Ram Rajagopalan of Zoom, head of product CX AI, and Joe Rittenhouse of Converged Technology Professionals, co-CEO, join a CX Summit session hosted by Bob Laliberte of theCUBE Research with co-host Zeus Kerravala of ZK Research. The session examines practical contact center artificial intelligence adoption, including agentic platforms, conversation-to-completion workflows, unified CX data layers and pragmatic deployment strategies.
Ram Rajagopalan outlines Zoom's conversation-to-completion approach and unified CX data layer. Rajagopalan explains that outcome-based pricing, automated testing with large language models and a common data layer enable reliable conversation-to-completion. They highlight measurable customer experience outcomes and practical testing methodologies for scaling AI.
Joe Rittenhouse shares partner deployment experience on use case selection, integrations and the operational choreography required to scale AI in contact centers. Rittenhouse emphasizes orchestration, a 30-day low-hanging-fruit launch cadence and C-suite-backed project management as critical factors to consider when expanding AI initiatives.
Bob Laliberte and Zeus Kerravala reinforce a pragmatic, metrics-driven implementation approach that prioritizes measurable outcomes and focused use cases. Watch for actionable guidance on contact center AI deployment, CX data integration and outcomes-oriented strategies for customer experience transformation.
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Joe Rittenhouse, Converged Technology Professionals & Ram Rajagopalan, Zoom
Ram Rajagopalan of Zoom, head of product CX AI, and Joe Rittenhouse of Converged Technology Professionals, co-CEO, join a CX Summit session hosted by Bob Laliberte of theCUBE Research with co-host Zeus Kerravala of ZK Research. The session examines practical contact center artificial intelligence adoption, including agentic platforms, conversation-to-completion workflows, unified CX data layers and pragmatic deployment strategies.
Ram Rajagopalan outlines Zoom's conversation-to-completion approach and unified CX data layer. Rajagopalan explains that outcome-based pricing, automated testing with large language models and a common data layer enable reliable conversation-to-completion. They highlight measurable customer experience outcomes and practical testing methodologies for scaling AI.
Joe Rittenhouse shares partner deployment experience on use case selection, integrations and the operational choreography required to scale AI in contact centers. Rittenhouse emphasizes orchestration, a 30-day low-hanging-fruit launch cadence and C-suite-backed project management as critical factors to consider when expanding AI initiatives.
Bob Laliberte and Zeus Kerravala reinforce a pragmatic, metrics-driven implementation approach that prioritizes measurable outcomes and focused use cases. Watch for actionable guidance on contact center AI deployment, CX data integration and outcomes-oriented strategies for customer experience transformation.
Joe Rittenhouse, Converged Technology Professionals & Ram Rajagopalan, Zoom
Joe Rittenhouse
Co-CEOConverged Technology Professionals
Ram Rajagopalan
Head of Product - AI Zoom CXZoom
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Ram Rajagopalan
>> AI has been part of the contact center conversation for years. Now, initially handling repetitive tasks such as call routing, basic self-service, and simple customer inquiries. Today, however, AI is moving into more complex workflows that can improve agent performance, automate multi-step processes, and reshape the customer journey. The opportunity is significant, but success requires more than just deploying another tool. Organizations need the right use case, trustworthy data, a pragmatic implementation plan, and clear metrics for determining whether AI is delivering value. Welcome to the CX Summit. I'm Bob Laliberte, Principal Analyst with theCUBE Research.
Joe Rittenhouse
>> I'm joined by my co-host, Zeus Kerravala, Principal Analyst and Founder from ZK Research.
Ram Rajagopalan
>> Welcome, Zeus. Hey, thanks, Bob. Great to be back here. Absolutely. And to help us explore how enterprises can move from this experimentation to measurable results, we're joined by Ram Rajagopalan, Head of Product AI Zoom CX at Zoom, and Joe Rittenhouse, Co-CEO, Converged Technology Professionals, a Zoom partner with extensive experience implementing contact center and AI solutions.
Joe Rittenhouse
>> So Ram, Joe, Welcome to the summit.
Ram Rajagopalan
>> Thanks, Bob. And Zeus, thank you for hosting me. It's a pleasure to be here. And Joe, good to see you again.
Joe Rittenhouse
>> Good to see you guys too, and thanks for having us. All right, so now let's have some fun.
Ram Rajagopalan
>> Ram, I thought I'd start with you and begin with the evolution of AI and customer experience.
Joe Rittenhouse
>> So a lot of organizations started with relatively simple capabilities, as I mentioned, right?
Ram Rajagopalan
>> Routing calls, answering their frequently asked questions and things like that, any kind of repetitive interactions.
Joe Rittenhouse
>> But how are customer requirements evolving today?
Ram Rajagopalan
>> And are there more sophisticated use cases that are becoming practical today? Yeah, that's a great question, Bob. Yeah, the industry is evolving rapidly with the advent of agentic platforms, especially in voice agents these days. Traditionally, many customers, even about a year, 18 months ago, many customers focused on purely measuring containment. But where I see conversations these days are going from not just looking at containment, but end-to-end resolution of consumer inquiries. So this is what we are calling within Zoom Conversation to Completion, where we are not just looking at answering the basic inquiries, deflecting the call from going to a human agent, but actually completing the task that the consumers or users are calling in for support. And we are also seeing use cases evolve from basic inbound calls to also going towards more automated outbound calls, outbound dialing, navigating complex IVR menus, things like that. And also we recently at Zoom this summer, we even introduced a new packaging called outcome-based pricing, which not only measures the call volumes or consumption, but also measures the outcomes that we are producing for customers through virtual agents. And then customers have an option to choose that if they want to just pay for the performance. So we see use cases evolving, both inbound as well as outbound.
Joe Rittenhouse
>> Hey Ram, the entire industry is moving to this vision of the rise of agentic AI impacting CX.
