(bright upbeat music) >> Hey everyone, welcome to theCUBE's AWS startup showcase. This is season two of the startup showcase, episode one. I'm your host, Lisa Martin. Pleased to be welcoming back one of our alumni, Alexis Richardson, the founder >> Hey. >> and CEO of Weaveworks. Alexis, welcome back to the program. >> Thank you so much, Lisa, I'm really happy to be here. Good to see you again. >> Likewise. So it's been a while since we've had Weaveworks on the program. Give the audience an overview of Weaveworks. You were founded in 2014, pioneering getopts, automating Kubernetes across all industries, but help us understand, unpack that a bit. >> Well, so my previous role was at Pivotal, where I was head of application platform and I was responsible for Spring and Vfabric, and some pieces of Cloud Foundry. And you may remember back in those days, everybody wanted to build like a Heroku, but for the enterprise. And so they were asking, how can we build more cloud services? And my team was involved in building out cloud services, but we were running into trouble with the technology that we had. And then when containers appeared, we thought this is the technology for us to roll out cloud services. So with some of my team, we decided to start a new company, Weaveworks, really intending to focus on developers. Because these new containers were pretty cool, but they were really complex operational centric tools, and enterprise developers need simplicity. That's what we'd learned from things like Spring. They want simplicity, productivity, velocity, all of that stuff, they don't want operational complexity. So Weaveworks' mission is to make applications easy for developers with containers. >> Talk to me about how you've accomplished that over the last seven years, and some of the things that you're doing to facilitate a DevOps practice within organizations across any industry? >> Yeah, well, our story is pretty interesting because of course in 2014, all of this was incredibly new. You couldn't even take two containers and put them together into a single application. So forget about enterprise. What we did was we built a network, which gave the company its name, Weave. But then we spent several years building out more and more pieces of the stack. We decided that we should go to market commercially because we're an open source company with a commercial SaaS. And we thought we would be like new Relic, that there'll be lots of customers in the cloud. And, therefore, they would need monitoring and management. And Weave started writing a SaaS based on Kubernetes, which was what we chose as our platform, back in the day, very, very, very early. We were one of the very first companies to start running Kubernetes in production other than Google. And so what we learned was customers didn't want to have management and monitoring for applications in the cloud, based on Kubernetes. Because they were all still struggling to get Docker working, to get basic Kubernetes clusters set up. And they kept saying to us "this is great, we love your tool, but we really need simpler things right now." So what we had done was we'd learned how to operate Kubernetes. And we discovered that we were doing it in this specific way, a way that meant that we could be reliable, we could set things up remotely, we could move things between zones. And so we called this approach getopts. So we've named the practice of getopts, which is really DevOps for Kubernetes. We decided that it was exciting after we had an outage and made a very quick recovery. Told people about it and they said, "well, we can't even Kubernetes started, let alone recover it from a crash." So we started evangelizing getopts and saying to people that we knew how to set up and run Kubernetes as operators for developers of apps, based on this experience. And people said, "well, why don't you help us do that?" So we pivoted the company away from a SaaS business, doing management, and straight back into enterprise software, providing a solution for people to run Kubernetes stacks, deploy applications, detect drifts, and operate them at scale. And we've never looked back. And since then we've built, very successfully, a big business out of telco customers, banks, car companies, really global two thousands. Starting from that open source base, continuing to respect that, but always keeping in mind helping developers build applications at scale. >> So in terms of that pivot that you've made, it sounds like you made that in conjunction with developers across industries to really understand what the right direction is here. What's the approach, what's their appetite? Talk to me about a customer example or two that really you think articulate the value and the right decision that that pivot was and how you're helping customers to really further their DevOps practice. >> Well, one of our first customers was actually Fidelity in this new world. Fidelity has a very advanced technology organization, a very forward thinking CTO, who I seem to recall is, or CEO, who I think is female. Really is into technology as a source of, you know, velocity and business strength. And we were brought to Fidelity by our partner, Amazon. And they said, "look, Fidelity have been using your open source tools, they want to run on Kubernetes, the early EKS service on AWS, but they need help, because what they want is a shared application platform that people can use across Fidelity to deploy and manage apps." So the idea Fidelity had was they're going to split their IT into a platform team, that was going to provide this platform, and a bunch of app teams that were going to write business apps like risk management, other financial processing. Paths, basically. And we came in to help Fidelity. And what we did was help Fidelity rollout, using getopts, a Amazon wide application platform. We also helped them to build, this was very early days for us post pivot, we really helped them to build an add on layer. So you could take any Kubernetes cluster and add other components to it, and then you'd have your platform right there. And the whole stack would be managed by getopts, which nobody had done before. Nobody who'd come up with a way of managing the whole stack, so you could start and stop stacks wherever you wanted, at will, correctly. I mean, if you talk to people about what's hard in IT, they'll tell you shutting down Kubernetes is hard, 'cause I know I'm never going to know how to start it again. So being able to start and stop things, move them around is really crucial. What Fidelity also wanted, which made I think the whole thing even more exciting, was to duplicate this environment on Azure and actually also on-premise later on. So where Fidelity are today is the whole Fidelity platform runs on Microsoft and on Amazon and on-premise, using three different implementations of Kubernetes. But using this platform technology and getopts that we helped Fidelity rollout. And if you want to know a bit about the story, type FIDEKS, F I D E K S into Google and you'll find a video of me three or four years ago on stage at Cube Con talking with a Fidelity chief architect about this story. It's pretty exciting and these are early days for these new Kubernetes platforms. >> Early days, but so transformative. And I can't imagine the events of the last few years without having this capability and this technology to facilitate such pivots and transformation where we would all be. I want to kind of dig into some use cases, 'cause one of the things that you just mentioned with the Fidelity example got me thinking use case of hybrid, multi-cloud, but also continuous app development. Talk to me about some of the key use cases that you work with customers on. >> Well you just named two. So hybrid and multi-cloud is absolutely critical, and also sovereign, which is when you're actually offline and you only update your cloud periodically. That's one of the major use cases for us. And what customers want there is they want consistency. They want a single operating model, across all of these different locations, so that all of their teams can get trained on one set of technologies and then move from place to place. They're not looking for magic, where apps move with the sun or any of that stuff. They just want to know they can base everything on a single, homogeneous skillset and have scale across their teams. Maybe tens of thousands of developers, all who know how to do the same thing. That's a really important use case. You also mentioned continuous delivery. That's probably the second really critical use case for us. People say, "I've got Kubernetes set up now, and I have Jenkins." At JP Morgan once told me they had 40,000 Jenkins servers, or something like that, you know, Jenkins at scale. And they're like, "okay, how do I push changes from Jenkins into the cloud?" So getopts provides a bridge between the world of CI and the runtime of Kubernetes. So one group of our customers is help me to put that middle piece of CD that gets you CI, CD, to Kubernetes, that's a classic. And then what they're looking for is an increase in velocity. And what we typically see is people go from deploying once every six months to deploying once a week, to deploying once a day, to deploying several times a day. And then they split things up into teams and suddenly, wow, that vision of microservices has come and everybody's excited 'cause IT velocity has gone up by two X. Another really >> So, >> Sorry, carry on. >> Go ahead, I was just going to say in terms of IT