Google Cloud Next '24 | Kathleen Walker | Elastic

Clips
News
More from Google Cloud Next '24

Kathleen Walker

Sr. Director of Product Marketing

Elastic

Progressing to search-powered AI: An overview of Elastic’s approach

Spawning hardware assistant devices, such as the Rabbit R1 and Humane Ai Pin, artificial intelligence has revolutionized search. With information now more democratized than ever, companies like Elastic B.V. are innovating with AI to overhaul enterprise logging, analytics, search and observability use cases.“All search experiences are a little bit different,” said Kathleen Walker (pictured), senior director of product marketing at Elastic. “If you think about almost a year ago, we were all talking about this new thing called generative AI

Elastic shares climb 8% on strong earnings and revenue beats in fourth quarter

Shares in Elastic N.V. rose nearly 8% in late trading after the enterprise-grade search software company today reported better-than-expected results in its fiscal 2024 fourth quarter.For the quarter that ended on April 30, Elastic reported adjusted earnings per share of 21 cents on revenue of $335 million, the latter up 20% year-over-year. Both were beats, as analysts had expected adjusted earnings of two cents per share on revenue of $329.2 million.Elastic’s better-than-expected headline figures were driven by growth in the Elastic Cloud, which saw revenue of $148 million in the quarter, up 32% year-over-year

Three insights you might have missed from theCUBE’s coverage of Google Cloud Next

Google LLC might be in third place for cloud market share behind Amazon Web Services Inc. and Microsoft Corp., but artificial intelligence could allow Google Cloud to narrow that gap quickly.TheCUBE on set at Google Cloud Next, wrapping up day 3.This was one of the takeaways from Google Cloud Next 2024 as theCUBE, SiliconANGLE Media’s livestreaming studio, interviewed company executives, partners and industry analysts over three full days of coverage. What emerged was a picture of Google seeking to aggressively redefine cloud computing through AI innovation and a differentiated product set

play_circle_outline Exploring AI Partnerships with Google: Leveraging Elastic's Search-Powered AI for Enhanced Customer Experience

play_circle_outline Elastic's partnership with Google and reference architectures for conversational experiences

play_circle_outline Impact of Elastic's solutions on customer support and efficiency

play_circle_outline Elastic's role in helping customers scale their solutions

Info

Kathleen Walker | Elastic

Kathleen Walker

Sr. Director of Product Marketing Elastic

search

(upbeat music)

>> Good afternoon, Google fans, and welcome back to beautiful Las Vegas, Nevada. We're here at Google Cloud Next, bringing on the back end of day one of three days of coverage here on "theCUBE." My name's Savannah Peterson, joined by Analyst Rob Strechay. Rob, what a power-packed afternoon we're about to open.

>> I think this is going to be a lot of fun. I am really excited for these, a lot of partnership with Google, and a lot of how you actually make this stuff work around AI, in particular.

>> Which is why we have such a fantastic guest. We've got Kathleen from Elastic with us. Welcome to the show, Kathleen.

>> Thank you. I'm glad to be here. Glad you're having me.

>> Yeah, we're having you back. You're a CUBE pro now at this point.

>> It only takes twice. Okay. (Savannah, Kathleen, and Rob laugh)

>> Well, you're alumni after,

so you're an alumnus after. >> Got it. All right.

>> So yes. >> Yeah, yeah. I mean, you know, that's all it takes in most cases, especially in AI. Only two prototypes, right? And then you're an expert.

>> Totally agree. >> How's the show going

for you so far? >> Yeah, it's going great. Just the usual. Fast paced, action packed. A lot of really interesting things going on here this year.

>> Yeah, a lot of really interesting talks I noticed too. Elastic is all about search-powered AI. Break that down for us a little bit. What are the advantages you're offering to your customers?

>> Well, as you can imagine, all search experiences are a little bit different. And so if you think about almost a year ago from now, we were all talking about this new thing called generative AI. And there are a lot of questions out there, like, is traditional search dead? Like, what's going on? You know, how is this going to change the face of this space? And what we found is it absolutely has, but it's actually drawn more attention to the need for some of that traditional search functionality. So if you think about, I have a customer. I want to build a search application that provides this awesome custom, you know, conversational AI experience. It needs the same underpinnings as, you know, traditional search applications that people were building years ago.