Ram Rajagopalan
>> And I do think the conversations to completion narrative that Zoom has been working with is unique, right? And so can you maybe give an example of how that might work in the contact center, particularly for a more complex type of interaction? Maybe you need to bring in a human agent as well. Yeah, I can tell you a couple of examples. We have one customer of Zoom who's using virtual agents today to perform political surveys. within a county. So this is virtual agents going through a list of residents within a county or a state and then qualifying by calling them up, by making an outbound call, asking some qualifying questions, and then taking them through a very complex survey of questions where each question depends on your response to the previous question. So this is not just qualifying the caller, but also going through complex survey questions, but writing all the results into a platform from which you can then create a complete comprehensive report around what were the interests. For this case, for example, they were doing a political survey. Where do they lean? Are they leaning left? Are they leaning right, et cetera? What are the key topics, key issues that they are— that the population cares about? And getting a full end-to-end report at the end of the day is a fairly complex endeavor. sometimes we call people, go to voicemail. So the virtual agent needs to be intelligent enough to detect when a voicemail is encountered and leave an appropriate message in the voicemail, in the voice message. So all of this is one example of how we are navigating complex use cases. And we are producing not just a completion record, but we are giving a full report of what was the outcome of those calls back to the customers. So this is where I think we are seeing use cases are evolving, and in healthcare too, we are seeing another place where we are seeing it evolving, where we are making outbound calls to remind patients of upcoming appointments, prescription refills, and even qualifying, connecting between healthcare providers and insurance companies where we need to navigate complex IVR menus. All these use cases are becoming more and more common these days. And Joe, I wanna bring you into the conversation here. And one of the reasons I like talking to partners is because you're actually measured on whether you're getting this stuff to work for customers, right? And we've talked about this a lot where, the executives of companies today are under tremendous pressure to deploy AI and move quickly, but that rubs up against the concerns of a contact center leader that might be concerned about disrupting customer service or perhaps just trying to do too much at once. And so when you work with customers, how do you help them identify the best first use cases for AI to help them get started?
Joe Rittenhouse
>> Yeah, and it also disrupts the IT team too, right? It's the disruption of the business, right? And so there's an anxiety, there's a push, but usually where we try to start is where's the low-hanging fruit? And it's in areas that you're usually not even typically thinking about, and it could be, and we see this from all different verticals, from healthcare to banking to professional sports to manufacturing. Just a simple question of what do you do after hours? What do you do after hours today? Well, we're usually staffed, we follow the sun from the East Coast to the West Coast, but then after hours we just have a general mailbox. How many calls do you get? What type of calls do you get? What's happening after hours? And the answer is typically, I don't know. Well, we can see that volume, we can project that volume. And so let's just go put a containment out there and go see what's happening. And some of the results we've seen, especially in the medical industry of just being able to book appointments or being able to have a follow up with a nurse or something along those lines, those are instant savings and instant revenue contributions. But that's not what we're looking for. We're just looking for a place that you can start simple to get these tools in place, have a pragmatic approach, get everybody comfortable. And get the organization used to having a choreography of how do we solve these complex business needs. Because it's not just the tools that are answering the surface questions. To Ram's point of the integration of maybe we want to write this to a ticket in Salesforce the following day. So when an agent comes in, that's the first thing that they do. And the methodology and the coaching that we have during that pragmatic approach is to say, as we get this in, it's like getting a new pair of shoes. You feel like you can run faster and you're going to want to do this and you want to do that. And we can do all those things, but we have to prioritize and we have to have a plan. And as long as we have a plan and we're working within the business, we can execute. We can start to take on those larger, more complex projects. But this is an evolution and it continues to go post-deployment and it is not something that you're just going to set it and forget it. And so you've got to get used to that mentality for the business too. There's change and this orchestration touches every level of the business. It is not just IT.
Ram Rajagopalan
>> Hey, Joe, because you're involved in the deployments and so forth, I'm wondering if you could share some knowledge with organizations out there that are thinking about adding AI and trying to do this. I'm wondering if you could talk about some of the common mistakes that you see organizations making, especially when they're trying to boil the ocean and they want to do everything at once and how to overcome that.
Joe Rittenhouse
>> Yeah, and I think the first question that we ask and where we're seeing some clients have some success is, Do you have an AI committee? And the answer is about 50/50 right now. And then the 50% that's saying, yes, we have an AI committee, it's like we're starting to put it together, but the fundamentals are there and they're starting to put it together. The groups that don't have an AI committee, it's for us to sit down and be like, how are you managing your AI strategy? Like, is it top-down? We just have to do it, we have to do it. Or is there an orchestration? And the reality is like you can't boil the ocean and there is a lot of orchestration between the various departments and what you're doing, but to present to the C-suite of like, this is the lift internally you're going to have to do. These are the requirements that your teams are going to have to participate in. They're going to have to drive this. These are the metrics we're driving. This is how we're prioritizing it. Do you guys approve of this? And then these are the deliverables that we'll give. The reality is, if we can't get that, that's not an implementation that's going to have success. And so it's more on the front end of the organization, we just have to really kind of think of how we want to handle our AI strategy? And there's an orchestration level from the C-suite to every department. And this is not just an IT project. This is not just a CX project. And there's a lot of overlap. And so you just have to have visibility of what's going on with the organization and have an orchestration, start small and go from there.
Ram Rajagopalan
>> Got it. And you come in as the voice of reason that can help organizations get through that and get down that path.
Joe Rittenhouse
>> Yeah, I think our pragmatic approach is, we're not here to sell you the product. You already want to buy the product, but how are you effectively going to do this and grow it? And it's going to evolve with you. And so it's not just the day one, hey, we're done, we're collecting revenue. This is an ongoing process and you're going to evolve with it.
