Practical applications for AI in your Cisco contact centre

So good afternoon, everyone. Welcome to today's webinar hosted by TTEC. My name is Linda Mullen. I'm the EMEA marketing lead here at TTEC Digital, and I'm thrilled to be kicking off today's session. Before we begin, just a few housekeeping items. Today's webinar is going to be recorded. If you miss anything or want to revisit the content later, we will provide the recording after the session. Please ensure you have stable Internet connection for your best experience, and the microphones and video should be turned off. We will have a q and a session at the end. So if you'd like to put any questions, put them into the chat. If you encounter any technical difficulties, please put them into the chat, or you can email me at linda dot mullen at t tech digital dot com. As I said before, we will have a q and a session at the end of this. We do value your questions and your input, so please feel free to do that. And now I would like to introduce our presenters, Caleb Johnson, who is responsible for our Cisco practice globally and who will be covering the core content today, and Neil Fulton, responsible for our Cisco practice here in Europe, and he will be covering the introduction. So now I'd like to hand it over to Neil. Right. Thanks, Linda. So we have an action packed agenda for you today lasting thirty to forty five minutes depending on questions. As Linda says, please do ask questions as we go along. So firstly, I'll quickly cover market updates in terms of what's happening with TTEC and our background and credit credibility in the AI and contact center space. I'll then hand over to Caleb who'll cover what's evolving with GenAI in in terms of large language models and other other other AI topics, and what this specifically means into Cisco contact center environment and a different deployment for the on prem and clouds. We'll then wrap up with some examples and move to quest to q and a. So if, some of the things that we cover today resonate with you, there'd be a section at the end about how you can engage further with TTEC. For some of you know TTEC in terms of the work we do with Cisco as a as a leading global partner, but I just wanna lay out our broader capability and background as it helped add some context to some of the points that were covered today. So TTEC, we've been providing customer experience solutions for over forty years. We're a global business with over sixty five thousand employees. We have a strong presence in EMEA with over hundred customers, you know, in this region and very much growing. And our business is essentially split into two main areas. That's our TTEC engaged business, which is our BPO business, where we provide people to run and manage contact centers all around the world for some of the leading contact center players, and then our TTEC digital business where Caleb and myself belong, where we provide customer experience technology. Some of the things we're looking to tap more and more into our customers is how can we bring the the insights and best practices from running contact centers into how our customers think about running their own. So as a leading service provider, we've invested heavily into our vendor partnerships over the years. We built out a leading Cisco global practice over the last two decades. And on more more recent times, we built out our broader capability with leading hyperscalers, AI platforms, and other value add solutions. And all of these very much complement the core Cisco platform. And we've seen things like AI really expand the scope of what you can achieve with the Cisco platform. And as Kate will now talk through next, this is becoming ever more important in recent times as customers are moving their contact centers to being cost centers to more strategic assets. Okay, Caleb. Over to you. Excellent. Thank you, Neil. Let I'll go ahead and share my screen here. Good morning. Good afternoon, everybody. So we're gonna take a few minutes and go through some examples of where we're really starting to see AI, large language models, Gen AI, come into the contact center space. And really kind of the evolution that we've seen over the last, let's say year and a half. I'm gonna start really quick though just kind of with a challenge statement because I think this is important to level set in that what we're finding in the market over the last kind of three years if I go back and look at even during the kind of the COVID period, there was a lot of conversations around the business challenges that people were anticipating happening, whether that was they were getting ready for technology changing, they felt like some of their technology was outdated, or they were having challenges with even from a customer attrition standpoint. And so they were starting the planning phase of, okay, how are we gonna solve this in the future? And this has kind of continued to evolve. They dug into the kind of the very specific business cases of really the challenges that we keep seeing that kind of pop up is, hey, there's either challenges on the actual interaction side where it's a disconnected journey across whether it's a voice channel or a chat channel or SMS. And really, there's a lacking intelligence across some of these things that sometimes will lead to poor customer interactions or or poor customer feedback. Right? Because customers are wanting to have a very frictionless experience when they interact with your brand. And so part of the challenge with that was some of the technologies