Hello everyone and welcome to today's CRM Magazine web event brought to you by NICE. I'm Bob Fernekees. I'm the Publisher of CRM Magazine, and I'll be the Moderator for today's broadcast. Our presentation today is titled "Transforming Quality Management with GenAI: Precision, Personalization, and Impact." But before we start, I just want to explain how you can participate in this live broadcast. At the end of the event, we will have a question-and-answer session, so if you have any questions during the presentations, just type them into the question box, hit submit, and we'll get to them at the end of the broadcast, and as always, if we can't get to yours, don't worry, we'll follow up in a couple of days with an email. Plus, if you'd like a copy of the presentation, you can download a PDF from the handouts tab on the console once the event is archived. So now to introduce our speakers for today, we have Shay Diner, Senior Product Marketing Manager, Senior Product Manager at NICE. Welcome, Shay, or Shay, and Lilach Zemach, Director of Product Management at NICE. Welcome, Lilach. So now I'm going to turn the event over again to Shay, Senior Product Manager from NICE. Welcome to the broadcast, Shay. Thank you so much, Bob, and good morning and good afternoon, everyone, and welcome to our webinar, "How will QA change with the rise of the GenAI?" So I'm Shay Diner, Senior Product Manager for CXone Quality Management and Coaching here at NICE. And let me introduce my wonderful co-presenter, Lilach. Hi everyone, my name is Lilach Zemach, and Bob, thank you very much for making justice to my name. I'm leading the CXone QM and Coaching product group here at NICE. Really happy to be here today and share some good times and good stories. Thank you, Lilach. We've got a great session lined up where we all explore how AI is transforming quality management, and together we'll walk you through the latest trends that are making waves in the industry and share some proven strategies that really work. Think of it like your guide to understanding of how AI is reshaping the way we approach quality management today. Ready to dive in? Let's get started. All right. GenAI is not just another tech trend. It's completely transforming the way we work, and we are seeing a boost in productivity across the board by taking care of those repetitive tasks, right, and letting people focus on the work that really needs human touch. You know, Lilach, since we're talking about AI today and since you are my boss, I just have to share something quite interesting. Did you know, did you know that recent Fishbowl studies found that 16% of employees are using ChatGPT at work without their managers knowing about it? And of course, I'm being completely open here about this webinar. But seriously, what is really fascinating is the transformation that we're seeing is not just limited to the workplace, okay? And let me share some fascinating figures about what's happening, for example, in education. So picture this: an AI tutor that actually understands how you learn best. It's like having a personal instructor that adapts to your unique style, gives you feedback, and makes sense for you, and knows exactly what additional materials you need to succeed. And it doesn't stop in education. In healthcare, for example, we're seeing AI revolutionize everything. And everything from personalized treatment plans to drug discovery and even assisting in robotic surgeries. It's making healthcare more efficient, more accurate, and more accessible even than ever before, and the numbers in healthcare are amazing. Listen to this. According to Accenture Research, AI could save the healthcare industry up to $150 billion annually by 2026. That's a billion with a B, so these examples: workplace, education, healthcare are just the tip of the iceberg, and GenAI is truly transforming every aspect of our lives. And you know what? We're only at the beginning of this journey, and the impact is just going to keep growing in the years ahead, and over to you, Lilach? Hello? Can you hear me? Yes, great. So the next thing that we would like to do is to ask a question for the audience, and let's see what you think. So how many? We're going to present a poll, and the question is, how many new jobs do you think AI is projected to create by 2025? 50 million? 75? 97? Or 120 million jobs? What do you think? How many new jobs? I cannot tell. But we're seeing something amazing. It's like equal. We'll give it a few more seconds. That's interesting guesses. Okay, so I think now we're going to reveal the results, and the answer is, oops, AI is projected to create 97 million jobs by 2025, so imagine, we're not talking about a decade for now. We're talking about next year. These numbers are staggering. It's really, really crazy, and you know, just to continue with some information, follow what Shay said about the different industries. The National Cancer Institute found that AI can detect breast cancer with an impressive accuracy of up to 99%, and I have to say, personally, it makes me happy. I'm a breast cancer survivor, and it's really so encouraging to hear how it can really help save lives, so that's a good point for AI. Yes, thank you, Lilach. And you know, while AI is doing wonders in the healthcare and other industries, let's talk about how it's transforming our world of contact centers. So AI is making significant impact in three key areas of the contact center. And Aberdeen conducted a