 Good afternoon, cloud community, and welcome back to beautiful Las Vegas, Nevada. We're here midway through day two of Google Cloud Next. My name's Savannah Peterson, joined by analyst Rob Stretchy. Rob, what an exciting week. This is, the enthusiasm is contagious here. I mean, it's awesome to see this many people turn out for this. It's huge. I mean, twice the size again of last year, which was only eight months ago. But, you know, again, it's great to see that and the enthusiasm. Yeah, it is great to see. And you've talked about it a lot on the show. Partnerships, collaboration, ecosystem, which brings me to our fabulous guest, the bulge. Michael, thank you so much for being here. Absolutely. You got to discover and Google on the desk. How is it going for you? It's buzzing. You were just beaming when you walked up to the desk. Are you having a good week? It is exhilarating. Yes. Yes. It's quite a marathon. It was, that it is. It definitely is a marathon. You two announced some exciting news yesterday, correct? Can you tell me about that, Michael? Yeah, absolutely. So, you know, first off, against the backdrop is, it's been an incredible week. And one of the things that we're most proud of is, is if you think about all the product announcements we've made. A lot. A ton. Yeah. And then when you think about all the customer stories that we've shared and discover is a great one that's associated with it, what is super clear is the moment has arrived for Gen AI and the enterprise. And what we're seeing is, we're seeing so many great examples where a year ago, it was, hey, I'll trial, I'll experiment, but leading companies are really now moving to production against great use cases that are associated with it. And so, we're thrilled to actually talk about real life examples. And for Discover, it is really about, how do I make that contact center that much more efficient and how can I help our folks? But let me not do a disservice. I'd love for you to talk a little bit about this. Sure. So, as you guys know, Discover is a direct bank and we are very proud of our award-winning customer experience. This is really a core pillar of our brand. And as we have been a long-time partner with the Google team, really about a year ago, Gen AI presented itself as, maybe this will help us solve problems that we have, what the previous technology just wasn't possible. And that's what we have been focusing on. Specifically, how do we make our customer service agents better at what they do? And if you consider what happened to customer service agents and their job, it becomes significantly more complex in the last five, 10 years. As digital banking really successfully penetrated the industry, you don't call a call center with easy questions anymore. You're not going to call because you want to know your due days or your payment, you get that from the app. You call when you have a really difficult question. And because of that, the customer service agents just really are fielding difficult question after difficult question. And the way it actually happens in the background is, you know, they have a great number of documents, hundreds of procedures that describe them in great length, what they need to do. And one of the absolute strength of Gen AI tools is summarization. So what we try to, what we set out to do is to change the customer experience when you call, you ask your question and then the agent has to say, can I put you on hold for a minute? And then they promptly go and search and try to find the right document and then they try to read it through and try to find something. So we introduce Generative AI, we create a summary that's really just a paragraph of what exactly is the answer, the bread and butter of Generative AI. So the agent can stay in the conversation flow, they can give you an answer immediately as you expect, you would have an answer immediately. And you know, because we use rags and we use embedding, it also gives the agent the actual source material so they can go back to the actual procedure itself and they can find it. But it really solves a major problem. When you call, we want to be there for you. The customer service agent wants to give you an answer right away and then you can build from that. And it's really good. And there is a multitude of other use cases that we have been working on. One of it is, well, why don't we just rewrite the procedure so they would be easier when the agent has to use it and Generative AI is great for that as well. So with the Google team, we built platform integration essentially within weeks and then we spent about six, seven months working through use cases, getting the organization up to speed and comfortable with the use of these tools. And pushing the first actual tools into production. How did the agents interact with the Gen AI, the LLM? How are they actually interacting with it? Yeah, so that's a great question. And what we try to do is try to make the transition as incremental, as seamless as possible. So, you know, technically we could pick up queues from the call automatically. But the agents traditionally are used to running a search that provided, you know, a bunch of procedures. Now they use a similar search, but it provides immediately a paragraph of a recap of the answer. So for one, we made it very easy for them. Two, the agents are really becoming the biggest asset in this game because your expert agents are the ones who can train the AI. So we take feedback from the agents and we explain it to them that we take feedback. They give thumbs up, thumbs down, and they give qualitative feedback on which are those questions, where the process is working and which are those where we need to fine tune more or test more. Again, the Google team was great to build automated tools to refine solutions. And, you know, we tried to go incrementally, make the customer experience measurably better, but stay in within measurable steps that's comfortable for the end user, the agent, the organization, again, it's very important that, you know, we save it in our risk guidelines. Until the agents put prompt engineer on their resumes, right? I'm joking. I mean, that's pretty not what you're getting there. I mean, it's not even, you know, I know you're being cheeky, but you're bringing up a great point. You know, people talk about the negative side effects of AI from certain skill jobs, versus in this case, you're empowering them to be able to do things faster and gain knowledge quicker. It's a fundamental change. So, with the traditional platform implementation, there is a lot of coding, a lot of integration. With this