Pedro Andrade of Talkdesk, vice president of artificial intelligence AI, joins theCUBE Research hosts Bob Laliberte and Zeus Kerravala at Contact Center Summit 2026 to discuss customer experience automation CXA and multi-agent AI in contact centers. Andrade describes customer experience automation CXA as an operating model that coordinates AI agents, humans, enterprise systems and knowledge. They address orchestration, prebuilt vertical integrations, differences between CXA and contact center as a service CCaaS, and workforce management.
Key takeaways emphasize orchestration and governance as primary barriers. Andrade cites compliance, security, disconnected systems and legacy infrastructure as top concerns. They state CXA can run atop existing contact centers and accelerate return on investment in weeks through verticalized prebuilt integrations. Analysts Bob Laliberte and Zeus Kerravala observe that CXA leaders report markedly higher Net Promoter Score NPS and improved cost per contact metrics.
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Pedro Andrade, Talkdesk
Pedro Andrade of Talkdesk, vice president of artificial intelligence AI, joins theCUBE Research hosts Bob Laliberte and Zeus Kerravala at Contact Center Summit 2026 to discuss customer experience automation CXA and multi-agent AI in contact centers. Andrade describes customer experience automation CXA as an operating model that coordinates AI agents, humans, enterprise systems and knowledge. They address orchestration, prebuilt vertical integrations, differences between CXA and contact center as a service CCaaS, and workforce management.
Key takeaways emphasize orchestration and governance as primary barriers. Andrade cites compliance, security, disconnected systems and legacy infrastructure as top concerns. They state CXA can run atop existing contact centers and accelerate return on investment in weeks through verticalized prebuilt integrations. Analysts Bob Laliberte and Zeus Kerravala observe that CXA leaders report markedly higher Net Promoter Score NPS and improved cost per contact metrics.
>> AI is rapidly becoming part of the customer experience environment, but many organizations are still using it to automate isolated interactions or assist individual agents. Now, Talkdesk is taking a broader approach through customer experience automation, or CXA, which uses multiple AI agents, enterprise data, and cross-system orchestration to resolve customer needs from beginning to end. Hey, welcome everyone to the CX Summit. I'm Bob Laliberte, Principal Analyst, joined by Zeus Kerravala, Principal Analyst and Founder of ZK Research. Welcome, Zeus Kerravala.
Zeus Kerravala
>> Bob, thanks. This event's been great.
Bob Laliberte
>> So yeah, looking forward to another great session here. And joining us to explain Talkdesk strategy and its latest innovations and how organizations can really translate AI investments into measurable business results is Pedro Andrade, VP of AI at Talkdesk. Pedro, welcome to the CX Summit.
Pedro Andrade
>> Hi, thank you so much, Bob. It's a great pleasure being here with you.
Bob Laliberte
>> Absolutely. So this is going to be a fun session, and I wanted to kick things off, and obviously I mentioned this in the opening, but you've been positioning yourselves as this customer experience automation company rather than, simply just a CCaaS provider. What does CXA mean and how is it different from the AI capabilities enterprises may already have in their contact centers?
Pedro Andrade
>> Yeah, well, it's a great question to start. So, Customer Experience Automation, CXA, is what we defined as an operating model. Okay, so it's not a category of technology. It's the system that coordinates a hybrid workforce of AI and human employees. Then connecting the systems, connecting knowledge, and also connecting the workflows. So, when the customer calls in, reaches out to a brand, they get what they need resolved end to end, independent of how many systems you need to get into and put together to resolve that issue. So for customers, this is not acquiring a technology just alone. It's an operating model shift. Okay. It's not a tool purchase. Majority of the enterprises already have some sort of technology about either generative AI or scripted AI. And so Talkdesk launched recently a survey on that with great interesting data. So around 74% of the enterprises already have some sort of generative AI. That's okay. That means that gets the work started. You can have some answers to your questions. It can eventually draft responses for agents. It can follow a script. But the problem is that that situation alone without having this operating model is that it stops the moment where you need a judgment or an action that needs to cross multiple systems. Okay, so basically then you need to have a human to take the data from place A to place B so things continue working. So that's the change. CXA is a layer that keeps that work moving across different systems. And so you get your work done across systems, knowledge, humans, and machines.