customers were on, really were not enabling the ability to go in and provide some of these new communication channels or be able to get easily the data that they were looking for to be able to make decisions and be able to surface new things for their customers. The other part of this from a business challenge as we've kinda collected this data over the last several years was really the shift obviously from cloud or sorry, from premise to cloud. And so one of the biggest themes was this idea of being able to stay up with the trends, moving from a model of I have one or two big releases every two to three years, and I get lots of new functionality, and then I need to figure out what to do with that. And we're shifting into that through software as a service model where it's a continuous release concept. The idea that monthly or quarterly, I'm getting new features, and I'm able to be much more agile in the way that I can change my approach to customer service or even to sales and be able to adapt to some of those trends and be able to provide new experiences for their customers and really even on the employee side as well. The last piece of this I'll touch on and kinda get into this connected journey concept using AI is really around the data side. And what we have found is that with a lot of the new developments in customer experience technology, the CX stack is the ability to start bringing together data across every single touch point from a customer's journey. Being able to understand where they start from, where they end, and everything that happens in between, and being able to utilize that data to actually make actionable decisions. Things like being able to figure out where to go do self-service, where to improve on that experience, or really where to also take high profile, high touch customer situations and be able to get them to the right person. So we're gonna spend just a moment on this slide because I think this helps kinda bring together what we have started to see is kind of an ideal scenario inside the customer journey. So if you think about from the very beginning on the left, think about the customer or employee side, somebody reaching out And in an ideal scenario, they're being meet met at that point of need by a virtual agent. And this is really kind of the one of the first places where we see AI starting to come into, the picture inside that customer journey. So I know chat bots, voice bots, they've been in in the market for a while now. Many companies have deployed them. The challenge was that the conversational AI from three years ago, it's almost apples and oranges to what's available today. And it's changed so significantly that the ability to go and deploy these voice interactions or chatbots in a highly intelligent way has become much easier to do. It's much more cost effective now, and AI is really kind of delivering on what our expectation was several years ago. And a lot of that's being driven out of the developments from generative AI and large language models. So that combination there, which has made all the buzz last year, is really enabling companies to go deploy this first touch point much faster. Now in a perfect scenario, that virtual agent is gonna be tied into, in an AI service, that conversational AI you see at the top of the screen, speech to text to text to speech, the natural language capabilities, but then now also generative AI. The idea that your data, your private data could be connected to a language model, and a generative AI algorithm can go and actually generate responses based on that data. So in traditional world, we might have called that an FAQ bot. You might have put something out there, can understand twenty or thirty questions, but it's a static experience. And so now you're talking about being able to expand the corpus of data significantly, train it on your conversations, and be able to put put out a highly intelligent interactive bot that can actually handle multi intent conversations all on the front end. And in a perfect scenario, that's actually containing many conversations, and it's simplifying the customer journey as they're trying to get help. Now when you actually make it into the contact center, for whatever reason, you kinda go through what you see there, the Webex omnichannel. We'll talk about more of how Cisco is enabling these true omnichannel interactions with AI and coming into the contact center. But now that you've actually gotten to the agent, the next piece where we see AI coming into the fold is this piece around conversation context and transcripts. The idea that I can actually take the conversation from the virtual agent, pass that to a live agent, put it on their desktop so they can actually see what someone was trying to accomplish. And that's gonna make for a much more seamless interaction. And nobody likes to go into the contact center and have to repeat three times what they're actually trying to accomplish. And so this starts to create more of that connected journey into the actual contact center. And then the last piece of this where we start to see AI come in to make an immediate impact is around CX analytics. So you hear this called conversation analytics as well. But the idea that I can take all of these conversations that are happening as well as the activity statements, things like the customer wants to reset their password, they need to change the address, and be able to actually take the data, analyze it, understand what are the top intents, what are people actually saying, what's