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AI is changing how we work, but the real breakthroughs come when organizations rethink their entire foundation.
This is AI-First, where Box Chief Customer Officer Jon Herstein talks with the CIOs and tech leaders building smarter, faster, more adaptive organizations. These aren’t surface-level conversations, and AI-first isn’t just hype. This is where customer success meets IT leadership, and where experience, culture, and value converge.
If you’re leading digital strategy, IT, or transformation efforts, this show will help you take meaningful steps from AI-aware to AI-first.
Ravi Malick (00:00):
It's important again that people understand how the technology works, how it will work in certain conditions, how it'll work in the absence of information, in the absence of a skill that it's been trained on or optimized on.
Jon Herstein (00:18):
This is the AI first podcast hosted by me, John Herstein, chief Customer Officer at Box. Join me for real conversations with CIOs and tech leaders about re-imagining work with the power of content and intelligence and putting AI at the core of enterprise transformation. Hello everyone and welcome to AI First Leadership, the show where we explore how AI is shaping the future of content, enterprise workflows and innovation. My guest today is Ravi Malik, the CIO of box, and I'm your host, John Hurston, chief Customer Officer. At Box each episode we talk with CIOs and AI pioneers to share real world insights you can act on today. Let's dive into today's conversation. Robbie, how are you,
Ravi Malick (01:06):
John, how are you? Good to see you.
Jon Herstein (01:08):
It's good to see you too. Let's start with a little bit of an introduction of you and your background. You and I first met when you were actually a box customer, so you talk a little bit about your career.
Ravi Malick (01:19):
Sure. My career has been a little interesting getting to the CIO seat. Most recently, as you mentioned, I was a customer. I was at a company called Vista Corp. I was the CIO there for several years. I was basically the CIO at TXU Energy, which is the retail business unit. So I spent about 11 years in the power generation, wholesale and retail industry, but didn't start my career in IT or energy. Actually started in investment banking, trader, institutional equity, fixed income, and then did consulting for several years at PWC, which is really where I kind of cut my teeth on the whole enterprise technology thing that was happening at that time. Always been a technologist at the bank, taught myself vb, automated models, reporting, et cetera. So I've always had that kind of inner technologist is like to say, technology at the heart business in the mind, and then went back into finance financial services for a little while before I made the leap, joined it at TXU Energy and haven't really looked back since. That was about 15 years ago and now I'm dating myself here.
Jon Herstein (02:42):
We've all got that problem. So you joined Box when?
Ravi Malick (02:47):
I have now almost four years. It's been four years. So I joined Box after taking a little time off and my role at Box is a little unique in that I split both internal and external. So internally I do the things that CIOs do, running IT and security and operations and externally I spend a lot of time with customers, with our sales teams, our customer success product and product strategy. And so it's a really nice blend of the first half and second half of my careers and I really do enjoy it. It's a great time to be at Box,
Jon Herstein (03:24):
And one of the reasons I think your role is defined the way it is at Box is that we spend a lot of time thinking about this idea of Box all in Box. So how do we leverage our own product, make our business better, and you being able to take that message out to our customers and prospects is incredibly helpful. Can you talk a little bit about this philosophy of Box on Box customer zero, whatever you want to call it, and just keep in mind that a lot of what we're trying to get from the podcast to our audience is practical advice and tips on how to think about their own business. So talk a bit about Box On Box.
Ravi Malick (03:57):
Yeah, so this is basically the concept of drinking your own champagne. How do we leverage and run our business on Box and basically push the platform to its limits and provide that feedback into the product roadmap and the product strategy. As you know, we've really formalized this over the last couple of years with your organization actually being set up as a customer doing the same things that any other customer would be as Customer Zero, which means engaging with our customer success team, having a rep assigned, getting product roadmap updates, doing quarterly reviews, all the things that you would want to be doing as a regular customer and being able to again, provide that feedback loop. One of the things about being able to go out and talk about how you run Box On Box, you really need to implement your own best practices. So one of the first things we did, we looked at our instance and our tenant and said, okay, we probably need to go implement and clean up a few things here.
(05:07):
The company having grown up on the platform and with help from the customer success organization and a small team that have my organization, we really made sure that everything from a security standpoint, from a management standpoint, administration was really top notch and at a point where we then can then take that out to customers and say, Hey, look, this is how we've done it. This is how we run things. And that's really a big part of the intent of that program, is to go out to customers and show them how we do it, how we integrate, how we leverage the integrations, how we leverage workflows, the automations, and be able to provide a real practical practitioner to practitioner viewpoint of, you know what, your business does run on content and we understand that and here's how we do it.
