In this CUBE Conversation, Dave Donatelli, chief executive officer of Riverbed, joins theCUBE's John Furrier to discuss the company's third-generation AI platform and its push toward full IT autonomy. Donatelli frames 2025 as the year when foundational bets made years earlier begin paying off in real, measurable outcomes. He details how Riverbed's centralized data store — collecting telemetry across applications, networks and devices — underpins a unified agent that collapses APM, NPM and device management into a single platform. With over 300 million autonomous operations completed and 59 customers running at least 10,000 automated workflows per month, Donatelli underscores that AI at Riverbed means machines resolving issues before humans ever notice them.
The conversation also explores the six new capabilities Riverbed is launching, anchored by IQ 4.0, a second-generation agentic intelligence layer that adds contextual awareness and MCP-driven LLM routing to cut past the 25% error rates Gartner attributes to generic large language models. Donatelli breaks down AI Assurance — Riverbed's new framework for tracking which AI models are running inside an enterprise, how much they cost and how agentic applications are performing. He also details Data Express, a SaaS-based solution that moves data 10 times faster, compressing 30-day model reloads down to three days. On the financial side, Donatelli shares that customers are seeing five-to-one returns on their Riverbed investment, with some reaching ten-to-one through smart hardware refresh and software utilization analytics. From collapsing IT silos with a unified agent strategy to giving every practitioner a natural language interface into complex observability data, Donatelli outlines a roadmap for how enterprises can achieve genuine autonomous operations while keeping ROI firmly in focus.
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Dave Donatelli, Riverbed | AI Autonomous Operations
In this CUBE Conversation, Dave Donatelli, chief executive officer of Riverbed, joins theCUBE's John Furrier to discuss the company's third-generation AI platform and its push toward full IT autonomy. Donatelli frames 2025 as the year when foundational bets made years earlier begin paying off in real, measurable outcomes. He details how Riverbed's centralized data store — collecting telemetry across applications, networks and devices — underpins a unified agent that collapses APM, NPM and device management into a single platform. With over 300 million autonomous operations completed and 59 customers running at least 10,000 automated workflows per month, Donatelli underscores that AI at Riverbed means machines resolving issues before humans ever notice them.
The conversation also explores the six new capabilities Riverbed is launching, anchored by IQ 4.0, a second-generation agentic intelligence layer that adds contextual awareness and MCP-driven LLM routing to cut past the 25% error rates Gartner attributes to generic large language models. Donatelli breaks down AI Assurance — Riverbed's new framework for tracking which AI models are running inside an enterprise, how much they cost and how agentic applications are performing. He also details Data Express, a SaaS-based solution that moves data 10 times faster, compressing 30-day model reloads down to three days. On the financial side, Donatelli shares that customers are seeing five-to-one returns on their Riverbed investment, with some reaching ten-to-one through smart hardware refresh and software utilization analytics. From collapsing IT silos with a unified agent strategy to giving every practitioner a natural language interface into complex observability data, Donatelli outlines a roadmap for how enterprises can achieve genuine autonomous operations while keeping ROI firmly in focus.
Dave Donatelli, Riverbed | AI Autonomous Operations
Dave Donatelli
CEORiverbed
In this CUBE Conversation, Dave Donatelli, chief executive officer of Riverbed, joins theCUBE's John Furrier to discuss the company's third-generation AI platform and its push toward full IT autonomy. Donatelli frames 2025 as the year when foundational bets made years earlier begin paying off in real, measurable outcomes. He details how Riverbed's centralized data store — collecting telemetry across applications, networks and devices — underpins a unified agent that collapses APM, NPM and device management into a single platform. With over 300 million autonom...Read more
exploreKeep Exploring
What is your take on AI becoming autonomous, and what news or updates does Riverbed have related to that shift?add
As an experienced operating executive, what does "execution" mean to you when turning a board-level AI strategy into real, deliverable business results?add
How does Riverbed collect and centralize data (from apps, networks, servers, cloud/on‑prem) in its data store to enable observability and AI-driven automation?add
How does Riverbed differentiate itself—what is its unified agent and 360 offering, and how do they combine APM, NPM, device management and data-store analytics to simplify monitoring, troubleshooting, and delivery to customers?add
How does AI assurance (IQ 4.0) address governance and observability—specifically tracking which AI/LLMs are used, controlling costs, and monitoring the behavior and performance of agentic applications?add
How does the current AI-driven FinOps spending chaos affect buyers of Riverbed and its platform users—particularly regarding ROI and cost-management concerns?add
Dave Donatelli, Riverbed | AI Autonomous Operations
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John Furrier
>> Welcome to theCUBE. I'm John Furrier, host here at theCUBE Studios in Palo Alto, California. We are here for our annual spring check-in with Dave Donatelli, the CEO of Riverbed. Every year, we get together and look at the review Dave took over as CEO of Riverbed years ago. It's our third year doing it. Dave, great to see you again, coming in for our annual tradition.
