Transforming Network Observability with AI Innovations with Riverbed’s Dave Donatelli

Hey guys, thanks with Joe. We're here with David Donatelli is the CEO for Riverbed and we're talking about how AI is gonna be applied to network observability with a bunch of new offerings that these guys are rolling out. Dave, welcome to the show.

Hey Mike, great to see you again. Good. Um, we've been talking about observability and networking for a long, long time and I guess everybody knows now there's this thing called AI in the land, but how do these things come together in some sort of, uh, primordial suit that becomes a catalyst for changing the way we think about networking and walk us through it a little bit.

Sure. Well, you know, Riverbed's had a very exciting year of innovation already. So we did a major launch in April of this year for our, in essence desktop and mobile ai, uh, around observability.

Then we did a huge acceleration launch in May and now this is our third launch we're doing right now, which is around our classic MPM products. And what we've done with those MPM products is we've not only updated the hardware aspect of them that runs now three times faster. But addition, we've added our IQ technology on top so that people can now collectively pull the data from MPM sources, from their desktop sources together and apply AI intelligence to it.

Whether that's generative AI or agentic AI or whatever, you know, all the major forms of AI available today on people spending more time observing network performance these days as it relates to applications, it seems to me a lot more of the applications are latency sensitive these days. And is this becoming something of a, uh, of art as much as it is science? Well, I think the art part is that it continues to become more challenging, right?

Because people are running applications, you know, in traditional environments they're running into the cloud, they have SaaS based applications and it gets more and more challenging for customers to sort out where problems are. For instance, in the SaaS world, although you're usually depending on your SaaS supplier to make sure that application works if there is a problem. And, and we've seen that with our customers, they've had issues.

It takes a while for them to really sort out where that issue is coming from. Is it on their side or on the SaaS provider's side as just an example. So therefore that elongates troubleshooting times, it certainly frustrates the end users who are looking for the availability of the application and therefore people are looking for tools to help sort through these issues.

And that's what we're delivering. So where are we on this AI spectrum? Because some folks early on we started with copilots and now everybody's talking about AI agents and are we just talking about something that uh, will alert me to an issue or might it go out and fix the issue?

Well, the good news is we, we offer what we call automations or remediations. So that gives you the customer the ability to put in pre-configured remediations of problems and then report what's happened in places like ServiceNow. So in essence we can automatically open up a call, fix a common problem you're seeing through automation, through AI and then also close that call out so you know what happened.

And you know, we see many of the large financial institutions as an example, using this technology to both reduce meantime to repair and at the same time reduce the total number of calls coming in since they, you know, through ai We can also do predictive work. So if we see something that's gonna run off the rails, actually launch a remediation before something goes bad. And you know, the best problem is they always say is a problem that doesn't occur and that's what this new technology allows.

So what will be the role in the network engineer going forward? 'cause everybody's having these kind of moments where they're ultimately thrilled that they don't have to do all this manual work and then, you know, somewhat concerned about what it is that they will be doing for a living. I think they're gonna be very busy for a long period of time.

You know, all we're trying to, you know, if you look at the technologies today, what they help to do is take out a lot of kind of the, in essence the drudge work of the job and let 'em focus on really the smart things that they do to really understand what's happening across the environment and fix things without, again, wasting a lot of time collecting data and trying to find the proverbial needle in the haystack. But if you look at the amount of data growth that's happening now, a lot of it actually even caused by ai. Um, everybody working in the network space has plenty of work to do even despite all the great new innovations that are out there.

Do you think that as we advance this, that there might be more collaboration across all these IT silos? Because the networking team has often been an island onto itself apart from the application development team and everybody else. And usually the one thing everybody can agree on is it's the networking guy's fault.

Yeah. A lot of the networking folks talk to me about what I, what they term mean, time to innocence, right? They spend a lot of their time trying to say, it's not me.

'cause everybody points, fingers at the network. What I mentioned with all these announcements we've been doing is all about building the Riverbed platform and it's ga it's in the marketplace today. Customers can run it.

What's unique about it is it expands applications, networks, and endpoints. So all the major things that you're gonna touch, you know, in, in terms of diagnosis or a problem, we can now look at and we put that at all that data into what we call the Riverbed data store. So we have a common pool of data across all those areas.

And in that common pool is where we apply our AI technology. So in essence, what it's meant to do is to start to solve that problem of finger pointing among silos. With unified data, you get to a more, you know, unified answer.

And by also unifying your data, you get to a more accurate answer because you have, you know, data from all these different places in one place at the same time. And uh, that speeds problem determination and certainly speeds problem resolution for our customers. So you've been at this a long time and, and I'm wondering, will the rise of AI and that common data pool start to help us to converge some of these job functions in it?

I don't think that the jobs are going away, but the way that we are structured might change. 'cause maybe we can be structured around some sort of outcome rather than around the core technologies that we're trying to babysit. Well, you know, I I I, I'd answer it slightly differently.