>> Yeah, I mean, in fact, part of this was we had a good discussion with Satchin Gupta earlier on today about infrastructure and distributed cloud, and he actually referenced Elastic. You know, unplugging AlloyDB on premise and using in that infrastructure the Google Distributed Cloud, using Elastic for the vector database. And it would seem like, I mean, to me, again, I've been saying it for a while, that vector is more of feature than it is an actual, you know, entire database. How has that really come out? Like, how has that manifested itself within Elastic and with your customers?

>> Yeah, so what we really say over and over again is you think about a vector database, and some people are thinking about it in the terms of, like, storing and searching embeddings. But that's really just the smallest use case. You really also need to understand how are those embeddings getting in there. And then once you have all this information there, like, how are you going to retrieve the best information? And that requires a lot more than just that kind of technical embeddings piece. You need to be thinking about, am I going to be able to aggregate my data? What about facets and filtering? Like, how am I actually going to run this? Can I do it on prem? Can I do it on the cloud? Am I going to have, you know, the technology around it for my specific, you know, use cases that might require things like text search? Because, you know, surprisingly, text search is going nowhere. It's still really applicable to a lot of those search scenarios, where, you know, I'm an e-commerce site. One of the most common searches I get is a part number. There's no real, you know, intent I need to infer from that. I have a part number, tell me more about it. So, you know, it still has a place in the search space, and you need more than just kind of these, you know, more later technologies, like semantic search, et cetera, to really fill a lot of these use cases.

>> Let's build on that a little bit. So that's a perfect example. I used to work on an e-commerce site with 3 million SKUs. Don't need to know much more detail beyond the intent of someone to learn or to purchase, or whatever it is they're doing there. What are some of the use cases that really sing for search-powered AI?

>> There's just so many. So a customer that we just recently had at one of our user groups, Stack Overflow, was talking about the fact that they have over, you know, 100,000 people contributing highly technical information to their environment. And within that environment, there are absolutely other people there that would benefit from that information. But, you know, that's this perfect scenario for semantic search, where the way that I say something and the way I share information may be different from somebody else. So you really need that technology to help you understand what's the intent behind this question so that it can match you with the best results. So, you know, that's a different scenario using some different kind of technology to answer that question. But what we're finding is most people actually need a combination of both. And so you need a situation where text, you know, traditional text search is going to be the best answer. There are others where semantic search is really required to get the most relevant answer. So that's what we're seeing hybrid search really taking off and exploding. You need your technology to be able to recognize, okay, which is the most relevant answer for this particular query, and then surface that to the person asking the question.

>> Is that where your partnership with Google comes in?

>> Yes, that and more. So one of the things that we've really kept at the forefront for Elastic is kind of our background in just being open. And so we recognize that there are different decisions that have different right answers across our customer base. So if you look at something that a lot of people are talking about right now, like, what is the right large language model for me to be using, you know, there's a lot of factors that go into making that decision and a lot of different answers that could be absolutely correct. And so what we focused on is making sure that we're partnering with these technology, you know, with these technologies, working closely with them to make sure that Elasticsearch works, you know, effectively, but also better, you know, than other things so that people can pick the things that are right for their use case. And Google's a perfect example of that, where we got together and we built some reference architectures that we could share with our mutual customers about how to build conversational experiences on Elasticsearch, on Google. We had a great webinar on that. It was one of our, you know, super high attended ones 'cause people are really interested in this topic, as you could imagine. (laughs)

>> Absolutely. And I can imagine they also like a little guidance from a company like Elastic on how to prioritize that and how to train those models to know which type of search to vault to in that scenario, which is really cool. You've got some power-packed logos on your website illustrating that there are some very large, well-known brands that have adopted this. You mentioned a use case with Cisco and how quickly they were able to make change over the course of a year. Can you tell us a little bit about that?

>> In fact, you had them on with us.