Dave Donatelli
>> Yeah, it is a tradition. Great to see you, John.
John Furrier
>> It's a good tradition. Wanted to catch up and do a check-in, but every year, you're delivering performance. When you first started, I remember our conversation, it was, wow, Riverbed is a huge opportunity. They're sitting on a lot of data. They have a huge install base. And you saw a really big opportunity. We chronicalized that last year, you came in again in typical fashion, hit the numbers, the cadence of the Dave Donatelli playbook, really transforming the product. It's been a really interesting year. This is the year though that AI is going autonomous, and this is the year that the benefits of good bets pay off. This is the year of not just hypie things like, "Hey, I'm using models to bolt onto something, and get an outcome or an answer." It's a year of architecture, real ROI kind of driven workflows. Okay, something that, again, proud to say we've covered and you've made that bet. What's your take on this? As we look at the autonomous, you've got some big news, well, news and updates around this. What's the big story this year?
Dave Donatelli
>> Well, it's just what you said. I like to say, at Riverbed, we're a four-year overnight success. We put the foundation for these things in. We've been talking about it for the last several years, and this year is the next iteration of it, delivering real value to customers, and for the first time, really moving to autonomy, so machines actually fixing problems for people before they occur.
John Furrier
>> Yeah, the zero disruption piece of it is interesting because, this year, there's not a lot of tolerance for disruption. There's real focus on execution. There's no strategy risk in the AI game right now. I talk to anyone at board level in any functional role, whether it's a C-suite, deep tech practitioner, or a developer, everyone knows infuse AI into the business, into your operations. Okay, you can say that, but everyone's talking about execution.
Dave Donatelli
>> Right.
John Furrier
>> You're a operating czar, you've been in many leading positions. What does that mean to you, because execution means different things to different things, different people.
Dave Donatelli
>> Yeah. Well, I've always been a fan of hype doesn't really do much. It's all about the reality of what value you're delivering to people. As you said, every board on earth asks their CEO every day, "What are you doing about AI, and how's AI going to work?" And then, that CEO leaves and goes, talks to their CIO and says, "Hey, go make this work. What's going to happen?" And we see that in our space as well is that people want to see results. They need a methodology in order to do that, and they need products that actually work. And so, one of the things I'm really proud of is we ship products that really work that help people deliver real business value that saves them money, or gives them better uptime today.
John Furrier
>> The interesting thing I've noticed this year that the theme that's jumping out, this is my view, is that it's injecting intelligence into everything. AI and ultimately what you're doing is injecting intelligence into the human, the worker, the knowledge worker, the process, the network, operations. This seems to be the big thing. Last year, when you came and showed the results, you really helped with the platform, the differentiation experience that Riverbed had as a product and market share. But, the key thing, you had the data store to insights, which I thought was very interesting. Let's pull up the platform slide. I want you to review this again. This is what we talked about last year. Take us through how this played out, and set the table for what's changed.
Dave Donatelli
>> Okay, sure. For those who aren't familiar, if you look across the bottom there, there we're showing applications, networks, servers, cloud, on premise, all the places Riverbed collects data. And what we've done, and the company's been in business 23 years, is what we've done is learn how to collect data in real time, real data at scale, okay? That's your foundation. The most important thing that we talk about is what's in the center there, the Riverbed data store. And what the data store can do is collect all that data into a centralized place. And just as importantly, it's an open architecture, so it collects our data and data from all other third party apps out there. Once you can aggregate that data and organize that data, then it gets very easy to apply all the various AI algorithms to it to get to automation. And as you know, people and enterprises have struggled with data and data organization since I've been in the business. And our unique technology is that data store that enables that to happen for the space we're in. Our space is observability and this is the premier way to collect data in the observability space.
