NICE Ltd. (NICE) Earnings Call Transcript & Summary

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 within a couple of days with an e-mail. 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 Manager at NICE. Welcome 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. And we've got a great session lineup where we all explore how AI is transforming quality management. And together, we will walk you through the latest trends that are making waves in the industry and share some proven strategies that really work. And think of it like your guide to understanding of how AI is reshaping the way we approach quality management today. So ready to dive in? Let's get started. All right. So 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. And 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 that recent [indiscernible] studies found that 16% of employees are using ChatGPT at work without telling their managers knowing about it. 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 your unique style, gives your feedback and make 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 it means by personalized treatment plans, to drug discovery and even assisting in robotic surgeries. It's making healthcare more efficient, more accurate and more accessible even 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 will it be. 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.

Can you hear me? Yes. Great. So the next thing that we would like to do is to ask the question for the audience and let's see what you think. Now 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 million, 97 million or 120 million.

What is your answer, Lilach? 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. Okay. So I think now we're going to reveal the results. And the answer is, 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 cr*zy. And 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.

Thank you, Lilach. And while AI doing wonders in the healthcare and other industries, let's talk about how it's transforming a world of contact centers. So AI is making significant impact in 3 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 processes inefficiencies compared to the 45% of those without AI. And moreover, 83% of AI-driven companies successfully use data for root cause analysis that impact 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 identification 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 counterparts. And finally, we can see that AI helps optimize outcomes. So companies that using AI seeing 3x greater year-over-year improvement in customer satisfaction scores and 4x 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 [indiscernible] research, AI and self-service are now handling, let me show you the number, 41% of customers interactions and that's an impressive [indiscernible]. But here's the catch. It means that our agents are left dealing with the more complex challenging cases that automation cannot solve. It means that today, agents need much higher skill level than before and they're not just handling simple queries. They are tackling the tough and tough 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 from 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.

So I'd like to continue. Shay shared the Aberdeen and [indiscernible] analysis of the evolution of GenAI and contact centers, but I wanted to share a report that was done by Frost & Sullivan. It was recently published, and it's describing 5 ways generative AI is transforming the quality management in the content center. So 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. So let's start with the first. The first point is transforming quality evaluations. So 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 team 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 #1. Now let's continue to bringing 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 insight 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 #3 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 skill 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 #4 is about empowering AI employees. So GenAI perspective or predictive analytics and data visualization tools streamline 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 process. 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 supervisor 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 you -- 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, et cetera. 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 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 like you tapping 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 3 key pain points. The first one is navigating through a lot of interactions, countless of interactions, which one should we choose. So I wanted to start with the point, and I'm sure you're all aware of, is that contact centers are usually running between 4 to 8 evaluation 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, customer will be able to dramatically increase the scales of the evaluation. And it doesn't have to be 100% of your volume, even if you are going to do, let's say, a statistical 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 #1. 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. So 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. And 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. So 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 agent. But 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. So 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 supervisor 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 3 pain points that we kind of aggregated across all the customer feedback and analysts and trends that we've seen.

Absolutely, especially that part of the human being in the loop. And I would like to stress something really exciting about the GenAI that I think that everyone will find fascinating. While everyone is talking about GenAI technology, what really makes the difference is the data behind this [indiscernible]. 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 center 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 center's operation. And -- but here, what really set us apart. It's our commitment for innovation. And our CEO, Barak Eilam, put it perfectly when he highlighted our investment in this space. And just last year, we invested over $300 million in R&D and had more than 3,000 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 a minute, that are specifically designed to transform contact center operations and enhance the customer experience. And now that you know the level of innovation and investments, 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're not going to see me. Just tell me if you can see my screen, entire screen. Sure.

Okay. Yes, now we can.

Thank you. 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 sounds familiar, right? So let me show you how the auto evaluate transforms this challenge into an opportunity. And while we're already having great tools of automation today, today, we have it, okay. And we're using categories and we're using sentiment analysis and -- but here, we are 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. And it's like having a brilliant assistant who not only understands your evaluation questions, but can analyze interaction transcript with incredible precision. And what make it truly special. It doesn't give you answers. As you can see here, I shows you the why, okay? And see these time stamps, for example. By clicking on this, it will take you to the right exact moment in the interaction that supports the AI evaluation. And just to break it down in a simple way, once you set up evaluation question that -- 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. And then it looks -- after the post call, it looks on the transcript and connects the dots and 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 word, 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 be picking from multiple questions or giving detailed writings. It gives you accurate answers every single time. No more guesswork, just confident and reliable results. 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 AI 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 100% that we are speaking about or close to 100% or significant sample of those evaluation that Lilach spoke earlier. Lilach?

And Shay, if I may, yes, I wanted to add one more thing here. So you have seen now how we can auto evaluate and now let's kind of merge it together with the data at scale. So 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. So you can look at those, you can take the outliers and see where the scores for specific agents are really bad versus really good, and 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. So that's how it ties to the data at scale.