the sentiment around those conversations, and feed that back into your model to be able to improve both the virtual agent side as well as training for the agents and being able to enable them to be more successful in those conversations. So we'll jump forward here and keep going. So when you think about this from a a Webex standpoint, I think this is a really important conversation that we've had with a lot of our customers over the last couple of years. Cisco has made tremendous investments into their contact center technology to really bring it both into the cloud as well as in a true software as a service model. And there's two core products that Cisco uses today to really address customer needs in the cloud. Webex contact center, which is the newest, product that's out there right now from a true omnichannel system, software as a service, continuous release model, and it incorporates all of the new digital capabilities that you would expect to find in a contact center as well as pretty significant advancements on the AI side as well as analytics natively bringing in these features into the system so that it's not a bolt on activity or it's not something that you have to go and and pay for necessarily a third party service. These are just out of the box functionality that's available. That same concept is influenced to Webex enterprise as well, which has been around for a few years, but really the large enterprise type of contact center, single tenant model, but it's designed to to handle lots of scale, lots of interactions, but still give you a lot of control, and from a customization standpoint, a desktop and integration, it's rich with capabilities. And all this is being powered around this with, with Cisco's cloud, technology as well as being able to, enable it with additional features, things like what you'll see in this Webex platform. So the introduction of a true CPaaS system with Webex Connect as well as Control Hub, which is really starting to blur the lines between, simplified administration in the contact center as well as from a calling standpoint, something that very much differentiates the overall Cisco, technology stack. So last thing I'll say on this, and we'll kinda get into some very specific use cases, is we've heard feedback from customers over the years of, hey. Cisco hasn't moved into omnichannel yet, or I'm missing this feature, or where where's the AI features I see in some of the other platforms? And I'll say this that it's all there now. So the good thing is from a confidence standpoint, we've got customers live on these systems today fully taking advantage of not just a voice interaction, but everything across the digital channels, whether it's email or chat or SMS or even your social messaging, but also the AI feature. Being able to have virtual agents fronting front ending all of the conversations, doing things like real time transcription of conversations, and even summarization of those of those conversations that bring you significant ROI into, your your day to day operations. Okay. So we touched on kind of these three points. I'm not gonna belabor these really quick because I'm gonna go into individual use cases with this. But I'm gonna layer on something here with the generative AI side. Conversational AI has been around for a couple of years, virtual agents, agent assist, even being able to analyze sentiment and conversations. All that changed last year with large language models. This idea now that we can use a much larger, processing capability to go in and actually build faster on the virtual agent side. On the agent assist side, the idea that I can absorb a significant amount of knowledge content, generate responses for agents much faster, and be able to give them much more intelligent responses so they can do their job better. And then also on the insight side, going way beyond just being able to do sentiment analysis or identifying intents, but actually being able to ask questions of your data. Things like show me the top fifty intents and what should I actually go in and move to self-service? And being able to have that data coming out real time, that's where conversational AI is finally kinda delivering on the promise that we are all expecting a few years ago. So let's look at a couple of things. On the front end virtual agent side, I've got a little, you know, video running here so you can kinda see a conversation. Traditionally, that virtual agent interaction, especially at the chat level, it was gonna be a fairly static linear experience. You go in, you ask a question, you get a response, and if you ask it something it doesn't know, it's probably gonna fail. Now with large language models, I'm able to bring in everything from data that I feed it. So if I wanna feed it my website, if I wanna feed it my, PDF documents on process or product information, I can do that. And the virtual agents are intelligent enough to be able to answer questions based off of that without me as the developer having to go in and build out intents for every single potential question that someone might ask. The other thing that's unique now is the idea of the multi intent or multi question interaction. Somebody coming in saying, hey. I need to change my address and I need to reset my password and I need to do a banking transaction. And the virtual agent is being able to be to process those multiple questions, segment them, handle them one at a time, and then be able to take action on it easily with a lot of the predeveloped functionality that now comes