study comparing best-in-class organizations that use AI against those that don't. And the results are eye-opening. So first, as you can see, AI helps improve decision-making. And we can see that 90% of AI-powered organizations can identify bottlenecks and process inefficiencies compared to the 45% of those without AI. And moreover, 83% of AI-driven companies successfully use data for root cause analysis that impacts customer experience, while only 48% of non-AI organizations can do the same. And you know what it means? It means that AI enables better identifications of issues related to product processes or skills. And then we can continue with the notion that AI-driven organizations see impressive increases in metrics year-over-year, right? So here you can see that they experience a 7.2x greater improvement in average handling time, the AHT, and 8.8x greater improvement in first contact resolution compared to their non-AI counterpart. And finally, we can see that AI helps optimize outcomes. So companies that are using AI see 3x greater year-over-year improvement in customer satisfaction scores and four times greater improvement in customer effort scores. But that's not all, right? Because there are some striking numbers that paint a clear picture of what's happening in the contact center today with another research. And according to Metrigy's research, AI and self-service are now handling, let me show you the number, 41% of customers' interactions. That's impressive. But here's the catch. It means that our agents are less dealing with the more complex, challenging cases that automation cannot solve. It means that today agents need a much higher skill level than before, and they're not just handling simple queries. They're tackling the tough stuff AI challenges. And this impact is clear, as we can see here. 51.5% of executives say that agent burnout is a serious issue. And by the way, this is something that I experienced for many years in this industry. And that's more than half of the industry leaders raising a red flag about their team's well-being. And it gets even more concerning that we're seeing 28.8% agent turnover rate in 2023. These are not just numbers, okay? These numbers represent a real challenge we're facing in contact centers. So overall statistic, while AI is fantastic at handling the day-to-day stuff, our human agents are dealing with more complex situations than ever before. I'd like to continue. Shay shared the Aberdeen and Metrigy analysis of the evolution of GenAI in contact centers, but I wanted to share a report that was done by Frost & Sullivan. It was recently published, and it's describing five ways generative AI is transforming the quality management in the contact center. You're going to see how perfectly it fits the approach we took here at CXone QM Advanced. And I'm also going to give you a little spoiler alert to what Shay is going to shortly demo afterwards. Let's start with the first. The first point is transforming quality evaluations. I wanted to start with automation. When you automate 100% of the evaluations, or close to 100% of your volume, it's going to drive a huge increase in the evaluation data. That means a much higher scale of QM data. No need for additional funding for quality teams to review because it's done automatically. Now, automating the evaluation also means more consistency of scoring. So, less focus on calibrations and appeals. And of course, substantial time saving for supervisor quality teams to focus on their agent growth and other strategic tasks to improve the quality. So that's point number one. Now let's continue to bring in enhanced data-driven decision-making. So we spoke about evaluation data at scale, which means you have now more data for the system to generate accurate insights and guide the supervisors and quality teams to take the right action to support their agents. For example, we have heard from some of our clients that they plan to leverage the quality team now that they have more and more automation, and with GenAI especially. So the quality team that has a lot of expertise and overall insights into the processes, they want to leverage them now to help, for example, with coaching. So you can still use your quality teams and supervisor, but focus them on other tasks that will help drive your business and the quality programs. Point number three is about powering the agent coaching to better outcomes. So with GenAI, agents receive exactly the support they need. It's tailored to their roles, their skills gaps, so the supervisor can really see tangible improvements in performance. Now, imagine agents can have a virtual coaching playground where they can practice in an isolated environment, but still using real-world scenarios. This is completely a game changer. As for their supervisor, they will save the time to actually go and talk to them about those examples as part of their coaching sessions. Point number four is about empowering AI employees. So GenAI perspective, or predictive analytics and data visualization tool, streamlines the workflows. It supports the team. It helps the employees understand the trends, the concept easily. For example, Copilot capabilities tailored for specific users and quality processes. It's really easy for the supervisors and agents to really use Copilot to help them drive their quality and processes in general. So when we look at empowering the users, it's not only about the agents. It's also about