John Furrier
>> If I look at the slide, I want to just point out there's little Rs next to the components of the bottom. You made a big bet with this platform, so nothing's really changed on this architecture.
Dave Donatelli
>> No. I came into Riverbed. I've been there about three years now, and the reason I came in is they were just releasing the data store. And I knew from my experience, I said, "This is what's going to be needed going forward with AI." And then, the idea was how do you operationalize that? And the operational piece of that was the platform. We're going to go and operationalize this for customers through a platform. And once we get the platform, if you think of it as a foundation, then it's easy to keep adding onto it, which is what we're doing.
John Furrier
>> Got it. I have to get into what's changed this year. Give us the stats, performance wise, it's been a busy year for you. You got a lot going on. What was the key milestones in terms of what's been shipped? What's changed from last year?
Dave Donatelli
>> Yeah. Well, incredibly busy year. If you look last year, we shipped over 30 new products, so that's a lot by any stretch of the imagination. Everything you see on that chart, every product we make was completely refreshed, and that was really exciting. What that also has enabled, to your point earlier about real people getting real results, is we did over 300 million autonomous operations last year, and we have over 59 customers today who are actually doing more than what I call what we call the 10,000 a month club. A minimum of 10,000, some do hundreds of thousands a month, but it's AI in practice. In AI, that's helping them reduce meantime to repair, it's AI that's helping prevent problems before they occur. And so, it was really a year about showing how this stuff works and provides value to our customers. And that's what's so exciting.
John Furrier
>> Let's pull up that customer utilization slide again. There was some stats here. 300 million ... I think I go to the high number first, but let's go there first. The 300 million annualized automations, what does that mean? Take us through, get monthly and annual. What is the monthly automations? Is that scoped a certain way, because I could say automated X and do 10 million.
Dave Donatelli
>> Right.
John Furrier
>> Share what automation means in your definition.
Dave Donatelli
>> Yeah, in our definition, it is literally having a computer do work that humans used to have to do. And so, it could be ... I'll give you one. Teams cash refresh, happens all the time. Cash gets full, you have to refresh it. You can call a help desk, and go through that. Takes a lot of time. Or we can just trigger an automation, does it automatically, no one has to call, and we update automatically in ServiceNow, so you know what happened, you know what occurred, and then it's resolved. As I mentioned, we have 59 customers who are doing a minimum of 10,000 of these a month. We set that as an arbitrary number. What's the floor that seems to make impact? And as I said, some of those same customers are doing hundreds of thousands per month, and it's really limitless and it's your path automated-
John Furrier
>> These are real workflow automations that would have humans-
Dave Donatelli
>> In the past, yes....
John Furrier
>> in the loop, and time wasted.
Dave Donatelli
>> Correct. Saves companies a lot of money, and saves users downtime. Both important factors, obviously.
John Furrier
>> All right, so how ... Taking all this experience, you're now on your third generation of AI. Let's get into the architecture slide, the platforms. Let's pull that slide, because I think this to me is a big story. The eternity platform strategy, let's review this. You got the stack. Most people have just network and application. These also could be siloed companies.
Dave Donatelli
>> Right.
John Furrier
>> Take us through what you've combined in this architecture. Take us through the blueprint.
Dave Donatelli
>> Yeah, so one of our advantages is, in addition to the data store, is Riverbed individually has been in the APM business, application performance management, the NPM business, network performance management, and then, the device management business. And all three of those are individual domains that have their own competitors, right? There's different people who do those three things. What we've been able to uniquely do is take all that technology and combine it into on place. We deliver that to customers on what we call the unified agent, which is one of the discussions we had a couple of years ago when we launched it. And in this case, I got to be clear, these are software agents, not Agentic agents.
John Furrier
>> Exactly.
Dave Donatelli
>> Yeah. But, the idea behind it was your PC there, if you're in a big company, you can have 30 agents on that. Very difficult, right? People hate agents, they got to manage them, they got to update them, security, all this stuff. We have a single agent where we can take all those different technology domains and put it in one place. Once we have that, that gives us a view of if you think about it, what's your experience? Well, you're either having a network problem, you're having a performance problem on the physical device itself, but the reality is why are you doing all that stuff? Because you want to run an application. Getting to understand all three of those elements at once is really important. We do that. And then, from there, because of our data store as we mentioned before, we apply all these different algorithms to it in order to automate identification of issues, prevention of issues and results. And then, finally, from a selling perspective, we offer all this in one SKU. We call it 360.