Jon Herstein (06:01):
Are there a couple of examples of use cases that you're particularly proud of that we've leveraged Box for?
Ravi Malick (06:07):
Yeah, so we're a big Salesforce shop as well, so the integration with Salesforce and Box As being Box on Box Box is our single content and unstructured data platform across the enterprise. I think the integration with Salesforce is probably one of the more meaningful and impressive aspects of how we leverage it to where all of the content, all of the information associated with a particular opportunity or a customer gets stored in Box, but even some of the automations that have been associated with that, setting up a new opportunity, a new count, whatever it may be, auto creating those, the directory, the customer information auto-populating as needed really does. It reinforces the structure that we've put in place and gives our sales teams and our go-to-market teams, the guidelines and the field of play, if you will, that they go execute against.
Jon Herstein (07:14):
Are you aware of any sort of efficiencies that we've gained by leveraging some of these capabilities?
Ravi Malick (07:19):
Yeah, so I mean kind of drafting off of that, some of the other really powerful use cases that we've seen with the platform, and we will start to get into ai, I'm sure about how we're thinking about it there as well, but our employee onboarding, our employee onboarding is we regularly hear from boxers that are onboarded and start with the company that we have one of the best. A big part of that is that we leverage Box, we leverage box from the point of engagement with a potential candidate all the way through to the point of onboarding with the company, and then we continue to extend that with as part of the onboarding experience with a Box 1 0 1 session that everybody goes through. So that includes leveraging Relay for automated workflows, leveraging box sign for signatures for employee documents and signatures. It's really another great example of how we have leveraged box to manage and own a critical process for us as a company.
Jon Herstein (08:27):
Yeah, I'll give you a pretty cool example from my own org. We just hired a vice president for our customer success management function, and we actually leveraged box, not just a bunch of files and folders that were resources for him to begin the onboarding process with, but we actually created a hub for him, put all the content in the hub and what yeah is actually used AI queries to ask questions about the organization, the people, the strategies, previous PowerPoint decks and team recordings and so forth. And it's been a really, really cool part of the onboarding process for him.
Ravi Malick (09:04):
And I mean just on that note, what we've done with your organization around using a hub with AI for product information and putting all the product information in there so that CSMs and CSRs have the ability to go into that hub, ask questions about the product if a customer has a question. And so I mean I think that has two benefits. One, if a customer has a question, if there's support issue, we can get to the answer faster and get that information back to a customer faster. And the second is when you think about the onboarding curve, typically in a support organization that could be anywhere from three to six months to where somebody is effective, really effective in their job and doesn't require having to reach out in other areas. And I think we've seen that be reduced pretty significantly because now they have the ability to access what was kind of tribal knowledge in a hub ask questions query, and that's part of their ramping up an onboarding process. And so they're learning faster, right? They're becoming more productive and more effective in their roles faster,
Jon Herstein (10:19):
Completely agree, and just that access to all the information that's relevant to them and their role is incredibly important. So we've talked about business process and some of the efficiencies gained there. Obviously we'll get to AI in a few minutes, but I want to touch on one other topic that is always near and dear the CIO's hard net security given your prior roles as CIO, how maybe you used to do things versus how you do them now and what the impact is from a security perspective.
Ravi Malick (10:44):
So security is always a top of mind mean that goes without saying, you think about what we do, what we have in the platform across all of our customer base, highly sensitive information, not just from regulated, highly regulated industries like financial services or health sciences, but government information as well. Being FedRAMP high, obviously there's a fair amount of sensitive government information that's in the platform as well. So security certainly from a product perspective is threaded into everything that we do in terms of safeguarding that information, making sure that all the appropriate, not just security measures, but compliance measures are applied that just drafts right into the IT organization as well and making sure that we are striking the right balance between boxer productivity and security. In any organization, people are your biggest risk, they're also your biggest asset. And so how do you make sure that you balance those two, enable people to be productive, execute on ideas and initiatives while making sure that they're doing in a very, very secure manner.
(11:58):
One of the challenges I think a little bit different than when I was at RA is at a technology company, I basically have, you have 3000 people who want to run their own personal IT and want to do things their way. So it's a great problem to have too because you get lots of ideas, you get lots of exposure to different aspects. The challenges is that most problems are solved with an application. And so getting people to funnel into what we have existing and making sure that things that are being experimented with are being done in a safe and secure manner versus time of ra, highly regulated industry, obviously power generation, much more of a top down, this is how we have to do it much more locked down in terms of the environment. Both have pros and cons, and I think both always come with the challenge of how do you innovate effectively? How do you make sure that you're maximizing your investment in technology and doing it with an eye towards business outcomes?