one, since we are Google Cloud user, that was measured really in weeks. There is hardly any engineering. That's awesome, by the way. Which is really good. It's a few weeks. I just want to point that out. That's really good. On the other hand, our traditional knowledge workers, technical writers who write the procedures, expert agents who do quality control on top of other agents, they are becoming the most important knowledge workers because they are the ones who help to fine tune the model output. So they are built into the process, and yes, their jobs is getting significantly richer. Yeah. So it is a great development, and this is something that also helps the other station of the tool. And you know, it is something that helps us go into the direction where we unlock customer on that needs. Yeah, absolutely. How's the team receiving it? Tell us about some of the impact. Are people excited about using it? Is they nervous? Yes. Well, let me cover quickly two things. So there is a little bit of an organizational approach to it, which is about a year ago when we started to work on these ideas, we built an AI governance console in the enterprise. We are financial services. A little bit of governance, yes. And that was very, very important, and I would recommend it to anyone who is heading down this journey. So this is senior people from legal, from compliance, from information security, from your modeling teams, engineering, and the business lines. So you can build a risk framework to identify lower risk ideas that you can start to experiment with, and that's really important. In terms of the actual use of the tool, so one of the use cases that we tried, which we went through in great details now, had to get information to the hands of the agents. We do have measurements with actual agents who are using the solution, and what we find is that they get to the information a little over 70% faster, which sounds like a subtlety, but it's very important because the traditional tool is usually measured in minutes. So again, we might have to put you on hold, and on a phone call, a couple of minutes is forever. The new solution is in seconds. So you can stay on the conversation and you can provide answers. So, very exciting. I can only imagine that you're going to hear it or see it in the way you measure customer satisfaction with this. Absolutely. That's exactly the ultimate measurement because that's what we are after. Yes, and you make the agents more productive. They're going to have greater calls. They get to resolution faster with customers and they feel like they're getting the best information in the right amount of time. So it's exciting. Yeah, it's exciting. How has that partnership really worked between the two of you as you brought this out? Frankly, I think we do our best work when clients trust us enough to sort of share what it is that they're solving for. And in this case, it was we love nothing better than trying to solve a tough technical challenge together. And the good news is if you look across every place in the globe and every industry, there are use cases now that customers can sort of say, I have high confidence that if I really want to think about making my employees more productive, this was a great one. Another one was Sintos. We talked about Sintos this week. They thought about getting a knowledge center of how can I make sure that my customer service reps or my sales reps can find the information they need to be able to better navigate sort of customer engagement. You think about examples like Best Buy. Best Buy is doing some incredible things on the customer experience front. It does start with a virtual agent, but imagine the world of being able to do product troubleshooting through a virtual agent, or even being able to reschedule deliveries. And there's so many more things that are going to come. And then it gets to even another regulated industries. Bear was an announcement this week and we're proud of the work we're doing to help build a radiology platform with them where they're able to deliver AI first sort of applications that helps the radiologists. It's all about shortening the time to diagnosis. So I think what we're seeing is, across all of these industries, you're seeing things that derive a direct business impact. For you, it's going to be agent productivity and it's going to be customer satisfaction. And so in many ways, you're starting to see that pattern. And for me, that's what's going to get people more comfortable and confident at this point. Yeah, absolutely. And I just got to ask you, because listening to you talk about the experience, you can just tell how proud and excited you are about this and what a difference it's going to make for those 10,000 agents. How does that make you feel when you hear a customer story like this? We love nothing better. I run our North America business and our goal is we wake up every single day focused on aligning our capabilities to the outcomes that matter most for our customers. And so this is the proof is in the pudding. Like all the hard work we did in hearing the business impact that this delivered, this is why we do what we do. And we absolutely love that. And how did you, or how did Discover, not just you, but look at this and say, hey, we want to go in on this with Google and this is the capabilities, they have it. And this is why we're going down the path with them. So we had a long history of working together with Google and finding customer solutions. It goes back actually to digital payment integration for many, many years and marketing applications. And so we've been working with the cloud team since 2017, great partnership. So as again, Generative AI came up, we looked to what's probably the most important for us. How can we use it to redouble on our competitive advantage, which is awarding customer experience, awarding customer service agents. So it seemed to fit naturally. The fact that Vertex AI is directly integrated into Google Cloud is fantastic. Gemini is extremely powerful and proved to be a very good solution. So it landed very naturally with the ecosystem. And yes, we're going to keep looking at other customer problems that we can solve. I was just going to ask if now that this has worked or is it looking like it's going to work, the results and the impact is already speaking for itself. Are you planning to roll this out across different parts of the business? Yes, that's exactly what we are doing now. So this agent solution, we are rolling out across all of our agents and it's very knowledge intensive. So it goes agent team by agent team on different