Hello, everyone, and welcome to today's webinar. Transforming content with AI, a catalyst for growth. Brought to you by technology and services industry association and sponsored by Kaveo. My name is Vanessa Luzaro, and I'll be your moderator for today. Before we get started, I'd like to go over a few housekeeping items. Today's webinar will be recorded. A link to the recording of today's presentation will be sent to you within twenty four hours via email. Audio will be delivered via streaming. All attendees will be in a listen only mode, and your webinar controls, including volume, are found in the toolbar at the bottom of the webinar player. We encourage your comments and questions. If you think of the question for the presenters at any point, please submit through the ask a question box on the top left corner of the webinar player and we will open it up for a verbal q and a at the end of today's presentation. Lastly, feel free to enlarge the slides to full screen at any time. By selecting one of the full screen button options, which are located on the top right corner of the side layer. I would now like to introduce our presenters today. John Ragsdale, the distinguished researcher and vice president of technology ecosystems for TSIA. Bonnie Chase, Senior Director, product and content marketing for Caveo, and Juanita Oldgreens. Senior director of marketing platform also for Caveo. As with all of our TSI webinars, She do have a lot of exciting content to cover in the next forty five minutes. So let's jump right in and get started. John, over to you. Well, thank you Vanessa. Hello, everyone, and welcome to our webinar. Obviously, AI and Gen AI is one of the hottest topics and tech today, and we're going to be talking about how we can leverage this cool technology to really transform content. Within support and education services. There are just so many opportunities when we look at the capabilities of Gen AI from creating content, whether that's knowledge articles, product documentation, customer learning content, also personalizing content, which we'll be talking a bit about today and translation and multilingual content is something else that a lot of companies struggle with and this is going to really help you, create content in the voice of your customers. So I wanna cover a couple of data points about, some of the business challenges that I think this technology is going to help with And the first is the reality is we know very few companies have as many dedicated knowledge workers or content writers as they would like. This data from our knowledge management survey says that about a quarter of companies say everyone is accountable, and I always say if everyone's accountable. No one's accountable. About half of companies have a mix of some dedicated workers and assuming everyone is pitching in. But I think we all know that, support people are so busy that when it comes time to going off phone or off chat and taking the time to actually write up and finalize articles, there's just never enough time. And a similar data point is around budget. And when we survey asking our members, if they have dedicated budget for knowledge management and knowledge workers. You see about half of companies, forty six percent say that it is a permanent part of their de departmental budget. Twenty eight percent have no budget whatsoever. Twenty percent are begging every budget cycle to try to get money. So if technology is going to help us automate and create this content. It's really going to help when you're struggling to try to get additional resources. Another thing, that I think is really interesting is how quickly the technology can generate content and this data is asking how many days on average does it take to publish a new article? And think about a new bug that breaks out. If you're a software company, you know you're not gonna get just one, ticket about that. And that first person that that if it's taking on average nine point seven days to create a knowledge article, that means every sub subsequent call during that ten days the the tech is gonna have to start researching that problem again. So you see a lot of people spinning their wheels, trying to, come up with the right knowledge article, and it just ends up duplicating a lot of efforts. And then on the customer learning content side, you you think it's expensive to create knowledge articles. Look at the the cost and the time involved in creating an hour of e learning content. Ninety three hours, fourteen thousand dollars on average, is the cost for an hour of learning content. Obviously learning content is more complex than knowledge articles. It's it's digital. You've got bite sized chunks of content. It has to be created in an intelligent way to string together and to learning paths. But this is something that companies constantly struggle with. And we know that about third of support incidents are because people don't know how to use the product and understanding those gaps and automatically filling them, with better learning content to make a part of onboarding for the customer can really help reduce support costs, support volume over time. Well, enough for me, I think it's time to turn it over to our experts on AI and Gen AI. And I'd like to turn things over to our first guest speaker Bonnie is the senior director of product and content marketing for Kaveo Conney. You've been on so many of our webinars. Welcome back. Thank you so much, John. And and, yes, I'm so very happy to be here today joined by by Juanita. You know, we really do have complimentary areas of focus where I'm more focused on the customer support side of things, wanting to really focus is on the employee experience side of things. And so I thought it'd be really good for the both of us to join you here today. And John, you know, you've shared some really interesting stats about time and resources when it comes to knowledge management, And ultimately, I think, you know, as what I've seen is, you know, traditional knowledge management has been difficult to put money behind