supervisors and managers. Now, Shay mentioned how with more automation, the human agents will be handling more and more complex problems. So I think here we have another big opportunity for empowerment. So how the agents and supervisors are going to really learn how to work alongside with GenAI tools to help them to get the data and make better decisions? It's not about replacement. It's about working together, use the fact that there is an automation, but then, of course, use the fact that the tools complete the work, augment the work. And I think this is another opportunity for our workforce to really learn how to work alongside with those tools. And the bottom line is that the outcome, of course, is a greater impact on the quality programs and the business KPIs. And we know that companies using this technology are reporting better decision-making, improved operations, greater customer satisfaction, and most importantly, higher revenue, which is always the bottom line. So with this report, I wanted to continue and describe CXone Mpower Quality Management Advanced. So while Shay is going to demo the new capabilities that we have, you at least know what the basic includes. So I wanted to introduce our solution. It's a one solution. It's a one-stop shop to run your end-to-end quality processes, and it includes, for example, automated sampling and distribution of interactions, coaching, appeals, calibration, self-assessment, and much more like BI reporting, hierarchy management, etc. Now, it doesn't matter what the channel your agents are using, either voice or digital. The QM capabilities support the processes for more than 30 channels, so it's up to you to decide, for example, if you'd like the same process for voice and digital, or maybe you want different processes. It's really configurable and flexible, and now, on top of that, let's add analytics and let's add GenAI. Of course, the outcome will be more automation and a much more optimized quality processes. So thanks, Lilach. And you know what? I'd like you to tap into your expertise here. So from your perspective, working with contact centers and quality management processes, can you share some of the primary challenges you're seeing in QM today? Sure thing, so as a leader in the quality space, we really stay close to our customer. It's really important for us. We do advisory boards. We have regular meetings, feedback sessions. By understanding the needs of our customers, but also analyzing trends and industry reports, we've identified three key pain points. The first one is navigating through a lot of interactions, countless interactions, and which one should we choose, so I wanted to start with the point, and I'm sure you're all aware of, is a contact center is usually running between four to eight evaluations per agent per month. It's about an average of 1% or even less of the overall monthly volume. So even though the process may be automated, it is still not a meaningful sample, so with GenAI, the ability to Auto-Evaluate, the customer will be able to dramatically increase the scales of evaluation. Now, it doesn't have to be 100% of your volume. Even if you are going to do, let's say, a statistically significant number like 380 evaluations per agent per month, or even 100, the sample size still makes a huge difference. And this data is going to then help the quality teams and the supervisor to see the overall trend and then focus on what they need per agent, per team, or per process. So that's point number two, or point number one. The second pain point is the misleading conclusion. So on top of the small sample size that I just discussed that is typically being evaluated, evaluations still are dependent on human. Therefore, evaluations are subjective and may be biased. And of course, subjectivity leads to inconsistent evaluation and frustration and some perception even that the process is not fair. When we use analytics and GenAI-based capabilities, we know that the data is consistent. That means less misalignment, less time spent on calibration and appeals. When we Auto-Evaluate 100% or less of the volume, depending on what we want, it's going to help, as I said earlier, to identify the trend. We have so much data. We know exactly what are the overall quality scores for all the interactions, for all the agents, for all the teams. Now, it's easy to say, "Okay, where are we focusing? For the team or for the agents?" I did want to mention that it's still important to keep the human in the loop, to review a small sample of this data and provide feedback so we can improve the models, we can improve the prompt. Automation, that's where we're looking at. We're looking at data at scale, for sure. But the human still needs to be in the loop for a small portion of that. And the third pain point is the generic coaching process. It's 2024. Agents and supervisors alike do not like a one-size-fits-all cookie-cutter feedback. So they want something personalized. They want everyone to understand it from their point of view. And it's not yet there. So these were the three pain points that we kind of aggregated across all the customer feedback and analysts and trends that we've seen. Absolutely. Especially the part of the human being in the loop. And I would like to share something really exciting about the GenAI that I think that everyone will find fascinating. And while everyone's talking about GenAI technology, what really makes the difference is the data