with GenAI, things like scheduling or automated flows that might be in the back end. So this is speeding up our development time, being able to roll this out for customers, as well as making a much more intelligent virtual agent, on the initial deployment. You have the next one on the agent assist side, and this is where we're actually starting to see some of the most, significant ROI, as I mentioned earlier. From an agent assist perspective, the idea that at the agent desktop, whether that's the native desktop in Webex contact center or enterprise with Finesse or you have it in a CRM like Salesforce or Dynamics, but being able to pick up the conversation, transcribe it in real time to show that to a an agent, And then at the end of the conversation, being able to actually summarize that. So taking the full text, creating a summary of it, being able to save that into whatever destination you want that's in your CRM. And that's effectively taking your post call wrap up time down from what might have been three or four minutes, somebody to capture their notes down to thirty seconds. And so we're starting to see that, have obviously an immediate impact, but this functionality is available today. And I think that's the the important takeaway here is this is not something you have to wait for. It's not coming. It's available. And then the last piece on the knowledge assist. So if you think about you have this real time transcription coming through, and now being able to enable out of the box the idea of looking for the intent in that conversation and being able to feed information to that agent to help them do that job. Whether it's a process script that you automatically, pop up, or it can be things like, a full knowledge article, but then taking it to the next step where I'm taking a knowledge article that might be three or four pages long, and I'm actually summarizing the answer and giving that right to the agent. That really, really helps them create a much more frictionless experience with their end customer. And I thought this is a good data point. I pulled this out from one of our partners because we get questions every now and then of how accurate is the summarization. Right? Both from a language standpoint as well as from a, just an accuracy of the actual conversation itself and the structure of it. And what we have found based on this is kind of empirical data based on the study is that it's just as good as having a highly trained human go in and be able to summarize the conversation where they're not even under the pressure of time. The accuracy levels are very, very high, and the good thing is you can put compliance controls around this. So if you wanna be able to redact data that's coming out of the conversation and put that into the summary, you can absolutely do that. So that's something what you're actually saving on the back end, you're not gonna run into any kind of regulatory issues. You'll be compliant in what you're actually storing and what you're actually processing. And you're getting that level of accuracy all at the same time at being much faster than having somebody go in and manually type this out. Okay. Let's talk about insights for a second. Because this is the other advancement area that we've seen really, really jump forward, much much faster than we had typically over the last five years. We had conversation analytics. Call recordings were being analyzed. Some of that's being used from an automated QA standpoint. But now what we're finding is I can feed a significant amount of call transcript, call recording, chat data, as well as the virtual agent data into a single place. There's tools like what Google provides with CCAI insights being able to go in and feed this information into their, their engine, and you're getting things like your intent and your topic modeling coming out of that. You're also gonna be seeing some new releases from them around automated QA, being able to automatically score conversations against, a scorecard that you provide the system. But it's also going to the next stage, which is where it really gets exciting, which is the automated bot creation. The idea that you're feeding it a tremendous amount of data on conversations, but now I can start to pull actionable data out of that. And the and the system itself will start to say, okay. You have these top five conversations happening. Here's a dialogue flow design that you should go use to actually go build a virtual agent. So I'm no longer having to use an analyst to go in and figure out some of these types of things. The system is simply providing me that guidance and being able to give me opportunities to go either deploy self-service or what I'll call kind of triage, virtual agents in the voice channel or the chat channel where I'm collecting data of things like, you know, processing authentication. Handling basic tasks long before it ever gets to a human. And so we're starting to see containment rates go from five or ten percent, and especially in the voice channel, to upwards of fifty percent containment because the virtual agents are something much more capable. This is all being powered by the data that comes out of insights. And so from a starting point, which is typically a question that we get from our customers is, okay, I wanna go do this. Now what? Right? This is a great place to start if you're trying to understand better what are the customers saying both from a voice of the customer perspective as well as what are those intents or those low hanging fruit where