because, you know, from an organizational standpoint, the revenue impact isn't immediately clear, and it can be difficult to prove. And so when we think about content, we think about how we can leverage it a catalyst for growth, you know, there are all sorts of content that your customers need, right? So it's your support content, the learning and education, as you mentioned, troubleshooting, all very, various different types. And it's important from a customer experience perspective to ensure that you're taking all of those different intents into consideration. So when we're talking about leveraging AI for knowledge today, it's not just one specific type of knowledge, we're really looking at the full spectrum. And taking a step back with generative AI, I think it is it's easy to fall into the trap of focusing on the technology, right, because it's new, it's shiny, you know, we're all learning about it. We all wanna use it, but we need to take a step back and think about what we're trying to achieve. Right? And and, ultimately, that's a better experience. And it's both the better customer experience and a better employee experience. We've been working with a lot of customers, in our beta program for generative answering, and we're seeing both use cases being really important. And there always has been a balance between EX and DX But, you know, as this stat shows here, those who do well at both will outperform those who don't. So if you continue to strategize in silos, you won't be able to the experience customers expect. Right? And we see this all the time where companies will create a self-service portal or make their knowledge base available, but it siloed from the learning content or the overview content. And so what ends up happening is that because the experience is destroyed, customers continue to struggle. And so, Juanita will talk through a little bit more about, you know, all those different silos that we end up having in the importance of bringing those together. Thanks Bonnie. I think I'll add also that without employees, you have no customer experience. So customers are, or sorry, employees are your customers too. So if we think about the fact that support and service often, operates within silos. We wanted to take a step back and lift up kind of the levels a little And what you see here is a typical buyer journey. Looking at your site visitors end to end. So we go from a presales or unknown stage where you have people clicking on sites, looking for information, as they progress down the buyer journey, they become known. Right? They're getting a little bit closer into deciding on whether you're the right, company that they wanna talk to or work with. And then we get into more of that post sales or authenticated side where your customers are visiting your support sites, coming to learn more and possibly, right? We hope that they'll be ready to buy more in the future and go down to down through this journey all over in. What we also know is that along each step of this buyer journey are all the sites, information, and materials that differ teams across the organization are managing, which also means that they need access to information from different teams as well. And So this essentially shows you from, on the left hand side, maybe low impact or low intent to the right hand side, high impact and high intent On the right hand side, you know these are your customers engaging. All the different sites, information, and materials that your teams need to be able to access not only internally, but be able to show externally as well. And of course, because I am representing more of the employee experience versus customer side, we have to show and acknowledge that, again, employees are your customers too, and they have their own digital journeys that we need to make sure we're helping them to unlock so that they can do their best work. If you're on the agent side, this is absolutely important because you're servicing customers. But we also often forget that agents also exist to service employees if you think of things of IT service management, there's an entire agent operation that is helping to solve employee HR and IT tickets. So hopefully this gives you an idea of how you can look at the end to end in the different and content that must be made available as people are going through your buyer journey. And often, your customer's probably going through this without being a known content act. So how are you able to track what they're doing even when they're not authenticated or logged into your sites? I think Bonnie, you're gonna tell us a little bit more about the results of these bad experience or what did disappointed experiences, I should say? Yeah. Absolutely. And and so, you know, we've been taught talking for years and years about, self-service and going digital, and and really that's table stakes now. Now it's, you know, how can we really make this effective? Because at the end of the day, you know, customers are spending a lot of effort bouncing between channels. And when you look at what the steps say, you know, seventy seven percent of customers say that offering poor self-service is worse than not offering any at all since it wastes time. And you think about the the, you know, the the chatbot hype, right, when chatbots first came out, everyone needed a chatbot, and everyone wanted to implement a chatbot, but it wasn't effective, and and it didn't work the way that we wanted it to work. Right? It wasn't the, it wasn't the silver bullet that everyone wanted it to be. You know, fifty seven percent of inbound service calls come from customers who went to the website first. So this is just another example, you know, thinking about that full journey that Juanita showed, you know, they're they may not know the URL to your support site. They may not know that you have a knowledge base, but they do know that you have a website everyone has a website. So that might be where they start their support journey. So ensuring that you have knowledge available at every stage is really key. And when you think