behind it. In our industry, it's all about having the right kind of data to train these AI models. And here at NICE, we've got something special because our LLMs are trained specifically on contact centers' interactions using what we consider the best-in-class data. And this means that we're not just using generic AI. We're actually using AI that truly understands contact centers' operations. But here, what really sets us apart is our commitment for innovation. And our CEO, Barak Eilam, put it perfectly when he highlighted our investment in this space. Just last year, we invested over $300 million in R&D and had more than 2,000 R&D employees working to bring innovation to our customers. What this means, we're not just following the AI trend. We're leading it. We're leading it with solutions that you'll see in a second, in minutes, that are specifically designed to transform contact center operations and enhance the customer experience. And now that you know the level of innovation and investment, let me show you how it gets reflected in some truly game-changing features for quality management. So here, you need to excuse me because I'm going to share my screen so you'll not see me. Just a second. Just tell me if you can see my screen. Entire screen. Sure. Not yet. Okay. Yeah. Now we can. Thank you, so my screen is shared, and now, before I'm clicking on play, I just want to say a few sentences, so picture this. You're managing a large team dealing with thousands of daily interactions, and it sounds familiar, right, so let me show you how the Auto-Evaluate transformed this challenge into an opportunity, and while we already have the great tools of automation today, today we have it, okay, and we're using categories and we're using sentiment analysis, but here, we're taking it one step further, so when it comes to tricky and complex questions that we spoke about that usually need human touch, that's where our LLM really shines, so instead of spending hours manually reviewing these challenging aspects, the Auto-Answer, the Auto-Evaluate steps into this heavy lifting. It's like having a brilliant assistant who not only understands your evaluation questions, but can analyze interaction transcript with incredible precision. What makes it truly special is that it doesn't give you answers. As you can see here, it shows you the why? Okay? See these timestamps, for example? By clicking on this, it will take you to the right exact moment in the interaction that supports the AI evaluation. Just to break it down in a simple way, once you set up evaluation questions, this is where the magic happens because the LLM is really smart about it. It takes those questions you've created and really understands what they are asking for. Then it looks after the post-call. It looks on the transcript and connects the dots. It matches what's happening in the interaction with what your question that you're trying to measure. And think of it like a small reader who not only sees the words, but truly gets the meaning behind the words. And the best part, it works with any kind of questions you need to answer. So whether it's a simple yes/no question, or you need to pick from multiple questions, or giving detailed ratings, it gives you accurate answers every single time. No more guesswork, just confident and reliable result. But as Lilach mentioned, and this is still important, we need to keep the human expertise, the human in the loop because human is irreplaceable. And that's why supervisors can review everything and make, as you can see here, they can make the change of the answer. They can add comments and even provide feedback through simple likes and dislikes. Okay? And think of it as a partnership between human expertise and AI capabilities. You get the efficiency of automation while maintaining complete control over quality. It's about working smarter, not harder, to achieve the scale of the 1% that we're speaking about, or close to 1%, or a significant sample of those evaluations that Lilach spoke earlier. Lilach? Shay, if I may, yeah, I wanted to add one more thing here. You have seen now how we can Auto-Evaluate. Now let's kind of merge it together with the data at scale. If you can Auto-Evaluate 100% of your volume, or as I said, something statistically significant like 300 per agent per month, for example, this is where you have so many evaluation scores. You can look at those. You can take the outliers and see where the scores for specific agents are really bad versus really good. This is where you have acknowledgment or recognition opportunity, but also coaching opportunity because you have so much data and it's a really meaningful sample, not as we do today, which is typically not that meaningful. That's how it ties to the data at scale. Right. And now, let's talk about the Evaluation Insights. Okay? So before I'm going into the actual demo, what you're seeing here, by the way, the screen is visible, right? Just. It is. Okay. So what you're seeing here is a typical evaluation form. Okay? And typically, it's the quality team doing most of the heavy lifting with evaluations, and supervisors use all the data to coach their team members. However, let's be real for a moment. Okay? Supervisors often face quite a challenge here. And you can see it here. You can see it in the form. Okay? They're looking at the pages of these evaluations, trying to piece together the full picture of what happens in each interaction. And it's like