velocity it sounds like that's a major business outcome that you're enabling for, whether it's teleco, financial services, or whatnot. That velocity is, as you just described, is rapidly accelerating. >> Yeah, if you go to our website, you'll find a bunch of these use cases. And one that I really like is NatWest mettle, which is another financial example. They're not all financial by the way. But there's some metrics in there. We're getting people up to two X productivity, which at scale is huge, really makes a difference. Also, meantime to recovery. If you know the metric space, you'll know these are all DORA metrics. And DORA, which was acquired by Google a couple of years ago, is a really fantastic analyst in the space that came up with a bunch of ways of thinking about how to measure your performance as a business and IT organization. Recovery time and things like this that you really need to focus on if you're in this world. >> Well, from an IT velocity perspective, if I translate that to business outcomes, especially given the dynamics in the market over the last two years, this is transformative and probably helped a lot of organizations to pivot multiple times during the last couple of years. To get to that survival mode and into that thriving mode, enabling organizations to meet customer demand that was changing faster, et cetera. That's a really big imperative that this technology can deliver to the business. >> Yeah, I mean, that's been huge for us. So when the pandemic first began, obviously, we had some road bumps and there were some challenges, but what we found out very quickly was that people were moving into digital much faster. And we've been mostly enabling them, not just in finance, as I said, but also, car companies, utilities, et cetera. The other one, of course, is modern operations. So, everyone's excited about the potential for automation. If I have thousands and thousands of developers and thousands of applications, do I need thousands of operations staff? And the answer is, with Kubernetes in this new era, you can reduce your operational loads. So that actually very few people are needed to keep systems up, to do basic monitoring, to do redeployments and so on, which are all boring infrastructure tasks that no developer wants to do. If we can automate all of that, we can modernize the whole IT space. And that's what I think the promise of Kubernetes that we're also seeing as well. So applications speed first and then operational competence second. >> So you guys had a launch, here we are in early calendar year 2022, you guys had a launch just about six or eight weeks ago in November of 2021, where you were launching announcing the GA of Weave getopts enterprise, which is a licensed product building on the free open source Weave getopts core. Talk to me about that and what the significance of that is. >> Well, this is an enterprise solution that helps customers build these critical use cases, like shared service platform or secure DevOps or multi-cloud, using getopts, which gives them higher security, lower costs of management, and better operations, and higher velocity. And all of it is taking all the best practices that we've learned starting from those days of running our own Kubernetes stack and then through those early customers like Fidelity into the modern era where we have an at-scale platform for these people. And the crucial properties are it provides you with a platform, it provides you with trusted delivery, and it provides you with what we call release orchestration, which is when you deploy things at scale into production, using tools like canaries and other modern practices. So, all of it is enabling what we call the cloud native enterprise, application delivery, modern operations. >> So what's the upgrade path for customers that are using the free open-source tier to the enterprise package, what does that look like? >> The good news is it's an add on. So, I have been in the industry a while and I strongly believe it's really important that if you have an open source product, you shouldn't ask people to delete it or uninstall it to install your enterprise product, unless you really, really, really have to. And I'm not trying to be picky here. Maybe there are cases where it's important, but actually in our case, it's very simple. If you're already using one of our upstream tools, like Flux, for example, then going from Flux to Weave getopts enterprise is an add-on installation. So you don't have to change or take out what you're doing. You might be using Flux without knowing it. You may not be aware of this, but it's also insight as your AKS and ARC, it's inside the Amazon EKS anywhere bundle. It's available on Alibaba, VMware have used it in cartographer and Tanzu application platform. And even Red Hat use it too in some cases. So you may be using it already, from one of the big vendors who are partners of ours, as a precursor to buying Weave getopts enterprise. So, you know, don't be scared. Get in touch is what I would say to people. >> Get in touch. And of course, folks can go to weave.works to learn more about that. And, also we want to watch the Weave.works space, 'cause you have some news coming out relatively soon that sounds pretty exciting, Alexis. >> Well, I mentioned trusted delivery. And I think one of the things with that is no CIO wants to go faster, unless they also have the safety wheels on, let's face it. And the big question we get asked is "I love this getopts stuff, but how can I bring my team with me? How can I introduce change?" I have all of these approvals mechanisms in place, can I move into the world of getopts? And the answer is yes, yes you can because we now support policy engines as baked into our enterprise product. Now, if you don't know what policy is, it's really a way of applying rules to what you're seeing in IT. And you can detect whether something passes or fails conditions, which means that we can detect if something bad is about to happen in a deployment and stop it from happening, this is really critical. It also goes hand in hand with things like supply chain and security, which I'm sure we read about in the news far too much. >> Yeah, pretty much daily supply chain and security >> Pretty much daily. >> is one of those things that we're all in every generation concerned about. Well, Alexis, it's been a pleasure having you back on the program, talking to us about what's new at Weaveworks, the direction that you're going, how you're helping organizations across industries really advance their DevOps practice. And we will check weave.works in the next couple of weeks for more on that news that you started to break a little bit with us today. We appreciate your time, Alexis. >> Thank you very much, indeed, take care. >> Likewise. For Alexis Richardson, I'm Lisa Martin. Keep it right here on theCUBE, your leader in hybrid tech event coverage. (bright music) (music fades)

>> Announcer: From around the globe, it's theCUBE! With digital coverage of IBM Think 2021. Brought to you by IBM. >> Well welcome everyone as theCUBE continues our IBM Think series. It's a pleasure to have you with us here on theCUBE. I'm John Walls, and we're joined today by Brian Loveys who is the Director of Offering Management for Customer and Employee Care Applications at IBM in the Data and AI Division. So, Brian, thanks for joining us from Ottawa, Canada. Good to see you today. >> Yeah, great to be here, John. And looking forward to the session today. >> Which, by the way, I've learned Ottawa are the home of the world's largest ice skating rink. I doubt we get into that today, but it is interesting food for thought. So, Brian, first off, let's just talk about the AI landscape right now. I know IBM obviously very heavily invested in that. Just in terms of how you see this currently in terms of enterprise adoption, what people are doing with it, and just how you would talk about the state of the industry right now. >> You know, it's a really interesting one, right? I think if you look at it, you know, different companies, different industries, frankly, are at different stages of their AI journey, right? I think for me personally, what was really interesting was, and we're all going through the pandemic right now, but last year with COVID-19 in the March timeframe, it was really interesting to see the impact, frankly, in the space that I play predominantly in around customer care, right? When the pandemic hit, immediately call centers, contact centers got flooded with calls, right? And so it created a lot of problems for organizations. But what was interesting to me is it accelerated a lot of adoption of AI to organizations that typically lag in technology, right? So if you think about public sector, right, that was one area that got hit very, very hard with questions and those types of things, and trying to, you know, communicate out information. So it was really interesting to see those organizations, frankly, accelerate really, really quickly, right? And if you actually, you know, talk to those organizations now, I think one of the most interesting things to me in thinking about it and talking to them now is like, hey, you know, we can do this, right? AI is really not that complicated. It can be simplified, we can take advantage of it and all of those types of things, right? So I think for me, you know, I kind of see different industries at sort of different levels, but I think with COVID in particularly, you know, and frankly not just COVID, but even digital transformation