Ram Rajagopalan
>> Excellent. Now that sounds great. Ram, I wanted to come back to you. When we talk about, contact center data, right? Right, really fragmented across virtual agents, human interactions, workforce management apps, right, knowledge bases, customer systems, all sorts of different places. How does your integrated CX platform help customers connect those interactions and turn the resulting data into better outcomes? Yeah, that's a great question. I think Joe touched on this a little bit. One of the things I think, as customers consider their AI solution, you have to think through the entire customer journey end to end. You have to think about the journey as they start. I call it the before, during, and after. But before, during, and after they interact with a human being. But you have to think through that entire process in terms of how, what are the kind of use cases where, a virtual agent can best serve a customer. And then when does it get elevated to a human being? And what happens after that engagement is concluded? And AI really plays a role in all of these three segments. And many times customers tend to think about, many, there are many solutions out there. Many vendors bring in very, point solutions for each one of these stages, whether it's before, during, or after. But the challenge that they will run into is the common data layer that connects all these three. You want to see the entire lifecycle of an engagement when the customer is with a virtual agent and what happens afterwards. With Zoom CX, this is one area where I think we tend to have a unified layer where a customer can see the performance of a virtual agent and see it side by side with a human agent. We have quality measurement tools that evaluate virtual agent and human agent side by side and tells you what was the CSAT at each stage, what was the sentiment at each stage, et cetera. So you have an automated way of evaluating both the human agent and the virtual agent. Another thing which I think Joe touched upon was when we speak to particularly large enterprises, like you said, this is an area which touches multiple subgroups within a company. Because for example, for a virtual agent, you need to provide it with context and knowledge, right? But sometimes the knowledge is maintained by a different group, and there is often a disconnect between the validity of that knowledge. Maybe the knowledge base is not up to date or is not updated, or there's conflicting information. But when you inject all of this into a virtual agent, the responsiveness of that agent can be determined by the quality of knowledge being provided. One of the examples that we ran into with a large consumer products company was the knowledge base was quite old, it was not up to date or it was not updated for a virtual agent to understand. In many cases, we were kind of escalating the call to a human being only to find out that the human beings were using their own tribal knowledge or knowledge that they gained through experience which is not documented elsewhere. But being part of the same platform allows a virtual agent to see not just its conversation, but the conversation that is handled by a human agent and learn from that conversation. We look at that and we look at whether there is a gap in the knowledge base? And then surface it back to the human administrators to see if that gap can be addressed by looking at the human transcripts and then feed that back into virtual agents so that they learn from that. So that in the future a similar question comes up, they don't have to elevate it to a human being, but they can answer it themselves. So that feedback loop is very critical. And that's one of the unique things about the connected CX platform from Zoom, where the virtual agent and human agent are always talking to each other and learning from each other. Hey Ram, there's more though to a contact center than just Zoom, right? You've got your application, there's a lot of third-party CRM systems, things like that. And so talk about how you integrate with those third parties just to make sure that there's a complete view of customer experience and companies can get away from these silos that they've had for a long time. Yeah, absolutely. So, many customers have different systems of record. It could be a ticketing system, a customer information system, and to provide a more customized experience for every time a customer of yours is calling into your contact center, we need to know who you are, what kind of previous history that you've had with the brand. And virtual agents can look into that by, we have nearly 40 out-of-the-box integrations with all kinds of systems of record from Salesforce, Microsoft Dynamics. And in the future, we'll have our own data layer, which kind of remembers the context of the caller. If they called back in the last 48 hours. There's a history of conversation that we need to take into account. And using that in a way to provide a very tailored experience back to the caller becomes very unique. And this is what customers expect. Even as a consumer, I would feel happy if I'm speaking to an agent that knows my history so that I don't have to repeat myself, or I can continue from where I left off. So all of that, needs to happen within, I would say between 500 and 1,000 milliseconds of every turn. We need to be, especially on voice conversations, that is the latency through which the virtual agent has to learn and respond to a customer. And we have a number of connectors, but we also support a lot of custom scripting within the product. So we have a lot of choices. It's a very open platform. And for customers, they can also rely on, people like Joe who can come in and hook it up and make it even more customizable and more personable for customers. Yeah, that— let's go back to Joe. Joe, you talk about the importance of picking the right use case to get started with, right? So once you help a customer walk through that and understand the right use case, typically how long is it before the customer gets it up and running and then it actually starts demonstrating value and a return on that investment?
Joe Rittenhouse
>> Yeah, with the low-hanging fruit methodology, that's why we start there, because it's easy to get it in place and to start capturing about the art of the possible, right? And so what that also does is it just starts to get the wheels in motion for everybody dreaming about the art of the possible, which can also be dangerous, but it just really starts to grab momentum. And so on a low-hanging fruit analogy of after-hours messaging and containment, it's going to continue to evolve, but you're just gonna put basic containment out there of what can we do? What do we think they're asking for and how can we do it? We're gonna roughly start to finish, that's 30 days from start to finish where you're gonna start seeing containment, start seeing data, and start seeing results. There was one analysis that we did for a physical rehabilitation facility and all their physical therapists were answering their phones. And so we asked what happens after hours? And they're like, nothing. And what are people calling for? And they're like, well, they just got out of the ER at 9 o'clock at night. They broke their ankle and they were told to call us. And if they don't get us first thing in the morning, they're just gonna call the people next to us and book with them. And just putting that containment on that front end resulted in millions of dollars in revenue. So you just don't know what you're gonna find, but you just start small. But to start small, roughly about 30 days to launch. And then from there it gets into a conversation of What are we going to prioritize and what's the most important and how can we leverage this tool more? And that's usually leading to data integrations and all that stuff that Ram was talking about.
Ram Rajagopalan
>> Ah, yeah.
Joe Rittenhouse
>> So, Joe, I liked your example of the containment.
Ram Rajagopalan
>> I'm thinking about as organizations are doing this and as you were talking about how do we get to value?
Joe Rittenhouse
>> Are you seeing with AI the metrics are changing?
Ram Rajagopalan
>> Is it resolution rate, is it handle time, customer sat, right, CSAT, is it agent productivity, is it cost per interaction? For the organizations that you've worked with that maybe are a little bit more mature than others, are you seeing a trend in the metrics? Are they changing in what they're looking at to define value of the solution?
Joe Rittenhouse
>> Yeah, the easy button answer is all of the above, but the organizations that are a little bit further along more mature are really making some good strides. And a big part of where some of the early adoption started was just overconsumption and not understanding pricing and volumes and understanding that there are consumptions, like there's outcome-based consumptions, there's metered consumptions, there's conversation consumptions, there's different ways that you have billing. And so understanding what are those results, to Ram's point of outcome-based rates, like what defines a successful outcome. you have to be extremely detailed of what you're contracting for. And so what we've learned from those mature— more mature organizations as they continue to progress is really understanding their volume and understanding their pricing models. Because a lot of people through the agentic projects that have ballooned up, they just got surprise costing of like, yeah, it's great, yes, we have call containment. Yes, we're driving revenue, but oh my God, my bottom line has gone up so much. And so it's because there wasn't a pragmatic approach to understand what is the volume going to be and what is— how is the pricing outcome going to be related to. So the more mature organizations are really focused on one, we know we can execute these things and we know we're going to get these returns, but let's be extra critical of what we know our ongoing costs are going to be.
Ram Rajagopalan
>> Got it. Great. And I'm sure that's something you're helping organizations with, right?
Joe Rittenhouse
>> Yeah, exactly. That's where we come in to just kind of sit down and help kind of break down the puzzle pieces. The product's going to work. What are we going to do and how are you going to accomplish it and how are you going to budget for it? Because the reality is it is going to grow.