Just recently. Yes. >> Yeah, last time you saw me, we had Cisco here with us. And, you know, one of the things that I was really impressed by was just how quickly they moved, embracing, you know, generative AI and building their own application. And the application that they built was something that helped their customer support engineers find answers for their customers who were calling in with problems. Always a sensitive one. Always one you want to handle quickly and efficiently. And so talking to them a year on out where, you know, they've got time under their belts, they've made improvements along the way, we've partnered with them the whole time, what they have found is that they can attribute 90% of the answers that they're surfacing to their support engineers are coming from this platform. And they've been able to save about 5,000 hours a month for these customer support engineers.

You have to imagine- >> Wow.

>> that has to have a huge impact on

customer satisfaction, right? >> That's massive. Yeah, absolutely.

>> I'm getting your answers fast, and I'm also able to serve more customers now, so.

>> And your agents feel better, too, because their time is better utilized, and you have a greater sense of purpose for other types of work.

>> Yeah, so just a really great example about being, you know, being an early adopter, building something really powerful, and getting some great results.

>> Do you see that people are coming to Elastic anew because of these features? Because, I mean, again, Elastic's been around for a while and been known in really how you aggregate data. A lot of pipelines into Elastic, like you talked about. Getting the data in is part of the problem. And then being able to do the vectorizing and all of the tagging and what have you. Is this a new introduction to people who may have not thought about Elastic that way?

>> I think I would say that Elastic has some great, you know, great recognition across the market. I think that when you think about search, a lot of people think about Elasticsearch, you know, right away. But I think that what we've been able to do is kind of take this moment, this market moment that's been presented, and really reeducate people on how much we have done over the last several years to really dive into the promise of machine learning, dive into the promise of AI. And it's already surfacing in our products. So, you know, when the big buzz started happening around vector database, for instance, it's like, we're the most downloaded vector database in the market. And that may have been a surprise to some people, but it's, you know, it's the truth. We've been around for a while. We've been investing in these technologies, and we're helping our customers see the promise.

>> Were you predicting this market moment?

>> I wish I could say that I predicted all market moments, but I think-

>> I mean, don't we all, for the record? Yeah.

>> I would say I was definitely, you know, pleasantly surprised by just the massive amount of buzz that happened. 'Cause I think, you know, all of us saw ChatGPT and those initial experiences and were like, "This is very cool." But I think it really opened everybody's eyes and minds to, okay, this has a place everywhere. I mean, we've got to figure out what that is, because if our competitor just do it first, then that's a, you know, we're doing a disservice to our customers. So I think, you know, that was really exciting. And interestingly enough, we just did a developer survey recently where, and these were Elastic and non-Elastic developers, but they all had search on their minds. And one of the things that we found were 87% of these developers had a generative AI use case in mind or in progress, but only 11% had gotten those out into production. And so that's one of the things

that we're seeing, is- >> Wow, yeah.

>> when you start kind of going down this path, all these considerations come up, like, okay, how am I going to keep my private data safe? You know, how are we going to make sure that we understand the costs around this? And working with somebody who's had, you know, a really long time, you know, leadership spot in the search space I think can help there.

>> Absolutely.

>> We had a really interesting insight from McKinsey earlier. There's a running joke of death by 1,000 POCs at this point. And so how are you helping customers go from experimentation phase to really figuring out their solution at scale?

>> You know, we have a lot of fun doing generative AI workshops for multiple customers at a time or for individual customers, if they want to use their own data. And a lot of times we're kind of starting to talk about it in the framework of like a playground, where it's like, let's just, you know, let's see what's possible. Or if you have something in mind, or if you're earlier on in the process, you know, let's come in, and by the time you leave, you're going to have written your first generative AI application. (laughs)

>> That's got to be really inspiring and empowering for people.

>> Yeah, it is because it's, you know, I think while there's a lot of considerations, our goal is obviously to make a platform that helps you get up and running quickly, helps you ingest data quickly and efficiently, and enhance that data as you bring it in, so that, ultimately, you know, you have a great experience at the end.

>> Makes total sense.

>> Yeah, it really does make total sense. So we've talked about the logistics and how it works a little bit. Are there any applications of this in the wild, or maybe in the Instant Pot, if you will, cooking for the future, that really have you personally excited not just as a company, but as an individual?