John Furrier
>> Yeah. And you guys also have DEX, which is digital experience, digital workers.
Dave Donatelli
>> Right.
John Furrier
>> Is that right?
Dave Donatelli
>> Yeah. The device is typically what we call the DEX space.
John Furrier
>> Okay. Device, not digital workers, not like agent agents, but like-
Dave Donatelli
>> We have those as well, but again, terminology.
John Furrier
>> Okay, so go to the next slide. I want to show the domain visibility. It's the pie chart. I think this illustrates what you were just saying. Does this represent a state of the customer, or the state of where Riverbed plays, or both?
Dave Donatelli
>> It's both actually. And that's what I was mentioning before. If you think of, we sell primarily to large enterprise, and big government, and everything else. And in most of those environments, all these things are siloed. You have certain people who just look at devices, what we call the deck space in industry terms. Some people just look at applications, some people just look at networks. Then a problem occurs, and they're trying to figure out hot potato, right? Where is it? Who's the problem? Uniquely, what we do is combine information from all three. And so, by combining information from all three, we can identify faster, fix faster, have a higher resolution percentage, meaning first time fix is higher.
John Furrier
>> One of the things I love about this market is, and the benefit of theCUBE, or you have the benefit of running a company and getting all these performance and these milestones. But, for theCUBE, I get to see a lot of things. And one of the things that's happened over the past year is what's the future of AI? And then, the recent SaaS Apocalypse came out, and everyone was saying, "Oh, my God, AI's going to kill this, and make this happen better." But, the game is still the same. When you're running a large scale operation in a business, or an enterprise, you just can't unplug ERP, and your CRMs, and you can't unplug your switches and your devices. They're out there. They're IT. Some say brittle. I'll just say they're out there, they're installed. And so, I was talking with the founder of of Apple, and he had a great answer on that.
And I think this is the success format that I think you've hit with your bet in this platform slide. This unified agent layer that you've combined NPM, APM, and device into one thing is what success looks like. And that is the iPhone. The iPhone from the Apple person told me, he's like, "Look at, there was a big debate at Apple around iPhone around 2005. The number one selling product at that time was the iPod. And there was a debate. Wait a minute, we can't put the iPhone. It's going to kill the iPod." Well, what is the iPhone? It's an iPod, it's a phone, it's a computer, and an app store. A better product for the next generation. REM could have done it, Nokia could have done it. For the historians out there, Apple did it. This is what AI looks like. Success is making the better product for the market. In some cases, it could be cannibalizing existing stuff. That's essentially the bet you're made.
Dave Donatelli
>> Exactly. Yeah, some of the things we now do on software used to sit on hardware appliances. And so, it's how do you take that off a hardware appliance, make it available in software. And again, aggregation is the key. Get rid of the silos, get rid of the handoffs, make it simpler to actually operate. It's a big deal. Part of it is we're lucky, because we have all those fundamental technologies, and we're very deep in them, and you need to be deep in them to really get the true, accurate answers.
John Furrier
>> And my premise is that those ERPs, the CRMs, the companies that don't transform to that, will probably go away or slide it to relevance and in some case be replicated. But, they can do it, because the system of records, the rules for governance, there's a lot of stuff in there that's worth a lot.
Dave Donatelli
>> Absolutely.
John Furrier
>> I think, and that's very instructive, that's a very nuanced point, but I want to put an exclamation point on that, because not everyone's going to ... AI's not going to replace everything. It's going to make it better. And the good companies can be agile too. That's the key. All right, let's get into the news. With that enablement, you can go faster. This year, the big story is the new capabilities. Take us through the new process. Pull up the six new products and capabilities you're launching.
Dave Donatelli
>> Yeah. To talk about how we got to these six products is we mentioned, look, it's our third generation doing this. And so, moving to the third generation, things are changing. This is just the point you were making is that, first of all, user interface, you're going to have any user interface you want. AI enables that. We all spent years talking about user interfaces, and people design, and all that stuff. And if that's primarily your business, AI's taking that away from you. The second thing you heard us talk earlier about automations. We're doing 300 million, more than five times since the last time I was here to talk to you. Well, now AI is going to help write more automations, and they're going to get better, and they're going to be easier for people to implement. Your environment is getting ready for autonomous through that. Third thing is personalities. A lot of these tools, DBAs, right, have a DBA language. As a regular executive, hard to figure out, right? Now we have common data, and with that common data, you can interact to it by your personality. And then, finally, with these new launches that we're going to go through, is every time something new gets invented, we're in the observability business, you need to invent an observability tool to observe it.
