 Welcome back everyone, it's theCUBE's coverage here in Las Vegas, I'm John Furrier, host of theCUBE with ReMars, Amazon ReMars. It stands for machine learning, automation, robotics, and space, a lot of great content encompassed on AI meets robotics and space, industrial, IoT, all things data. We've got two great guests here to unpack the AI side of it, the new serve out of managing director of AI ecosystems at Deloitte and Art Merit, conversational AI lead at AWS. I know it's great to see you, CUBE alumni. All right, welcome to theCUBE. Thanks for having me, I appreciate it. So AI is the big theme, obviously. The big disconnect in the industry has been the industrial, OT versus IT, that's happening. Now you've got space and robotics meets what we know is machine learning and AI, which we've been covering. This is the confluence of the new IoT market. It absolutely is. What's your opinion on that? Yeah, so actually it's taking IoT beyond the art of possible, right? One area that we've been working very closely with AWS where a strategic alliance with them. And for the past six years we've been investing a lot in transformations, transformation as it relate to the cloud, transformation as it relate to data modernization. The new edge is essentially on AI and machine learning. And just this week we announced a new solution which is more focused around enhancing contact center intelligence. So think about the edge of the contact center where we all have experiences around dealing with customer service and how to really take that to the next level. Challenges that clients are facing in every part of that business, so clearly. Well, conversation on AI is a good topic. Talk about the relationship with Deloitte and AI, Amazon for a second, around AI, because you guys have some great projects going on right now that's well ahead of the curve on solving the scale problem. Because there's a scale problem, practical problem, and then scale. What's the relationship with Amazon and Deloitte? It's a great, we have a great alliance and relationship. Deloitte brings that expertise to help folks build high quality, highly effective conversation on AI. And enterprises are implementing these solutions to really try to improve the overall customer experience. So they want to help agents improve productivity, gain insights into the reasons why folks are calling, but it's really to provide that better user experience being available 24 seven on channels users prefer to interact. And the solutions that Deloitte is building are highly advanced, super exciting. Like when we show demos of them to potential customers, their eyes light up and they want their positions. What do you show them? One solution is called multimodal interfaces. So what this is, is when you're calling to like a voice IVR, Deloitte's solution will send the folks, say a mobile app or a website. So the person can interact with both the phone, touching on the screen and the voice, and it's all kept in sync. So imagine you call the doctor's office or say I was calling an airline and I wanted to change my flight, or sort of change the seat, if they were to say seat 20D is available. Well, I don't know what that means, but if you see the map while you're talking, you can say, oh, 20D is the aisle, I'm going to select that. So Deloitte's doing those kind of experiences, it's incredible. Manos, this is where the magic comes into play. When you bring data together and you have the integration like this, asynchronously or synchronously, it's all coming together, you have different platforms, phone, voice, silo, databases, potentially the old way. Now the new way is integrating. What makes it all work? What's the key to success? Yeah, it's certainly not a trivial feat, right? Bringing together all of these ecosystems of relationships, technologies all put together. We cannot do it alone, right? This is where we partner with AWS, with some of our other partners like Salesforce and One Reach, and really trying to bring a symphony of some of these solutions to bearer. When you think about, you know, going back to the example of Contact Center, the challenges that the pandemic posed in the last couple of years was the fact that it was a humongous rise in volume of number of calls. You can imagine people calling in, asking for all kinds of different things, whether it's airlines, whether it is doctor's office and retail. And then coupled with that is the fact that there's the labor shortage, right? And how do you train agents to get them to be productive enough to be able to address hundreds or thousands of these calls? And so that's where we have been starting to, we have invested in those solutions, bringing those technologies together to address real client problems, not just slideware, but actual production environments. And that's where we launched this solution called TrueServe as of this week, which is really a multimodal solution that is built with preconceived notions of technologies and libraries where we can then be industry agnostic and be able to deliver those experiences to our clients based on whatever vertical or industry