 Hello and welcome to this special CUBE conversation here in Palo Alto, California. I'm John Furrier, co-host of theCUBE. We are here with Jonathan Rosenberg, CTO, Chief Technology Officer and Head of AI for 5.9. Jonathan, great to see you. Thanks for coming in. Thanks, my pleasure to be here. So you've had a stellar career, certainly technical career going way back, the loose in technologies now here at 5.9, Cisco along the way. You've been a real technical guru. You've seen the movie before. This is happening, every wave of innovation, multiple waves you've been on. Now you're on the next wave, which is cloud AI as CTO 5.9, rapidly growing company. Yes, it is. What attracted you to 5.9? Great question. There's actually a lot of things that brought me to 5.9. I think probably the most important thing is that I've got this belief and I'm very motivated for myself at least to do technology and innovate and create new things and this belief that we're on the cusp of the next generation of technology in the collaboration industry and that next generation is going to be powered by artificial intelligence. And one of the ways I sort of talk about this is that if you look at the entire history of collaboration up till now, meetings, telephony, messaging, it was figuring out a way to get the bits of data from one person to another person fast enough to have a conversation. That's it. You know, once we got the audio connected, we just moved the audio packets and the video packets and the messaging from one place to another. And we didn't actually analyze any of that because we couldn't. We didn't have the technology to do that. But now, with the arrival of artificial intelligence and particular speech recognition and natural language processing, we can apply those technologies to that content and take all this dark data that's been basically thrown away the instant it was received and get to process it and do things. And that is going to completely transform every field of collaboration from meetings to messaging to telephony. And I believe that so strongly that I said, great, that's going to be my next job. I want to work on that and it's going to start in the contact center because the contact center is the ideal place to do that. It's the tip of the spear for AI in collaboration. And it's a real great area to disrupt and create innovation opportunities. So take us through the impact because one of the things I've observed in this industry is you have, you know, I don't want to say mainframe client server to go back to date myself, but there was that wave of client server computing. And mainframe is cool again. We just call it cloud now. Exactly. So you have these structural industry waves. Take us through the waves of how we got here and what's different now and why can't the old guard or the older incumbents survive? And if you're not out in front of that next wave, you're driftwood. So what do these waves mean? Why is this important? What has to change to be successful in any way? So there's been this whole, like you said, these waves. So the first wave of telecommunications was like hardware, circuit switching, big iron switches sitting in telco data centers, you know? And then that era of transition to software. And that was with the arrival of voice over IP and technologies like SIP. And that made it more less expensive and anyone could do it and it transformed the industry. The next wave, the third wave, we're still like halfway through and in some areas actually just beginning context centers early here, the third wave is cloud, right? Is now we're moving that software to a totally new delivery vehicle that allows us to deliver innovation and speed. And that wave has now enabled us to start the next wave, which is only in its infancy, which is AI, right? And the application of machine learning techniques to automate all kinds of aspects of how people communicate and collaborate. I think cloud is a great example. We've seen AI, which has been a concept around when I was in computer science back in the 80s. There was AI theory and the science of it is not so much change, but computing is available. The data to be analyzed for the first time is available. You mentioned analyzing the bits is now a key part. What does it actually mean to someone who either has a contact center or has a large enterprise says, you know what? I got to modernize. How does AI fit them? What is actually going on? Right, great question. So AI actually can solve lots of different problems. At the end of the day, again, AI is like, it's the biggest buzzword, right? It's in my title, so like I'm a little guilty, right? Definitely got to pay raise for it. But what it comes down to is really this core idea of machine learning, which is really like a fancy new algorithmic technique for taking a bunch of data and sort of making a decision based on it. So, and it turns out as we've learned, if you have enough data and you can have enough computing and we optimize the algorithms, you can do some amazing things, right? And it's been applied to areas like speech recognition and image recognition and all these kind of things, self-driving cars that are all about decision processes. Do I go left? Do I go right? Is this Bob? Is this Alice? Did the user say and or did they say or, right? Those are all decision processes that these tools can automate. What does it mean in the context center? It means everything in the context center. If you look at the context center, it's all about decision processes. You know, where should this call get routed? What's the right agent to handle the call right now? When the agent gets the call, what kind of thing should they be saying? What do I do with the call after the call is done? How should the agent use their time? All those things are decision processes and