alongside COVID is really driving a lot of AI in an accelerated manner. The other thing that I'll kind of talk to a little bit here is I still think we're very much in the early innings of this, right? There's a tremendous opportunity to innovate in this space. And I think we all know that, you know, data is continually being created every single day. And as more people become even more digitalized, there's more and more data being created. Like it's how do you start to harness that data more effectively, right, in your business every day. And frankly, I think we're just scratching the surface on it. And I think tremendous amount of opportunity as we move forward. >> Yeah, you really raised an interesting point which I hadn't thought about in terms of, we think about disruptors, we think about technology being a disruptor, right, but in this case it was purely, or really largely environment, you know, that was driving this disruption, right, forcing people to make these adoption moves and transitions maybe a little quicker than they expected. Well, so because of that, because maybe somebody had to speed up their timetable for deployments and what have you, what kind of challenges have they run into then, where, because as you describe it, it's not been the more organic kind of decision-making that might be made sometimes, situation dictated it. So what have you seen in terms of challenges, you know, barriers, or just a little more complexity, perhaps, for some people who're just now getting into the space because of the environment you were talking about? >> I think a lot of this is like, you know, people don't know where to get started, right, a lot of the time, or how AI can be applied. So a lot of this is going to be about education in terms of what it can and cannot do. And then it all depends on the use cases you're talking about, right? So if I think about, you know, building out machine learning models and those types of things, right, you know, the set of challenges that people will typically face in these types of things are, you know, how do I, you know, collect all the data that I need to go build these models, right? How do I organize that data? You know, how do I get the skillsets needed to ultimately, you know, take advantage of all of that data to actually then apply to where I need it in my business, right? So a lot of this is, you know, people need to understand those concepts or those pieces to ultimately be successful with AI. And you know, what IBM is doing right here, and I'll kind of, this will be a key theme throughout this conversation today is, you know, how do you sort of lower the time to value to get there across that spectrum, but also, you know, frankly, the skills required along the way as well? But a lot of it is like, people don't know what they don't know at the end of the day. >> Well, let me ask you about your AI play then. A lot of people involved in this space, as you well know, competition's pretty fierce and pretty widespread. There's a deep bench here. In terms of IBM though, what do you see as kind of your market differentiator then? You know, what do you think sets you apart in terms of what you're offering in terms of AI deployments and solutions? >> No, that's a great question. I think it's a multifaceted answer, frankly. The first thing I'll kind of talk through a little bit, right, is really around our platform and our framework, right? We kind of refer to as our AI ladder, but it's really an integrated, you know, sort of cohesive platform for companies around the journey to AI, right? So kind of what I was mentioning a bit earlier, right? If you think about, you know, AI is really about supplying the right data into AI, and then being able to infuse it to where you need it to go, right? So to do that, you need a lot of the underlying information architecture to do that, right? So you need the ability to collect the data. You need the ability to organize the data. You need the ability to build out these models or analyze the data, right? And then of course you need to be able to infuse that AI wherever you need it to be, right? And so we have a really nice integrated platform that frankly can be deployed on any cloud, right, so we get the flexibility of that deployment model with that integrated platform. And if you think about it, we also have built, right, you know, sort of these industry-leading AI applications that sit on top of that platform and that underlying infrastructure, right? So Watson Assistant, right, our conversational AI which we'll talk probably a little bit more on this conversation, right? Watson Discovery focused on, you know, intelligent document processing, right, AI search type applications. We've got these sort of market-leading applications that sit on top, but there's also other things, right? Like we have a very, very strong research arm, right, that continues to invest and funnel innovations into our product platform and into our product portfolio, right? I think many people are aware of Project Debater we took on some of the top debaters in the world, right? But research ultimately is very much tied, right, and even, you know, some of the teams that I work with on the ground, we've got them tied directly into the squads that build these products, right? So we have this really big strong research arm that continues to bring innovation around AI and around other aspects into that product portfolio. But it's not just- >> I'm sorry go ahead, please. >> Go ahead, sorry. >> No, no, you go, (laughs) I interrupted, you go ahead. >> Don't worry, I was just going to say, the other two things I'll say like, you know, I'm saying this right, but we've got a lot of sort of proof points in around it, right, so if you talk about the scale, right, the number of customers, the number of case studies, the number of references across the board, right, in around AI at IBM it is significant, right? And not only that, but we've got a lot of, sort of I'll say industry and third-party industry recognition, right? So think about most people are aware of sort of Gartner Magic Quadrants, right, and we're the leader almost across the board, right, or a leader across the board. So, you know, cloud AI developer service, insight engines, machine learning, go down the line. So, you know, if you don't trust me, there's certainly a lot of third party validation around that as well, if that makes sense. >> Yeah, sure does. You know, we hear a lot about conversational AI and, you know, with online chat bots and voice assistance, and a myriad applications in that respect. Let's talk about conversational right now. Some people think is a little narrow, but yet there appears to be a pretty broad opportunity at the same time. So let's talk about that conversational AI element to what you're talking about at IBM and how that is coming into play. And perhaps is a pretty big growth sector in this space. >> Yeah, I think, again, I talk about scratching the surface, early innings, you'll see that theme a lot too. And I think this is another area around that, right? So, listen, let's talk about the broader side. Let's first talk about where conversational AI is typically applied, right? So you see it in customer service. That's the obvious place where I've seen the most deployments in. But if you think about, it's not just really around customer service, right? There's use cases around sales and marketing. You can think about, you know, lead qualification for example, right. You know, I'm on a website, how can I get information about a product or service? How can I automate some of that information collection, answering questions, how can I schedule console? All those things can be automated using, right, conversational AI, but organizations don't want these sort of points solutions across the customer journey. What they're ultimately looking for is a single assistant to kind of, you know, front that particular customer. So what if I do come on from a lead qual perspective, but really I'm not there for lead qual, I'm actually a customer, and I want to get a question answered, right? You don't want to have these awkward starts and stops with organizations, right? So on the customer side where we see the conversational AI going is really sort of covering that whole gambit in terms of that customer journey, right? And it's not just the customer journey, but you also want to be across channels, right? So you can imagine not just, you know, the website and the chat on the website, but also, right, across your messaging channels, across your phone, right? And not just that, but you also want to be able to have a really nice experience around, hey maybe I'm on a phone call with some automation, but I need to be able to hand them off to a digital play, right? Maybe that's easier to sign up for a particular offer, or do some authentication, or whatever it might be, right? So to sort of be able to switch between the channels is really, really going to become more important in terms of a seamless experience as you do kind of go through it, right- >> So let's talk about customers- >> Oh, go ahead sir. >> Yeah, you talked about customers a little bit, and you mentioned case studies, but I hope we can get into some specifics, if you can give us some examples about people, companies with whom you've worked and some success that you've had in that respect. And I think maybe the usual suspects come to mind. I think about finance, I think about healthcare, but you said, "Hey buddy, but customer call