Jon Herstein (13:10):
Yeah, it's that sort of constant push pull of standards and compliance and security versus maybe it's not versus, but vis-a-vis innovation and how do you, people thinking on their own and coming up with ideas and some of those may make it into the mainstream of what you support from an IT perspective and others may not, and how do you make those decisions?
Ravi Malick (13:33):
Yeah, no, that's always the challenge for CIO or technology leader is how do you balance that? How do you tap into, again, how do you tap into the asset in a way all of your people in a way that is beneficial, that is inclusive and gets the best of what the organization has to offer, while at the same time maintaining that incredibly high level of security,
Jon Herstein (14:02):
Which kind of brings us kind of neatly to AI because with the advent, what, two and a half years ago now of chat-based ai, this introduced a whole set of capabilities that I think enterprises weren't totally ready for. And so maybe before we get into Box and how Box thinks about ai, maybe a little bit about your perspective on what that meant and how it relates to this idea of letting people innovate and come up with ideas versus thinking about the enterprise.
Ravi Malick (14:32):
Yeah, this was for sure a watershed moment in terms of technical advancement, having been in this game for quite some time and seeing the internet come to fruition and blossom mobile big data, some pretty critical and major events and innovations in the tech space. I think this by far just outweighs those by an order of magnitude. The speed at which it's developing, the speed at which it's being adopted and included, I think is just at a rate that we've never really seen before. And so the promise of it, I think is really what we're spending a lot of time focusing on and figuring out what does that exactly mean, and then how do we implement that in the enterprise and really drive value from it.
(15:39):
The release of chat GBT and the sort of digital assistant capabilities of this at an individual level, it was amazing. And to see how quickly it was adopted and how quickly people were experimenting and incorporating it some faster than others was sort of, I would say that's kind of phase one. Now. The next phase is really how do you translate that to enterprise value? And I think that's what we've been focusing on. Certainly. How do we look at that? How does it make sense across an entire business process and how does it really drive, not just efficiency, because I don't think this is just about cost and how do I reduce cost, but scalability, how do I scale? And I think that's what I'm really excited about is the promise of, and I think the realization of real material exponential scalability with technology.
Jon Herstein (16:42):
Yeah. Well, and I think the other thing, to your point about it's not just about cost. I think that the gut reaction on things like this where there's some new technology is, oh, how do I apply it to do the things that I do today faster, more cheaply, more efficiently, et cetera? And that's fine. And obviously there are some applications of AI for that purpose, but I think what's more interesting are things that either you hadn't thought of doing or couldn't do either because the capabilities didn't exist or you couldn't possibly throw enough people at it. And I think that's what's really interesting is where you can start to, and Aaron talks about this a lot, right? This idea of you can actually unleash things that you just couldn't have done before,
Ravi Malick (17:23):
Couldn't have done before. Absolutely. And that's, I think that gets back to the real ability to scale is being able to unlock that, unlock those things, those ideas, those concepts, those innovations, whatever it may be. Because it was unobtainable before, to your point, you couldn't throw enough people at it. I mean, take for example, metadata, metadata population at scale. This has been elusive for years. And if you've ever had to do an implementation where metadata was a part of it, you always found yourself in these, how do I minimize the scope? How do I reduce the scope to only what is essential? Because the reality was the tech to do it was very limited. The effort was incredibly manual. We're talking multiple, multiple folks working on an effort for months, in some cases years to populate metadata to apply it to an existing corpus of work, to do it to newly generated content. And what we have found is that generative AI reduces that to minutes.
Jon Herstein (18:42):
Yeah,
Ravi Malick (18:42):
Probably grossly underestimating the level of effort that's been reduced as a result of ai.
Jon Herstein (18:49):
And I think one of the key reasons why a lot of the legacy ECM projects just didn't ever pay off the way they were expected to was this challenge of metadata. And if these things to be effective had to have accurate, concise, up-to-date metadata, and you were relying on people to make that happen, error prone, people didn't want to do it. It was taxing. And so what do you wind up with is people saying, well, it's too much trouble to put this content into this ECM, so I'll just manage it somewhere else. And so you never got the benefit, right?
Ravi Malick (19:23):
Yeah,
Jon Herstein (19:24):
For sure. One of the areas that you've been looking into for box that takes advantage of these metadata capabilities is actually in legal and contracts. So can you talk a little bit about that use case, what the possibilities are there? Maybe what some of the constraints were historically and what sort of capabilities this unlocks?