Ram Rajagopalan
>> Yeah. Yeah. But if I may add, one of the things we are seeing, and I think this is becoming more and more mainstream, is definitely this is one of those products where We are extremely data-driven, right? You have the call volume, you have the handle time, you have the containment rate, but we are going further and going beyond. Some customers are happy with that, but some customers wanna go beyond that. And when you go beyond that, we look at resolution and resolution is measured in multiple ways, right? Every customer has a definition of a resolution, right? And it varies from, industry to industry, vertical to vertical, customer to customer. So you want to have a platform that kind of scales with that and also works out of the box. So we have what we call implied resolution and explicit resolution. Explicit resolution is at the end of the call, we have a 2-question survey. Hey, did we solve the call? How do you rate it from 1 to 5? So you get a very, very objective score and a yes or no answer through which you know whether you served the customer or not. This is one way, and this kind of tells you, but not everybody's gonna stay at the end of the call to give you a survey response. So that's one signal. But we complement that with what we call implied resolution, which we're using an LLM to evaluate the outcome of a call. So a different LLM to evaluate the outcome of the call, wherein you can, we have some standard signals wherein we don't wanna count customers being frustrated and dropping off as a call resolution. It is certainly contained, but we didn't resolve the customer and we didn't answer the question from the customer. And sometimes we provide an answer and the customers just might get the answer and they drop off. So we have built a composite metric which kind of takes into account different behaviors, patterns, and at the same time we also allow customers to customize and define what are the key signals that they want to get from a call and allow us to measure that automatically for every call. So you have a very thorough way of measuring not just containment but also resolution. Then you combine this with CSAT and sentiment score. You have a very deep level understanding of how the virtual agents are performing in the contact center. Okay. No, I think that's great. And it actually leads a little bit into my next question I wanted to ask you, right?
Joe Rittenhouse
>> As these agents are taking on more complex tasks, interacting with all these different systems, right?
Ram Rajagopalan
>> You're coming up with different metrics for them to track, but organizations also want to have confidence that everything's performing correctly across all these different things. So how are you helping also customers build, test, and continuously improve the agents before and after they're in production? Yeah, that's another excellent question, and I think Joe touched upon it. Because this is not once you deploy once and it's done. So if you think even about a year ago, everybody were rushing into how can I build an agent that is voice agent or a chat agent and deploy it very quickly. The testing was manual calls or manual engagement. You do, you know, your team maybe makes a few calls, see whether the agents are performing more or less the way you want it. And then you go into production. But where Zoom is going and where we see the future is, we want agents to do two things: be able to solve complex problems and be able to build these agents and test them programmatically and build the confidence. What I mean by that is we want to go away from just making manual testing calls to programmatically using LLMs to evaluate LLMs, for example. So we have a voice agent that makes programmatic calls based on the goals and the criteria that you define and the outcomes that you expect. And then we run that with a simulated number of calls against the virtual agent that you have built. And then we have a scorecard and a metric that tells you how the agent is performing. So you can really stress test the agent, and you can stress test it under different conditions, accents, background sounds, et cetera, and then see how the agent is performing. And then we also have recommendations on if there are gaps in knowledge, for example, or gaps in the way certain tools are being called. We identify those gaps and recommend what kind of remedies can be put in place in terms of prompting that can be adjusted into the agent guidance. So this is something that we've championed and we are putting it out there for customers to test. And this is coupled with the native A/B testing that is already available in the product. So you can really, as you evolve from one version of an agent to another version with newer models, you can test it along with our built-in A/B testing where you can split the traffic and test the outcomes of the agent and then decide whether you're ready to upgrade to the newer model or go to a newer version and do it in a very methodical, data-driven way. Got it.
Joe Rittenhouse
>> Yeah, and, Joe, we—
Ram Rajagopalan
>> I want to wrap this up with, some advice to users. So there's a very fine line between AI success and AI failure.
Joe Rittenhouse
>> I think everybody's got great intentions, and so, you've done a lot of implementations.
Ram Rajagopalan
>> So from your experience, what separates the companies that deploy it and generate meaningful value from those that start the projects and stall or fail.
Joe Rittenhouse
>> Yeah, I feel like I'm kind of a broken record on that, but it's orchestration. But I'll go back to another layer there of where we're seeing successful organizations is there's, and in large part when this AI stuff came out, there was a fear that it was going to replace all these jobs and we haven't seen that. We're enhancing the response we're giving to customers or enhancing the ability to drive sales. We haven't seen replacement of jobs, but what we have seen is a pretty significant creation of jobs in project management. And it's project management within the organizations that report to the C-suite that manage their AI infrastructure. And these PMs within these organizations that are really executing are the orchestration level between the various business units and understanding requirements and prioritizing and understanding, getting back to the AI committee of what projects are we going to take? And then the PM is responsible for making sure that the appropriate parties from the organization attend the meetings that are required to execute these projects. Because that's usually where most projects fail, is there's a complete misunderstanding of the commitment of time that your business is going to have to put in here, to Ram's point, of what happens from start to finish in implementation. And there's multiple departments on almost every implementation with different knowledge sets, and all that has to be choreographed. And so how do you coordinate that orchestration within the business to effectively affect change? And it's measured. And so the organizations that we're seeing that are really starting to thrive have a PM-based model now that reports to the C-suite that is the choreographer of the business. And they own the projects. And that's a significant step that wasn't there before. You always had PM potentially in IT and larger enterprise, but now we're seeing it in the mid and the major market too. And that's the difference of success that we're seeing. It's execution. These tools work. You just can't boil the ocean. we use the analogy of eat the pizza piece by piece. You don't fold it in half and scarf it down typically. But it's one bite at a time. Start small and it'll grow.
Ram Rajagopalan
>> Yeah, and then Ram, to wrap up, let's pick up on the thread that Joe had about the humans still being important.
Joe Rittenhouse
>> And so companies are gonna have to manage their agentic deployments as managing the relationships with the human agents.
Ram Rajagopalan
>> So when you look ahead, how do you expect agentic AI to change that relationship between human and virtual agents and give us some advice on how companies should think about managing them. Yeah, definitely. I think we have to think through this in two ways, right? Like what Joe said, this, when we, you know, with virtual agents, you have an opportunity to serve more customers who are calling in or engaging with your brand, be it online or on the call? What we are really seeing here is the transition between virtual agents and human agents. So when we transition or when the teams that are building these agents, whether it's building agents for the virtual agents or for human agents, having that common layer that kind of where you're dipping into the same knowledge source, you're using similar tools that are available between human agents and virtual agents, you basically reduce the burden on the IT administrators who are managing these agents that are being deployed for different stages in the CX flow. But more importantly, when the call the engagement is transferred between a virtual agent and a human agent, you wanna transfer the full context, the call, the reason why they're calling, the context of collected variables that you need to pass on to a human being so that that human being who's answering or helping that customer can get on with the job and get it done without having to repeat themselves or look at 10 different places of record just to answer a simple question. So that relationship is critical and this is a two-sided story where you have a customer-facing experience and an admin-facing experience. So as customers think about deploying Agentic AI within CX, they have to think about one system where they can have one place to manage the entire AI for the CX and with a common infrastructure, common responses from language models and a common set of tools that are deployed between virtual agents and human agents. Well, guys, hey, I really appreciate this. This has been a fantastic conversation.