Zeus Kerravala
>> Yeah, thanks. Pedro, thanks for the update on what CXA is, and it's a great pivot for Talkdesk. Now Talkdesk is though by and large one of the leaders in the contact center industry, right? And you scored very well on all the different rankings. And so talk about the relationship of CXA to the contact center platform? Is it a replacement for it, does it sit on top? How does Talkdesk think about the relationship with the two?
Pedro Andrade
>> All right. So different from other situations or other companies, other providers that sell technology infused in the contact center. So we hear that term very, very often about infused AI. Actually, Talkdesk, when we decided many years ago that we want to move from a human picking up an interaction, a phone call or an SMS or an email, and try to do that manually, we understood that other contact centers also have the same problem. And so instead of building a platform fully infused in the contact center. We built CXA as a platform, as a different offer. So Talkdesk has two offers, CXA and CCaaS, and the CXA platform can be offered on top of other contact centers beyond Talkdesk Contact Center. Because the reality, Zeus Kerravala, is that the majority of the seats are still on-prem. And those customers that are still on-prem for multiple reasons, contract reasons, because moving the contact center to the cloud is still a big project, they can't afford to miss the opportunity of AI. So CXA works as well as a standalone on top of other contact centers to help relieve that pressure for the customers that are still on-prem that cannot get the latest and the greatest of AI on their on-prem system. But to be able to, again, to use CXA to relieve that pain and start gaining those productivity gains on top of an existing contact center.
Zeus Kerravala
>> Okay, thanks for that explanation. And I think the ability to work with the legacy ones actually, it really helps customers modernize now. You mentioned some of the research that Talkdesk has done. We actually looked through it in preparing for this and we saw an interesting data point that 98% of companies have deployed AI somewhere, and that's probably going to be 100% pretty soon, somewhere on the customer journey, but only 15% combine agentic AI with cross-departmental orchestration. So why do you think that gap is so big right now?
Pedro Andrade
>> Well, the reality is that adoption is easy. The orchestration is the hardest part. So adoption is easy. You can get to a provider, buy a chatbot, put it to run, and it's easy. The problem that you are mentioning and the reason why only 15% use agentic cross-departmental orchestration in these journeys is specifically for two reasons. Number one is that Agentic orchestration requires AI to maintain a context across all those systems. A solution is not just get my balance, which is only one place. You are going to resolve a problem of a situation with one of your transactions that may require multiple interactions across multiple systems to get that problem solved. The majority of these steps, are steps that require an interaction with a human. Someone is going to approve something. It's a whole journey that embeds multiple systems, multiple humans involved, machines involved. And today that situation is fragmented. Those workflows are fragmented. They are not linked and connected to each other. So orchestration is really the hardest part. Currently, you know this, right? We still have companies that have information in silos. And when you ask them to solve this end-to-end, you will need to orchestrate across these multiple systems. There are those, this is the reason why this requires the connections between multiple systems it's a blocker when you reach customers that are still working with systems in isolation and then expect humans to connect the dots and to transport the data from one place to the other. And the reality is that, for example, in that report, 64% of the organizations, they are already running some sort of AI agents for specific functions, for example, billing or for example, identity, right? It may sound like it's coordination, but the reality is that this handoff between those agents still requires someone to go there and move the data from one place to the other. And I told you there are two reasons for that gap. So from the 98 to the 15, the second reason is governance. Okay, governance connects and connecting to that report. It is more related to an enterprise readiness than actually just an AI capability. And if you take a look at the 4 top reasons why people— to justify that difference of adoption is because number 1, compliance, taking 50% of all the reasons. For that adoption cross-departmental and this orchestration. Second, with 48% of responses, is about security. Disconnected systems takes third place with 45% of the responses pointing to that as the issue. And with 44% are legacy infrastructure, systems that don't talk, systems that are legacy, still on-prem. And so that was the four reasons Zeus Kerravala, to answer your question, is you need to orchestrate that. But beyond the orchestration, governance, including compliance, security, disconnected systems and silos, is being the top barriers. And that differentiates those customers that are part of the 98% that deploy something versus the ones that are able to actually harness those agents into one experience that goes cross-departmental and cross-system.