they're key to the context center. So AI and ML are going to transform every aspect of it and most importantly, analyzing what the person is saying, connecting with the customer and allowing the agent to be more fair. You know, I think this is really one of the most cutting edge areas of the business and the technology and Rowan, the CEO was talking about, you know, emotional cognitive cognition around, you know, connecting with customers and data certainly is going to be a big part of that. But as machine learning continues to get at sea legs, you're seeing kind of two schools of thought. I call it the Berkeley school, hardcore mathematics, throw math at it and then you've got this other side of machine learning, which is much more learning, you know, it's less math, more about adaptive and self-learning. One's deterministic, one's non-deterministic. You're starting to see these use cases where, yeah, there's a deterministic outcome. Right. Okay, throw machine learning at it. Great, exactly. Boom, help humans can curate, create knowledge, create value. Then you've got a new emerging use case of non-deterministic like machine learning environments where I can be driving my Tesla down the road or my company's running the contact center. I got to understand what's going to happen before it happens. Talk about this. What's your thoughts on this? This is in a really new pioneering area. What's your view on this? Yeah, so I think it actually illustrates sort of a key point. I want to narrow it up from what you said, which is that a lot of these problems still, it's about the combination of man and machine, right? It's that there's things that, you know, are going to be hard for the machine to predict. So the human in their usage of the product teaches the machine and the machine, as it observes, helps the human achieve mastery. And that human part, by the way, is even more important in the contact center than anywhere else. At the end of the day, you're a customer and you call up. You're reaching for a human connection. You're calling this, you want to talk. You've got a problem. You need someone to not just give you the answers, but to empathize with you, to understand you, right? And if you go back, John, you think about the best experience you've ever had when you called up for support or got a question answered. It was like, it was someone who understood you, who was friendly, polite, empathetic, funny, and they knew exactly what they were doing, right? And they sold it for you. So the way I think about that is that actually the future of the contact center is a combination of human and machine. And the human delivers the heart and the machine delivers the mastery. Yeah, and I just noticed you're, I'm looking at Twitter right now. You just tweeted this 14 minutes ago, the future of the contact center. That's nice. Combination of human and machine. That's accurate. The human delivers heart and the machine delivers mastery. I think this is so important because unpacking that, words like trust come out. Yes. Truth. Exactly. So you may ask about my experiences. It's when I've gotten what I needed. Knowledge or the outcome I wanted. Plus I felt good about it. I trusted it. I trusted the truth. And you're seeing that in media today, with fake news, you're seeing it with digital has kind of almost created this anonymous, non-trustworthy, it's data. There's been no real human packaging. So I think you're, I'm hearing you, you're on the side of humans and machines. It's not just machines being the silver bullet. Absolutely, absolutely. And again, it goes back to sort of the history of the contact centers. There's been this desire to like just make it cheaper, right? But as the world is changing and as customer experience is more important than ever before. And as now technology is enabling us to allow agents and human beings to be more effective through this symbiotic relationship that we're going to form with each other. Like we can actually deliver amazing customer experiences and that's what really matters. And that idea of trust, I want to come back to that word. That's like super central to this entire thing. You know, you have to, as a user you have to trust the brand. You have to trust the information you're getting from the agent. You have to trust the product that you're calling them and talking about. And that's central to everything that we need to do. In fact, it's a fundamental aspect of our entire business. In fact, if you, again, think about it for a moment here. We're going to customers who are looking to buy a contact center and we're saying trust us. We're going to put it in the cloud. We're going to run it. We're going to operate it for you. And we're going to deliver a great, highly reliable experience. That takes trust too. So one of the things that, back to your early, early question, why did it come to 5.9? One of the things it has done is build this amazing trust with its customers. It's huge, amazing reliability, uptime, a great human process of how we go and work with our customers. It's about building trust at every single level. So I want to put you in the spot here because I know you've seen many ways of innovation. You've seen a lot of different times, but now it's more accelerated. You got cloud computing, you got AI. Much more accelerated innovation cycle. So as users expect to interact with a certain kind of environment, Rowan talked about this in his interview, the CEO, Rowan Troll. So users want to be served on the channels that they want to be served in. So having a system that they have to go to to get support, they want it where they are. So how does the future of the customer interaction, whether it's support or engagement is going to take place in context to non-linear discovery progression? Meaning, are they going to service