 From around the globe, it's theCUBE with digital coverage of IBM Think 2021. Brought to you by IBM. 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 Lovies, 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. Looking forward to the session today. Which by the way, I've learned Ottawa 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, 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 played 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 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, 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, I kind of see different industries at sort of different levels, but I think with COVID in particularly, 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 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 innovated in the space. And I think we all know that, data is continually being created every single day 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, he 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, we're 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 are just not 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 skill sets 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, you know, 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 through this conversation today is, you know, how do you sort of lower the time to value to get there across, you know, 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, you know, the competition's pretty fierce and pretty widespread. There's a deep bench here. In terms of IBM now, 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 it 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 little bit earlier, right? 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 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 build out these models, right? 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 in 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 product 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, right? We've got them tied directly into the squad 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, right? I'm sorry, go ahead, please. Go ahead, sorry. No, no, you go, I interrupted you, go ahead. No, I was just gonna say that the other two things I'll say, like, you know, I'm saying this, right? We've got a lot of sort of proof points in around it, right? So, right, 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 and 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 it, most people are aware of sort of Gartner Magic Quadrants, right? And we're the leader almost across the board, right? Or a leader and across the board, right? 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, you know, third party validation around that as well, if that makes sense. Yeah, it sure does. You know, we're hearing a lot about conversational AI and, you know, with, you know, online chatbots and voice assistants and a myriad applications in that respect. Let's talk about conversational right now. Some people think it's 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, and again, I talked about Scratch and 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 is typically applied, right? So you see it in customer service. That's the obvious place. We've seen 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 a console? All those things can be automated using, right? Conversational AI. But organizations don't want these sort of point solutions across the customer journey, what they'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 leadfall perspective, but really I'm not here for leadfall, 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 AI going is really kind 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, right? Not just, you know, the website and the chat on the website, but also, right, across your messaging channels, right? 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 might be, right? So to sort of be able to sort of switch between the channels is really, really going to become more important in this sort of seamless experience as you kind of go through it, right? So that's fine by customers, yeah. Yeah, you've talked about customers a little bit and you mentioned case studies, but can we get, 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 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're kind of mentioning, you can come by sort of industries that are relevant, right? So the ones that I think are most relevant that we've seen are the ones with the biggest sort of consumer sort of side, right? So clearly in financial services, banks, insurance are clearly obvious ones, telecommunications, 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, Oil Bank of Scotland, they've now changed their name to Nat West out 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 while 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, Nat West, 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 right through an IVR system, right? So through over the phone, right? So I can call instead of getting that 1-800 number, I'm gonna get a nice natural language experience over the phone to help employees with common problems that they have with their health 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 their cost centers. So the United West is a great example. CIBC, another bank in Canada, Toronto is a great example. And the nice thing about what CIBC is doing, and we have four big banks here in Canada, what CIBC can do is really focusing a lot on the transactional side. So making it really easy to do interact transfers or send money or 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 buzz where 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 difference is it making? Wow, that's a loaded, not net to language processing, it is a loaded term in a buzzword, I completely agree. I mean, it's, 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, you know, reset a password, right? So being able to understand that inquiry that that user has that's coming in and being able to understand what the intent is behind it, right? That's sort of one key, 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 is extracting certain things that you need to know. And again, using the conversational eyesight 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. We call them entities a lot of the time in sort of the ice bake 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 of 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, right? So you can imagine having a contract, right? There were 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 or 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, you can 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, the better off we're going to be, right? And handling the problem. So, 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 theCUBE. We appreciate the time today, Brian. Thank you very much. You bet, Brian Levy'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.