Here's what I do think is happening, like short term and then we can talk a little longer term. I think short term, what I see with most of our customers around the world is they're all doing some form of tools consolidation. And if you think about it, having all these very distinct point product tools really causes some of the silos that you see, right?

'cause people are just expert on their tool, then they have to talk to someone else who's expert on another tool. So clearly customers are now starting to consolidate down because they literally have dozens and dozens of tools in these very large organizations trying to figure out what's going on. So I think that's a first step.

And as that happens, then you can start to get to, to the point you just mentioned, Mike, which is then you can start to have people look more cross domain and start to break down some of those silos. AI also demands this because people, you know, what's the biggest challenge around ai For most enterprises and organizations, it's getting your data in one place in a common format. So, you know, I think the first piece that's practical that's happening right now that we see everywhere again is app application consolidation on observability.

From there then you start to centralize your data and from there then you'll start to see people again looking more cross domain would be my view of it. Do you think folks are distinguishing between monitoring and observability, or do they all just consider one extension of the other? Because, um, you know, when I think of monitoring, I'm like, well, it's a bunch of predefined metrics that we're gonna track.

Observability to me is more about I can query stuff and go look for that needle in a haystack that I might not know existed in the first place. Yeah, I, I, I agree with you. It is different and really what observability is moving towards very rapidly is actioning.

So it's one, I, I personally never liked the word observability in a sense 'cause it sounds so passive, right? Someone observing what's happening and what people really want to do is prevent problems or if they have a problem they wanna fix it very quickly. And so when we talk about automations and remediations, we have, you know, on the AI front something called predictive AI where you can keep things and and and understand that they are about ready to go south.

You need to take action, alert somebody, things like that. You make the products much more action oriented and then much more effective for customers 'cause they're getting better value by fixing things quicker or preventing problems from happening in the first place. And that to me is the exciting part of where the technology is heading.

And you know, this is always done against a backdrop of more and more complex environments. You know, it environments are more complex today than they were a year ago, uh, because of all the new technologies that's coming out all the time. So it's always this race between can the tools keep up with the innovation and vice versa.

Mm-hmm. You mentioned earlier, meantime the innocence. And I've always argued that the second most important metric is meantime to remediation after that, which is also known as meantime to return to innocence.

Um, is that gonna get faster as we go along? I mean, because the amount of time that I have an issue I should be able to reduce. 'cause I have some AI tool that will discover it faster and hopefully fix it faster.

Well, we're shipping products today that literally will alert you to your phone that says you're having a problem. So let's say you're at a scenario, right? You've left work, maybe you're out eating lunch and um, you'll get an alert on your phone, you're having a problem, which everybody dreads.

And then we, we give you in, in essence what this three different circles, network application, uh, endpoint. And from there, you know, green, green, red, well the red one means this is what AI is pointing to the problem. It will prompt, it'll tell you what the problem is and then suggests an automated remediation that you can trigger from your phone to fix.

And that's available today. General, you know, generally available now. And this technology is only gonna progress further and and quicker.

So, you know, we're on a pace to do that now and it's moving as everybody knows very, very quickly in the marketplace. The, the ability, you know, what we can do this year versus what we could do last year has progressed immensely and it's on a pace to keep going because again, the more data you have and the more you run your algorithms, the smarter they get and the more they can handle. So I think it's a very exciting future.

And I think, again, in my, in my judgment and observability, it's nothing to fear. It actually makes people's lives much more enjoyable in terms of doing their, their job. Mm-hmm.

Speaking of observability, we've always thought about networking in terms of number of endpoints and number of end users accessing. How many things in the back end will AI just throw that math out of the window? Because as I look at it, I'm like, well now there's gonna be AI agents, hundreds of thousands of these things that are essentially end users and they're gonna be calling more data than ever.

So, uh, are we gonna have just a network bandwidth issue because we're gonna have all this stuff on the front end and the back end that's gonna be exponentially greater? Well, uh, well I'll give you a real example. So, you know, we, I i, I don't, I can't mention names, but I'll say, look, we've been talking to, let's call them one of the major cloud providers and they're, they're referencing their customers who to reload their AI models is taking days, not hours, you know, days over, you know, over a week, which is a little bit like back to the future.

I feel like I went back to the 1980s right when moving data was so slow and it's just the sheer volume of data required. So that's why if you look at our acceleration products, you know, there, we grow our acceleration business very rapidly in the first half of this year. 'cause people need those in order to speed data even though the network pipes have never been fatter, right?

So they, so they need that. The new technology is driving those types of bottlenecks where people need these new solutions. And um, so it's very interesting if you, if you look at the agents themselves, you know, you're having bots now talking to bots.

What I think will be kind of the, the interesting thing to see is, you know, we allow ours to be configured by customers. 'cause they get somewhat nervous of, Hey, how many bots are you gonna create? What is this gonna do to my network?