about, you know, what's really driving people from, you know, whether they're becoming a new customer or they're adopting your product, or they're expanding to new products, you know, ultimately knowledge, you know, your your knowledge base, your content. That's really the core of every stage of the journey, and that's what informs them to help them move through. And then the final set here showing, you know, thirty six percent of consumers saying the most frustrating aspects of poor customer service is when the agent doesn't have the knowledge or skills I mean, that just shows, again, they are dependent on the knowledge that you have, you know, in all of your systems. And so ensuring that they're whipped to solve these cases is really important, and that's where, you know, knowledge plays a really big part. And from an AI per active. You know, we talked about different intents. And so just kind of wanted to walk through a few examples of this. So, you know, John mentioned the stat about you know, a lot of the support cases, you know, are are learning how to, how to do something. Right? And so when we think about intent, you're they're learning how, you know, how does the product work, how is it supposed to work, fixing a problem. So this is the break fix, you know, they encountered an issue and they need to learn how to fix it. There's a discovery intent, so discovering other ways to use product and see how others are using it. So, you know, as an example for me, if I if I downloaded the new, the new software update for iOS. Maybe there's some new features, and I don't necessarily have a problem, but I'm just curious What are the new shortcuts? Are there other ways that I can be using it? So that's that's another way. And and the community is a really good place for that happen. And then finally understanding concepts. So these are understanding the broader themes related to the product or the business perspective. So for example, you know, as we're all researching generative AI and how to bring that into, you know, our our experience, you know, you might be, you might be looking at content around, okay, how do I, how do I inject generative answering into my experience? Or maybe if you if you playing with a generative solution and you encounter an issue, you you wanna fix it. Obviously, I think, a big thing that has been happening is people aren't quite sure what use case for generative AI. So, you know, that's that discovery. You know, what are other people doing with it? And then finally understanding the con steps. Right? So so what is a large language model? What is, you know, you know, all these different pieces that are involved with it. Right? So really looking at all these different intents. They can live in different areas. They're not always in the same place. So, again, just the importance of bringing that content together. And when you're thinking about generative answering and, and being able to provide answers from this content, that's that tool needs to be able to to look at all of this. So, you know, moving on to, you know, personalizing at scale. You know, from a data perspective, how AI can help personalize at scale is it's able to capture, you know, the user pro file. So this is really the data that lives in your CRM, your ERP, your homegrown solution, is taking that so we can understand who that user is. And then it combines that with the customer journey. So all of those different touch points that your customer may be, you know, going through as they're navigating your sites and and your different touch points. And I can take both of those and combine them together to really understand the best content to recommend to that person at each stage in the journey. So that's really that feedback loop that we look when it comes to AI, with content. And as you think through, you know, there's there's, you know, the the the normal kind of experience that you have, and now we're layering generative AI, and it's not it's not necessarily a new concept, right, it's new technology, and we're still everyone's still kind of learning about it. But at the end of the day, it's providing answers, and you wanna be able to provide answers in the most relevant way possible to that fits that customer's need. And so, again, really thinking about that full experience and and how you're setting that system up for success. And so here, you know, you can see we've we've talked a little bit about the different intents and how it can inject that personalized experience at scale, but it can also help understand that the intent and the key content that, you know, that customers are finding as part of that. So a big piece of eye, in addition to the experience that it provides, is the analytics side that it captures. So looking at, you know, what's available who creates it and where it's stored. So, again, if you have twenty different content systems and five different teams working on content, they may not necessarily know each other's plans or strategies but the AI can bring that together and help you see where those things are happening. Again, you know, seeing how content is engaged and then really identifying those priction point. So, you know, where are they struggling to find what they need? Where are those gaps? Where are those, you know, where are they searching and not getting an answer? And again, this can happen with generative, answering as well where, you know, if you're not if you're not able to generate results, you know, we wanna make sure that we know what that query was so that we can, we can find that. And then, you know, finally, just as an example on this piece is, you know, looking at generative answering in the flow of the experience. So again, here's an example of generative answering Coveo, where the generated answer is displayed right within the search results. So it's not dramatically changing the user's experience. They still have access to the search results. Just given a, a, like, a direct answer to exactly what they need. So really pinpointing those relevant answers and again, the