Joe Rittenhouse
>> So thank you, Ram, Joe, for joining us.
Ram Rajagopalan
>> Thank you, Bob.
Joe Rittenhouse
>> And yeah, thanks for having us. This was fun. Thank you. Absolutely. And the key message is that Contact center AI doesn't have to begin with a massive transformation, right?
Ram Rajagopalan
>> Organizations can start with a focused use case, establish measurable goals, and expand as they gain experience and confidence, as Joe repeated multiple times in this call.
Joe Rittenhouse
>> Yeah, no, but at the same time though, it's important to understand that long-term success does depend on more than just automation.
Ram Rajagopalan
>> Enterprises need connected data, reliable knowledge, disciplined testing and architecture that could support an increasing amount of different customer journeys, as well as that combination of human and virtual agents. Yeah, absolutely. it's clear AI is raising expectations for both customer experience and operational efficiency, right? And the organizations that can create the greatest value will be those who move with purpose, really start pragmatically measure the results, and continuously improve. And you
Joe Rittenhouse
>> hear—heard today about how the tools and the solutions, the platforms enable you to do that.
Ram Rajagopalan
>> So again, thank you all for joining, and thanks to everyone watching this CX Summit, and stay tuned for more.
Joe Rittenhouse, Converged Technology Professionals & Ram Rajagopalan, Zoom
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Ram Rajagopalan
>> AI has been part of the contact center conversation for years. Now, initially handling repetitive tasks such as call routing, basic self-service, and simple customer inquiries. Today, however, AI is moving into more complex workflows that can improve agent performance, automate multi-step processes, and reshape the customer journey. The opportunity is significant, but success requires more than just deploying another tool. Organizations need the right use case, trustworthy data, a pragmatic implementation plan, and clear metrics for determining whether AI is delivering value. Welcome to the CX Summit. I'm Bob Laliberte, Principal Analyst with theCUBE Research.
Joe Rittenhouse
>> I'm joined by my co-host, Zeus Kerravala, Principal Analyst and Founder from ZK Research.
Ram Rajagopalan
>> Welcome, Zeus. Hey, thanks, Bob. Great to be back here. Absolutely. And to help us explore how enterprises can move from this experimentation to measurable results, we're joined by Ram Rajagopalan, Head of Product AI Zoom CX at Zoom, and Joe Rittenhouse, Co-CEO, Converged Technology Professionals, a Zoom partner with extensive experience implementing contact center and AI solutions.
Joe Rittenhouse
>> So Ram, Joe, Welcome to the summit.
Ram Rajagopalan
>> Thanks, Bob. And Zeus, thank you for hosting me. It's a pleasure to be here. And Joe, good to see you again.
Joe Rittenhouse
>> Good to see you guys too, and thanks for having us. All right, so now let's have some fun.
Ram Rajagopalan
>> Ram, I thought I'd start with you and begin with the evolution of AI and customer experience.
Joe Rittenhouse
>> So a lot of organizations started with relatively simple capabilities, as I mentioned, right?
Ram Rajagopalan
>> Routing calls, answering their frequently asked questions and things like that, any kind of repetitive interactions.
Joe Rittenhouse
>> But how are customer requirements evolving today?
Ram Rajagopalan
>> And are there more sophisticated use cases that are becoming practical today? Yeah, that's a great question, Bob. Yeah, the industry is evolving rapidly with the advent of agentic platforms, especially in voice agents these days. Traditionally, many customers, even about a year, 18 months ago, many customers focused on purely measuring containment. But where I see conversations these days are going from not just looking at containment, but end-to-end resolution of consumer inquiries. So this is what we are calling within Zoom Conversation to Completion, where we are not just looking at answering the basic inquiries, deflecting the call from going to a human agent, but actually completing the task that the consumers or users are calling in for support. And we are also seeing use cases evolve from basic inbound calls to also going towards more automated outbound calls, outbound dialing, navigating complex IVR menus, things like that. And also we recently at Zoom this summer, we even introduced a new packaging called outcome-based pricing, which not only measures the call volumes or consumption, but also measures the outcomes that we are producing for customers through virtual agents. And then customers have an option to choose that if they want to just pay for the performance. So we see use cases evolving, both inbound as well as outbound.
Joe Rittenhouse
>> Hey Ram, the entire industry is moving to this vision of the rise of agentic AI impacting CX.
Ram Rajagopalan
>> And I do think the conversations to completion narrative that Zoom has been working with is unique, right? And so can you maybe give an example of how that might work in the contact center, particularly for a more complex type of interaction? Maybe you need to bring in a human agent as well. Yeah, I can tell you a couple of examples. We have one customer of Zoom who's using virtual agents today to perform political surveys. within a county. So this is virtual agents going through a list of residents within a county or a state and then qualifying by calling them up, by making an outbound call, asking some qualifying questions, and then taking them through a very complex survey of questions where each question depends on your response to the previous question. So this is not just qualifying the caller, but also going through complex survey questions, but writing all the results into a platform from which you can then create a complete comprehensive report around what were the interests. For this case, for example, they were doing a political survey. Where do they lean? Are they leaning left? Are they leaning right, et cetera? What are the key topics, key issues that they are— that the population cares about? And getting a full end-to-end report at the end of the day is a fairly complex endeavor. sometimes we call people, go to voicemail. So the virtual agent needs to be intelligent enough to detect when a voicemail is encountered and leave an appropriate message in the voicemail, in the voice message. So all of this is one example of how we are navigating complex use cases. And we are producing not just a completion record, but we are giving a full report of what was the outcome of those calls back to the customers. So this is where I think we are seeing use cases are evolving, and in healthcare too, we are seeing another place where we are seeing it evolving, where we are making outbound calls to remind patients of upcoming appointments, prescription refills, and even qualifying, connecting between healthcare providers and insurance companies where we need to navigate complex IVR menus. All these use cases are becoming more and more common these days. And Joe, I wanna bring you into the conversation here. And one of the reasons I like talking to partners is because you're actually measured on whether you're getting this stuff to work for customers, right? And we've talked about this a lot where, the executives of companies today are under tremendous pressure to deploy AI and move quickly, but that rubs up against the concerns of a contact center leader that might be concerned about disrupting customer service or perhaps just trying to do too much at once. And so when you work with customers, how do you help them identify the best first use cases for AI to help them get started?