Ravi Malick (19:41):
Yeah, so this is excited about this for a number of different reasons. One, every company has this problem. Every company has to deal with contracts. This is an area that has typically taken a fair amount of energy across numbers of teams, depending on where the contract originated, who's the ultimate stakeholder. I mean, you're talking sourcing, you're talking legal, you're talking maybe one or two business teams associated with signing a new deal, renegotiating renewal, even employment. So you think about the number of sectors or areas where contracts can be applied to across a company. Then you think about how do you manage that? How do you manage it at scale? How do you make sure that you aren't in a situation where you have overextended your liability, you have over indemnified, you've created a riskier situation that you're aware of either with a customer or with a vendor.
(20:49):
This is all of that really pertains to metadata and being able to extract those super salient points out of a contract, elevate them, and then organize and structure all of that unstructured data in a way that makes sense, makes sense to sourcing, it makes sense to legal, to where it can be queried. You can understand, hey, out of I have a thousand customers and I have a hundred contracts that are custom that well, that's a big deal because you want to understand those terms. Do you have a hundred unique contracts? Each one of those is unique, or does that a hundred subset break down into categories as well? And the only way to do that is through metadata. And then now you can use that data to drive dashboards, to drive deeper insights, to drive analysis, to drive roadmaps. So as a pretty much a hundred percent SaaS technology stack, it's incredibly important for me to know when my renewals are, how big, not just the spend of those contracts, the renewals, cancellation policies, indemnification liability.
(22:03):
Because if we are developing a roadmap that requires that we're going to consolidate and we're getting rid of applications, I need to be able to easily extract that information and apply it to the roadmap from, I mean, just from a cost and a risk management standpoint. And that applies to any part of the business as well. And then if you're looking from a legal and sourcing standpoint, the ability to at a macro level understand all of that information and break it down in a way that I can understand my risk exposure, I can understand my renewals, I can understand all of the customers that I have in different industries in different spaces. On the sourcing side, I understand my vendors, who are my critical vendors, where do I need to focus on potentially renegotiating contracts? I mean, it drives so much activity around business execution and scalability. Again, that just the potential that it unlocks is really exciting.
Jon Herstein (23:05):
And that's just in one business area of contracts. And you can apply that to all sorts of things
Ravi Malick (23:10):
Or applying that to marketing content and context creation and organization and campaigns. And obviously we do different campaigns based on the business size or the region, geographical location. So you start to think about metadata in that context, and again, getting back to how companies, they really run on content, on unstructured data and the importance of it to be able to harness that and then leverage it for the benefit of the
Jon Herstein (23:36):
Company. And I think the key concept in what you were just articulating is this idea of bringing structure to unstructured content because the insights you're talking about, it's not that they weren't available before. Those contracts have existed forever, those clause has been in those contracts, but finding them and finding them ones that are unique or non-standard or higher risk for the company, that was the hard part. So the idea of bringing structure to unstructured content is really the key. And at Box, we refer to that as intelligent content management. And so I'm wondering what does that mean to you when we say intelligent content management, what does that actually mean and how should customers and other companies think about that idea?
Ravi Malick (24:17):
Yeah, I think it's a few things. And if you think about how the tech space and how content has evolved over the last 30 to 40 years, we had back office automation around core business processes, whether that was manufacturing or you were in tech. I mean, that was the first wave, creating these foundational core systems that very quickly evolved into the digital experience and the customer experience and web and mobile and how do you optimize and tailor that. And that created now a new set of information bodies both structured and unstructured. And then now you have ai, which is leveraging a lot of the information and the artifacts that have been created through those first two phases, but now also being able to tap into all of the unstructured data that was associated with that. So I think it's the evolution and the mixing of technology areas to now ultimately be B, have an experience and drive.
(25:40):
Again, real scalability, real automation and intelligent content management means, one, you have the ability to understand where your content is, right? So you have an architecture that allows you to take advantage of all of your own structured data. Two, you have the ability to leverage and layer AI on top of that and get the insights and analytics and extract value, extract metadata, use that to then drive workflows. And then I think three, being able to integrate and expose those capabilities across your environment. So whether that's integrating back into core apps that leverage that content, developing agents as part of this agentic workforce that then can expose that content and interact with other applications. So I think when I think about intelligent content management, it's those three things in combination working together.