issues, you know, service centers, that kind of thing would certainly come into play," but can you give us an idea or some examples of deployments and how this is actually working today? >> Oh, absolutely, right? So I think you were kind of mentioning, you were talking about sort of industries that are relevant, right? So, you know, the ones that I think are most relevant that we've seen are the ones with the biggest sort of consumer side of it, right? So clearly in financial services, banks, insurance are clearly obvious ones. Telecommunication, retail, healthcare, these are all sort of big industries with a lot of sort of customers coming in, right? And so you'll see different use cases in those industries as well, right? So the obvious one, we've got a really good client, Royal Bank of Scotland, they've now changed their name to NatWest in Scotland. So they started out with customer service, right? So dealing with personal banking questions through their website. What's interesting, and you'll see this with a lot of these use cases is they will start small, right, with a single use case, but they'll start to expand from there. So for example, NatWest, right, they're starting with personal banking, but they're now expanding to other areas of the business across that customer journey, right? So that's a great example of where we've seen it. Cardinal Health, right, because we're not dealing with customers in terms of external customers, but dealing with internal customers, right, from an IT help desk standpoint. So it's not always external customers. Oftentimes, frankly, it can be employees, right? So they are using it through an IDR system, right? So through over the phone, right, so I can call, instead of getting that 1-800 number, I'm going to get a nice natural language experience over the phone to help employees with common problems that they have with their help desk. So, and they started really, really small, right? They started with, you know, simple things like password resets, but that represented a tremendous amount of volume that ultimately hit at their call centers. So NatWest is a great example. CIBC, another bank in Canada, Toronto, is a great example. And the nice thing about what CIBC is doing and they're a big, you know, we have four big banks here in Canada. What CIBC do is really focusing a lot on the transactional side. So making it really easy to do interact transfers or send money, or all those types of things, or check your balance or whatever it might be. So putting a nice, simple interface on some of those common, transactional things that you would do with a bank as well. >> You know, before I let you go, I'd like to hit just a buzzword we hear a lot of these days, natural language processing, NLP. All right, so NLP, define that in terms of how you see it and how is it being applied today? Why does NLP matter, and what kind of differences is it making? >> Wow, natural language processing is a loaded term as a buzzword, I completely agree. I mean, listen, at the 50,000 foot level, natural language processing is really about understanding language, right? So what do I mean by that? So let's use the simple conversational example we just talked about. If somebody's asking about, you know, "I'd like to reset my password," right? You have to be able to understand, well what is the intent behind what that user is trying to do, right? They're trying to reset a password, right? So being able to understand that inquiry that user has that's coming in and being able to understand what the intent is behind it. That's sort of one key aspect of natural language processing, right? What is the intent or the topic around that paragraph or whatever it might be. The other sort of key thing around natural language processing, the importance of extracting certain things that you need to know. And again, using the conversational AI side, just for a minute, to give a simple example. If I said, "You know what, I need to reset my password." I know what the intent is, I want to reset a password, but, right, I don't know which password I'm trying to reset. Right, and so this is where sort of you have to be able to extract objects, and we call them entities a lot of the time and sort of the (indistinct) or lingo. But you got to be able to extract those elements. So, you know, I want to reset my ATM password. Great, right, so I know what they're trying to do, but I also need to extract that it's the ATM password that I'm trying to do. So that's one sort of key angle, natural language processing, and there's a lot of different AI techniques to be able to do those types of things. I'll also tell you though, there's a lot around the content side of the fence as well. So you can imagine how like a contract, right, and there were thousands of these contracts, and some of your terms may change. You know, how do you know, out of those thousands of contracts where the problems are, where I need to start looking, right? So another sort of key area of natural language processing is looking at the content itself, right? Can I look at these contracts and automatically understand that this is an indemnity clause, right? Or this is an obligation, right? Or those types of things, right, and being able to sort of pick those things out, so that I can help deal with those sort of contract-processing things. So that's sort of a second dimension. The third dimension I'll kind of give around this is really around, you can think about extracting things like sentiment, right? So we talked about, you know, extracting objects and nouns, and those types of things, but maybe I want to know in an analytics use case with customers, you know, what is the sentiment and, you know, analyzing social media posts or whatever it might be, what's the sentiment that people have around my product or service. So natural language process, if you think about it at the real high level is really about how do I understand language, but there's a variety of sort of ways to do that, if that makes sense. >> Yeah, no sure, and I think there are a lot of people out there saying, "Yeah, the sooner we can identify exasperation (laughs) the better off we're going to be, right, in handling the problems." So, it's hard work, but it's to make our lives easier, and congratulations for your fine work in that space. And thanks for joining us here on theCUBE. We appreciate the time today, Brian. >> Thank you very much. >> You bet, Brian Loveys, he's talking to us from IBM, talking about conversational AI and what it can do for you. I'm John Walls, thanks for joining us here on theCUBE. (upbeat music) ♪ Dah, deeah ♪ ♪ Dah, dee ♪ (chimes ringing)

>>from >>Around the globe. It's the cube with digital coverage of IBM think 2021 brought to you by IBM >>Well welcome everyone is the cube continues or IBM Thanks series. It's a pleasure to have you with us here on the cube. I'm john walls and we're joined today by brian loves who is the director of offering management for customer and employee care applications in the at IBM in the data and AI division. So brian, thanks for joining us from Ottawa Canada, good to see you today. >>Yeah, great to be here john I'm looking forward to the session today >>which by the way I've learned Ottawa is the home of the world's largest ice skating rink. I doubt we'll get into that today, but it is interesting food for thought. Uh so brian first off, let's just talk about um the Ai landscape right now. I know IBM obviously very heavily invested in that uh just in terms of how you see this currently as in terms of enterprise adoption, what people are doing with it and and just how you would talk about the state of the industry right now, >>you know, it's a really interesting one, right? I think if you look at it, you know different companies, different industries frankly are at different stages of their Ai journey, right? Um I think for me personally what was really interesting was, and we're all going through the pandemic right now, but last year with covid 19 in the March timeframe, it was really interesting to see the impact, frankly in the space that I played predominantly in around customer care, right? When the pandemic hit immediately call centers, contact centres got flooded with calls, right? And so it created a lot of problems for organizations. But it was interesting to me is it accelerated a lot of adoption of ai to organizations that typically lag and technology. Right? So if you think about public sector, right, that was one area that got hit very, very hard with questions and those types of things and trying to communicate and communicate out information. So it was really interesting to see those organizations frankly accelerate really, really quickly, right? And if you actually talk to those organizations now, I think one of the most interesting things to me and thinking about it and talking to them now is like, hey, you know, we can do this right, AI is really not that complicated, it can be simplified, we can take advantage of it and all of those types of things. Right? So I think for me, you know, I kind of see different industries that sort of different levels, but I think with Covid in particularly, you know, and frankly not just Covid, but even digital transformation alongside Covid is really driving a lot of ai in an accelerated manner. The other thing I'll kind of I'll kind of talk to a little bit here is I still think we're very much in the early innings of this, right, there is a tremendous opportunity innovating in the space and I