be what you want to do. But how do you help businesses push themselves beyond the oh yeah, we can get a chatbot out of this.

A (22:16)

Chatting with an intelligent system is actually, I think one of the interesting revolutions that has happened over the last few years. When it first started, it seemed quite weird to talk to an AI system, but now it's become very common. It's become like almost like a new interface. In fact, the people in the industry, they'll often call it the ax, the agentic experience. And so like, and this is, you know, this is not a term that existed all that long ago, but now many companies, including Box, has an ax, an agentic experience overall. So one of the keys is that talking to an intelligent system is actually becoming a more common way that people are interacting in their personal lives in addition to at work. And so it's not that the chat itself, like you were saying is problematic, but it is like the idea of simple chats is you got to kind of move beyond that. In the early days of something like ChatGPT, you'd only talk to the model about what it was trained upon previously. So in that world, you just kind of ask it a general question and it would give you a general answer. And it was great, but it sort of was quickly limited. But one of the next steps would be that having it to be able to do things like helping people who take orders. So if somebody says, I want to potentially buy something, can you start that transaction? Many times what you want is not just to have an AI respond to you, but you want to have AI start to do work for you. And this is usually where we get in the world of talking about things like AI agents, usually the chat experiences where you're talking to an AI agent is almost like talking to somebody in your organization who then has a job, it has a role, it has a function, it has access to certain systems, systems. And that's I think, where being able to then have AI agents that you have inside of your organization started to either customize or develop or use from a platform to that basically say, I can do some specific work for you. And then being able to think of it this way. So you say, I wish that AI could help me with this task of going through this set of financial data so that I can understand my customer better. Well, if you think of that, like, I wish I had an AI agent that could do that. So you can make an AI agent who has an objective, who understands how to go look at that data, has access. They didn't understand the general output. It understands the kind of format the person the advisor is looking for. So making an AI agent be able to do that, you could then chat with it and ask questions and say, I'm going to have this meeting with this customer soon. I can't remember this detail. And the AI agent will answer it for you. And so the idea of these agents that have access to this is one of the key things. But then quickly as you go down this route, you're like, well, wait a minute, like, if I'm going to do this every time, like, do I really need to go always ask that agent something? Do I need, always need to chat with it? Do I always need to make it do things? And this is where the world of having AI agents not only available to talk to you, but also available in the background to do work work for you. So why not have it such that it knows that I'm going to have a meeting with this customer and then it basically will go pre prepare the work in sort of a workflow. So it'll realize that like based on integration with whatever system, it'll say like, I see that on Tuesday you have one of these types of meetings. I will go spend maybe like minutes or some cases, hours of work to go through, process all the data, understand it all, look at it, and then they're able to say before your meeting, I've done this work for you. Because asynchronously in the background, using some sort of a workflow system that usually built in platforms, I have done this work for you. So this is the world usually where it's not about necessarily the chat interface being bad, but it's more about like, why not have AI work more for you in this agentic form? Because they're capable these days of doing things that are more complex. We've moved past the world where the agent will give you an answer in 10 seconds or the AI will respond in 10 seconds. And we move much more towards the agents that think that reason that loop. And then they're able to then have access to tools to go and be able to accomplish these more complex things. Things.

B (25:44)

Finally, someone who can explain agentic in a way that anyone can understand. Thank you for that. Ben. Give us an example. Pushing an organization on a bit. This idea that you might think it can help in this quite small way, but actually think 5x bigger, think 10x bigger. One of the biggest problems is people understanding you don't know what you don't know and therefore you don't always know what AI could do for you.

A (26:09)

You.

B (26:09)

Because it's a whole different paradigm at times.

A (26:13)

Yes.

B (26:13)

How do you support organizations? Have you got an example of where AI can deliver 5x improvement, 10x improvement.

A (26:24)

The phenomenon that you refer to is not only challenging by itself, but is actually extra challenging because everything keeps changing. I think in the last 12 months there has been something like 15 major model releases across these different vendors. They have these different names and each of these arguably was the most intelligent piece of software ever created in the history of humanity. Right. But like most people don't even know the names of these or the versions of these like Opus 4. Five, they kind of named in this funny way. And so like it's hard to keep up with the changes. However, at some point, the thing that you are familiar with a year ago or maybe six months ago or two years ago, depending on how much time you spend with it, it has dramatically changed what's possible. So oftentimes people would go back and they would say like, ah, yeah, I know about AI, I've kind of tried it, I played with it, not realizing that it has dramatically changed. And it has this kind of almost like a bold frog aspect where it's like gidd better and better, but people aren't necessarily realizing it because it's just a lot of information to consume in the world to see how well they're doing things. And certainly it takes a lot of time to go test all this stuff. One of the major areas that we've seen in the last 12 months has been when people first started to see that the AI can write code for you. It was this revelation of like, whoa, that's crazy. I can't believe if I write a comment at the top, it'll be able to decide based on this. It'd be like, I can write that for you. Then that was like, that's amazing. But then when the new coding tools came out with this idea of agentic coding where basically you can not just say, help me complete this function, but help me write some major new aspect, help me go find this bug. And the AI would go think for a long time, it would process, it would come back, sometimes taking minutes or hours, like, this was a new paradigm of people of like, how to think about this overall. And this was like dramatically different. And like many software engineers, they'll be able to come in in the morning, they'll be able to kick off many of these different things, do this work for me. And then when the AI agent comes back and does some great things, another AI agent comes back and it's able to maybe didn't do it quite well, so you kind of like ask it to try again. But this idea of managing agents going forward is actually one of critical insights. But it's very hard for people to understand it until you actually sit down and use it and realize that you have to get good about the way that you're managing these agents. The key will be for me to sit down and try things, to experiment with it. What is possible now with the latest models, with the latest platforms, with the latest way that companies are integrating their data into this, into these AI systems. And then at some point you realize, I can't believe that it's able to do this stuff. I think this is the revelation that we're going to see in 2026 and beyond, which is that people basically saying, I wonder if AI can do this job for Me and help it and then getting back sometimes amazing results, sometimes not that that great. But then maybe again trying a little bit later as like you know, the new model came out or a new capability. Those kind of things I believe will be the way by which people will start to say I'm understanding better what AI can do and I'm starting to use it more. Mostly because I tried it and I'm familiar with this new paradigm. I'm familiar with the agentic experience, I'm familiar with the way you talk to these things. So this is the kind of evolution of using this technology that I think we'll start to see.