Jon Herstein (26:38):
One of the challenges that I hear a lot from the CIOs that I talk to our customers is this issue of, okay, AI's becoming very, very prevalent. It's being introduced into basically certainly every SaaS application. I think it takes a bit longer if you've still got on-premise applications to get those capabilities. And if you're an enterprise that has 200 or 300 or 400 SaaS applications, all of a sudden you've got to think about the AI capabilities across all of them. And that presents a real challenge for CIOs because the question is, well, which of these things actually add value versus being some sort of gimmicky capability? So maybe just sitting in your CIO chair, you can give some thoughts to the audience around how do you think about that? How do you think about vetting those, determining value? And then we'll get into governance in a few minutes, but just starting with just how do you find the signal and the noise there?
Ravi Malick (27:33):
I think you go back to some of the foundational principles that have always been part of it, any sort of successful technology roadmap and successful information implementation of technology, and that's you get back to the value. What's the value that's going to be created? SaaS certainly led to a democratization of technology across the enterprise and that it was very easy to turn things on, very easy to start using things. I think AI continues to amplify that. And so you really have to get back and focus to, it's not that difficult. It's the simple things is ask the question, what value is it creating? That's number one. I think number two is what does the architecture look like? We know not all AI is created the same. Different models will produce different results under different conditions or under certain conditions. That probably is going to be the case for a few years, as well as how apps incorporate that into their technology is different since some it's, I'm slapping this on
(28:46):
To my existing platform, whereas others, it's actually fundamentally part of the architecture and part of the core part of the product. And so I think between those two filtration points of view, you can start to get rid of the noise. I'm very fortunate in that we run box on box and the first answer to any sort of AI question is what can box do it? But leveraging that as a point of advice for other CIOs, I think that the more you can simplify when somebody comes with an AI use case and separate the ai, look at the use case, what is the use case? What's the value of it? Being able to answer the question, well, can box handle that, can fill in the blank handle that? I think that at least gives you a unified point to start with. If the answer is yes, okay, very simple, right?
(29:44):
Well now how do we go implement it? If the answer is no, okay, well, what are the other solutions that we're looking at? Why do we have to look at them? What is the uniqueness about this use case? What is the uniqueness about the value that it's going to create? And that's kind of the operating principle under which we function at box. If box can't handle it, why can't it handle it? Well, there are certain things in areas that we're probably not going to play in as well as others. Code generation, really hardcore coding, product development, IT coding, application development, that kind of stuff. That's not really our space. So it makes sense for us to maybe look at some of the other platforms out. There are probably maybe a handful of unique use cases where it doesn't necessarily make sense for us, and we will evaluate those as they come along.
Jon Herstein (30:39):
It seems like the most value is going to be added when AI is sort of seamlessly embedded into an existing process or workflow as opposed to being bolted on. And so as you said, if it's a workflow like an engineer building code, you'd want those capabilities to be right there in the tools that they use to build code.
Ravi Malick (30:58):
And that gets back to the creating a user experience. So again, the productivity side of things. How do you create something and do something that's both secure, but value add? And this is a challenge that's sort of been in play for quite some time, again with the SaaS market, creating a little bit of this is that disjointed user experience. And I think you hit the nail on the head, that is an ideal situation and that somebody in the tool that they're in, that they spend the majority of their time and they're able to leverage AI and utilize it. And I think that's a key part of our operating tenant as we think about integrations and how do you expose AI capabilities into other parts of your tech stack that orient around content. I think that's incredibly important because as I mentioned, AI sprawl is a thing, and you could in a matter of a few months, find yourself in a situation where you've got 30 different AI platforms, capabilities, functions, whatever
Jon Herstein (32:11):
Being
Ravi Malick (32:11):
Utilized or exposed across the environment. And we've seen some of the interesting side effects of different AI interacting with each other and what kind of results that produces.
Jon Herstein (32:26):
And then it gets into the governance questions of how many different models are being used, what are they being used for? Where's my data going? Et cetera, et cetera. Right? I do wonder if you think about the relatively early days of SaaS, for a long time the fact that the software was in the cloud was a marketing asset. It was like, oh, this is cloud-based. And it was a way for people to differentiate from legacy and on-premise and so forth. But it gets to a point where it's so mainstream that no one talks about the fact that it's a cloud-based solution because everything is right. Do you think AI is evolving to that same point where right now, again, it's a marketing asset to be able to say, oh, we've got AI embedded into the product, or we've attached AI to it or what have you, but two years from now, three years from now, is it just going to be expected that there's AI features embedded into every software product?