skills. Well, we are looking at similar solutions that are helping, for example, a handoff between our digital assistant and an agent. If you want to cut over at any time from the digital assistant to an agent, summarization is a great tool to recap the chat history. So when the agent takes over, they don't have to read sometimes the many pages that you already racked up with the digital assistant, the agent just get really quick. So solving very specific use cases that are all directed to make the customer experience always just a tad bit better. We find that that's really difficult things to copy. And that's something that can contribute to sustainable advantage in customer experience. Yeah, absolutely. And that helps with churn in terms of customers. I mean, if people are happy, that customer experience is such a pivotal moment. Exactly, exactly. We have probably the longest and your card member base, the longest loyalty, the strongest loyalty. And we certainly are working on having the strongest loyalty and that comes from many things, many digital things, but also the customer service agents. So if they can do their job much easier than anywhere else, that's exactly what we are shooting for. Then they're going to have better conversations than anybody else. And that's what we are looking for. This is such a good application and such a tangible thing to wrap your mind around because we've all called in. I mean, I'm thinking about it. My airline loyalty is to Alaska because they have the best customer service. Anytime something happens, I know I'm taking care of it, only takes a couple minutes. Now in the future, probably a couple seconds. But it matters to me. And I don't even price shop anymore at a certain point, you know? So you, yeah, I can see how this is all. Your experience is not unique. Right. That's exactly. Yeah, exactly. And I think what's interesting is, and I think for people who don't know, I mean, we know Discover pretty well because we cover you guys, you're at CNCF at UConn and CloudNativeConn. So you guys know what you're talking about. This is not like you're a technology company. For sure. And providing that customer service is enabled by that technology. Is that really one of the tenants that you guys are aiming for going forward in how this partnership has really grown? I have to agree with Michael that by now, we are looking for things that unlock specific unmet customer need. Maybe a year ago, it was a little bit, wow, this is fascinating. This could do so many things. You know, with the AI governance framework, it is more building a steady pipeline, a relentless pipeline of ideas that we can explore. If they pan out, we can fine tune and scale for the enterprise. So it is becoming part of how we work. And yeah, I think this year is very different from last year. I agree. And hopefully we can, some of the other things we're doing in the industry can spark some ideas. Because we're seeing financial services organizations think about how can I drive efficiency in my organization? How can I bolster the productivity of employees? Or how can I transform products and services? We're a couple of other ones that we talked about this week was, for Moody's, they're using all the capabilities to more quickly and more thoroughly analyze all financial reports. And so think about what that would mean for employee productivity. And another one's for Symphony. It's like purpose-built voice analytics for the financial services space. And especially with all the regulations that are involved, how important that is in sort of the flow of their business. So my hope is that we're going to continue to build on this work and then hopefully we'll spark ideas with what we're seeing from other customers to then think about where we go next. I think that's fantastic. Speaking of closing question for you and nice pun with the next, Google Next. When we are having a chat next year, I have an idea of what you might say to this, but we're having a chat next year. What do you hope to be able to say that you can't say today? That's a good one. So most I'm going to start with you. So where we are, and I think where we're going to be next year is, I think this year the big news, and here at the conference as well, you can hear it all around, that there are first use cases that are reaching production. The platforms are up and running. It's not as much blind experimentation. I think next year, the way I would expect it, you know, financial services, maybe other regulated industries, incremental, incremental progress. What I would expect next year is, there's going to be a greater number of companies with deeper use cases. But again, I think we all try to make sure that we have a responsible approach to it, and we are deploying the solutions for solving specific problems while we are managing risk around it. So I do expect next year it's, the excitement is going to be about specific use cases that people are putting into production and they are trying as compared to this year, which is, hey, we have use cases that we are putting into production. So I expect next year there's going to be tracks, tracks of specific solutions that you do. You know, maybe my perspective would be, in many ways, these are leading companies doing leading things within their respective industries. And so when we think about whether we use sort of the paradigm of crossing the chasm, it's really thinking about how it gets more pervasive across the industry, where they're no longer concerned about, you know, well, I'm not sure how to calculate the ROI of this. We have enough proven pathways and enough sort of proven success stories that all customers sort of realize that these are great ways that I can accelerate today with proven ROI models so that we bring the rest of that maturity curve along. That's what I'm really hoping for. And we're proud of leading customers such as yourself, really paving the way. And I think we're just going to get great momentum in the next 12 months. I agree with both your predictions. I think you're absolutely right. On that note, Michaels-Bolch, thank you both for being here. This was absolutely fantastic. I'm excited. You can tell how powerful your partnership is. And I'm pumped for all those agents and your customers who are going to have such a better experience now. Rob, a fantastic job this morning so far. And thank all of you for tuning in wherever you might be watching on our beautiful planet Earth. We're here at Google Cloud Next in Las Vegas, Nevada. My name's Savannah Peterson. You're watching theCUBE, the leading source for enterprise tech news.