Zeus Kerravala
>> All right.
Zeus Kerravala
>> Thanks for your thoughts on that.
Bob Laliberte
>> Yeah, Pedro, I'm interested in this as well because it sounds like some organizations are a little bit more mature. Clearly, you've seen a lot of this activity, these organizations, what works, what doesn't work. How are you at Talkdesk helping customers integrate those AI agents with all those systems of records, right? Whether it be CRM or ERP or others, and helping them to get over this bottleneck, get over these challenges that they're having and accelerate that time to value.
Pedro Andrade
>> All right, so over time, Talkdesk created a gazillion sets of integrationsbecause automation, Bob, it's not new, right? We already have automation on IVR. Press 1, press 2, and you still get your balance. You still file your claim. And the fundamental piece that was required to do that level of automation is that one way or the other, some customers needed to put some of these solutions in place. Some issues, right, sometimes is that building an integration requires a whole project that takes months. With Talkdesk specifically in industries, we relieve that pain because all those integrations that you require to have an integration, let's say with Epic, right, that requires integrations with specific healthcare protocols, or with majority of the banking systems. Talkdesk offers that out of the box. You have those integrations out of the box and allows you to, with minimum configurations and setup, allows you to have these agentic systems ready to connect to those data sources with quality, reliability, with security that is fundamental. So over the years we created those connections, we created that relationship with those partners with those providers that now we are reusing, especially then on our verticals to accelerate the adoption and the time to value.
Bob Laliberte
>> Excellent. Yeah, that makes a lot of sense. And especially for those specific verticals where you've got those tight integrations to be able to accelerate that. And it leads me to another question I have for you because we know there's a lot of the CX leaders, other business leaders, they're all under a lot of pressure to show that AI is creating value. And in this space, it's about not just call deflection and maybe even headcount reduction. When you're thinking about CXA, what business outcomes should organizations expect? And are there new measurements and new metrics that they should use to measure them?
Pedro Andrade
>> Oh, absolutely, yes. And if I had $1 every time that I get this question, I could retire myself today. So Bob, the majority of the times I get this question is, is AI going to help me to reduce my headcount? People are just so, so crazy about this and nothing wrong about that. But let me share some additional data that we also got in that survey. And I think that that kind of needs to be used to change the way you need to think when you look at adopting AI. If you are just thinking about reducing headcount and do these savings, you are missing part of the story. Okay, so I'm going to give another example. So let me give some data and some examples here. In our report, we created 4 categories of different customers depending on their maturity level, or their maturity to adopt AI. The top ones are what we call CXA Leaders compared to the ones that are right before these ones, right below these ones. They are getting much more results in terms of NPS gains than the ones that we call Agentic Scalers. So we have the Agentic Scalers and on the top, the CXA Leaders in terms of NPS. The difference is crazy. It's about 4 times. So you have 5% better NPS scores for the ones that are Agentic Scalers. So they have some level of maturity. They are running agentic AI in their contact center. They are deploying it in production. But they are just not like the leaders that they are harnessing those agents to resolve end-to-end. The difference between those two is 4 times, 22% for the CXA Leaders in terms of NPS gains versus 5% for the Agentic Scalers in that KPI. So what is interesting is that the cost per contact also improves but not as dramatically as NPS, for example. So for the CXA Leaders, you get 57% improvement on costs per contact versus 48%, which is a moderate reduction in terms of your— between the Agentic Scalers versus the CXA Leaders. So that suggests that Savings alone don't tell the whole story. They understate the value that you can get with AI. So the other way then to look at that is to look at retention and revenue. And let me give you some numbers. So for example, the customers that are using CXA to run predictive modeling, they are getting way better results if they are on the top tier of this adoption. 51% better results in predictive churn modeling versus the 28% that the group of customers, the Agentic Scalers, right, that they are seeing. Also personalized recommendation. All right? 44% of the companies that are running personalized recommendation, they are seeing gains if they are part of the CXA Leaders versus 19%. So again, it's almost double of the gains if you are increasing your maturity of your AI adoption. And again, that's using it as a harnessing system that connects those agents, humans, systems, and knowledge. So the summary is that if you took a look at all the spectrum of companies that are adopting AI, it's true that only 5% can say that, oh, I have a clear way of measuring impact. But reality is that 46% of all CXA Leaders, they have the impact, they have measured, and it is a good impact across metrics that are just not cost saving. They are reduction of churn and increase their revenue through for example, personalized recommendations.