think we all know that you know data is continually being created every single day and as more people become even more digitalized, there's more and more data being created. Like how do you start to harness that data more effectively, right in your business every day? And frankly I think we're just scratching scratching the surface on it and I think tremendous amount of opportunity as we move forward. >>Yeah, he really is really raised an interesting point which I hadn't thought about in terms of, we think about disruptors, we think about technology being a disrupter, right? But in this case it was purely really, largely environment that was driving this disruption, right, forcing people to to make these adoption moves and transitions maybe a little quicker than they expected. So because of that, because maybe somebody had to speed up their timetable for deployments and what have you what what kind of challenges have they run into them? Where because, as you describe it, it's not been the more organic kind of decision making that might be made, sometimes situation dictated it. So what have you seen in terms of challenges, barriers or just a little more complexity perhaps for some people who are just not getting into the space because of the environment you were talking about? >>I think a lot of this is like people don't know where to get started, right, a lot of the time or how ai can be applied. So a lot of this is going to be a bad education in terms of what it can and cannot do, and then it all depends on the use cases you're talking about, right? So if I think about, you know, building a machine learning models and those types of things right? You know, this set of challenges that people will typically face in these types of things are, you know, how do I collect all the data that I need to go build these models? Right? How do I organize that data? Um you know, how do I get the skill sets needed to ultimately, you know, take advantage of all that data to actually then apply to where I needed in my business? Right, So a lot of this is, you know, people need to understand, you know, those concepts are those pieces um to ultimately be successful with AI and you know what IBM is doing right here and I'll kind of this will be a key theme through this conversation today, is how do you sort of lower the time to value, to get there across that spectrum, but also, you know, frankly the skills >>required along the way as >>well, but a lot of it is like people don't know what they don't know at the end of the day. Mhm. >>Well, let me ask you about about your AI play then, a lot of people involved in this space, as you well know, you know, competitions pretty fierce and pretty widespread, there's a deep bench here um in terms of IBM know, what do you see is kind of your market different differentiator then, you know, what what do you think set you apart in terms of what you're offering in terms of AI deployments and solutions? >>No, that's a great question. I think it's a multifaceted answer, frankly. Um the first thing I'll kind of talk through a little bit right, is really around our platform and our our framework, right? We could refer to as our air ladder, um but it's really an integrated, you know, sort of cohesive platform for companies around the journey to AI, right? So kind of what I was mentioning earlier, right? If you think about, you know, AI is really about supplying the right data into A I. And then being able to infuse it to where you needed to go. Right? So to do that, you need a lot of the underlying information architecture to do that, Right? So you need the ability to collect the data, you need the ability to organize the data, you need the ability to to build out these models, right? Or analyze the data and then of course you need to be able to infuse that ai wherever you need it to be. Right. And so we have a really nice integrated platform that frankly can be deployed on any cloud. Right? So we got the flexibility that deployment model with that in greater platform. And you think about it? We also have built right, you know, sort of these industry leading Ai applications that sit on top of that platform and that underlying infrastructure. Right? So Watson assistant, Right. Our conversational AI, which we'll talk probably a little bit more on this conversation. Right, Watson discovery focus on, you know, intelligent document processing, right. AI search type applications. We've got these sort of market leading applications that sit on top, but there's also other things, right? Like we have a very, very strong research arm right, that continues to invest and funnel innovations into our product platform and into our product portfolio. Right? I think many people are aware of project debater, we took on some of the top debaters in the world, right? But research ultimately is very much tied, right? And even some of the teams that I work with on the ground, we've got them tied directly into the squads that build these products, Right? So we have this really big strong research arm that continues to bring innovation around AI and around other aspects into that product portfolio. But it's not just go ahead, >>Please go ahead. three. No, no. You know, I interrupted you. Go ahead. >>No, I was just gonna say that the other two things, I'll say it like, you know, I'm saying this right, but we've got a lot of sort of proof points and around it. Right? So, if you talk about the scale right? The number of customers, the number of case studies, a number of references across the board, right? In around AI AT IBM It is significant, Right? Um, and not only that, but we've got a lot of sort of, I'll say industry and third party industry recognition. Right? So think about most people are aware of sort of Gartner magic quadrants, right? And we're the leader almost across the board, Right? Or a leader across the board. So cloudy I developer service inside engines, machine learning go down the line. So, you know, if you don't trust me, there's certainly a lot of third party validation around that as well. That makes sense. >>Yeah, it sure does. You know, we're hearing a lot about conversational AI and, you know, with online chat bots and voice assistance and a myriad applications in that respect. Let's talk about conversational right now. Some people think it's little narrow, but, but yet there appears to be a pretty broad opportunity at the same time. So let's talk about that conversational AI um, uh, element um, to what you're talking about at IBM and how that is coming into play and, and perhaps is a pretty big growth sector in this space. >>Yeah, I think again, I talked about scratching the surface early innings. You'll see that theme a lot too. And I think this is another area around that. So listen, let's talk about the broader side. Let's first talk about where conversation always typically applied. Right? So you see it in customer service, that's the obvious place we're seeing the most appointments in. But if you think about, it's not just really around customer service, right? There's use cases around sales and marketing. If you think about, you know, lead qualification, for example, right? How can, you know, I'm on a website, how can I get information about a product or service? How can I automate some of that information collection, answering questions? How can I schedule console? All those things can be automated using great conversationally. I, the organizations don't want these sort of point solutions across the customer journey. What we're ultimately looking for is a single assistant to kind of, you know, front right, that particular customer. So what if I do come on from a legal perspective, but really I'm not here for legal. I'm actually a customer and I want to get a question answered, right? You don't want to have these awkward starts and stops with organizations, Right? So on the customer side where we see the conversation like, hey, I going and it's really kind of covering that full gambit in terms of that customer journey, right? And it's not just the customer journey, but you also want to be across channels, right? So you can imagine right now, not just, you know, the website and the chat on the website, but also right across their messaging channels, right across your phone. Right. And not just that, but you also want to be a really nice experience around, hey, maybe I'm on a phone call with some automation, but I need to be able to hand them off to a digital play. Right? Maybe that's easier to sign up for a particular offer or do some authentication or whatever might be, right. So to sort of be able to sort of switch between the channels, it's really, really going to become more important in this sort of sort of seamless experience as you just kind of go through it. Right? >>So you're coming by customers. Yeah. >>You talked about customers a little bit and you mentioned case studies, but can we get, I hope we can get into some specifics. You can give us some examples about people, companies with whom you've worked and and some success that you've had that respect. And I think maybe the usual suspects come to mind about finance. I might health care, but you said anybody with customer call issues, service centers, that kind of thing would certainly come into play. But can you give us an idea or some examples of deployments and how this is actually working today? >>Oh, absolutely. Right. So I think you kind of mentioned you become sort of industries that are relevant. Right? So, you know, the ones that I think