I can go create automation. This is one of the areas of opportunity. So we typically say start here with understanding what's actually happening in the conversations. Let's use that data to make intelligent decisions on what we're gonna go deploy as a phase one. And then we can move into an agile model where it becomes a much more, frequent build, frequent release type of scenario where you may have a virtual agent that's handling several scenarios today, and you can continue to expand on that as you get more data and as you start to understand the business case and the business outcome and the value around that. Okay. So, Neil, I'm gonna pause actually just for a second, see if there's any questions that we need to address before we kinda go on to the next piece here. Yeah. I can't I can't see any questions in the minute, Caleb. If if anybody has any questions, just please, put them into the the chat panel, and we can answer as we go along. Perfect. No worries. Okay. So the last piece I wanna touch on here from a functionality standpoint, and I think this is really important, kind of bringing everything together. There's obviously been a a massive shift to more digital, digital channel interactions, both as companies, kind of going back to the business challenge, as companies are trying to identify areas to save costs, as well as simply be able to be more competitive to offer new ways to interact with their brand. So one of the biggest investments from a Cisco standpoint is really Webex Connect, which is the CPaaS platform. Some of you might be familiar with this system. This is already integrated into both Webex contact center as well as Webex enterprise. So from a capability standpoint, this is available. And it's the idea of enabling really seamless interactions both at the SMS and the mobile level, which is where we see ninety percent of customer, contact center interactions start from, is still at the cell phone level. Being able to automate a lot of these flows to be able to, you know, simplify things to the actual customer. And then bringing all of that conversation detail into the contact center itself, and then finishing that up with the actual automation that you wanna have around things like surveys as well as proactive outreach. And one of the biggest areas that we still see is kind of untapped potential for for companies to be able to make a a big improvement on the customer experience side is around the proactive communication side. So So think about not just a a typical thing that we would see in a retail scenario where it's a shipping notification. You get an SMS, your package is on the way. But even more things like, hey. Your bill is coming up at the end of the month. You've got five days to take care of that. If you need to speak with somebody, please reach out to us. So you're starting to get ahead of some of these, the rush that you might see inside of the contact center to try and help bring those awareness to customers about activity that's happening, in in the kind of their customer journey, customer life cycle. With Webex Connect, you also get the advantage of taking or you also get the, ability to take advantage of functionality that's in the mobile app itself. Think about being able to push things, push notifications directly into a wallet, or even embedding this into a mobile app itself so that you get to start taking advantage of interactions native in the phone. Things like being able to use, face ID to be able to authenticate somebody and being able to interact at that level instead of pushing somebody to a web page or always into a voice interaction. So this is a big, big jump forward. It's something that's available to to customers today to be able to utilize and something that's kind of growing. We're seeing a big demand around this. Okay. So, Neil, I'm gonna stop here. We've kinda gotten through a lot of our content, and we can get into some of the, the convert the questions if we want to. I'll pull this up, yeah, as we have some some questions. And if we don't, that's okay. I've got one or two things I can touch on after this. Great. Thanks, Kaida. I so if anyone else got any questions, maybe ask from the chat or we can unmute as well, Linda, if you wanna do that. So if anyone's got any questions that they could just maybe indicate or put in the chat, that'd be great. Yeah. Perfect. So while while we're waiting on that, I pulled up an example here of the Cisco Spinesq desktop, which if you've been on UCCE, you're probably familiar with some look and feel of this. But the thing I wanted to call out here is this transcript and agent answers piece. And this is a a big advancement in the the life cycle of an agent. Being able to have this kind of information directly at their disposal. When they have the transcript of the conversation, they can scroll back through it, understand capture key information from a copy and paste standpoint. But what you're really seeing here is what we're calling our auto summarization. Right? So in this scenario, there was a virtual agent on the front end. It actually captured a conversation, and then you it's actually taken the virtual agent conversation that summarized it for the agent. So they immediately know in one sentence what their and the intent is, what they need to handle. And at the end of their own conversation with the customer, whether it's a voice or it's chat, it's also gonna summarize that conversation. And here, it's actually giving you the option to copy the text of that, or you can actually insert