trying to solve a puzzle with too many pieces. And it's time-consuming and sometimes frustrating. So they need to understand the context, spot the patterns, right, and figure out what's really important for coaching while managing their other responsibilities. And that's exactly why we develop the Evaluation Insights. So think of it as having a super smart assistant by your side, and it cuts through all those noise and gets straight to what matters. And this way, supervisors can spend less time digging through the data and more time doing what they do the best, coaching and developing their team. Now, let me show how it actually works. And I'm going to share my screen again. Is my screen visible? Yes, it is. Great. Sentence before. What makes this tool special is how it gives evaluators and supervisors a complete view of the agent performance, and it doesn't look at the numbers in the evaluation. It analyzes everything in the evaluation, including performance and the full picture. Now, let me take you through the key features so as you can see here, this is a typical evaluation that you can see, and right from the beginning, you can see a hint, and every hint that you are seeing here is AI-generated, and it's broken down into sections, so here, you can see the card, and I get the hint. And then, by clicking on the summary details, I can go here with the same evaluation form, but you can see the Evaluation Summary tab, and this is the short summary. It's like a quick snapshot of how the agent is doing, and it's highlighting their wins and the areas for growth. And then we have the section of the Overall Summary. So this is where you get the deep dive, the full story of agent performance. And by the way, this is my favorite part, the Strengths and Improvements. Okay? So it identifies the top three areas where the agent excelled and where it needs support based on score and weightage. And here, in the Suggestions section, you will get a quick overview of how the agent did, plus some practical tips on what they can do better. So think of it like a simple, and this is for me as a product manager, so it's like a simple roadmap for improvement that both managers and agents can easily understand and act on. And last but not least, you can see here we have the Like and Dislike. So this is the feedback section. This is where I can share my thoughts on how helpful the Evaluation Insights are. So before I'm continuing, Lilach, would you like to add something? No, you're right on point. Thank you. So there you have it. Evaluation Insights, LLM, making sense of complex data and saving time and giving the supervisors insight to make a better decision about the agent performance. And this is so great. Now I would like to go into another area. And I'm going to share another amazing feature. Amazing. Okay. And this is the Coaching Simulator. And this is something that's truly going to revolutionize the agent coaching. So imagine yourself a virtual coaching playground where agents can practice customer interactions without any real work pressure. And I'm always describing it like a time machine. You can take the agent and the things that the agent experienced in the day-to-day and things that they perhaps had to have some improvement, and then take it back in time and let the agent practice again in the same environment. What makes this tool special is how realistic it is. We're talking about scenarios that perfectly mirror the kind of calls the agent handles every day. Here's what I love the most. We can actually recreate those challenging situations like a time machine and these situations that agents might have struggled with before and giving them a chance to master this interaction. Think of it as a safe space for learning. Here, by the way, the system acts like a coach. This is the actual simulation. The system acts right now as a customer. This is the agent providing the reply. What's amazing is this one. We'll see it in a minute. This one, the feedback. The feedback is a game changer because it's immediate, like you're seeing here, and completely objective based on clear criteria. This means that agents get a fair, consistent feedback on the spot and focus purely on their performance and helping them to identify exactly where they excel and what they need to do. Okay? And it's truly transforming how we approach the agent coaching. It's about building confidence and competencies in an environment where mistakes are just stepping stones to improvement. Lilach? Thank you, Shay. So I think you've seen a lot of the good features and really innovative capabilities that we're developing at the moment to complement our existing solution. But it's only a glimpse. And as we continue to invest in GenAI and push the boundaries of what's possible in the contact center quality management, you'll be sure to expect more powerful tools and features that will help you drive success. So we are diligently working on that. And from our perspective, it's really changing the boundaries and changing the paradigm of quality management in the contact center. So please stay tuned for more updates. And I'm going to hand it back to Bob now. Hey, great. Thanks so much. Really interesting presentation. I believe we're into the question-and-answer period so if anybody's got any questions for either Shay or Lilach, please type them in right now, and we'll get to them so I'm going to jump to this first question and I'm just going to put it out