more access to content, the better that, generate answer can be, especially when you're thinking about the different intents. So, you know, being able to say, okay, this person is at this stage of the journey with this question, This is the type of of content that I need to look at, and I'll pull the answer from this and present it to that customer. Now you know, we we think about generated answers and content. I think there's there's always that question of, you know, how much how much content can come from generative AI versus humans. And at end of the day, you know, obviously the the human aspect of knowledge management is still required. Right? Because you need quality If you don't have good quality content, you're not going to have quality answers. You need the accuracy, you need relevancy, and you need the expertise that comes from humans. On the AI side, it really is about speed, relevancy, prioritization, you know, being able to reduce duplication and identifying those gaps, that can help accelerate, content creation. It can help accelerate, the answers that we get from that. Anything to add here, Juanita? Yeah. Great points that you're covering. I think, just to add, you know, these generative models are not gonna be able to provide an answer if that content doesn't exist as well. So to your point, there's still work for humans to do in this whole process and and look between man and machine. The other thing that I that I feel, generative AI in particular, generative answering in particular, help to do or areas he helped to improve. It kinda goes back to John what you were covering earlier in the presentation, and that's that learning aspect. Generative answering allows people to become more proficient. And this is the word we've talked about a lot. Employee proficiency. And, you know, previously I think to a certain, regard, it was yes proficiency in being able to find and then click on and then read and then digest. But now, generative answering is doing this for us. It's summarizing for us. So it's helping employees be more proficient. And another word I like to use is self sufficient, right? You in in in the speed that we operate today, especially depending on what industry you're at, The need to be proficient and self sufficient is at an all time high. And generative answering, is really helping to accelerate this at a rate and at a pace that I think we haven't seen before. Absolutely. Now, I'm gonna take a step and and kind of look at the analytics side of AI and and really what what it should be capturing. We talk about the full journey And, you know, this is where we can really start analyzing which channels customers are using. We can start looking at how the AI is performing. And I think this is a really important piece of this, you know, if you're leveraging AI for your knowledge management strategy, being able to see where it's impacting your knowledge is really important, especially if there's automatic tuning and the results are changing. You wanna be able to ensure that it's learning the right things, and it's, and it's coming up with the, the best results. And then, of course, the typical kind of search, analytics as well identifying what customers searching for, where are those gaps, again, you know, all of that information around the content. And then looking at from a generative AI perspective or generative answering, it's still just as important to be, be able to see, you know, what kind of queries are being asked, what kind of, you know, confident are the answers, are the responses to those queries? So again, analytics, AI go hand in hand, and that's really how you can see how the machine is learning. When we talk about the impact of effective knowledge sharing, with self-service and even, from assisted service perspective. It really impacts, you know, a lot of different things. We've seen cost savings, right? So it's about ship lifting left, deflecting those known issues so that you're reducing those case volume, unnecessary truck rolls, reducing that time to resolution, and improving that first contact resolution, you know, the better knowledge that you have, you know, the easier it is for them to find answers, and and we can cut back on some of these costs. But it's also important from a customer satisfaction perspective where we're really looking at reducing friction for empowered success. So it's that self sufficient, piece that Juanita mentioned. You know, we wanna be able to increase that customer success and adoption because again, knowledge being the core of what, you know, helps people move, you know, throughout your product adopting and and expanding, you know, having that good knowledge strategy will really help with that. And of course, know, people are able to find the answers. It's increasing trust and convenience, loyalty, and advocacy, and then, of course, improving that digital satisfaction. And then finally, employee proficiency. So empowering employees with effective knowledge, increasing that their proficiency and subject matter expertise reducing that time spent searching and and enabling them to effectively support customers. Anything to add on this one, Juanita? Not much I would add. I think these are all great points. I think one thing that we've not talked about. I said I don't have anything to add now. I have two points. My first point is, you know, I think it's important to also understand, you know, knowledge doesn't have to always be so formal. It can always be informal. I mean, we hear from our own customers about, the use, increasing use of of like wikis or other like blogs that they're, you know, indexing to pull that content and information and knowledge from, being able to do that on the informal area really can help with increasing the speed at which these key stakeholders get access to that information. So definitely something I think that's worth mentioning. And yeah, I think, again, being able to just kind of almost in a way see both customers and employees as key stakeholders that often are looking for similar information