John Furrier
>> Got it.
Dave Donatelli
>> And that's what you see ... Those principles I just talked about, those four things are what you see in these launches. First, we talk about, we introduce what we call our IQ 4.0. IQ is our intelligence layer that interacts with that data store we've been talking about. Obviously, a 4.0 means we had three other, we had many revisions before this. But, this is where we start to bring in our second generation Agentic. And so, what's the difference between first generation and second generation? First generation obviously interacts with large language models, all good. But, what you find is without proper context, it's not the most effective way it can be. We all know LLMs, if you look at Gartner numbers, they'll tell you they have a 25% error rate. And in this space, the reason why you can get to these error rates is that again, we'll look at your device. You can have this device, I can have the device, LLM will look at it and give you an answer to the same problem I have. You get one answer, I get a different answer. That's why context matters. That's what we've added here. We've added to the foundation around Agentic, contextual awareness, Agentic skills, and then the ability through analytics, and other paths we have through MCP access to train the large language model that if you're having a specific problem, this is the route you go to get to the precise data you need that's going to give you .
John Furrier
>> You have data that steers the intelligence-
Dave Donatelli
>> Totally....
John Furrier
>> not trying to make up an answer on the fly basically, and using maybe bad data or misinformation.
Dave Donatelli
>> Correct. And it learns. The fact that it learns, you're not starting over from scratch every single time. Again, that's what second generation adds to that. And again, accuracy comes from that, and the ability to move to autonomous. What it also has to it on autonomous, which big customers care about is an audit function. It tells you what you're doing, and that way, it also allows you to decide as a customer, do you want to go autonomous or not?
John Furrier
>> And the governance and observability piece becomes key, so that's a great leverage. As the Agentic world comes, they got to be observed.
Dave Donatelli
>> Exactly.
John Furrier
>> How does that fit into here? Does it fit into IQ 4.0?
Dave Donatelli
>> That's our announcement there on AI assurance. The whole idea behind AI assurance is just as you said, now that it's here, people need to be able to observe it and figure out what's going on. And there's really three elements to this. First, is what AI and large language models are being used in your enterprise, and how much are they being utilized? And we talk about there's shadow AI and in some companies they're okay with that, and other organizations they're not. This gives you a centralized place to know what's going on. The second piece of AI assurance is cost. And as we know, you get large token, some guy kicks something off, and your cost goes through the roof. Well, you want to know who's using and how much they're using. That's in there. And then, the third piece of it, as you just mentioned about it, as you write Agentic applications, someone's got to know what all these agents are doing, how do you understand them, how do you understand how they're performing, and that's part of the AI assurance.
John Furrier
>> The IQ 4.0 and AI assurance, that's all autonomous?
Dave Donatelli
>> All of it, yeah.
John Furrier
>> That's got to be set up. Is it leveraged? How does that work? Take me through, and it just happens?
Dave Donatelli
>> Well, the great news we do the work for you. Again, as I mentioned, it's built on our data store. The fundamental thing that if everybody can keep this in mind, if you get your data right, so to speak, the rest of it becomes simpler. And then, what we add to it is when we talk about Agentic, and Agentic skills, and analytics, and CP serving and stuff like that, it's all about how do I get to a more precision view of this data?
John Furrier
>> In this year's announcement-
Dave Donatelli
>> We did all that....
John Furrier
>> you did work around the data store, so there's been work done on the foundation of the data. That's driving the autonomous intelligence. Is that the way right think about it?
Dave Donatelli
>> I think the way to think about it is we've added intelligence to how you access the data that's there, and how you couple intelligent access with the LLM. The second big piece that goes with this is natural language interface. Back to those principles we talked about. By adding a natural language interface, you can just talk to it in the way you want. I'll give you a couple examples. If you work on a service desk all day, you probably work in ServiceNow as an example. You can access it through ServiceNow. If you're like me, I spend a lot of my day on Teams these days, video meetings all the time. I can access the data through Teams. If you're executive, there's a lot of different software you use. It's usually-
John Furrier
>> Multi-tool access, whatever environment I'm in, I can get it to you.
Dave Donatelli
>> Whatever environment, whatever language, and even if you want to use voice, knock yourself out and you can get it.