Right. And now let's talk about the evaluation insights. So before I'm going into the actual demo, what you're seeing here, by the way, the screen is visible, right? 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 detailed 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, all while managing their other responsibilities. And that's exactly why we developed the evaluation insight. 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. 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 [indiscernible] performance and paints 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, we can see every hint that you're 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 win and the area 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 strength and improvements, okay? So it identifies the top 3 areas where the agent excel and where it needs support based on score and weightage. And this makes it so much easier to develop targeted coaching plans. And here, in the suggestion 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, you would like to add something?

No, you're right on point.

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 is truly going to revolutionize the agent coaching. So imagine yourself a virtual coaching playground where agent 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 experience in the day-to-day and things that he got perhaps had to have some improvement and then take it back in time and let the agent practice again in the same environment. And what makes this tool special is how realistic it is. So we're talking about scenarios that perfectly mirror the kind of course the agent handles every day. And here what I love the most, we can actually recreate those challenging situations like a time machine and the situation that agent might have struggled with before and giving them a chance to master this interaction. So think of it as a safe space for learning. And here, by the way, the system acts like a coach. And this is the actual simulation. The system acts right now as a customer. And this is the agent providing the reply. And what's amazing is this one, you'll see it in a minute, this one, the feedback. And the feedback is a game changer because it's immediate like you're seeing here and completely objective, based on clear criteria. It 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 are 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 for 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.

Great. Thanks so much. It's a 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 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 will -- 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 solutions directly address 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 kind of consistent and objective evaluations 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 is kind of the performance. The professional development, we can think about personalized coaching recommendation and structural learning pattern, safe practice, safe environment with the role play. So the agent does not need to worry about his cost will get escalated. It's a safe environment. In the worst case scenario, he will use points. So we're speaking about skill development tracking and it all comes into this stress reduction because you can come up with measurable outcomes, okay? You're improving the agent confidence. You're -- obviously, having this will get the higher job satisfaction, which we addressed in the first slide about the managers that were in it. And there is other research that say that if you have higher job satisfaction, there is a correlation to better performance. And based on that, reductions in turnover. So these are like the 3 main pillars that we can think about the agent retention challenges with our competencies.

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 just -- 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 on all kind of coaching methodologies. And here in CXone we have a real-time guidance. So the agent can get some real-time guidance based on all kind of predefined criteria. And it goes in the post call activities. So once the coach sits 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 involving the practices of the coach in the contact center. And it's interesting because the measurement of getting this 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 agents 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.

So first of all, it's amazing that we have QMA customers in this session. And just by thinking of it, the QMA, and it's like the leap forward with using GenAI. And I love this question. Let me -- just 2 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 the handling in 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 excel it to most significant sampling. And here is where GenAI can understand the evaluated new scenarios that conventional automation might miss, including context tone and subtle interaction details. And second, I think -- and I spoke about it, but let's iterate again. It's the scale, okay? So current automation typically evaluate limited percentage of interactions and GenAI theoretically can analyze, not theoretically, practically, okay, 1% of your inactions, and it's providing complete operational visibility. And I think that I have more, okay? And this is -- so -- we're speaking about GenAI, we're speaking about QMA. And I think here, GenAI offers 100% transparency. So it doesn't simply provide answers like you see in the auto evaluate, right. It explains the why. It explains the reasoning by highlighting specific moments in the conversation that support 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 are closing the gap with the new technology.

Okay. Great.

You can tell the QMA customer that he 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.

This is an amazing question because once we created these features and here, I have [indiscernible], we started speaking about how it is going to speak across the entire suite of CXone and the integration is seamless with NICE CXone and power ecosystem. So whatever we are creating these new generic capabilities, these announce the existing workflow and rather requiring all kind 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.

Good. So here's the question that I always love. What kind of ROI can we expect and in what time frame? 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. 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, experiencing significant improvement across key metrics, and this is something that we know for fact. But yesterday, the Aberdeen study that showed that AI-powered organization achieving, for example, [indiscernible] talk about 7.2x greater improvement in average handling time and 8.8x greater improvement in the first contact resolution. And just remember, the slide before, it's fresh in my head. And we also mentioned today about the 40% to 60% reduction in the evaluation time. And by the way, one of our customers told us that instead of reviewing of the evaluation insights, for example, in one [indiscernible], instead of spending 35 minutes, it got reducted 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 enhances quality management efficiency and improves the agent engagement and provides more consistent evaluation and better coaching opportunities. And I think that most organizations begin seeing measurable improvements 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? I know we've been through a big piece...

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, et cetera, within the contact center. That's my take.

That's great. So how do you feel like 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.

Yes. 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. And we can't wait to hear the feedback as well.

That's great. Well, nobody heard of GenAI 2.5 years ago. So it's made quite a run. So 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. And 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 and don't worry, we will send you a link with all this information in it tomorrow once the event is archived. So that concludes our broadcast for today. Thanks, everyone, for joining us.

Thank you.

Thank you.