might be helpful in seeing maybe there's opportunity to, you know, even decommission sites that are duplicate in nature, right, when they're actually, surfacing the same or housing the same knowledge as well. Those are just a couple of thoughts I have on that one. Alright. And, so Juanita, why don't you tell us a little bit about how we think about, you know, we we've shared some general kind of concepts and best practice isn't about AI generally, but the Coveo perspective, I think we'd like to bring it as well. Yeah. Thank you. So I think we've talked about CX and EX. That is essentially the formula that makes up total experience we obviously think about it as total experience AI because you think about that customer journey and there needs to be mall and ways to help amplify a user's experience across that journey. The way we think about total experience AI also surrounds how we're able to help companies solve for multiple use cases internal and externally. So whether that's agents helping the customers on a support site, or on the knowledge worker site, you know, marketing, helping to ensure the right materials are on the dot com site to help with like an upsell or a cross sell. We wanna make sure that we're helping our customers solve for these multi use case errors across the company. Because at the end of the day and what and what this image effectively represents is a company. Right? A company is essentially a collection of departments. Right? You have corporate HQ, your sales, and marketing product, finance, customer service, legal, and within each of these departments, are a specific set of locations. Right? Sales might use Salesforce. Customer service might use ServiceNow Salesforce for something else. Right? So each department is using their own systems And within those systems are different data sources, information formats. But we need to access or have the ability to access this information across departmentally. And so I think oftentimes companies think we need to centralize everything. We need to centralize everything. And by now, I hope we all know that centralization is not the answer. The answer is unified access to information sources wherever they exist. And the last point I would probably talk about here, and if you were wondering, there is a there is either department that is missing those gray boxes. Yes. I see you're looking. And that department is IT and technology teams who are essentially the core team that is serving not just our customer teams, but our employee teams. And IT teams need to have access to not only, you know, the latest tools, but cutting edge technology that helps them increase their capacity and their ability to make an impact. And that is really the promise of AI, right? It was always promised to augment our capacity as people. But also to help team scale. And we all know that IT is serving lots of stakeholders across the business. So the more that we can help equip them with a solution that is best of breed that can be used enterprise wide. The better everyone else is not only from innovation and having access to the latest capabilities, but also getting, consistent support delivering consistent experiences externally. That's what it means to be a total experience AI platform. And that's how we think about it at Cobello. Additionally, if we take, look at what is the impact of working with a total experience AI platform. Really the benefit is that you are seeing positive outcomes across the different divisions that I mentioned. On the service and support side, yes, you're gonna see an increase in that Csat, you're gonna see maybe a decrease in handling time. But there's also the website, right? Your dot com, you're gonna see an increase in conversions, a decrease in bounce rates. Maybe a decrease in the total customer acquisition cost on the workplace, or employee experience side. And I realize agents, again, our employees too, whether they're servicing end customers or internal employees, you're gonna see, improve self-service, lower ticket ticketing on both sides, employee or customer facing. And you're gonna see an increase in esAT and productivity. And lastly, especially, important depending on what industry you are, you can see positive e commerce outcomes. So if you're a business that exists to sell, you're selling a product, AI is only going to make this better and more amplified for you because it's giving users exactly what they're intending to So hopefully this gives you an idea of what is possible, what is this total experience AI thing, how it helps, but also the benefits that you can effect across the enterprise. So, Bonnie, I think you're gonna take us through, case study. A real example of Yeah. Yeah. So, definitely I have this, I linked to this case study. Feel free to to go here and and check it out. But ultimately, Forest Point is an example of a, a customer of ours who, really took this to heart. Right? And they they made changes to impact both their employee experience and their customer experience. And they really were able to see some really great results. You know, their CSAT was boosted. Their, time into resolution was decreased. And, ultimately, they've been able to provide this experience across multiple touch points and channels. So, I wanna leave this here, for you to take a look at. And with that, I think, we're ready to answer some questions. Okay. Thank you all so much for a great presentation. And now we do have a little bit of time left to open it up for our audience Q and A. So just a reminder, on the top left corner of the webinar player, and we're going to get through as many questions as time allows. We do have quite a few questions in queue already. So I'm gonna just jump right in. And our first question comes from Susan. And they say, do you have any best practices around preparing content for gen AI? I'm happy to to jump in. And, if if John and Juanita wanna add more, feel free. I would say from a concept best practices. There's nothing too crazy different from what I would consider knowledge management best practices