Joe Rittenhouse
>> Yeah, and it also disrupts the IT team too, right? It's the disruption of the business, right? And so there's an anxiety, there's a push, but usually where we try to start is where's the low-hanging fruit? And it's in areas that you're usually not even typically thinking about, and it could be, and we see this from all different verticals, from healthcare to banking to professional sports to manufacturing. Just a simple question of what do you do after hours? What do you do after hours today? Well, we're usually staffed, we follow the sun from the East Coast to the West Coast, but then after hours we just have a general mailbox. How many calls do you get? What type of calls do you get? What's happening after hours? And the answer is typically, I don't know. Well, we can see that volume, we can project that volume. And so let's just go put a containment out there and go see what's happening. And some of the results we've seen, especially in the medical industry of just being able to book appointments or being able to have a follow up with a nurse or something along those lines, those are instant savings and instant revenue contributions. But that's not what we're looking for. We're just looking for a place that you can start simple to get these tools in place, have a pragmatic approach, get everybody comfortable. And get the organization used to having a choreography of how do we solve these complex business needs. Because it's not just the tools that are answering the surface questions. To Ram's point of the integration of maybe we want to write this to a ticket in Salesforce the following day. So when an agent comes in, that's the first thing that they do. And the methodology and the coaching that we have during that pragmatic approach is to say, as we get this in, it's like getting a new pair of shoes. You feel like you can run faster and you're going to want to do this and you want to do that. And we can do all those things, but we have to prioritize and we have to have a plan. And as long as we have a plan and we're working within the business, we can execute. We can start to take on those larger, more complex projects. But this is an evolution and it continues to go post-deployment and it is not something that you're just going to set it and forget it. And so you've got to get used to that mentality for the business too. There's change and this orchestration touches every level of the business. It is not just IT.
Ram Rajagopalan
>> Hey, Joe, because you're involved in the deployments and so forth, I'm wondering if you could share some knowledge with organizations out there that are thinking about adding AI and trying to do this. I'm wondering if you could talk about some of the common mistakes that you see organizations making, especially when they're trying to boil the ocean and they want to do everything at once and how to overcome that.
Joe Rittenhouse
>> Yeah, and I think the first question that we ask and where we're seeing some clients have some success is, Do you have an AI committee? And the answer is about 50/50 right now. And then the 50% that's saying, yes, we have an AI committee, it's like we're starting to put it together, but the fundamentals are there and they're starting to put it together. The groups that don't have an AI committee, it's for us to sit down and be like, how are you managing your AI strategy? Like, is it top-down? We just have to do it, we have to do it. Or is there an orchestration? And the reality is like you can't boil the ocean and there is a lot of orchestration between the various departments and what you're doing, but to present to the C-suite of like, this is the lift internally you're going to have to do. These are the requirements that your teams are going to have to participate in. They're going to have to drive this. These are the metrics we're driving. This is how we're prioritizing it. Do you guys approve of this? And then these are the deliverables that we'll give. The reality is, if we can't get that, that's not an implementation that's going to have success. And so it's more on the front end of the organization, we just have to really kind of think of how we want to handle our AI strategy? And there's an orchestration level from the C-suite to every department. And this is not just an IT project. This is not just a CX project. And there's a lot of overlap. And so you just have to have visibility of what's going on with the organization and have an orchestration, start small and go from there.
Ram Rajagopalan
>> Got it. And you come in as the voice of reason that can help organizations get through that and get down that path.
Joe Rittenhouse
>> Yeah, I think our pragmatic approach is, we're not here to sell you the product. You already want to buy the product, but how are you effectively going to do this and grow it? And it's going to evolve with you. And so it's not just the day one, hey, we're done, we're collecting revenue. This is an ongoing process and you're going to evolve with it.
Ram Rajagopalan
>> Excellent. Now that sounds great. Ram, I wanted to come back to you. When we talk about, contact center data, right? Right, really fragmented across virtual agents, human interactions, workforce management apps, right, knowledge bases, customer systems, all sorts of different places. How does your integrated CX platform help customers connect those interactions and turn the resulting data into better outcomes? Yeah, that's a great question. I think Joe touched on this a little bit. One of the things I think, as customers consider their AI solution, you have to think through the entire customer journey end to end. You have to think about the journey as they start. I call it the before, during, and after. But before, during, and after they interact with a human being. But you have to think through that entire process in terms of how, what are the kind of use cases where, a virtual agent can best serve a customer. And then when does it get elevated to a human being? And what happens after that engagement is concluded? And AI really plays a role in all of these three segments. And many times customers tend to think about, many, there are many solutions out there. Many vendors bring in very, point solutions for each one of these stages, whether it's before, during, or after. But the challenge that they will run into is the common data layer that connects all these three. You want to see the entire lifecycle of an engagement when the customer is with a virtual agent and what happens afterwards. With Zoom CX, this is one area where I think we tend to have a unified layer where a customer can see the performance of a virtual agent and see it side by side with a human agent. We have quality measurement tools that evaluate virtual agent and human agent side by side and tells you what was the CSAT at each stage, what was the sentiment at each stage, et cetera. So you have an automated way of evaluating both the human agent and the virtual agent. Another thing which I think Joe touched upon was when we speak to particularly large enterprises, like you said, this is an area which touches multiple subgroups within a company. Because for example, for a virtual agent, you need to provide it with context and knowledge, right? But sometimes the knowledge is maintained by a different group, and there is often a disconnect between the validity of that knowledge. Maybe the knowledge base is not up to date or is not updated, or there's conflicting information. But when you inject all of this into a virtual agent, the responsiveness of that agent can be determined by the quality of knowledge being provided. One of the examples that we ran into with a large consumer products company was the knowledge base was quite old, it was not up to date or it was not updated for a virtual agent to understand. In many cases, we were kind of escalating the call to a human being only to find out that the human beings were using their own tribal knowledge or knowledge that they gained through experience which is not documented elsewhere. But being part of the same platform allows a virtual agent to see not just its conversation, but the conversation that is handled by a human agent and learn from that conversation. We look at that and we look at whether there is a gap in the knowledge base? And then surface it back to the human administrators to see if that gap can be addressed by looking at the human transcripts and then feed that back into virtual agents so that they learn from that. So that in the future a similar question comes up, they don't have to elevate it to a human being, but they can answer it themselves. So that feedback loop is very critical. And that's one of the unique things about the connected CX platform from Zoom, where the virtual agent and human agent are always talking to each other and learning from each other. Hey Ram, there's more though to a contact center than just Zoom, right? You've got your application, there's a lot of third-party CRM systems, things like that. And so talk about how you integrate with those third parties just to make sure that there's a complete view of customer experience and companies can get away from these silos that they've had for a long time. Yeah, absolutely. So, many customers have different systems of record. It could be a ticketing system, a customer information system, and to provide a more customized experience for every time a customer of yours is calling into your contact center, we need to know who you are, what kind of previous history that you've had with the brand. And virtual agents can look into that by, we have nearly 40 out-of-the-box integrations with all kinds of systems of record from Salesforce, Microsoft Dynamics. And in the future, we'll have our own data layer, which kind of remembers the context of the caller. If they called back in the last 48 hours. There's a history of conversation that we need to take into account. And using that in a way to provide a very tailored experience back to the caller becomes very unique. And this is what customers expect. Even as a consumer, I would feel happy if I'm speaking to an agent that knows my history so that I don't have to repeat myself, or I can continue from where I left off. So all of that, needs to happen within, I would say between 500 and 1,000 milliseconds of every turn. We need to be, especially on voice conversations, that is the latency through which the virtual agent has to learn and respond to a customer. And we have a number of connectors, but we also support a lot of custom scripting within the product. So we have a lot of choices. It's a very open platform. And for customers, they can also rely on, people like Joe who can come in and hook it up and make it even more customizable and more personable for customers. Yeah, that— let's go back to Joe. Joe, you talk about the importance of picking the right use case to get started with, right? So once you help a customer walk through that and understand the right use case, typically how long is it before the customer gets it up and running and then it actually starts demonstrating value and a return on that investment?