themselves in the organic digital space? I honestly want to go to a site per se. How do you see the future evolving around this notion of organic discovery, talking to their friends, finding things out? Does that impact how 5.9 sees the future? Yeah, absolutely. And I think it gets back to sort of an old idea of Omni-Channel. I mean, this is something that the contact center people have been talking about for like, forever, like the last 10 years, right? And its original meeting was just this idea of, oh, you know, you can talk to us via chat or you can send us an email or you can send us a text or you could call us, right? And we'll work with you on any of those. But like you said, actually what's more interesting is as customers and users move between those things and it actually switches from reactive to proactive, right? Where we actually treat those channels as well, depending on what the situation is, we're going to gather information from all these different data sources and then we're going to find the right way to reach out to you and allow you to reach out to us in the most efficient way possible. So you see a real change in user expectation experience with real contact. Yeah, yeah. I mean, the one thing that technology has delivered is a change in user expectations on how things work. And if you look at the way we as human beings communicate with each other, it's dramatically different today than it was really just a few years ago, right? So John, let's look under the hood now in terms of the customer environment because certainly I've seen legacy after legacy systems being deployed, it's almost like cybersecurity kind of matches the same kind of trend that in your world which is throw money at something and then build it out. So there's a lot of sprawl of solutions out there trying to solve these problems. How does the customer deal with that? And they're going forward, they're on this new wave, they want to be modernized, but they've got legacy, they've got legacy process, legacy culture. What's the key technical architecture? How do you see them deploying this? What's the steps that they should take in your opinion? It will surprise you not one drop when I say it's go to the cloud, right? And there are real reasons for it. And by the way, this is going to be, I'm going to be talking about this at Enterprise Connect. So tune in Enterprise Connect, I'm going to be talking about this. There's a ton of reasons, five huge ones actually, about why people need to get to the cloud. And one of them is actually one of the ones we've been talking about here, which is a lot of this modernization is rooted in artificial intelligence. And it turns out you just cannot do artificial intelligence on premise, you cannot. So the traditional gear, which used to be installed and operated by legacy vendors like Avaya, they go in and Genesis, they go in, they install a thing and it works just for one customer at a time. The only way artificial intelligence works is when it gathers data across multiple customers. So multi-tenancy and artificial intelligence go hand in hand. And so if you want to take any benefit from the stuff that we've been talking about in this conversation, the first step is you've got to take your context under the cloud just to begin building and adding your data to the set and then leverage the AI technologies as they come out. So data is the central equation in all this because good data feeds good machine learning, good machine learning feeds great AI. So data is the heart of this. So data making data in the cloud addressable seems to be a key thought. Your reaction and what are you guys doing with respect to that? Absolutely, absolutely. And this is by the way another reason why I joined 5.9 that I've been speculating in here. I said, all right, if AI, if the future is about AI, as I said that's what I want to do in collaboration, you need data to do that. You actually have to work for a company that has a lot of data. So market leadership matters. And if you go look at the contact center and you go look at all the industry and analysts reports, it made it pretty obvious like who to go to. There was like the leader in cloud contact center with tons of agents and tons of data is 5.9. And so that's why I'm here. So building the data, aggregating data, that's one of the first things I'm working on here is how do we increase and utilize the data that we've been gathering for years. And we've had this conversation with many customers before about silos. Silo's kill innovation when it comes to data, addressability, your thoughts on that and what customers can do to start thinking about breaking down those silos. Yeah, exactly. So in fact, silos have been a big part of the history of like especially on premise systems. Yeah, in fact, often you had one silo for inbound contact center, a different one for outbound. Different departments, by the way, also had their own different contact centers. And then you had other tools that have other data. You'd have like a separate tool over there for CRM and a different tool over there for WFO and WFM and something else for QM. And all these things were like barely integrated together. In the cloud, that becomes much more natural to bring these technologies together and the data can begin to flow from these systems in and out of each other. And that means that we have a much greater access to data and correlated data across these different things that allows us to automate all over the place. So it's this positive reinforcement cycle that you only get when you've gone to the cloud. So the question I want to ask you is more customer. So I'm pretending I'm a customer for a second. I want to ask you, Jonathan, what's the core innovation for me to think about and bring to my organization if I want to go down