they are in. Take me through the client's engagement here because I can imagine they want to get a practical solution. They're going to want to have it up and running, not like just a chatbot, but like a completely integrated system. What's the challenge and what's the outcome first set of milestones that you see that they do first? Do they just get the data together? Are they deploying a software solution? What's the use cases? There's a couple different use cases we see. There's the self-service component that we're talking about with the chatbots or voice IVA or IVR solutions. There's also use cases for helping the agent, so real-time agent assist. So you call into a contact center, it's transcribed in real-time, run through some sort of knowledge base to give the agent's possible answers to help the user out. Tying in, say, the Salesforce data, CRM data to know more about the user. Like if I was to call the airline, it's going to say, are you calling about your flight to San Francisco tomorrow? Notice who I am, it leverages that stuff. And then the key piece is the analytics, knowing why folks are calling, not just your metrics around length of calls or deflections, but what were the reasons people were calling in because you can use that data to improve your underlying products or services. At least are the things that enterprise are looking for. And this is where someone like Deloitte comes in, brings that expertise, speeds up the time to market, and it really helps. What was the solution you mentioned that you guys announced? Yeah, so this is called Deloitte TrueServe. And essentially it's a combination of multiple different solutions, combinations from AWS, from Salesforce, from OneReach, all put together with our joint engineering and really delivering that capability. Enhancing on that is the analytics component, right? Which is really critical, especially because when you think about the average contact center, less than 10% of the data gets analyzed today. And how do you then extract value out of that data and be able to deliver business outcomes? I was just talking to somebody the other day about Zoom, how everyone records their Zoom meetings, and no one watches them. Who's going to wade through that? Call center is even more high volume. We're talking about massive data. And so do you guys automate that? Do you go through every single piece of data, every call, and bring it down? Is that how it works? Go ahead. Oh yeah, there's just some of the things you can do. You can analyze the calls for common themes, like figuring out like topic modeling, what are the reasons people are calling in, summarizing that stuff so you can see what those underlying issues are. So that could be, like I was mentioning, improving the product or service. It could also be for helping train the agent. So here's how to answer that question. And it could even be reinforcing positive experiences. Maybe an agent had a particular great call and that could be a reference for other folks. Yeah. Yeah, and also during the conversation, when you think about, within 60 to 90 seconds, how do you identify the intonation, the sentiments of the client customer calling in, right? And be able to respond in real time for the challenges that they might be facing and be the ability to authenticate the customer at the same time be able to respond to them. I think that is the advancements that we're seeing in the market. I think also you're point about the data having residual value is also excellent because there's a long tail of value in this data, like with predictions and stuff. So NASA was just on before you guys came on and we were talking about the Artemis project and all the missions. And they have to run massive amounts of simulations. And this is where I've kind of seen the dots connect here. You can run with AI, run all the heavy lifting without human touching it to get that first ingestion or analysis. And then iterating on the data based upon what else happens. This is now the new normal, right? It is and it's transfers towards, across multiple domains. So the example we gave you was around conversational AI. We're now looking at that for doing predictive analytics, right? Those are some examples that we're doing jointly with AWS SageMaker. We are working on things like computer vision, right? With some of the capabilities on what computer vision has to offer. And so when you think about the continuum of possibilities of what we can bring together from a tools, technology, services perspective, really the sky's the limit in terms of delivering these real experiences to our clients. So take me through a customer, pretend I'm a customer, I get it, I got to do this. It's a competitive advantage. What are the outcomes that they are envisioning? What are some of the patterns you're seeing with customers? What outcomes are they expecting and what kind of high level upside that you see them envisioning coming out of the data? Yeah, so when you think about the CXOs today and the board, right? A lot of them are thinking about, okay, how do you do build more efficiency in your system? How do you enable