it into the notes. And so the agent can then make edits to that. And then if they want to, they can actually push this into a CRM. So if you think about your system of record from a data standpoint, you've now got much more than just a long form conversation text that you need to go back and read, but you're gonna also have the summarization points at the agent conversation as well as the virtual agent conversation. And we're seeing a the ROI for these the summarization piece is so high, that you're talking about being able to reduce post call time by, you know, seventy, eighty percent, which just gives you a much, larger capability to handle more interactions and be able to provide better, better customer service. Okay. So last thing here, while while we're talking about more while we're waiting on questions and whatnot. Okay. This is a question coming from Matt Drayton. Matt, I'll just unmute you. You wanna ask the question, Caleb? Can you just spare me a second? Yep. Thank you for the information you've shared. There was a customer in the UK in the news, this week where they were using large language models, and, part of the responses that were coming back from the chatbot were not ideal to the customer. And, some of it was inappropriate, actually. And I just wondered I mean, in you in t with TTEC's customers at the moment, are a lot of the customers exploring large language models as an evolution to things like Google Dialogflow where they have kind of predictive responses, or, or not, I suppose. I I was just wondering what the balance was between the kind of predictive response versus the true, AI instigation of large language models. Yeah. Thank thanks, Matt. It's a it's a great question. It's very topical. So let's talk about it for a second. And we'll we'll talk about Google's technology stack just, since we're talking about dialogue flow there for a moment. We are seeing this. It's across the board. Everybody's interested in knowing what it can do and how it can bring their business benefit, and we're seeing it really come from an executive level down. A lot of these conversations are, the executive suite, the c suite wanting to say, I'm hearing lots about Gen AI, large language model. What do I what do I need to go do with? There's a couple of important things when you think about what's what's possible with the language model. Google's approach so far has been to say, I'm gonna take a large language model that Google has developed, and it's gonna be static. And so I can put customer data on top of that large language model, but the actual language model itself is never going to ingest any kind of private customer data, and all of that's gonna sit within a customer's, Google Cloud tenant. So you have complete control over the data, where it sits from a security standpoint, and even by region, you can control what's what's going on there. And then you'll also be able to control from the actual conversations that may be happening with that language model. All that data can be written to something like BigQuery, which again is staying controlled inside of your Google Cloud Center. The next piece of that from a security standpoint is there is a level of rigor, if you wanna call it that, that needs to happen around what you are providing to the actual, generative AI and large language model capability. And so what we find is that customers the the easiest place to start is to say, you have approved data that's on your website, for example, or it may be in product documentation that you provide out to the public, whether it's a manual or it's insurance benefits, details, things like that that truly would be public information. So I'm gonna I'm gonna provide that data to the language model to say, these this is the dataset that you're allowed to answer questions against, but you can't answer any other questions outside of this data because you don't know anything else. The language model itself simply understands the grammatical and formatting of how a a language response should happen, but it doesn't have access to the entire Internet to be able to answer questions that it shouldn't. And the last piece of that is from a prompt management standpoint. And so there is some skill and there is some conversational design that needs to happen to go in and basically put guardrails around that virtual agent to say, alright. You're not God. You can't answer questions about politics. You can't answer questions about religion, and you can only answer questions about this data that I have provided you. Nothing else. And so you can put that the entire virtual agent along with, along with your dataset in a box and be able to control it from that standpoint. The failure kinda go back to what you're talking about, Matt. The failure piece that we see, which is the challenge, is companies are taking things like ChatGPT, which is connected to the Internet as its data source, and they're wanting to provide guidance on that and use their website still as a reference point. But essentially, that that virtual agent then has access to a tremendous amount of information that's outside of your approved content. And so that there's a lot of risk involved anytime you see the chat GPT coming in into play of of how that bot is going to respond and what it thinks it can and can't do. And so we find that going into this private model is much more secure. It's much more recommended. And the conversations we've had with chief security officers and CIOs is that they're kind of preferred path at the moment. Happy