are most relevant that we've seen are the ones with the biggest sort of consumer sort of side to it. Right? So clearly in financial services, banks, insurance, and clearly obvious ones telecommunications, retail, healthcare, these are all sort of big industries with a lot of sort of customers coming in. Right? So you'll see different use cases in those industries as well. Right. So the obvious one, we've got a really good client, Royal Bank of Scotland, they've now changed their name to natwest Open Scotland. Um So they started out with customer service. Right? So dealing with personal banking questions through their website, what's interesting and you'll see this with a lot of these use cases is they will start small, right with a single use case that they'll start to expand from there. So, for example, >>natwest right there, starting with they started with personal banking, but they're not expanding to other areas of the business across that customer journey. Right. So it's a great example of where we've seen it. Cardinal Health Right. We're not dealing with customers in terms of external customers but dealing with internal customers right from the help that standpoint. So it's not always external customers. Oftentimes frankly it can be employees. Right? So they are using it right through an I. V. R. System. Right? So through over the phone. Right. So I can call instead of getting that 1 800 number. I'm going to get a nice natural language experience over the phone to help employees with common problems that they have with their health does so. And they started really, really small, right? They started with simple things like password resets but that represented a tremendous amount of volume but ultimately headed their cost cost centers. So not West is a great example. C I B C. Another bank in Canada Toronto is a great example and the nice thing about what CNBC is doing and there are big, you know, we have four big banks here in Canada, what have you seen do is really focusing a lot on the transactional side. So making it really easy to do interact transfers or send money or over those types of things or check your balance or whatever it might be. So putting a nice simple interface on some of those common transactional things that you >>would do with the bank as well, >>you know, before I let you go, uh I'd like to hit this of buzz where we hear a lot of these days natural language processing. NLP Alright, so, so NLP define that in terms of how you see it and and how is it being applied today? Why why does NLP matter? And what kind of difference is it making? >>Wow, that's a loaded natural language processing. There's a loaded term in a buzzword. I completely agree. I mean listen, at the 50,000 ft level, natural language processing is really about understanding length, Right? So what do I mean by that? So let's use the simple conversational example. We just talked about if somebody is asking about, I'd like to reset my password right? You have to be able to understand what is the intent behind what that user is trying to do right there? Trying to reset a password, right? So being able to understand that inquiry that the user has that's coming in and being able to understand what the intent is behind it. >>That's sort of one, you know, aspect of natural language processing, right? What is the intent or the topic around that paragraph or whatever it might be. The other sort of key thing around natural language processing the importance, extracting certain things that you need to know. And again using the conversational ai side, just for a minute to give a simple example if I said you know what I need to reset my password, I know what the intent is. I want to reset a password but Right I don't know which password I'm trying to reset. Right? So this is where you have to be able to extract objects and we call them entities a lot of time in sort of the ice bake or lingo but you've got to be able to extract those elements. So you know I want to reset my A. T. M. Password. Great. Right so I know what they're trying to do but I also need to extract that it's the A. T. M. Password that I'm trying to do. So that's one sort of key angle of natural language processing and there's a lot of different techniques to be able to do those types of things. I'll also tell you though there's a lot around the content side of the fence as well, right? So you can imagine having a contract, right? And there are thousands of these contracts and some of your terms may change. How do you know, out of those thousands of contracts where the problems are, where I need to start looking, Right? So another sort of keep key area of natural language processing is looking at the content itself. Can I look at these contracts and automatically understand that this is an indemnity clause, Right? And this is an obligation, right? Or those types of things, right? And be able to sort of pick pick those things out so that I can help deal with those sort of contract processing things. That's sort of a second dimension. The third dimensional kind of kind of give around this is really around. You can think about extracting things like sentiment, right? So we talked about, you know, extracting objects and downs and those types of things. But maybe I want to know and analytics use case with customers. Um you know, what is the sentiment and you know, analyzing social media posts or whatever it might be. What's the sentiment that people have around my product or service? So naturally this process, if you think about it, the real high level is really about how do I understand language? But there's a variety of sort of ways to do that if that makes sense? >>Yeah, sure. And I think there's a lot of people out there saying, yeah, the sooner we can identify exasperation, the better off we're going to be right and handling the problems. But it's hard work but it's to make our lives easier and congratulations for your fine work in that space. And thanks for joining us here on the cube. We appreciate the time. Today, brian, >>thank very much. >>You bet BRian Levine is talking to us from IBM talking about conversational Ai and what it can do for you. I'm john Walsh, thanks for joining us here on the cube. Mhm. >>Mhm.

>> Announcer: From around the globe, it's theCUBE, with digital coverage of PegaWorld iNspire, brought to you by Pegasystems. >> Hi everybody, welcome back. This is Dave Vellante, and you're watching theCUBE's coverage of PegaWorld iNspire 2020. Kerim Akgonul is here. He's the senior vice president of product at Pega, Pegasystems. Kerim, great to see you. Thanks for coming on. >> Hi Dave. Thanks for having me. Yeah, I mean I wish we were face-to-face at your big show, but this is going to have to do. A little different this year doing the virtual event. You're used to a big stage, big audience, lots of clapping and buzz. How's it been for you, this virtual pivot? >> It's been different, it's definitely been different, especially since the last few years we had it in Vegas, so it was a big Vegas show. Now we're in my living room. Not the same vibe, but nevertheless we have a lot of new products and new stories to tell, new experiences to share with the clients, so we're focusing on those aspects. >> Yeah, I'm excited to get into that, but I mean your whole raison d'être is you guys build for change, and obviously we've been thrown this curve ball, more than a curve ball, knuckle ball. Maybe talk about what you're seeing your customers do in terms of being able to rapidly adapt to this new abnormal. >> Yeah, so we've seen, obviously, across the globe, right, not just with Pega, not with just our clients, we've seen a tremendous amount of change. We've seen change in how we work, how we communicate, how we collaborate, how we get into meetings, and a lot of our clients, of course, had to quickly adjust to these recent changes as well in these last couple of months, and in many cases they had to make technology choices, and we're pretty excited that basically Pega technology has been on that top shelf of technologies that our clients chose to leverage in this time of crisis. They chose to use the technology to better engage across their organizational work that they do. They use the Pega technology to actually digitize how a lot of the work that gets done in their organization. They use it as a COVID-19 response. They use it to engage directly with the consumers, so it's been on, as I said, the top shelf of technologies that they had to leverage to adjust and transform, so it's been very busy, Dave. >> Obviously a lot of companies have been hit, and some industries have been very hard hit in the shutdown, but I want to pick a couple of examples. Let's start with healthcare. I mean they've been hit like no other, front lines. Do you have some examples that you can share, or any example in healthcare, how they pivoted? I mean have they been able to even spend time on anything that's not emergency? Maybe you could share some of your experiences there. >> Absolutely. Actually a lot of the healthcare organizations that we're working with, the front line workers, obviously, the way that they engage has changed quite a bit, but also the people that work in the corporate, in the back office, in the technology, they have changed as well as they had to really respond to the changes in the scale of their operations, changes in how they engage with their customers, with the other organizations that they work with, and how they operated their processes. We did have one of the