there so whoever wants to answer it, it's fine. The question is, how does this solution address the overall agent retention challenges? Do they like this? Is this something that they feel will help them? Or does this feel like something that they will be judged on at a level that they can't compete at? How does this affect the agents in the chairs? Can I take this, Lilach? Of course. That's a very interesting question, I must say. Let me think of it for a second. Okay. I think that, first of all, our GenAI solution directly addressed the key factors of affecting agent satisfaction through multiple approaches, and it's in the presentation, so we have the performance and professional development and stress reduction, and let's think about performance, so here we can provide all kinds of consistent and objective evaluation, like we're seeing, and we can see in the coaching real-time guidance and support and clear performance metrics, and in the Evaluation Insight, we can see fair and transparent assessment, and this has covered the professional performance. The professional development, we can think about personalized coaching recommendations and structural learning patterns, safe practice, safe environment with the role play. So the agent does not need to worry about his calls would get escalated. It's a safe environment. And the worst-case scenario, he will lose points. So we're speaking about skill development tracking. And it all comes into the stress reduction because you can come up with measurable outcomes. Okay? You're improving the agent confidence. Obviously, having this will get the higher job satisfaction, which we addressed in the first slide about the managers that were in this. And there is other research that says that if you have higher job satisfaction, there is a correlation to better performance. And based on that, reduction of turnover. So these are like the three main pillars that we can think about the agent retention challenges with our competencies. I hope this answers. Okay. Great. One of the things that you had mentioned when you were going through just the coaching application is that the agents could actually see immediately where their problem was if they had a problem. That was in the coaching module. Is that also the way it works when it's live? "Hey, I think I blew that call. Let me review and see how I could have done this better." Is that how it works? Or do they have to wait a period? So it depends. There are all kinds of coaching methodologies. And here in this one, we have real-time guidance. So the agent can get some real-time guidance based on all kinds of predefined criteria. And it goes in the post-call activities. So once the coach speaks with the agent, they bring the examples of what they can do in order to excel. So it goes into the real-time and the post-calls, and it's involving the practices of the coach and the contact center. And it's interesting because the measurement of getting the feedback immediately, you can see it impacted later on after the coaching session in the performance. And then you can correlate the session itself to the topic of the coaching where the agent can improve their performance. Okay. Great. Here's another question from someone. And they say, "I'm currently using QMA with analytics for automated evaluations. What makes GenAI different? How is that different? Can I take this one, Lilach? Sure. You can take all. Oh, thank you. So first of all, that's amazing that we have QMA customers in this session, and just by thinking of it, QMA, it's like the leap forward with using GenAI. And I love this question. Let me just two seconds to rearrange my thoughts, and then I can think about something. Okay. So I think that while QMA with analytics provides valuable automation capabilities, GenAI represents a significant advancement in the evaluation technology. So if we can speak about the key differences, we can say that first, GenAI excels in handling complex questions. And like we said in this webinar today, traditional automation works effectively for faithful, rule-based evaluations. And Lilach mentioned the scaling part. And here, we're taking the 3% that is currently being made manually and excited to most significant sampling. Here is where GenAI can understand the evaluated scenarios that conventional automation might miss, including context tone and subtle interaction details. Second, I think, and I spoke about it, but let's iterate again, it's the scale. Okay? Current automation typically evaluates limited percentages of interactions. GenAI theoretically can analyze, not theoretically, practically, okay, 1% of your interactions. It's providing complete operational visibility. I think that I have more. I have more. Okay? This is, so we're speaking about GenAI. We're speaking about QMA. I think that here, GenAI offers unprecedented transparency. It doesn't simply provide answers, like you're saying in the Auto-Evaluate, right? It explains the why. It explains your reasoning by highlighting specific moments in the conversation that support it in the evaluation. This allows supervisors to validate decisions and maintain a control effectively. So think about it as enhancing your current capabilities rather than replacing them. And here, GenAI fills the gap where traditional automation reaches limits, particularly understanding the complex customer interactions that we spoke about. And here, we're closing the gap with the new technology. Okay. Great. And