Is, you know, as you said, is the network just gonna be too bogged down to really work through all this? So it's, it's a transitional time. Um, it is very exciting but I think it is gonna be a transition versus a revolution mainly.

'cause customers I think are not gonna wanna unleash, um, ag agentic without restrictions in their environment. 'cause they're worried about, as you said, secondary effects, like slowing their network too much. Alright, and then I'm gonna heading them back to the future.

When we were both younger, they taught us that nothing good happens when you move data and you should always bring the compute to the data. And then the cloud came along and we wound up moving a lot of data to the compute. Have we come full circle now and are we kind of going back to, well let's bring the compute to the data because uh, the networking essentially needs to be more efficient and that will work better if we have more cash at the place where the data's being accessed?

Yeah, I mean, I, if you look at our data store, so our data store by the way is a free product we make, and this is part of this, you know, announcement that, that runs all this ai, we actually leave the data in place and only call data as necessary. So our, our algorithms are smart enough to know, hey, this is a hot zone where the problem is only pull that data versus to your point, if you just tried to stream all the network data out there constantly all the time, one, you couldn't have enough place to store, you know, you'd have to have a whole different storage form just to do that. Probably couldn't scale well and would really crush your network.

So yeah, I think they agreed upon architecture out there is leave as much data in place as you can, don't move it as much as you can. You heard my example of taking over weeks for people who are trying to move and it's the most practical way to do it. Now the challenge with that, of course is getting your, your data store and ability to actually work that way.

You know, in our case it's a proprietary design we designed from the ground up ourselves strictly for that purpose. Um, most conventional technology though, doesn't work that way outta the box. So that, that requires other, you know, architectural considerations.

Ultimately then what's your best advice for folks who are in the networking space these days? Should they just sit tight and wait for all this to come to be? Or are there things they should be doing more proactively to get ready for what amounts to a new era of computing?

Well, I think first of all, I think sitting tight is, is the worst thing you can do right now in, in that sense. I, you know, to me, and I, as you mentioned, I've been doing this, you know, for decades. This is the most exciting change I've seen in my career in the sense that it really does fundamentally change everything.

And I think, um, you know, most people are gonna be like pre AI workers, post AI workers, right? It's if your career ended before ai, you, you're really gonna be kind of set back. 'cause you know, the future I think is very exciting.

I will say there's tools available to them now. Those tools do speed troubleshooting. We've got tons of real world customers already doing it.

Um, you know, I try not to overhype, you know, we, we have not solved world hunger yet. That's gonna take time. But I do think that, uh, the progression of the technology we see has, has fundamentally altered the game for people.

Like when you have people say, Hey, I've reduced my meantime to repair by 30%, I've reduced the amount of calls that come in by 30% all helpful. And it allows them to stay ahead of this ever complex curve that they're always battling. So there's real things they can do today.

And I'm very convinced, you know, if you look at our rate of innovation, we've launched 20, 85 new products in the last two years, um, with the rate of innovation, there's gonna be better and better solutions coming. So it's important to kind of get on the track to learn it. And then so you're ready as all the new stuff comes along as well.

Yeah. And of course a lot of folks are concerned about how their job may or may not exist in the age of the enterprise where we can scale more with fewer people on the IT team. However, conversely, it also seems to me that more organizations will be able to afford to build more complex networking than ever.

So might not there be ultimately more organizations is looking to hire networking people than there were before. Ai. If you look at the history of our market, it's, it's actually a history of what I call the expanding circles.

So I mean, when I first started mainframe, you know, dating myself a little mainframe was the core architecture. And you know, desktop PCs were just coming in and people were like, oh, client. And then obviously client server was a part of this whole movement and what happened, guess what?

Here we are now, decades later, we're still selling mainframes, you know, and we're still selling desktops and everybody's got mobility and then everybody has the cloud, everybody has SaaS and now we're doing ai. And I, I, my, I'm doing my hands moving outward 'cause why? Because the total market did nothing but grow, right?

There's more people working in tech than there were when I started. The market size itself, you know, now measured it, you know, in the trillions is much bigger than it's ever been. And I don't think that changes, you know, the form might change a little bit, but my joke is as well that, you know, when the cloud first came out, what did everybody say?

It's timeshare. You know, if you're, if you're around in the sixties, so, you know, we do things a little differently, but the fundamental principles are all still the same. You know, ai, I think people way overcomplicate, right?

It's about centralizing data, running very smart algorithms against it, getting those algorithms smarter than applying them to a problem in your business. If you think about it that way, it's not so scary. And, um, and again, I think it opens up more opportunities for people and I think our industry continues to grow.

And as you said, the less expensive we make things, the more use cases we find. And it's always been that way, and I don't think that's gonna change. All right folks.

You heard it here. The best time to be in it and networking specifically is right now. Hey, nice to meet on the show, Mike.

Great to see you again. Thank you. All right.

And back to you guys in the studio.