Zeus Kerravala
>> Yeah, Pedro, I'm glad you're actually focused on a lot of the revenue generating type of metrics, right? We see a lot of cost cutting and I'm curious within the customer base, what's the typical time frame for these customers to start seeing meaningful ROI? Because I think a lot of companies really aren't— they want to invest, but they're not sure of when to start, when they'll start seeing the upside.
Pedro Andrade
>> Well, that answer is less of a pattern. Right? It's going to depend a lot on, where your current pains are. You may have a solution that you can spin up and put to run in a few weeks, maybe 2 weeks. You can put it to run in two, three weeks. And if you, for example, have a problem of optimizing your assignment of the right people, imagine that you are a company, you are an insurance company, you are in a season of renewal of policies. The biggest problem that you have is that you need to guarantee for a specific scenario for customer, you need to have the right person to do that because it's sales. It requires a touch, a personal touch. The AI job here is to connect the right people. So for example, intelligent decision routing is one of the typical use cases that may affect a customer in an insurance company, you can put them to run in a few weeks. Understand the business rules, understanding their business, put that into an intelligent system and make guarantee that the user is not forced to press 1, press 2, press 5, and hopefully it gets to someone that is going to help to renew their policy. Or in case you are doing outbound, you need to make sure that you connect the right people depending on the right customer profile. So other scenarios may take a little bit more time depending on what is the pressure point. You can run for 2 weeks to 4 weeks If it goes more than 2 months, maybe you are trying to boil the ocean. There's so much to do that you should break the problems in smaller pieces and maybe you are not attacking the right pain point there and understanding. So understanding first what is your journey, where the friction of your journey is the first thing that we do when we interact with a customer doing consulting service, trying to understand where we can help them. That is the first thing that we do. Instead of answering, yes, we can do whatever the customer asks, we do the questions first. We try to diagnose the pain points, the friction points, and try to see where we can automate. Sometimes it's not a voice system, it's not a bot. Sometimes it's an operation on the back office that is breaking the whole experience that you have. This is where we start. So basically, to answer directly your question, Zeus Kerravala, this is a matter of weeks, not a multi-month project. Something is going to be really wrong if you need to spend multiple months doing a project in the CX space. Yeah.
Zeus Kerravala
>> Now I suspected your answer would be it depends, which it sounds like it is, but I'm glad you time-bound that within a couple of months because I think that gives businesses some sort of frame of reference to work with. Now, I want to shift gears a little bit here, Pedro, into the workforce and Talkdesk recently introduced your CXA Operations Center to manage both AI and human agents. And I know workforce is really, a hot topic right now. And so when you look ahead with what CXA is, what's that operating model that CX organizations will work with as AI agents assume more responsibility, but we still have to rely on our humans.