customers that I talk about, HCA, one of the Pega customers, they basically implemented a Pega solution just in a couple of days, and rolled it out into production just a couple of days to keep track of their employees, the volunteers that basically work with them, to keep track of people who are impacted by COVID-19, and they have about 200,000 people that they need to manage the availability in the schedules, and they decided to use Pega technology to be able to manage that across the enterprise, which has been a great experience for us working with them. >> So Kerim, how would that work? So they're an existing Pega customer, they spun up a new module, they sort of developed it themselves. You guys helped them. Describe how that sort of became real. >> Sure, so we actually have a couple of different examples of these types of applications that went live in the last couple of months, from the healthcare organizations, we had it from some organizations in the telecommunications industry, we had state governments and different public sector companies. It works differently for each one of them, but it all starts with really having somebody, having a clear idea on exactly what they want to actually do. What do they want to keep track of? What do they want to operate? What do they want to be able to actually get done? And having somebody to have that vision and being able to articulate that in the Pega construct to automate it to define the process, to define what they're going to keep track of, to define the journeys of those things that they're going to keep track of, and a lot of the clients that have centers of excellence in their organizations with Pega experts, some of our clients work with our great set of partners who have come up with ideas and brought them into these organizations, and we also get pulled into a couple of these implementations, and like you said, Dave, we always talk about being built for change, and this is a time of crisis. This is a time of change, and Pega's technology is perfectly structured to be able to get things quickly done and up and running, but what it really needed at all times is somebody to actually have the vision and the ability to make a decision and go execute on it. And we know that the people are there. We know the technology is there, and that's how a lot of the results got done. >> Yeah, very fast decisions had to get made. Another example is we've been tracking the telecom space, and the whole work-from-home pivot has really put stress on distributed networks, the traditional corporate networks. Now everybody's at home. We've all experienced this, whether video calls, et cetera. The kids are at home, at school, sometimes gaming, so the internet, it didn't blow up, luckily, but still major change in the telco industry. >> Absolutely. How lucky we are to actually have access to all this technology, to all this internet capacity, and yeah, it's been a big change. Obviously the demand on their business has increased quite a bit in the telecommunications industry. One of our clients that basically had contact centers in other countries where the agents actually didn't have an opportunity to go into the contact center, and they couldn't actually enter the building. They weren't even allowed to be on the streets, out on the streets, so what they did, and while this is happening, right, while basically the agents are not able to go to work, at the same time the volumes are increasing through the roof, right? There's a tremendous amount of urgency and higher levels of volumes of requests coming in from the end customers, the end consumers coming in, right? It's basically a perfect storm of things happening, so what our clients have done is a couple of things. One, they created new sets of processes, and they created an army of volunteers from within the business to be able to respond to customer requests from home, and two, they really completely ramped up the pace of taking processes and making them self-service available on the mobile apps, on the website, on the IVR, because customers, consumers have a sense of urgency. They need an answer. They need something to get done quickly, and they want to be able to avoid waiting on line for four hours, right? We saw that, we saw a lot of the websites that says, "Hey, if you call our contact center," some companies put up these messages, "it's going to be so many hours." So our clients were able to take the processes that they have defined for their contact center agents and actually pushed them to self-service channels like the mobile channel, like the web self-service channel, as well as chat and chat bot channels, to be able to get the answers that the consumers need quickly and get their work done, respond to them quickly while in this time of amazing change. >> Yeah, so that enables scaling. Self-service is critical. Yeah, I want to ask you about digital transformation. It's a theme of PegaWorld iNspire. There's been a lot of talk the last three, four years about digital transformation. Frankly, a lot of lip service. I think it was Satya Nadella said we've accelerated. We've pulled two years of digital transformation into two months, but again, you guys are all about digital and digitizing processes, so kind of I want to know if you can talk about that theme of the show, kind of what it means to you and your client. >> I think it's been amazing. I think, like you said, there's been a lot of talk about it in several years, and there have been lots of initiatives, but I think it was missing the urgency that it needed to be able to get moving and get things done. We have had so many discussions. So many people have talked about what do we need to do, do we need to do it now, can we basically wait? Long meetings and long delays on making decisions to actually move forward, and this just basically changed all that, right? There's no more the question of do we need to go through a digital transformation? Everybody knows it's a yes. We had to do it, no question about it. There's no more question of can we do it. Yep, we know we can do it. Do we have the technology, do we have the people? Yep, got it. All that is in place. Now really the thing that we're seeing people succeed in is the ability to make a decision to move forward, to move forward aggressively, and having now proven that the people and the technology is there, and that they can get done, and it really basically requires decisiveness and leadership. >> Yeah, I think the word you use, 'urgency,' because there was a lot of complacency leading up to this, but the good news was there was also a lot of experimentation going on. So COVID obviously accelerated that urgency. Anna Gleiss from Siemens is an example of somebody who spoke during your keynote. Big industrial exposed with a huge supply chain, which for years some of that's been really opaque, and digitize that, now you get greater transparency. What were the key learnings from her discussion? >> Right, so Anna and the team have done a spectacular job, and like I say, they didn't need a worldwide pandemic to get going, and they basically approached theirs systematically with a great plan, and what they basically were able to do is really do that, another thing that people have done a lot of lip service in the past is IT and business collaboration. They actually executed brilliantly from that perspective where the IT organization, technology organization sort of delivered, on top of the Pega platform delivered a platform to be able to manage all the technical aspects of business applications that all the processes that seems needed, and in different departments and different divisions were able to leverage those assets and be able to quickly get applications up and running, and being able to dramatically increase the speed of innovation while at the same time dramatically reducing the cost of getting these things done and running them. So basically they built that environment where IT provided the technical aspects as a service to business applications so that they can quickly get things done, automate their processes, and deliver tremendous amount of operational efficiency into the organization. >> Now Kerim, of course, is the head of products. I want to get into some of the product discussion, some of the hard news that you have at PegaWorld. This notion of the Pega Process Fabric, I mean the metaphor is very strong. You think about digital, you think about a fabric. But what do we need to know about the Pega Process Fabric? >> Dave, it's a great solution that I believe corporations, especially enterprises, need to be able to make their staff more effective, streamline their work, getting them to a world where they don't have to personally navigate through dozens of different applications just to achieve an outcome, because whenever you basically have a situation where an employee of an enterprise has to jump through six, 10, 12 different applications just to be able to get something done for the customer, there's a tremendous amount of efficiency that's lost, there's a tremendous amount of training that's required to be able to actually get people to be able to manage all these, working across all these applications, and of course it's very easy to make mistakes. And whenever you have an environment that's built out like that, it inevitably gets exposed to the customers, and they basically, their experiences realize that there's a lot of jumping around. The Process Fabric is around bringing an experience to the users that is