>> Well, I think, yeah, we've been having a lot of exciting conversations about just the art of the possible, and I think some of the things that our customers bring to us are like, wow, this is extremely inspiring. And so I think, you know, you have your traditional experiences, like, Cisco has jumped on and, like, others, where, you know, I have so much internal data that I want to surface to my employees to help them, you know, work more efficiently. You know, that efficiency has only increased. So I think if you'd asked me which spaces are you seeing the fastest adoption of this technology, I'd say, like, customer success type or internal workplace search. These are two areas where people are jumping on quickly, 'cause it's just so obvious that there are benefits to jumping into this space. But then, you know, we've also seen customers go out and do really exciting things about, you know, anomaly detection and threat monitoring and things like that that are really helping keep the world safer. And so that's very exciting too.

>> Yeah, I was going to say, what was the top use case that you're seeing from your customers? Where are they focusing when they get started a lot of times?

>> Yeah, like I said, I think it's let's, you know, potentially create a experience where our customers can come and ask questions, too, and have kind of a conversational experience at our support site. So I'm having this issue, how can I solve it? But outside of kind of that traditional, 'cause that in and of itself is not new. But what can be new is if you think about I know who this is, I know what kind of certifications they have, I know what products they have of mine, I know what maybe a recent issue that has surfaced is. All this information can come together with your standard documentation, troubleshooting guides, and provide, like, a step-by-step. Yes, we understand this is what you have. This is what's happening. This is how you can fix it. And, you know, lovely to be able to just get a step-by-step guide to solve my problem without having to call anybody, right?

>> Absolutely. And also better for them to... I know how many times I've called up, and they don't know how, yes, I know that. I've rebooted it. I've done all this stuff. Here's where I am in your call screen. That makes a lot of sense.

>> Yeah. So I think between that and just, again, if you think about your corporate intranet and evolving that into a, you know, interaction scenario where you can ask, what's my 401k policy? Well, I know who you are, I know what region you're from, you know, and in some cases I know what role you are. So if I'm asking a more sensitive question like, what's the compensation ratio for this title at this level, you know, I, as an individual contributor, probably shouldn't be seeing that information. But as a manager of that type of role, I should. And that's where things like role-based access control become really important. Making sure that we only serve the data to the people that have the right to see it, so. (laughs)

>> That's a really good point. I'm glad you said that. Closing question for you, 'cause time has flown. You gave us that great Cisco example here today, a year later. What do you hope, the next time we have you on "theCUBE," that you can say a year from now that you couldn't say today?

>> Oh, there's just so much exciting stuff that our customers are working on. I feel like we just have endless possibilities to talk about next year, which we hadn't, you know, necessarily even thought about two months ago. And it's kind of the combination of the creativity of the people that were working on the customer side with, you know, a technology. That we're willing to hear that input and take the product in the direction that's going to serve them best.

>> I love it. Well, we look forward to telling that story with you on stage at the next Google Cloud Next. Kathleen, thank you so much for being here. Rob, fantastic insights and questions, as always. And thank all of you for tuning in around the world here for our live coverage from Google Cloud Next. It's the end-ish of day one of three. My name's Savannah Peterson. You're watching "theCUBE," the leading source in enterprise tech news. (upbeat music)

Kathleen Walker | Elastic

search

(upbeat music)

>> Good afternoon, Google fans, and welcome back to beautiful Las Vegas, Nevada. We're here at Google Cloud Next, bringing on the back end of day one of three days of coverage here on "theCUBE." My name's Savannah Peterson, joined by Analyst Rob Strechay. Rob, what a power-packed afternoon we're about to open.

>> I think this is going to be a lot of fun. I am really excited for these, a lot of partnership with Google, and a lot of how you actually make this stuff work around AI, in particular.

>> Which is why we have such a fantastic guest. We've got Kathleen from Elastic with us. Welcome to the show, Kathleen.

>> Thank you. I'm glad to be here. Glad you're having me.

>> Yeah, we're having you back. You're a CUBE pro now at this point.

>> It only takes twice. Okay. (Savannah, Kathleen, and Rob laugh)

>> Well, you're alumni after,

so you're an alumnus after. >> Got it. All right.