a technology or solution for them to not only infuse the top line but as well as their bottom line? How do you enhance the customer experience? Which in this case is spot on because when you think about when customers go repeat to a vendor, it's based on quality, it's based on price. Customer experience is now topping that where your first experience, whether it's through a chat or a virtual assistant or a phone call is going to determine the longevity of that customer with you as a vendor. So clearly, when you think about how clients are becoming AI fueled, this is where we're bringing in new technologies, new solutions to really push the limit and the art of possible. And you got to play both to do this. Yeah, yeah, absolutely, we have done that. And in fact, we're now taking that to the next level up. So something that I've mentioned about this before which is how do you trust an AI system as it's building up? Hold on, how do I put the plug in? The book is here for a reason to remind me. Now, but also trust is a big thing. Just put it trustworthy. This is an AI ethics question. It's a big, let's get into it, this is huge. Yep, data is data. Data can be biased from coming in. This part of it, there are those concerns you have to look at the bias in the data. It's also how you communicate through these automated channels, being empathetic, building trust with the customer, being concise in the answers, and being accessible to all sorts of different folks and how they might communicate. So it's definitely a big area. I mean, you think about just normal life. We all live situations where we got a text message from a friend or someone close to us where, what the hell, what are you saying? And they had no contextual bad feelings about it. Or, well, there's misunderstandings. Because the context isn't there, because you're rapid fire, I'm on the subway, I'm riding my bike, I stop the text, okay, okay. Church response could mean I'm busy or I'm angry. Like, this is now, what you said about empathy, this is now a new dynamic in here. The empathy is huge, especially if you're, say, a financial institution or building that trust with folks and being empathetic. If someone's reaching out to your contact center, there's a good chance they're upset about something. So you have to take that in- Calm them down first. Yeah, and not being like the, you know, the false like, platitude kind of things, like really being empathetic, being inclusive in the language. Those are things that you have conversation with designers and linguistics folks that really look into that. That's why having domain expertise from, you know, folks like Deloitte come in to help with that. Because maybe if you're just building the chap on your own, you might not think of those things, but the folks with the domain expertise would say like, hey, this is how you script it, it's the power of words. You know, getting that message across clearly. The linguistics matter. Yeah, yeah. It does. By vertical too. I mean, you pick any, the tribe, whatever orientation and age, demographic, genders. Yeah, all of those things that we take for granted as a human, when you think about trust, when you think about bias, when you think about ethics, it just gets amplified, right? Because now you're dealing with millions and millions of data points that may or may not be, you know, the right direction in terms of somebody's calling in depending on what age group they're in. Some questions might not be relevant for that age group, right? Now a human can determine that, but a bot cannot. And so how do you make sure that when you look at this data coming in, how do you build models that are ethically aware of the contextual, you know, algorithms and the alignment with it? And also enabling that experience to be much enhanced than taking it backwards, right? And that's really there. You can imagine it getting better as people get scaled up a bit, because then you're going to have to start having AI to watch the AI at some point, as they say. Where are we in the progress in the industry right now? Because I know there's been a lot of news towards around, you know, ethics and AI and bias, and it's a moving train, actually. But still, problems are going to be solved. Are we at the tipping point yet, or are we still walking before we crawl or crawling before we walk, I should say? I mean, where are we? I think we are in between a crawl and a walk phase. And the reason for that is because it varies depending on whether you're a regulated industry or an unregulated, right? In a regulated industry, there are compliance regulations requirements, whether it's the government, whether it's banking, financial institutions, where they have to meet, you know, Sarbanes, Oxleys, and all kinds of compliance requirements. Whereas in an unregulated industry like retail and consumer, it is anybody's game, right? And so the reality of it is that there is more of an awareness now, and that's one of the reasons why we have been promoting this jointly with AWS. We have a framework that we have established, right? Where there are multiple pillars of trust