basically a single experience, even though work is coming from many different applications in the organization, right? You talk to any enterprise in anywhere in the world, and you basically name any enterprise software company, and they'll tell you, "Yeah, we got that." They have it. >> Yeah. >> They have Microsoft, they have Salesforce, they have ServiceNow, they have Pega, they have it, and users, employees have to juggle through all of these systems to be able to actually get their work done. The job of Process Fabric is to actually bring all these tasks, bring all this work that the workers, and then on behalf of the customers, have to get done, and weave them together into a single experience so that they don't have to jump around. There's much more efficiency. Get work done fast, and the organization then also has control around how the work is prioritized across different systems. How the work is managed through how it gets assigned, how to handle key customers and be able to see all the work that we're doing on behalf of them across all the different systems, and be able to actually bring a home all of these efforts and provide that experience to the user. >> So Kerim, what's the secret sauce there? Is it a combination of using APIs to those applications, and machine intelligence, and machine learning? >> There's a little bit of many things. The key is, one, we basically come with standard connectivity to standard enterprise solutions. We come prepackaged with connectivity to Pega environments within the organizations, as we have many customers that have deployed dozens of different Pega applications. We come with a standard open API approach to be able to provide connectivity, and then we use our decisioning capabilities and process capabilities to manage the prioritization, to be able to manage the routing and the experience for the end users. >> Okay, and the prioritization is something that's determined by business rules, is that correct? Or how does that all work? >> Absolutely. Absolutely, so the idea is to be able to leverage the business rules capabilities of the Pega platform to be able to handle the prioritization and the routing and sort of collating things together that are associated with the same work streams and for the same customers. >> When Alan Trefler started Pega it was right around the time I started in the industry and AI was the hot buzzword, and it took a while to get here, but it feels pretty real right now. How do you look at machine intelligence and the role that it plays? You've used the term real realtime AI. >> Right. >> What do you mean by that, and what's so special about your AI? >> Well, our realtime AI is real, so that's one of the main specialties, but look, there's a lot basically technology out there. There's a lot of great technology out there with great use cases that can look at historical sets of data and be able to actually generate predictive models from them, and those are great. Those are very, very valuable. But we believe that especially when we're directly engaging with customers, that is not enough. That you need actually realtime, real realtime AI. Let me give you an example. If you are basically running some predictive models against a set of customer data, say basically in January and February and using them in March, you will not get the right results that are basically for each individual customer, because things have changed dramatically between February and March. You couldn't make decisions about a customer based on what happened in their activity in January based on what's today. One of our telecom... One of our, I'm sorry, banking clients, for example, used their customer data in the UK, NatWest, used their customer data and identified people that work for the National Health Services and provided realtime programs that are specifically tailored for them, right, so that's basically being able to actually leverage the power of AI and be able to change how you engage with customers. They looked at customer data who might be at financial risk due to the crisis and actually changed programs and payment programs for them, because things have changed dramatically in the timeframe. Our AI leverages predictive models based on historical data, which is great, but actually also adds on top of it the ability to evaluate realtime data based on the real context of the end customer at this point in time, at this point on their experience on the website, on the IVR, on the mobile app, and be able to determine the best way to engage with that customer at that moment in time, and be able to deliver that one-to-one personalized experience. And this has been basically one of the major capabilities of Pega technology. That's how we differentiate in the marketplace in our ability to actually drive the AI capabilities in realtime interactions. >> Wonder if I could ask you about one of the trends in the marketplace, and you're seeing it in the equity markets, these private equity robotic process automation. People, I think, sometimes misunderstand you, and I've said, I've reported a number of times that RPA's just a small part of what you guys do, but at the same time you're seeing a lot of energy in the marketplace, money, billions of dollars, billions, yeah, have poured in. How do you look at RPA? Where does it fit in the Pega platform? >> Yeah, so RPA's absolutely a part of the overall journey. We look at things from an end-to-end automation perspective, essentially we need to do something for a customer, on behalf of a customer, to get an outcome delivered to a customer, and there's a process associated with it. And this process is frequently going to touch through a bunch of different systems. And some of these systems it's going to touch are old. They've been around for a very, very long time. They're a pain point for a lot of organizations. What RPA does really well is it basically lets you put a robotic process, essentially, a process that runs on the desktop and to be able to sort of execute that process inside that old system automatically. And that saves time and saves money, and there's basically a clear ROI associated with it, but it doesn't eliminate that old technology. It just puts, essentially, a veneer in front of it so that the end user doesn't have to key into some old application. It just does it on their behalf. We think that's a part of an end-to-end process automation, and as you go through different steps you might have to execute these robotic process automations, but it's not digital transformation. You're not really transforming it, right? You are basically eliminating that pain point for time being, and it will become a problem maybe for the next person that has to deal with it. We believe that robotic process automation is a great way to automate stuff, but each one of those elements need to go through that transformation as a part of the modernization, digital transformation journey. >> So it's that systems view that you would stress, and obviously you've always taken a systems view. You've got a platform that is an end-to-end platform. That's really what you mean by the end-to-end is that systems view, correct? >> Well, what we mean, really, by end-to-end is a customer comes in and they have a need, and we basically get them what they come in here for, and whatever is in between, whatever processes, and systems, and integrations, and technologies that sit in between, that's sort of the second part of the story. The main important part is work that needs to get done, we get the work done. And we will do anything in between. We'll do integrations, we'll do routing, we will do automation, we'll do business rules, we'll do AI, we'll do robotic process automation, anything that is necessary to basically drive that outcome, drive efficiency, faster response times, and better customer experience. >> Okay, so those are the key metrics. You just answered that other question. Last question, then, is we've got uncertain times. We've talked the gamut of digital transformation, but what advice would you give to customers given this uncertainty? How should they be best prepared? >> I think it's most important, really, to pay attention to the end consumers, and look at it from a perspective of empathy. What is the end consumer worried about right now? What is difficult for them? What is it that they need from your organization given their current circumstances, and make sure the experience that your corporation provides to them is the right experience. This is, I think, a time for a lot of corporations to build some incredible loyalty with their end customers, with the consumers. This is an amazing opportunity to basically have great engagement and to be able to have people realize that yeah, they were there for me. It was a good experience, it was an easy experience, it was a seamless experience, and I would mostly emphasize on that empathy factor. Make sure that we understand what's going through, what's happening in their lives, what they need, and when they engage with the corporation make sure that we provide a seamless experience to them. >> I think that's a great point. We're not going back to the customer experiences of the 2010s. We're entering a new decade, and Kerim, thanks so much for your insights and coming on theCUBE to share them. >> My pleasure, thanks for having me. >> You're welcome, and thank you for watching, everybody. You're watching theCUBE's coverage of PegaWorld iNspire 2020. Be right back right after this short break. (smooth music)