Ravi Malick (33:14):
I do. I think it's reasonable to have that expectation. I think you have a very telling indicator of that is if you look at the current generation of high school students and middle school students, I mean they're already, IT is, I think they have an expectation that they can use AI and they will use ai. And so I think as that generation starts to move through higher ed and that into the workforce, which is certainly going to be depending on where they are in that spectrum, the next three to five years, that expectation will absolutely be there. And you're already, again, if you want to look for leading indicators of this, go no further than Amazon or any sort of eTail platform, and you'll already see the effects of AI embedded into that user experience. And it's almost like do you expect now if you do a Google search, that you're going to get an AI summary of those results with highlight key points? I think I sort of expect that and look for that now for sure. And Amazon, do I expect I can get a summary of all of the user reviews and make an educated decision without having to spend time sifting through
Jon Herstein (34:37):
Them? And there's a point where you won't even really care that it's AI making that possible. It's just going to be an expected capability of the products that you use, right?
Ravi Malick (34:45):
Yeah. But I do think where we need to make sure that we keep our eye on this and continue, there's some discussion around shopper agents and around making decisions. And for me, I think that's a very critical point then that the human in the loop, I think still needs is very important. Even with all the expectations of AI and the benefits that it's going to get and how we're going to expect it to be as far of that experience, I still expect to make the decision. I still expect to make the call on whether I buy something or whether we implement something or whether an action we take with a customer or an action that we take internally or decision we make internally. I think that expectation should remain prevalent.
Jon Herstein (35:40):
Well, there's two components to it. One is who makes the ultimate decision? And then two is who has the accountability for that? And what we're seeing is the human in charge of that function still has accountability for the decision that was for the tools that were used. You're not going to be able to just say, well, AI generated that, so it wasn't me. You're accountable.
Ravi Malick (35:59):
And if you think about maybe the workforce of the future where you have a mix of people and agents that are getting that work done, I don't think the accountability or the responsibility matrix shifts or has some new kind of model. The accountability is still the manager. If you're a manager of both AI and people, you're still ultimately accountable.
Jon Herstein (36:21):
Totally agree. Just to your peers in the CIO community, what other things would you recommend that they think about as we start evaluating, looking for value in these AI solutions, maybe on the governance path? Just any tips or thoughts for other CIOs
Ravi Malick (36:39):
Finding the balance, and this is, I'm very much a practicing pragmatist when it comes to technology, and I think what we're seeing, certainly what I'm seeing is a trend to be more that way. I think early on, if I was pressed, if I were pressed by somebody to say, Hey, can you define two camps? I would say, yeah, there are two camps. There are those that are blocking AI and there are those that are leveraging it and experimenting. I think that has started to shift a little bit in that the folks that were blocking early are moving rapidly into the experimentation and leveraging phase. So very rarely do I encounter somebody that is just outright blocking it. You could kind of break that down by industry, and I get that there's are regulatory aspects of certain industries where they have to do it, even just optically, because I think the reality is people are going to use ai.
(37:35):
This blurring of work and personal has been going on for several years. And so whether if you're blocking it at work, I promise you people are using it outside of work and they're probably using it work things, you should just kind of have that expectation. And so I think leaning into that, leaning into, I think you have a technology now that will enable people who have maybe been hesitant with technology or feel like they don't know technology all that well, to now be a stronger part of the community, a stronger advocate, and have deeper understanding of how to leverage this in their roles. And for enterprise value. I would say my number one advice, lean in, right? Educate. I think the number one thing is one, educating yourself, but then also educating your organization. I think it's an opportunity for CIOs to really, again, lead the charge and be very center of how this really changes and evolves the business. And that's educating peers, educating colleagues, educating the board, and really understanding and articulating, Hey, here's the value, here's where I see this can go. And providing some of those use cases and providing a way to experiment and start small and iterate.
(39:08):
I think those are the things that are, I'd say our where to focus.
Jon Herstein (39:14):
Yeah, I mean, it's so clear to me that companies are looking to their CIOs for guidance on all this. You're the technology expert, you're the thought leader, and it's not just IT as an enabler for the business. The way we may have thought about it historically, but actually guidance and guardrails, I think is what businesses are looking for. And to your point, CIO is being on the leading edge of that and bringing solutions. And interestingly, your point about experimentation, here's how we should start going after these things. It's just so new.
Ravi Malick (39:50):
Yeah, very much. And you're seeing the advent of the chief AI officer, right?
Jon Herstein (39:58):
Oh, interesting. Okay. I've not heard that.