today. Right? You know, I think when it comes to creating knowledge, ensuring that, you know, you have that single topic, per article, you know, as you're as you're writing, that information should be chunked so that relevant information that's related to each other, you know, they're kind of in same place in that same paragraph. When you think about, generative answering and how it's pulling together, it's pulling together pieces from a bunch of different articles. Right? It's not just coming from one. And so you wanna make sure that that information is is easy to find and easy to read. So again, it's it's not, so different from normal best practices, but thinking about it in a way where you know that, you know, it's not just going to be one piece of content that's providing the answer. It could be multiple pieces of content. Anything to add there? I would add that we are seeing from early adopters that if you have not done any maintenance on your knowledge base, for years and years, it's going to come back to haunt you because the system is going to index all of the content, whether it's good content or bad content. And, again, we're hearing from early adopters that the the generative answers are spitting out bad answers because it's pulling in incorrect procedures or outdated procedures. So if you haven't done any cleanup on duplicate and outdated information. You know, in theory, and correct me if I'm wrong, I think that the machine learning will understand what are the correct answers and hone that, but you'll save yourself a lot of time if you just clean out any garbage before you start indexing. And so I'd I'd recommend to clean up. We totally agree. I would also add. I think this also assumes that you have knowledge management SME subject matter experts, right, that are maintaining that content, John. So, of course, the best results are those where you do have people that know the content. So even if you are implementing generative answering and you get some wonky answers. Let's say you do get some wonky answers. Who's who's gonna know it's wonky or not. Right? Who's gonna know what's bad or not, it's gonna be that subject matter expert that knows that content that knows that space. So I would, you know, ask yourselves that question. Do you have someone that knows the content in that knowledge base in that, you know, in that intranet collection who's gonna be the person that's helping to maintain it? Is the real question? Okay. Our next question comes from Matt And they asked, you talked about proficiency. Can you say more about how you measure that? Yeah. I think I'll start by taking that one. So we've talked about proficiency at Cobeo for a very long time and and to be honest, I always felt a little iffy about that word because to me, proficiency means I understand this content error area. And if you gave me a quiz, I could ace that quiz because I'm now proficient in the subject matter area. So previously, I would say we roughly did provide that with our existing AI models. Right. We're recommending content. We even have our smart snippets AI model that's giving you the one to one direct answer to information you're looking for, not generative, more the one to one answer, meaning it's finding that existing answer in your databases. So there, I thought we, you know, we got closer to, okay, we're giving people the answer that they can easily consume and say, I now know this moving forward. With generative answering, I think we're taking that to the next level because we're now able to go to many different sources. Right? So it's a it's a wider breadth information that we are now simplifying into bullet points or very simplified response, again, to make it quicker and easier for that employee or agent to consume and now know this information moving forward. So for proficiency to me has to do with I'm able to read, understand, digest, move forward, and take action with what I'm doing in my work, my work effort here. Anything you'd add, Bonnie or John? Nothing to add on my end. Okay. So our next question comes from Victoria, and they're asking does Coville offer generative answering? Yeah. So, I'll take that one, and that's definitely something that we are in the midst of working on. So we have a beta for generative answering right now. It's, it's been a closed beta that we've invited select group of customers to participate with us in. The plan is to launch that by end of year. We do have it up on our doc site right now, which is docs dot covayo dot com. So it is the beta. So if you wanna go and play with it and kind of see how it's working, it's it's up now. So, but we're planning on launching by end of year. Okay. So then we have a question from Tom that says, kind of along those same lines. What are the different use case is you're seeing with the generative answering. Yeah. That's a good one. Maybe Juanita. I know you you've been working with some that have a lot of use cases. Yeah. Really they're very. There's there's various that we're seeing in the in the beta customers we have today, which Bonnie and I are both working with. We see use cases on both the CX and EX side. From an EX side, we are seeing, use cases on the intranet actually. So, you know, especially for massive enterprises and organizations, the intranet is the digital, front door to that company. And so that means it's the first place employees go to to navigate the organization. And so, again, the internet is is big. It's broad. There's a lot of information in there. And so what we see our customers do kind of to your your earlier advice Bonnie was narrow in on a particular focus and use case. And so on the internet, we are seeing, our customers explore the HRI t aspect. So how do I, you know, reset my password? How do I get access to certain systems and applications as an employee. So we're seeing our customers explore that area from an employee experience perspective. And then on the customer, experience side, We also see various use cases along the, customer self-service. If you think about your community sites or