>> So yes. >> Yeah, yeah. I mean, you know, that's all it takes in most cases, especially in AI. Only two prototypes, right? And then you're an expert.

>> Totally agree. >> How's the show going

for you so far? >> Yeah, it's going great. Just the usual. Fast paced, action packed. A lot of really interesting things going on here this year.

>> Yeah, a lot of really interesting talks I noticed too. Elastic is all about search-powered AI. Break that down for us a little bit. What are the advantages you're offering to your customers?

>> Well, as you can imagine, all search experiences are a little bit different. And so if you think about almost a year ago from now, we were all talking about this new thing called generative AI. And there are a lot of questions out there, like, is traditional search dead? Like, what's going on? You know, how is this going to change the face of this space? And what we found is it absolutely has, but it's actually drawn more attention to the need for some of that traditional search functionality. So if you think about, I have a customer. I want to build a search application that provides this awesome custom, you know, conversational AI experience. It needs the same underpinnings as, you know, traditional search applications that people were building years ago.

>> Yeah, I mean, in fact, part of this was we had a good discussion with Satchin Gupta earlier on today about infrastructure and distributed cloud, and he actually referenced Elastic. You know, unplugging AlloyDB on premise and using in that infrastructure the Google Distributed Cloud, using Elastic for the vector database. And it would seem like, I mean, to me, again, I've been saying it for a while, that vector is more of feature than it is an actual, you know, entire database. How has that really come out? Like, how has that manifested itself within Elastic and with your customers?

>> Yeah, so what we really say over and over again is you think about a vector database, and some people are thinking about it in the terms of, like, storing and searching embeddings. But that's really just the smallest use case. You really also need to understand how are those embeddings getting in there. And then once you have all this information there, like, how are you going to retrieve the best information? And that requires a lot more than just that kind of technical embeddings piece. You need to be thinking about, am I going to be able to aggregate my data? What about facets and filtering? Like, how am I actually going to run this? Can I do it on prem? Can I do it on the cloud? Am I going to have, you know, the technology around it for my specific, you know, use cases that might require things like text search? Because, you know, surprisingly, text search is going nowhere. It's still really applicable to a lot of those search scenarios, where, you know, I'm an e-commerce site. One of the most common searches I get is a part number. There's no real, you know, intent I need to infer from that. I have a part number, tell me more about it. So, you know, it still has a place in the search space, and you need more than just kind of these, you know, more later technologies, like semantic search, et cetera, to really fill a lot of these use cases.

>> Let's build on that a little bit. So that's a perfect example. I used to work on an e-commerce site with 3 million SKUs. Don't need to know much more detail beyond the intent of someone to learn or to purchase, or whatever it is they're doing there. What are some of the use cases that really sing for search-powered AI?

>> There's just so many. So a customer that we just recently had at one of our user groups, Stack Overflow, was talking about the fact that they have over, you know, 100,000 people contributing highly technical information to their environment. And within that environment, there are absolutely other people there that would benefit from that information. But, you know, that's this perfect scenario for semantic search, where the way that I say something and the way I share information may be different from somebody else. So you really need that technology to help you understand what's the intent behind this question so that it can match you with the best results. So, you know, that's a different scenario using some different kind of technology to answer that question. But what we're finding is most people actually need a combination of both. And so you need a situation where text, you know, traditional text search is going to be the best answer. There are others where semantic search is really required to get the most relevant answer. So that's what we're seeing hybrid search really taking off and exploding. You need your technology to be able to recognize, okay, which is the most relevant answer for this particular query, and then surface that to the person asking the question.

>> Is that where your partnership with Google comes in?