here's something. You can tell the QMA customer that they can reach out to me later on, and we can discuss because it's interesting. Okay. Great. And this is actually kind of a follow-up question where the question is, and this can—or how can this integrate with our existing NICE solutions? This is something that this module can be just upgraded? No, but this is an amazing question because once we created these features, and here I have evidence, I have Lilach, we started speaking about how it is going to speak across the entire suite of CXone. And the integration is seamless with my CXone Mpower ecosystem. So whatever we're creating, these new GenAI capabilities, these enhance the existing workflow and rather than requiring all kinds of separate systems or processes, it's seamless. You don't need anything. You just come to us, and we're going to do the integration. You don't need integration. It's like a building. Okay, all right, good, so here's the question that I always love. What kind of ROI can we expect, and in what timeframe? and you started off things saying almost 30% of agents turn over every year, so any kind of impact on them not turning over at the rate of 30% a year would probably have a huge financial impact, so what ROI can somebody expect, and how long does that take, realizing that some of these things will take a long time just because it takes a long, anything that affects an agent will take a long time to see some sort of payback? That's a tough question. Let me think of it for a second. I'll give you all the hard ones, Shay. Yes, yes, yes. It's really hard, but I think that we kind of covered it. Based on our research, and we're always doing data implementation, organization expressing significant improvements across key metrics. This is something that we know for a fact. We used the Aberdeen study that showed that AI-powered organizations achieving, for example, a monetary benefit. We spoke about 7.2 times greater improvement in average handling time and 8.8 times greater improvement in the first contact resolution. Just remember the slide before. It's fresh in my head. We also mentioned today about the 40%-60% reduction in the evaluation time. By the way, one of our customers told us that instead of reviewing the review of the Evaluation Insights, for example, in one of our beta, instead of spending 35 minutes, it got reduced to less than 10 minutes. Less than 10 minutes. That's remarkable, Bob. 35 minutes to prepare for the coaching, for example. Yes. Yes, and we have the qualitative benefits, so with this one, we can say that it enhanced quality management efficiency and improved the agent engagement and provides more consistent evaluation and better coaching opportunities, and I think that most organizations begin seeing measurable improvement within the first month of the implementation, with benefits accelerating, the system learns from your specific use cases, so the ROI is very, very straightforward, and you can see it immediately. Fantastic. That kind of looks like all the questions that we have right now. Is there anything that you want to mention to just kind of summarize or sum up what we've talked about so far? Wow. I know we've been through a big piece of. I think I would say with GenAI, the most important thing is to generate data at scale, so you're really able to create those meaningful sampling and automate everything. You can still control it, and you should still review it as a smaller sample out of it, but I think that the main thing is the data at scale and the tools that help augment agents, supervisors, and quality teams as part of the quality processes, and I think this is bringing a shift into how the day-to-day look for the different people in the contact center and how they do the work and how they learn to kind of free the time to, for example, handle more complex interactions for agents or focus on growth or more strategic tasks, etc., within the contact center. Fantastic. That's my team. That's great. So how do you feel that these GenAI tools will play out in the contact center in 2025? It looks like it's going to be the year of GenAI if this year wasn't. Next year certainly will be. Yeah. Definitely. I think that the organization, and we see it across the analysts, are investing more and more in technology and in GenAI and want to learn how to think about it, how to kind of design their day-to-day processes to fit with GenAI, to maybe some of them to be switched by GenAI. So definitely, 2025, we'll see, based on analysts and what we feel from our client base, more traction and more adoption. Fantastic. We can't wait to hear the feedback as well. That's great. Well, nobody heard of GenAI two and a half years ago, so it's made quite a run. Hey, I'd like to thank everybody that joined us today, everybody that asked questions, especially our speakers and sponsors, Shay Diner, Senior Product Manager at NICE, and Lilach Zemach, Director of Product Management at NICE. If you'd like a copy of the presentation, you can download it once the event is archived. You can use this web address if you'd like to send it to a colleague or review it yourself. You can use the same web address that you used for today's event. It will be archived for 90 days. Don't worry, we will send you a link with all this information in it tomorrow once the event is archived. That concludes our broadcast for today. Thanks, everyone, for joining us. Thank you. Thank you.