Joe Rittenhouse
>> Yeah, with the low-hanging fruit methodology, that's why we start there, because it's easy to get it in place and to start capturing about the art of the possible, right? And so what that also does is it just starts to get the wheels in motion for everybody dreaming about the art of the possible, which can also be dangerous, but it just really starts to grab momentum. And so on a low-hanging fruit analogy of after-hours messaging and containment, it's going to continue to evolve, but you're just gonna put basic containment out there of what can we do? What do we think they're asking for and how can we do it? We're gonna roughly start to finish, that's 30 days from start to finish where you're gonna start seeing containment, start seeing data, and start seeing results. There was one analysis that we did for a physical rehabilitation facility and all their physical therapists were answering their phones. And so we asked what happens after hours? And they're like, nothing. And what are people calling for? And they're like, well, they just got out of the ER at 9 o'clock at night. They broke their ankle and they were told to call us. And if they don't get us first thing in the morning, they're just gonna call the people next to us and book with them. And just putting that containment on that front end resulted in millions of dollars in revenue. So you just don't know what you're gonna find, but you just start small. But to start small, roughly about 30 days to launch. And then from there it gets into a conversation of What are we going to prioritize and what's the most important and how can we leverage this tool more? And that's usually leading to data integrations and all that stuff that Ram was talking about.
Ram Rajagopalan
>> Ah, yeah.
Joe Rittenhouse
>> So, Joe, I liked your example of the containment.
Ram Rajagopalan
>> I'm thinking about as organizations are doing this and as you were talking about how do we get to value?
Joe Rittenhouse
>> Are you seeing with AI the metrics are changing?
Ram Rajagopalan
>> Is it resolution rate, is it handle time, customer sat, right, CSAT, is it agent productivity, is it cost per interaction? For the organizations that you've worked with that maybe are a little bit more mature than others, are you seeing a trend in the metrics? Are they changing in what they're looking at to define value of the solution?
Joe Rittenhouse
>> Yeah, the easy button answer is all of the above, but the organizations that are a little bit further along more mature are really making some good strides. And a big part of where some of the early adoption started was just overconsumption and not understanding pricing and volumes and understanding that there are consumptions, like there's outcome-based consumptions, there's metered consumptions, there's conversation consumptions, there's different ways that you have billing. And so understanding what are those results, to Ram's point of outcome-based rates, like what defines a successful outcome. you have to be extremely detailed of what you're contracting for. And so what we've learned from those mature— more mature organizations as they continue to progress is really understanding their volume and understanding their pricing models. Because a lot of people through the agentic projects that have ballooned up, they just got surprise costing of like, yeah, it's great, yes, we have call containment. Yes, we're driving revenue, but oh my God, my bottom line has gone up so much. And so it's because there wasn't a pragmatic approach to understand what is the volume going to be and what is— how is the pricing outcome going to be related to. So the more mature organizations are really focused on one, we know we can execute these things and we know we're going to get these returns, but let's be extra critical of what we know our ongoing costs are going to be.
Ram Rajagopalan
>> Got it. Great. And I'm sure that's something you're helping organizations with, right?
Joe Rittenhouse
>> Yeah, exactly. That's where we come in to just kind of sit down and help kind of break down the puzzle pieces. The product's going to work. What are we going to do and how are you going to accomplish it and how are you going to budget for it? Because the reality is it is going to grow.
Ram Rajagopalan
>> Yeah. Yeah. But if I may add, one of the things we are seeing, and I think this is becoming more and more mainstream, is definitely this is one of those products where We are extremely data-driven, right? You have the call volume, you have the handle time, you have the containment rate, but we are going further and going beyond. Some customers are happy with that, but some customers wanna go beyond that. And when you go beyond that, we look at resolution and resolution is measured in multiple ways, right? Every customer has a definition of a resolution, right? And it varies from, industry to industry, vertical to vertical, customer to customer. So you want to have a platform that kind of scales with that and also works out of the box. So we have what we call implied resolution and explicit resolution. Explicit resolution is at the end of the call, we have a 2-question survey. Hey, did we solve the call? How do you rate it from 1 to 5? So you get a very, very objective score and a yes or no answer through which you know whether you served the customer or not. This is one way, and this kind of tells you, but not everybody's gonna stay at the end of the call to give you a survey response. So that's one signal. But we complement that with what we call implied resolution, which we're using an LLM to evaluate the outcome of a call. So a different LLM to evaluate the outcome of the call, wherein you can, we have some standard signals wherein we don't wanna count customers being frustrated and dropping off as a call resolution. It is certainly contained, but we didn't resolve the customer and we didn't answer the question from the customer. And sometimes we provide an answer and the customers just might get the answer and they drop off. So we have built a composite metric which kind of takes into account different behaviors, patterns, and at the same time we also allow customers to customize and define what are the key signals that they want to get from a call and allow us to measure that automatically for every call. So you have a very thorough way of measuring not just containment but also resolution. Then you combine this with CSAT and sentiment score. You have a very deep level understanding of how the virtual agents are performing in the contact center. Okay. No, I think that's great. And it actually leads a little bit into my next question I wanted to ask you, right?
Joe Rittenhouse
>> As these agents are taking on more complex tasks, interacting with all these different systems, right?