your community portals, our customers are trying to, apply generative answering, onto those portals again to help with that case deflection experience helping customers self serve and also become proficient themselves because we talk about proficiencies and being self sufficient internally for agents and employees, but our customers are smart and intelligent and they wanna be self sufficient as well. So we're seeing, various types of uses of generative AI with our, with our beta customers. And I'll just add, you know, we we, it really also depends on, you know, where your organization is within that digital journey. So, you know, there are certain customers we have in different industries, whether it's financial services or health care where No. They're not quite ready to make that available to customers, but they do wanna test it internally. So that's something to take into consideration as well as kind of where you are on that Alright. We have Allison asking, what are the biggest risk you see with GenAI, it feels risky to put this new technology in front of our customers. There's quite a few risks. I would ask, so Bonnie do help me with this. I will actually start because we're talking content knowledge management. I'll start there. I think one of the biggest risks and we talked about it a little bit is not having those dedicated subject matter experts that are managing that content and knowledge. You because these AI models and large language models exist to consume, right, those large amounts of information, that that assumes that the information exists and is accurate. And so when we're guiding our own customers with our generative answering capability, They're the ones also proactively telling us so they must be best in class, by the way, that they're gonna bring in their SMEs to help validate whether the responses are actually accurate. Again, I mentioned, like, if if if there's no one managing this and you apply generative AI to a set of knowledge and information. And yes, it's gonna it's gonna give you answers and responses, but who's gonna be the one to tell you whether it's accurate not. So to me, that is one of the biggest risks. And especially if you are in a more sensitive or highly regulated industry health or financial services who I think are the most kind of waiting to see what happens before they're using this gender to you definitely, and absolutely must have someone that can validate, yes, that is correct information. Dan, I'll just quickly, quickly add, you know, outside of the the technical risks, which, you know, I think we're all aware of. You know, privacy, data security, all of that. Another one that's outside of that technical side is is treating generated answers as new concept pieces. So I think it's important to make the distinction between, you know, a generative solution that summarizing a conversation and creating an article out of that versus an answer that's generated from existing content. And so, you know, of course, we've been looking at a bunch of different solutions in the market right now. And, you know, it's it's exciting that you can have you can be, you know, solving a case and having conversation with a customer and identifying their needs, summarize that, and that can be the knowledge article. That's great. But if you're, you know, going and searching, and then you're getting an answer, and then you're creating an article from that answer, and then you just end up getting stuck in a loop of of creating articles from generated answers that come from other articles. So may just keep that in mind that you're not getting stuck in that loop because, again, at the end of the day, That's where the human involvement is with the accuracy and the quality of the content, and the generative answering is really just kind of pinpointing the the specific answer that Yeah. I would just add that, I I understand what privacy issues and and HIPAA and health care and financial services. But in the general tech industry, if you think you have no inaccurate information on your website or your community today, you're probably fooling yourself. So I realize you don't wanna give the wrong answer to a customer, but, I suspect that that is happening a little bit already. But I would say the one risk you don't have to worry about is adoption because customers have been not enthusiastic about previous generations of chatbots, which were usually like answer repairs or menu driven and they were frustrating to use and ultimately didn't deliver a very good customer experience. But this is technology customers really wanna try. So I, you know, I wouldn't, be too afraid to release it. I think you can put a disclaimer on there that, you know, this is, a pilot project and, you know, verify information before destroying data or something like that. But I do think you're going to find that customers will be much faster to adopt and use these tools, and they have other self-service tools you have introduced in the past. Great. Okay. Well, unfortunately, we have come to the conclusion of today's webinar Obviously, this is a hot topic. There are quite a few questions that we still have not been able to answer here, but don't worry. We haven't forgotten about you, and we absolutely will make sure to follow-up with you. And with that, a few reminders before we sign off for today, there will be an exit survey at the end. And if you can please take a few minutes to provide your feedback on the content and your experience by filling out that survey, And then know that a link to the recorded version of today's webinar will be sent out within the next twenty four hours. I'd now like to take this time to thank our presenters John Bonnie and Juan for delivering an outstanding session. And thank you to everyone for taking the time out of your busy schedules. So join us for today's webinar, transforming content with AI, a catalyst for growth brought to you by technology and services industry association, and sponsored by Caveo. We look forward to seeing you at our next TSIA webinar. Take care, everyone.