bias, privacy, and security that companies and organizations need to think about, right? Our data scientists, ML engineers, need to be familiar with it. But because while they are super great in terms of model building and development, when it comes to the business, when it comes to the client or a customer, it is super important for them to trust this platform, this algorithm. And that is where we're trying to, you know, kind of build that momentum, bring that awareness. One of my colleagues has written this book, Trust for the AI, we're trying to take the message out to the market to say, there is a framework, we can help you get there, and certainly that's what we're doing today. Just call it, you'll load it up and you'll be able to take care of them. Yeah. On the Amazon side, Amazon Web Services, I always interview Swami every year, three in there, and he's always got the updates. He's been bullish on this for a long time on this conversational AI. What's the update on the AWS side? Where are you guys at? What's the current trends that you're riding? What wave are you riding right now? So some of the trends we see in customer interest, there's a couple of things. One is the multimodal interfaces we were just chatting about, where the voice IVA is synced with like a web or mobile experience, where you take that full advantage of the device. The other is adding additional AI into the conversational AI. So one example is a customer that included intelligent document processing as part of the chatbot. So instead of typing your name and address, take a photo of your driver's license. It was an insurance onboarding chatbot, so you could take a photo of your existing insurance policy and extract that information to build the new insurance policy. So if folks get excited about that, in the third area we see interest is what's called multi-bot orchestration. And this is where you can have one main chatbot, marshal or user across different sub chatbots based on the use case, persona or even language. So those things get people really excited and then Aided Rest is launching all sorts of new features. I don't know what you're gonna say right now. He wouldn't tell me tomorrow. I know something's coming out tomorrow. I tried to corner him. He's big smile on his face. He wouldn't tell me. Yeah, we have, for folks like the Closer Alliance really is just we're able to give previews so there's a preview of all the new stuff and I don't know what it could. Yeah, this is pretty exciting. You get in trouble if you didn't spill the beans here. Don't be careful, I'll watch you. Yeah. We'll talk after up camera. Oh, exciting stuff. Yeah, yeah. I think the orchestrator bot is interesting. Having the ability to orchestrate across different contextual data sets is interesting. One of the areas where it's particularly interesting is in financial services. Imagine a bank could have consumer accounts, merchant accounts, investment banking accounts. So if you were to chat with the chat bot and say I want to open an account, well, which account do you mean? So it's able to figure out that context and navigate folks to those sub chatbots behind the scenes. And so it's pretty interesting stuff. Awesome. Who knows, while we're here, take a minute to quickly give a plug for Deloitte, what your program's about, what customers should expect if they work with you guys on this project. Give a quick commercial for Deloitte. Yeah, no, absolutely. I mean, Deloitte has been continuing to lead the AI field organization effort across our client base. If you think about all the Fortune 100, Fortune 500, Fortune 2000 clients, we certainly have them where there are in advanced stages of multiple deployments for AI. And we look at it all the way from strategy to implementation to operational models. So clients don't have to do it alone. And we are continuing to build our ecosystem of relationships, right? Partnerships, like the alliances that we have with AWS, building the ecosystem of relationships with other emerging startups to your point about how do you continue to innovate and bring those technologies to your clients in a trustworthy environment so that we can deliver it in production scale? That is essentially what we're driving. Well, Art, there's a great conversation and the AI will take over from here as we end the segment. I see a bot coming on theCUBE later and there might be CUBE replaced with robots. Right, right, right. I'm John Furrier, calling him from Palo Alto someday. Yeah, you can just say, Alexa, do my demo for me or whatever it is. Yeah, yeah, yeah. Oh, just a little twin for John. We're going to have a robot on earlier or do a CUBE interview with Metal Dave. I don't think he should pipe his voice in. Well, thanks for coming on. Great conversation. Thank you. Yeah, thanks for having us. Okay, CUBE coverage here at Remars in Las Vegas. Back to the event circuit. We're back in line, reinforced and don't forget reinvent at the end of the year. CUBE coverage of this exciting show here. Machine learning, automation, robotics of space. That's Mars. It's Remars. I'm John Furrier. Thanks for watching.