Ram Rajagopalan
>> You're coming up with different metrics for them to track, but organizations also want to have confidence that everything's performing correctly across all these different things. So how are you helping also customers build, test, and continuously improve the agents before and after they're in production? Yeah, that's another excellent question, and I think Joe touched upon it. Because this is not once you deploy once and it's done. So if you think even about a year ago, everybody were rushing into how can I build an agent that is voice agent or a chat agent and deploy it very quickly. The testing was manual calls or manual engagement. You do, you know, your team maybe makes a few calls, see whether the agents are performing more or less the way you want it. And then you go into production. But where Zoom is going and where we see the future is, we want agents to do two things: be able to solve complex problems and be able to build these agents and test them programmatically and build the confidence. What I mean by that is we want to go away from just making manual testing calls to programmatically using LLMs to evaluate LLMs, for example. So we have a voice agent that makes programmatic calls based on the goals and the criteria that you define and the outcomes that you expect. And then we run that with a simulated number of calls against the virtual agent that you have built. And then we have a scorecard and a metric that tells you how the agent is performing. So you can really stress test the agent, and you can stress test it under different conditions, accents, background sounds, et cetera, and then see how the agent is performing. And then we also have recommendations on if there are gaps in knowledge, for example, or gaps in the way certain tools are being called. We identify those gaps and recommend what kind of remedies can be put in place in terms of prompting that can be adjusted into the agent guidance. So this is something that we've championed and we are putting it out there for customers to test. And this is coupled with the native A/B testing that is already available in the product. So you can really, as you evolve from one version of an agent to another version with newer models, you can test it along with our built-in A/B testing where you can split the traffic and test the outcomes of the agent and then decide whether you're ready to upgrade to the newer model or go to a newer version and do it in a very methodical, data-driven way. Got it.
Joe Rittenhouse
>> Yeah, and, Joe, we—
Ram Rajagopalan
>> I want to wrap this up with, some advice to users. So there's a very fine line between AI success and AI failure.
Joe Rittenhouse
>> I think everybody's got great intentions, and so, you've done a lot of implementations.
Ram Rajagopalan
>> So from your experience, what separates the companies that deploy it and generate meaningful value from those that start the projects and stall or fail.
Joe Rittenhouse
>> Yeah, I feel like I'm kind of a broken record on that, but it's orchestration. But I'll go back to another layer there of where we're seeing successful organizations is there's, and in large part when this AI stuff came out, there was a fear that it was going to replace all these jobs and we haven't seen that. We're enhancing the response we're giving to customers or enhancing the ability to drive sales. We haven't seen replacement of jobs, but what we have seen is a pretty significant creation of jobs in project management. And it's project management within the organizations that report to the C-suite that manage their AI infrastructure. And these PMs within these organizations that are really executing are the orchestration level between the various business units and understanding requirements and prioritizing and understanding, getting back to the AI committee of what projects are we going to take? And then the PM is responsible for making sure that the appropriate parties from the organization attend the meetings that are required to execute these projects. Because that's usually where most projects fail, is there's a complete misunderstanding of the commitment of time that your business is going to have to put in here, to Ram's point, of what happens from start to finish in implementation. And there's multiple departments on almost every implementation with different knowledge sets, and all that has to be choreographed. And so how do you coordinate that orchestration within the business to effectively affect change? And it's measured. And so the organizations that we're seeing that are really starting to thrive have a PM-based model now that reports to the C-suite that is the choreographer of the business. And they own the projects. And that's a significant step that wasn't there before. You always had PM potentially in IT and larger enterprise, but now we're seeing it in the mid and the major market too. And that's the difference of success that we're seeing. It's execution. These tools work. You just can't boil the ocean. we use the analogy of eat the pizza piece by piece. You don't fold it in half and scarf it down typically. But it's one bite at a time. Start small and it'll grow.
Ram Rajagopalan
>> Yeah, and then Ram, to wrap up, let's pick up on the thread that Joe had about the humans still being important.
Joe Rittenhouse
>> And so companies are gonna have to manage their agentic deployments as managing the relationships with the human agents.
Ram Rajagopalan
>> So when you look ahead, how do you expect agentic AI to change that relationship between human and virtual agents and give us some advice on how companies should think about managing them. Yeah, definitely. I think we have to think through this in two ways, right? Like what Joe said, this, when we, you know, with virtual agents, you have an opportunity to serve more customers who are calling in or engaging with your brand, be it online or on the call? What we are really seeing here is the transition between virtual agents and human agents. So when we transition or when the teams that are building these agents, whether it's building agents for the virtual agents or for human agents, having that common layer that kind of where you're dipping into the same knowledge source, you're using similar tools that are available between human agents and virtual agents, you basically reduce the burden on the IT administrators who are managing these agents that are being deployed for different stages in the CX flow. But more importantly, when the call the engagement is transferred between a virtual agent and a human agent, you wanna transfer the full context, the call, the reason why they're calling, the context of collected variables that you need to pass on to a human being so that that human being who's answering or helping that customer can get on with the job and get it done without having to repeat themselves or look at 10 different places of record just to answer a simple question. So that relationship is critical and this is a two-sided story where you have a customer-facing experience and an admin-facing experience. So as customers think about deploying Agentic AI within CX, they have to think about one system where they can have one place to manage the entire AI for the CX and with a common infrastructure, common responses from language models and a common set of tools that are deployed between virtual agents and human agents. Well, guys, hey, I really appreciate this. This has been a fantastic conversation.
Joe Rittenhouse
>> So thank you, Ram, Joe, for joining us.
Ram Rajagopalan
>> Thank you, Bob.
Joe Rittenhouse
>> And yeah, thanks for having us. This was fun. Thank you. Absolutely. And the key message is that Contact center AI doesn't have to begin with a massive transformation, right?
Ram Rajagopalan
>> Organizations can start with a focused use case, establish measurable goals, and expand as they gain experience and confidence, as Joe repeated multiple times in this call.
Joe Rittenhouse
>> Yeah, no, but at the same time though, it's important to understand that long-term success does depend on more than just automation.
Ram Rajagopalan
>> Enterprises need connected data, reliable knowledge, disciplined testing and architecture that could support an increasing amount of different customer journeys, as well as that combination of human and virtual agents. Yeah, absolutely. it's clear AI is raising expectations for both customer experience and operational efficiency, right? And the organizations that can create the greatest value will be those who move with purpose, really start pragmatically measure the results, and continuously improve. And you
Joe Rittenhouse
>> hear—heard today about how the tools and the solutions, the platforms enable you to do that.
Ram Rajagopalan
>> So again, thank you all for joining, and thanks to everyone watching this CX Summit, and stay tuned for more.