>> Yes, that and more. So one of the things that we've really kept at the forefront for Elastic is kind of our background in just being open. And so we recognize that there are different decisions that have different right answers across our customer base. So if you look at something that a lot of people are talking about right now, like, what is the right large language model for me to be using, you know, there's a lot of factors that go into making that decision and a lot of different answers that could be absolutely correct. And so what we focused on is making sure that we're partnering with these technology, you know, with these technologies, working closely with them to make sure that Elasticsearch works, you know, effectively, but also better, you know, than other things so that people can pick the things that are right for their use case. And Google's a perfect example of that, where we got together and we built some reference architectures that we could share with our mutual customers about how to build conversational experiences on Elasticsearch, on Google. We had a great webinar on that. It was one of our, you know, super high attended ones 'cause people are really interested in this topic, as you could imagine. (laughs)

>> Absolutely. And I can imagine they also like a little guidance from a company like Elastic on how to prioritize that and how to train those models to know which type of search to vault to in that scenario, which is really cool. You've got some power-packed logos on your website illustrating that there are some very large, well-known brands that have adopted this. You mentioned a use case with Cisco and how quickly they were able to make change over the course of a year. Can you tell us a little bit about that?

>> In fact, you had them on with us.

Just recently. Yes. >> Yeah, last time you saw me, we had Cisco here with us. And, you know, one of the things that I was really impressed by was just how quickly they moved, embracing, you know, generative AI and building their own application. And the application that they built was something that helped their customer support engineers find answers for their customers who were calling in with problems. Always a sensitive one. Always one you want to handle quickly and efficiently. And so talking to them a year on out where, you know, they've got time under their belts, they've made improvements along the way, we've partnered with them the whole time, what they have found is that they can attribute 90% of the answers that they're surfacing to their support engineers are coming from this platform. And they've been able to save about 5,000 hours a month for these customer support engineers.

You have to imagine- >> Wow.

>> that has to have a huge impact on

customer satisfaction, right? >> That's massive. Yeah, absolutely.

>> I'm getting your answers fast, and I'm also able to serve more customers now, so.

>> And your agents feel better, too, because their time is better utilized, and you have a greater sense of purpose for other types of work.

>> Yeah, so just a really great example about being, you know, being an early adopter, building something really powerful, and getting some great results.

>> Do you see that people are coming to Elastic anew because of these features? Because, I mean, again, Elastic's been around for a while and been known in really how you aggregate data. A lot of pipelines into Elastic, like you talked about. Getting the data in is part of the problem. And then being able to do the vectorizing and all of the tagging and what have you. Is this a new introduction to people who may have not thought about Elastic that way?

>> I think I would say that Elastic has some great, you know, great recognition across the market. I think that when you think about search, a lot of people think about Elasticsearch, you know, right away. But I think that what we've been able to do is kind of take this moment, this market moment that's been presented, and really reeducate people on how much we have done over the last several years to really dive into the promise of machine learning, dive into the promise of AI. And it's already surfacing in our products. So, you know, when the big buzz started happening around vector database, for instance, it's like, we're the most downloaded vector database in the market. And that may have been a surprise to some people, but it's, you know, it's the truth. We've been around for a while. We've been investing in these technologies, and we're helping our customers see the promise.

>> Were you predicting this market moment?

>> I wish I could say that I predicted all market moments, but I think-

>> I mean, don't we all, for the record? Yeah.

>> I would say I was definitely, you know, pleasantly surprised by just the massive amount of buzz that happened. 'Cause I think, you know, all of us saw ChatGPT and those initial experiences and were like, "This is very cool." But I think it really opened everybody's eyes and minds to, okay, this has a place everywhere. I mean, we've got to figure out what that is, because if our competitor just do it first, then that's a, you know, we're doing a disservice to our customers. So I think, you know, that was really exciting. And interestingly enough, we just did a developer survey recently where, and these were Elastic and non-Elastic developers, but they all had search on their minds. And one of the things that we found were 87% of these developers had a generative AI use case in mind or in progress, but only 11% had gotten those out into production. And so that's one of the things

that we're seeing, is- >> Wow, yeah.

>> when you start kind of going down this path, all these considerations come up, like, okay, how am I going to keep my private data safe? You know, how are we going to make sure that we understand the costs around this? And working with somebody who's had, you know, a really long time, you know, leadership spot in the search space I think can help there.

>> Absolutely.

>> We had a really interesting insight from McKinsey earlier. There's a running joke of death by 1,000 POCs at this point. And so how are you helping customers go from experimentation phase to really figuring out their solution at scale?