the modernization? How do you answer that question? What is the core innovation strategy that I should have? Obviously moving to the cloud is one. But beyond that, is it just cloud then, what else? What do I need to be preaching internally and organizing my culture around? Yeah, great question. So I mean, I think the cloud is sort of the enabler of many of these different pieces of innovation, right? So velocity and speed is one of them. Again, setting up and adjusting these things used to be super, super hard. You wanted to add agent seats. Oh my gosh, you don't have to go buy new hardware and rack and stack boxes and whatever. So even simple things like reactiveness, right? That's something that's important to talk about is that many of our customers in our businesses are highly seasonal, right? We've seen like someone showed me a graph. This was like, oh my gosh. Like it was a company that was doing a telethon. And they said, here's how many agents they have over this year. It was like two agents. And then it shut up to like 500 agents. Like two days, exactly, it dropped back down. And I'm like, if you think about a business like that, you could never even do that. So cloud is nice, but the way you talk about it, and as an IT buyer of these technologies, you talk to your business owners about reactiveness, speed, velocity, right? That's what matters to a business. And then customer experience. You know, one of the things, just to kind of end the segment, I want to get your thoughts on, I'm going to bring kind of an industry trend that's I think might be a way to kind of talk about some of these core problems on data. Most mainstream people look at Facebook and saying, wow, what a debacle. They use my data, they're using it against me. I'm not in control of my data. You're seeing that weaponization, people are saying Alexis were rigged. So weaponizing data for bad means there's content and there's context and infrastructure cloud. But there's also the other side, which is you actually make it for good. So you start in thinking about this, people starting to realize, wow, I should be thinking about my data and the infrastructure that I have. It's create a better outcome. That's right. Your thoughts on that as people start to think about AI in terms of the business context. How do they get to that moment where they can say, I don't want anyone weaponizing data against me. I want to use it for good. How do they actually think about it? It comes back to trust, by the way, right? Is that, you know, and to some degree that's an uphill battle due to some of these debacles that you just talked about. But contact center is a different beast of the whole thing. And interestingly, it's an area where there's already been an assumption by users that when they interact with the contact center, that data is sort of used to improve the experience. I mean, every contact center, first thing you say, by the way, this call may be recorded for training and monitoring purposes. They opt in. Right, it's already opt in. There's an assumption that that's exactly how that is being used. So it's, and this is another reason, by the way, what's a contact center? Is it was this tip of the spear because it was a place where there was already permission, where the data is exactly the kind of stuff that had already been subject to analysis and a customer expectation that that's actually what was happening. The expectation was there. The ability to action that data, what was missing? So now we're filling in the ability to action on all that data with artificial intelligence. And final question, what's your vision going forward? A CTO and AI, what's the vision of 5.9? What do you see the 20 mile stare for 5.9 within context of the industry we just talked about? Yeah, yeah. So it's about revolution, I'll be honest, right? And I tell people like, I'm not like an incremental steady Eddie CTO. Like I do things because I want to make big changes. And I believe that the contact center is on the cusp of a massive change. And my boss Rowan said this, and this has been actually central to how I'm thinking about this. The contact center in the next five years will be totally different than the 25 years before that. As a technologist, I say, wow, five years. Like that's not very long in terms of software development. That tells me we're going to pretty much rewrite our entire stack over the next five years and show what should that start to look like? So for me, it's about how do we completely reimagine every single aspect of the contact center to revolutionize the experience by merging together human and machine and totally new ones. And the innovation strategy is cloud and AI. Cloud and AI. And data, great. Jonathan, great to have you on. My pleasure. Great conversation, quick plug for you guys. You're going to be at Enterprise Connect, theCUBE will be there covering the event as well. What are you going to talk about there? What are some of the interactions? What will be the hallway conversations? What's your objective? What's your focus there? Exactly, so I'm going to be having my own session where I'm going to be talking about the five reasons that you may not think about to go to a contact center in the cloud. I've hinted already at just one of them. Well, that's how you count it. AI is clearly central. And I'm going to sort of talk about the other four. Great, great conversation. Lot of change, massive change happening. Great innovation strategy. Great mission here at 5.9. Great mission around changing and reimagining more changes in the next five years in the past 25 years. Again, cloud computing and AI is doing it. There'll be winners, there'll be losers. We'll be following it here on theCUBE. Jonathan Rosenberg, CTO and head of AI at 5.9. I'm John Furrier with theCUBE. Thanks for watching.