John Furrier
>> You and I were talking before we came on camera catching up on some other things. You mentioned there's been some customer interest in data movement. Where does that fit in here? Is that on the data foundation piece, the data express?
Dave Donatelli
>> That's the data express. Really interesting problem is the amount of data people are moving to feed their AI models is massive, and it's taken them a long time, and people are moving from prem to cloud, from cloud to cloud, you name it, the various combinations out there. Again, we have technology that we've developed. It's a SaaS-based application that moves that data 10 times faster. I'll give you some real numbers that I think are stunning. Customer was taking 30 days to reload their model. We can take 30 days down to three days. If it's 10 days down to one day. And by also doing that, we save them a lot of money, because they're spending a lot of money to move all this data. It's both speed, which is critical to people and money savings, which is also really important.
John Furrier
>> You're hitting operational efficiency, actually autonomous. The automation number is 300 million plus and growing. You have the foundation and the data, but the cost side is huge. Expand more on that, because I love the automation. I could talk about intelligence autonomous all day long.
Dave Donatelli
>> Right.
John Furrier
>> But, the number one thing that's coming out of this AI error is a lot of FinOps. Remember the cloud days, FinOps?
Dave Donatelli
>> Yeah.
John Furrier
>> We're just getting that reigned in and that's almost a decade in to cloud. AI is like, I mean, talk about chaos.
Dave Donatelli
>> Exactly.
John Furrier
>> People are spending so much money on tokens. It's just a free for all right now. And so, smart executives are like trying to reign that in. How does that play into the buyer of Riverbed and the platform users? Because that's a number one concern that's going to pop up because ROI is on the table right now.
Dave Donatelli
>> Yeah, so we proudly give all our customers ROIs. In fact, I just met with a customer last week, and in a year, we're giving them five to one with their spending on us.
John Furrier
>> What does that mean?
Dave Donatelli
>> If they give us a dollar, we're giving them $5 in hard savings. Their number's not our numbers, so meaning we calculate with them, but they determine what the savings is, and they get those savings from a number of different places.
John Furrier
>> You don't just hand wave that. You sit down and scratch on paper, or spreadsheet, or whatever their economics go to that.
Dave Donatelli
>> Absolutely.
John Furrier
>> Five to one.
Dave Donatelli
>> In that customer, yeah, some customers 10 to one. In fact, that five to one customer, I gave them eight other things in the software that they're not using yet. They're going to drive increase save. They already paid for it. It's like, all you got to do is turn it on. You're going to drive even more savings. And that's what we're working with them on.
John Furrier
>> That validates the thesis I've been saying on theCUBE, not to take liberties here, but we're in an era where you're starting to see builders, operators, and investors, not like VCs or financial investors. In AI, the budgets are so tight that the practitioners have to reinvest the savings, because what's happening, they're reinvesting savings into forward frontier projects.
Dave Donatelli
>> Exactly.
John Furrier
>> The DevOps/network people running infrastructure are actually investors, because they have to essentially look at their budgets. There's no IT master budget. Here's your budget. It's not getting bigger by order of magnitude like the costs are. You're seeing smart technical people and business people saying, "Okay, if you're going to build and operate, you got to factor in reinvestment." The cost takeout is not a strategy. It's a table stakes to feed the projects.
Dave Donatelli
>> No, it's a requirement. And I always mention to people, as a person running a company, I'm both a seller and a buyer, right? We build products, we sell, but I buy stuff as well. And when people come in my office, first thing I say is, "Okay, what's my return?" And the people can articulate return quickly, get their projects approved quickly. The people who can't.
John Furrier
>> You're an easy customer. Show me the numbers.
Dave Donatelli
>> But, I'll give you a real practical one right now is one of the things we have is what we call smart hardware refresh and software utilization. Using data, not guessing, we can figure out on all your installed, let's say the PCs here, who's using in terms of what ... Are you a power user? Do you need a bigger CPU? Do you need more memory, everything else? As we know, costs are through the roof, right-
John Furrier
>> Yeah....
Dave Donatelli
>> because of the shortages. Memory costs are up, processor costs are up. We've saved many customers millions of dollars just by having that data. On the software side, what we could figure out, and I'll give you a Riverbed example. When I got to Riverbed, our Salesforce bill, everybody had to have Salesforce. Everybody's got to have it. Half of them never opened it. I was able to reduce in half, because we got to the people who actually used-
John Furrier
>> People don't have time. They don't have the time to go after all this stuff sometimes.