to get way more technical and detailed on that piece of it in a separate session, but, there is a level of discipline that's definitely required to make sure that, you're not answering the wrong questions. Put it that way. Okay. So last thing here, kind of looking forward of what's coming up. I'm gonna leave you guys with a thought on on where we're seeing things go and kind of what's next. So there's a feature coming out, from Google called, Call Companion. And to me, it's one of the most exciting pieces where when you're interacting at the voice level, the virtual agent can actually offer to say, hey. Can we also interact via SMS at the same time? Sometimes we find it in the link in the in the virtual agents. Things like alphanumeric codes are difficult to understand, especially when you're going across multiple languages, as well as obviously being able to input just, lots of information. There might be background noise, etcetera, at the voice channel. And so with call companion, you can actually start a multichannel conversation. So you're in the voice channel, it stays connected, but you can have a an SMS conversation going at the same time. The virtual agent is tracking that conversation with you, so you're seeing it kind of step by step with all the responses. You could input information. The voice agent will actually confirm that information that you put in via text, and then this full conversation can be saved and passed on to the actual live agent if needed. And so the the call companion can also do things like push images, push links, be able to refer you back to a different page if you need to to be able to to finish your interaction. But this is really taking the interaction to the next level because at some point, you can also drop the voice channel if you need to, and you can continue it in an asynchronous model directly via SMS. And so this is where, we're starting to see a tremendous amount of advancement. It's a combination of true omnichannel, generative AI, large language models, conversational AI, and starting to kinda bring all of those elements together. Alright. So I'm gonna stop sharing here for a second, and then we can start to kinda wrap things up. There is another question. It says, hi. What do you see as the biggest barrier that organizations face in updating their legacy CX stack? And where would you recommend they start? Excellent question. So I think one one of the biggest challenges that we see is making sure that you have an extremely strong business case to be able to justify, okay, we need to go and complete things. So we see companies that are premise still today, say, I'm I'm primarily voice, my customers like voice, and we don't really have a great business case to change because that's there's no evolution there. There's not gonna be an ROI. So being able to identify that ROI that's there with the, the modernization of the customer experience stack, I think it's a great place to start. We do that with things like this AI readiness assessment, which I I pulled up here. This is something that we do for free with our customers where we go through and kinda look at what the tech stack is today, and we start to figure out, okay, where are there opportunities to bring a more modern experience? And we give you a readout and kind of documentation around that. But the next piece of that too though is there is a lot of business benefit that we can help document for you as you start to look at things like the digital channels, as well as just moving to cloud and some of the benefits of getting out of the data centers. And so those are all places that we like to dig into to help kind of build that business case with you. But there's also there's always gonna be some hurdles. Things like security is a big conversation. The The good thing is I think Cisco has has proven their kind of, top tier nature when it comes to their products from a security standpoint and being able to manage that. So that's definitely something we can bring in and and have a conversation around as well. Okay. So if there's nothing else, then I'll leave this page here for the moment. I think this is really good. There is an AI readiness assessment, which I talked about. It's a great place to connect with, TTEC on, and and we're happy to dive deep into where AI fits within your business and kinda give you recommendations. We've also got other resources on our web page. Lots of thought leadership that TTEC publishes around customer experience, both on the actual human service side of things, as well as on the technology side. And so these are great places to dig into. And if you want to specifically connect with somebody, Neil is a great resource. You can absolutely reach out to him, set up time to have a conversation, and we'll bring in some of our experts to really kinda talk about where things are going both from a Cisco standpoint and and some of the awesome things that we can do within the Webex context in our suite, as well as, you know, bringing in AI and some of these modernization efforts. They really start to expand way outside the contact center into things like CRM and analytics. So there's lots of exciting things that are happening in twenty twenty four. So I'll finish with that. Thank you everybody for taking time to join us for, you know, about forty minutes, taking time out of your day. And so, hopefully, we'll be doing some more of these sessions over the year. We'll make sure that we stay in touch. Thank you, everybody. Have a great day. Alright. Thank you.