B (29:16)

I love the idea of AI being a constant process as well as a degree of decision making. I think that's a really useful lesson to take. Let's just go on then finally to step five, which is about measurement, which I think you've touched on actually. Ben, regularly through this great conversation about measuring what you're doing and then rethinking and testing again and improvement with constant backwards and forwards with the AI products you're working, working with. But you've gone through these four great steps that we've outlined. You're at this measurement stage where you're trying to push and maintain momentum and as you say, not forget that the world's improving out there in this area every single day of the year. How do you then embed measurement and understanding within your organization?

A (30:04)

So in general it's always critical to make sure that you have an ROI on something that you're looking looking at. Because we talk so much across the industry about technology and the capabilities associated with it. Sometimes people would just be like I'm using AI, therefore good. But that's probably not right. Probably you want to make sure that you actually are delivering business value and then usually a mechanism to measure that is critical so that you can have these KPIs or these kind of okr style of what is the result that you actually are looking for. The way that I'd recommend is that rather than start with a very high level KPI and say we're going to move this instead say what is the most important business value but then what is a more immediate and direct metric that we can then measure that we know ladders up to that. So for instance, let's say that you're interested in efficiency overall for your organization and so instead you say can I make this one process more efficient? And then can I make it go from 10 hours to one? And so somebody who's maybe more Skeptical might say, wait a minute, that one process going down and being 10 times faster doesn't change this high level metric immediately, which I think is a trap. And you need to make sure that you have many different processes that are all becoming faster, hopefully 10x faster, so that they all then add up. And then this trailing metric that you might be looking at up here is something that will improve over time. And so basically starting smaller, starting on things that matter, making sure that you're moving those metrics and then you believe over time that if you do this repeatedly you'll be able to move these higher level metrics. That's one of the key sort of management approaches from people who are successfully adopting this.

B (31:36)

Thanks so much Ben. Look, we've gone through a fantastic list of how to prepare yourself for successful use of artificial intelligence products. That's all about the audit, the single source of truth, getting the right governance and operational controls in place. The idea I think really, really important of starting small, small. I love the thought you had or the point you made Ben around you can still have a transformational sort of top line vision of where you want to be, but you started in small places and actually you will learn a lot from that about whether that top line vision actually works. I thought that was fascinating. This idea of pushing beyond Q and A and I think actually understanding agentic now is much higher for me having listened to your explanation of how agentic AI can, can support that. And I think measuring value and it's not just cost or productivity, but it's actually true value for your business and for your business's customers, which in the end is who you want to serve in the best way possible. Just thinking about the sort of the next three to six months. I'm a CEO of a company or I'm a chair of a board or I'm on the leadership team. What decisions should I be making in the next three months that would really set me on the track to being successful as a company with integrated AI in my business?

A (33:05)

One of the keys for these organizations is do you have the foundation enabled for AI overall and hopefully many organizations. But yes, I've been looking at this for many years. We've started to put this in place or we have it in place now that our data, the things that are very critical to our organization are available to everyone AI. Now I know many organizations are still working on this, getting the foundations so that data is available for AI. And then one of the keys is then to say how can I have AI specifically and typically in the form of these AI agents that have these objectives and these goals and then able to then often access multiple of these systems. Oftentimes you have an AI agent who's going to try to accomplish something and just like a person, you need to use different types of data. And so then the AI agents will then talk to other AI agents of these systems to be able to then go do something more complex. So this idea of an ecosystem of AI agents that all specialize in their area, but then come together to then accomplish a bigger task that I think is the world of in 2026 and beyond, where you start to have AI agents that can do more and thus provide more of this value. And so sort of focusing on that. I've got my data, I've got it available to AI, but now I need AI agents and then how do they qualify, operate to then accomplish more things? That's kind of the next generation of challenges that many platforms, many software companies like Box, we work in this, in agentic ecosystem are providing. And then now it's time for organizations to start to see how they can use that to then solve all the kind of challenges that they're interested in.

B (34:32)

That's the perfect place to wrap up today's discussion. It's clear that AI ready companies aren't the ones, as we've discussed, with the best models or the most expensive tools. They're the ones with the best foundations. It's about understanding your data, breaking down silos, experimenting with purpose, scaling intelligently and measuring the value that really matters. Thank you so much, Ben, for joining us for this fascinating conversation. Part two of our podcast series with Box. Thanks to you, of course, for listening. This podcast was brought to you by Intelligence Squared in collaboration with Box sa.