Dave Donatelli
>> Right, so we can do that from a centralized location, facts, not guessing, and save people real money that they can then turn into either savings or new projects.
John Furrier
>> Dave, it's one of these things where, it's not a cliche, but it's a strategy breaking down the silos. In AI, silos kill. And you're seeing that at an executive cultural level, and in all departments, silos being broken down, interdisciplinary skills. You collapsed three major categories into one product feature like the iPhone did.
Dave Donatelli
>> Right.
John Furrier
>> We're seeing that in people's roles. The CFO's now becoming more operational, just doing a little bit more HR. HR is doing more CFO, CIOs doing a little HR. You're starting to see a blending of skillsets collapsing into roles like a DevOps engineer, or an infrastructure IT engineer, our operator has to be a builder, and operator, and investor. Builder, operator, and investor, everyone has to take on these shared functional slice of roles.
Dave Donatelli
>> It's never been a more exciting time to work. I am so excited about the rate of change that's happening, what can happen. And we can talk about this by every job. If you're a product manager, which is a job I always love, now you're not a product manager, you do the prototype. You actually build the prototype of the product, and then hand it to the engineers and go, "Go make this really work," right?
John Furrier
>> Yeah, yeah.
Dave Donatelli
>> And so, in our space, when we talk about how do you automate things, and how do you break down silos, again, this is where AI is helping, because if you look at the network data as an example, if you're not a network practitioner, this could look ... Packets can look like, what am I looking at? But, AI can aggregate that, and simplify it in a way that a mere mortal can take that, and now, you don't have to be just a specialist. You can be a generalist across multi-
John Furrier
>> And you have the AI now with the autonomous where you can actually do the analytics from a business user. I can look right at what I can speak business.
Dave Donatelli
>> Yes, exactly.
John Furrier
>> It's interesting. I was talking to an NVIDIA executive recently at their big conference, GTC, and he was a senior purse. He was just below Jensen. And after the interview was over, I'm like, "Hey, how are you doing?" And he was lit up like a kid. He goes, "I'm an engineer. And the only reason why I'm an manager, is I'm an older and I'm experienced." But, he was an engineer where all senior people were engineers at one point, or they had a core job, and he says, "I code on the weekends, and I go back to my team and say, let's do this."
Dave Donatelli
>> Some of the products you see on that sheet right there started just as you said, is a product manager went home over a weekend, and said, "I think we can do this." And then, they bring in a prototype, and so, the speed at which we can develop now is so much faster, or so much less friction.
John Furrier
>> Yeah. And you're starting to see Agentic too also create digital twin capabilities where you can say, "I want to just make a replica of myself." Skills are being embedded into products where your old thing was, oh, upskilling and reskilling, we're always on the table in any organization. You can actually embed that into the product.
Dave Donatelli
>> Right.
John Furrier
>> You can build those skills in. Dave, it's always been great to catch up with you in the spring catch up. Bottom line, where's your head at now? How are you feeling? Obviously, you're excited about the market. We all are. It's a great time to be in tech if you're an engineer or a progressive smart thinker. What's on your plate now, now that this is going to be out? What's next? What's the next horizon?
Dave Donatelli
>> Well, I think to really get to full autonomy is a journey, right? This is a very important step to it. And the next thing on the horizon is getting people to the point where we can prevent problems before they occur. We give people a better experience, and your success rate of autonomy continues to rise, and that'll keep us pretty busy this year. But, what I love the fact is, and we mentioned before, is customers are using this stuff in the real world at increasing rates all the time, and it's giving users a better experience.
John Furrier
>> Well, congratulations. And again, I'll just say I've noticed that the people who have done the work, usually a year minimum in advance, actually can reap the rewards, and if they make good bets. You have done that. Thanks for coming on. Looking forward to catching up soon again. Thanks for coming on.
Dave Donatelli
>> Thanks, John.
John Furrier
>> Dave Donatelli, CEO of Riverbed. Again, experience comes to the table, Riverbed, infusing the platform, making the bets, changing the products, unifying, but to enable change, faster innovation, leveraging all the capabilities with that platform for customers. Of course, we're doing our job here at theCUBE to bring you these updates. I'm John Furrier, your host. Thanks for watching.
John Furrier