>> You know, we have a lot of fun doing generative AI workshops for multiple customers at a time or for individual customers, if they want to use their own data. And a lot of times we're kind of starting to talk about it in the framework of like a playground, where it's like, let's just, you know, let's see what's possible. Or if you have something in mind, or if you're earlier on in the process, you know, let's come in, and by the time you leave, you're going to have written your first generative AI application. (laughs)

>> That's got to be really inspiring and empowering for people.

>> Yeah, it is because it's, you know, I think while there's a lot of considerations, our goal is obviously to make a platform that helps you get up and running quickly, helps you ingest data quickly and efficiently, and enhance that data as you bring it in, so that, ultimately, you know, you have a great experience at the end.

>> Makes total sense.

>> Yeah, it really does make total sense. So we've talked about the logistics and how it works a little bit. Are there any applications of this in the wild, or maybe in the Instant Pot, if you will, cooking for the future, that really have you personally excited not just as a company, but as an individual?

>> Well, I think, yeah, we've been having a lot of exciting conversations about just the art of the possible, and I think some of the things that our customers bring to us are like, wow, this is extremely inspiring. And so I think, you know, you have your traditional experiences, like, Cisco has jumped on and, like, others, where, you know, I have so much internal data that I want to surface to my employees to help them, you know, work more efficiently. You know, that efficiency has only increased. So I think if you'd asked me which spaces are you seeing the fastest adoption of this technology, I'd say, like, customer success type or internal workplace search. These are two areas where people are jumping on quickly, 'cause it's just so obvious that there are benefits to jumping into this space. But then, you know, we've also seen customers go out and do really exciting things about, you know, anomaly detection and threat monitoring and things like that that are really helping keep the world safer. And so that's very exciting too.

>> Yeah, I was going to say, what was the top use case that you're seeing from your customers? Where are they focusing when they get started a lot of times?

>> Yeah, like I said, I think it's let's, you know, potentially create a experience where our customers can come and ask questions, too, and have kind of a conversational experience at our support site. So I'm having this issue, how can I solve it? But outside of kind of that traditional, 'cause that in and of itself is not new. But what can be new is if you think about I know who this is, I know what kind of certifications they have, I know what products they have of mine, I know what maybe a recent issue that has surfaced is. All this information can come together with your standard documentation, troubleshooting guides, and provide, like, a step-by-step. Yes, we understand this is what you have. This is what's happening. This is how you can fix it. And, you know, lovely to be able to just get a step-by-step guide to solve my problem without having to call anybody, right?

>> Absolutely. And also better for them to... I know how many times I've called up, and they don't know how, yes, I know that. I've rebooted it. I've done all this stuff. Here's where I am in your call screen. That makes a lot of sense.

>> Yeah. So I think between that and just, again, if you think about your corporate intranet and evolving that into a, you know, interaction scenario where you can ask, what's my 401k policy? Well, I know who you are, I know what region you're from, you know, and in some cases I know what role you are. So if I'm asking a more sensitive question like, what's the compensation ratio for this title at this level, you know, I, as an individual contributor, probably shouldn't be seeing that information. But as a manager of that type of role, I should. And that's where things like role-based access control become really important. Making sure that we only serve the data to the people that have the right to see it, so. (laughs)

>> That's a really good point. I'm glad you said that. Closing question for you, 'cause time has flown. You gave us that great Cisco example here today, a year later. What do you hope, the next time we have you on "theCUBE," that you can say a year from now that you couldn't say today?

>> Oh, there's just so much exciting stuff that our customers are working on. I feel like we just have endless possibilities to talk about next year, which we hadn't, you know, necessarily even thought about two months ago. And it's kind of the combination of the creativity of the people that were working on the customer side with, you know, a technology. That we're willing to hear that input and take the product in the direction that's going to serve them best.

>> I love it. Well, we look forward to telling that story with you on stage at the next Google Cloud Next. Kathleen, thank you so much for being here. Rob, fantastic insights and questions, as always. And thank all of you for tuning in around the world here for our live coverage from Google Cloud Next. It's the end-ish of day one of three. My name's Savannah Peterson. You're watching "theCUBE," the leading source in enterprise tech news. (upbeat music)