Ravi Malick (40:01):
Oh, yeah. Oh yeah. Yeah. That is definitely a role that is coming up. And look, I think in certain situations, and we've seen this in the past with the chief data officer and the chief digital officer, there is probably a need to create some focus and some elevated leadership to go drive this and take advantage of what the technology has to offer. But ultimately, you see those roles start to fold back into the core it because at some point you have to incorporate your legacy platforms, you have to tie back into them, you have to tie back into the things that the company has been, the foundations have been built upon. And so I think it's really important to make sure that that tie exists, right? Again, I'd encourage CIOs to lean in if your company has decided they want to chief AI officer, that should probably be your best friend. Because I think ultimately whatever is developed there has to at some point to really create true value has to tie back into what you've already built and developed.
Jon Herstein (41:07):
Can we go back to the idea of experiments briefly? Because I think one of the things that I'm seeing a lot is lots of experiments, lots of pilots, but making that jump from that to, okay, we're comfortable enough with this to go into production is still bit of a gap. So any thoughts on that and how people should approach that decision of taking something from pilot to production or ga?
Ravi Malick (41:33):
Yeah, again, I would say the great thing is that we have well developed processes for doing that. I don't think AI should have, its sort of a separate process for how you bring new fruition. Maybe it needs to have a faster process and maybe you figure out how do you maybe get it to the front of the line?
(41:54):
But again, there are well-established practices on how you develop and deploy technology in a safe, secure, manageable way. I don't think AI is really any different. And when you start thinking about agents and how you deploy agents and manage those agents, I think you are going to want those processes because if you're in a situation where anybody can deploy an agent anywhere in the environment, and we saw some of this a little bit with RPA, you can get off the rails pretty quickly and have situation. So again, it gets back to the, this is technology. You will need people who understand how to manage, administer secure technology in a way that maximizes the investment, but also does it in a way that continues to be scalable and absolutely continues to be secure. So maybe that you can use AI to maybe identify pinch points in the process and figure out how you can be more efficient about doing it. But I think you still have the concept, right? Again, ultimately these things are going to tie back into the core, your production data, your production systems, things that impact how the business operates, things that impacts the customer. So I think that discipline in how you deploy the technology and bring it to production will still be essential.
Jon Herstein (43:21):
Yeah. Well, I love that point that you made at the beginning of that, that you have processes for doing this today. Don't think of this as some completely foreign concept. So we've talked about value, but there's sort of three things that I think about a lot in my role. Customer success and value is one of them. In other words, are you getting value from the solutions that you're implementing? But the other sort of two aspects that I think about a lot are culture and experience. And one of things we haven't touched on that much is the impacts to culture and change management and the actual employee experience or the customer experience when you roll out these tools that are so different than what we've had in the past. So can we touch on that a little bit? Just how you think about the other two things, culture and experience in employing these kinds of solutions.
(44:10):
I've been thinking about this a lot recently. I think AI is one of the first pieces of technology where people's point of view about it in their professional life could be very different from their point of view about it in their personal life. There are a lot of people who have a lot of concerns about the implications of AI for society, for their children, for all sorts of things, but in the workforce completely embrace it because it makes them faster, more efficient, higher quality, et cetera, et cetera. And I think this is a very interesting thing. So that to me, that's one of the things that's very different about this. People didn't have dread about cloud or mobile in the way that I think a lot of people do about ai. That's a great point. It's a little bit of cognitive dissonance for people, I think, in thinking about just the implications of AI outside of their work life.
(45:00):
So I think that's one thing that's different. The other thing is I think people still wonder, is it okay to use ai? Am I cheating, right? If I use AI to help me do a work task, do I have a leg up that I shouldn't have? Or is it ethical? And I think there's a whole aspect of culture and change manage just around what is okay and what's not okay. And if I'm submitting a piece of work to my employer that I didn't entirely do on my own, shall I feel fine about that or not? Right?
Ravi Malick (45:35):
No,
Jon Herstein (45:36):
Those are two things that strike me as being very different about this than anything else.
Ravi Malick (45:40):
I think that they do create this different mindset, and as you mentioned this cognitive dissidence, I think that people are probably less, and I think you highlighted this well, they're less likely to use that filter at work. I think they're like, Hey, I used AI to create this work product. Do I feel, how do I feel about that? I think that's probably less likely to happen, which makes it really important to ensure that people understand the potential regulatory and compliance aspects of AI within the workplace. So education, as I mentioned earlier, and making sure that folks understand that across the enterprise I think is super important. A lot of that is very similar to how we educated people around data, residency, privacy, those kinds of things.
(46:35):
I think that's super important. I think you talked about experience, skillset sets, things of that nature. I think it will be important to tap into the experience of those that have gone through these technology evolutions and have seen how these things play out and can understand how to manage that, right? I think it's important that we make sure we learn from past history and we take advantage of that and incorporate it. So governance is important, and that's something we recognize very early on at Box, the making sure that we understood and we conveyed to our customers our principles around ai.
(47:28):
What we believe is incredibly important to this safe and ethical use of ai, both how we want it to incorporate in the product and how we use it internally is something that has been very important to us because we've seen the challenges and the potential for bias. When you think about how AI is used in the hiring process, the interviewing process, things of that nature. We don't want to reinforce things that have negatively impact people. We've seen issues with hallucinations and that in the absence of concrete information, AI is perfectly fine saying making up stuff. It's important again that people understand how the technology works, how it will work in certain conditions, how it will work in the absence of information, in the absence of a skill that it's been trained on or optimized on. And that gets back to the human in the loop and why that continues to be important and why I think we have to be very meticulous and very deliberate about how we deploy the technology, what capabilities we give it, what responsibility and accountability we assign to it.
Jon Herstein (48:48):
But it sounds like overall you're an optimist.
Ravi Malick (48:51):
Yeah. Lemme be clear, I'm a glass half full person, right? Because I think from the very start, I've always been an efficiency person. My mind has always been around efficiency. How can I do things more efficiency so I can basically unlock time to go do other things, whether that's work things or things that I enjoy. So I always have this goal in the four hour work week or the eight hour work week, how can you achieve your goals and outcomes and potentially even exceed them by being as efficient as possible? And what does that free up time for family, kids, other interests, potentially new creative ideas.
(49:36):
So I am an optimist and how I think this technology can be applied and can really unlock a lot of things that have been elusive for a while. I'm a firm believer in the scalability, and you see this, we've seen this with, as technology has evolved, the cloud enabled that reduced the barrier of entry for software companies significantly. We've seen now AI has amplified that a hundred, even a thousand times more. I think it can be ultimately can end up being a great equalizer in communities that have been underserved, underdeveloped potentially gives folks the skillset, the capabilities to be on a more even playing field,
Jon Herstein (50:28):
Right?
Ravi Malick (50:29):
With those that were a little more privileged. So I'm an optimist. I'm definitely an optimist. The potential is there, and I'm very excited about it.
Jon Herstein (50:42):
Well, I am too, and I think that's probably a great place to leave the conversation on that note of optimism and excitement about what's possible, the ability to scale, to make things more efficient, to make people more productive, to set more of a level playing field for folks to really be successful and hopefully to free up time for people to do things they enjoy. Right?
Ravi Malick (51:03):
Absolutely.
Jon Herstein (51:04):
Well, Ravi, it's been a great conversation. We're very lucky to have you leading our technology teams here at Box and to have you out in the world talking to our customers and prospects about the possibilities and the capabilities, and really, really appreciate you. So thank you.
Ravi Malick (51:18):
Thank you. Well, it's easy when you work with somebody as awesome as you are, John, just a little promo there. But honestly, when you have people around, you have teams around that lean in heavily that are excited about it and bring the excitement and that innovation to the company every day and to our customers every day. It makes it easy. It's easier. I'd rather be in a situation where I'm having to corral people and pull back on the reins than having to prod people to get to do stuff. So it's a great problem to
Jon Herstein (51:53):
Have. Yeah, it's a great point. I am saying to my teams, keep pushing, keep pushing, and let's make Robbie cry, uncle, on all the innovation and ideas that we're coming up with, right?
Ravi Malick (52:02):
Absolutely. Absolutely.
Jon Herstein (52:04):
And it's also a good note for our audience too, is encourage your teams to pushing the envelope of what's possible, and maybe at some point you become a little bit of the gating factor in saying, Hey, hey, too much. Let's slow down, but let's not do that yet.
Ravi Malick (52:19):
Yeah, agreed.
Jon Herstein (52:21):
Well, thank you Robbie, and thank you all for listening to today's episode of the AI First Leadership Podcast. Really, really appreciate Robbie, your insights and your openness to sharing what we're doing at Box and what you're seeing with the customers and other CIOs that you speak with. Folks, if you found value in this episode, please subscribe and share it with your professional network. We've got additional resources and companion materials that you can find on box.com, and we invite you to join us for our next episode as we continue exploring artificial intelligence, content innovation, and the evolving landscape of enterprise technology. Thanks. Thanks for tuning into the AI First podcast, where we go beyond the buzz and into the real conversations shaping the future of work. If today's discussion helped you rethink how your organization can lead with ai, be sure to subscribe and share this episode with fellow tech leaders. Until next time, keep challenging assumptions, stay curious and lead boldly into the AI first era.