Pedro Andrade
>> Yeah, it's a great point. And this is one of the strongest beliefs that we have at Talkdesk. We believe in the hybrid workforce. And so the CX operation manager is a role that emerges from that. When you have, when you have, imagine this, imagine a conveyor belt. You are going to place machines and humans as the work passes through the conveyor belt. This harnessing is the most important thing. That is what optimizes your operation. So the place where that shift is from having people that before they were doing the job, now they are monitoring. So the supervision, for example, shifts from building scripts into behavior monitoring. How is your harnessing, your agent, your machinery, your hybrid workforce working? Are you seeing problems when an AI hands off to a human? Are we losing something here? And what about when a human engages with an AI to complete part of the job? Are we losing something? So that behavior monitoring, it's important. And exactly what it means, it means basically 3 things. Guarantee that your non-human workforce is ready to go before you launch them into production. What is the quality of this skill that you are about to launch as part of your team? It's like recruiting, exactly the same thing. You do interviews. Well, in AI world, you are doing evaluations and that's a job for the CX operation manager. After you hire that agent, not a human agent, but an AI agent. The second thing that you are going to do is exactly what you do with human agents. You are doing evaluations of performance. You are going to do observability. You are going to understand how it is performing. What are the errors? What is the error rate that they are doing? So, do you do course correction? You do training, you do an optimization of an instruction. That is again the job for the operation manager. It's not a technical skill, but it's a behavior changing or a behavior monitoring skill. And finally, the last piece of this skill of this emerging role is understanding how your hybrid team is operating and how this is affecting your business KPIs. It shifts. Now you don't measure just average handle time. You are going to measure how much time it takes from opening of a problem until it gets closed. Not because average handle time just measures the time of an agent, but what about the rest of the process? Or what about the other systems? What about the people in the back office? They are not counted traditionally in those KPIs. But now that you have a hybrid workforce, you need to measure the efficiency of the whole process from the beginning until the end, even if it takes 300 interactions to get solved.
Bob Laliberte
>> Pedro, this is really fascinating for me because, developing these new skills and in a lot of cases I often look at AI and refer to it as the time to get comfortable with the technology and so forth. And now you're talking about a kind of almost a cultural shift of these supervisors having to manage these hybrid environments, learning new skills, So how is Talkdesk helping them make that shift? Are you actually offering some guidance, classes, things like that to help them accelerate and understand this is gonna be their new role and these are the new skills that they're going to need?
Pedro Andrade
>> Bob, at the beginning, I thought that it will be much, much more difficult. Reality shows that when you talk about bringing AI into the contact center, a first reaction that you get is scare. People get scared, people get fear. That fear is the first reaction is this is going to mess up big time because I see ChatGPT messing up big time as well. So the question is how do I guarantee that this is not going to create a problem for me? So the answer to your question comes very naturally. It's a need. People didn't even know that they need a role in the contact center that is the CXA Operations Manager. The reality is when you present it as that is the response to their fears. So the adoption and the reorganization internally to move people from previous roles into this role comes very naturally. They, from day zero, they are involved in the designing of the solution. So Bob, this is not like a traditional SaaS sale where you install the product and then here's the video, here's the training team. Actually, the customers are involved from the very beginning in co-development. So we work with them and as we work, We present these tools, we present here's what, here's the agent that we're just deploying, here's the quality provided by this eval report. And after the launch, we keep monitoring and they have access to all of that data. So for them, it's a fundamental part of their journeys where you don't need to have a specific action on that because it comes very naturally. The customers are involved with those tools and in this role of operation manager from the very beginning.
Bob Laliberte
>> Excellent. Now that's great. And I also— this next question I wanted to ask you is kind of a follow-up. We've talked about it a little bit earlier on and you've talked about the value of it, but I know you've done a lot of specialized capabilities for verticals, whether it be healthcare, financial, insurance, retail, etc. Why do you believe that vertical specialization is going to be essential for that successful agentic AI adoption? Yeah.
Pedro Andrade
>> So it's fundamental because when you look at industries, their level of maturity differs a lot from one to the other. Retail is the most mature vertical, where for example around 24% of all the retail organizations reach the top tier of maturity. For example, comparing to what the average in terms of maturity is of 15%. So this tells you that your adoption, the way you adopt is going to be very close to your level of maturity of those industries. The way to accelerate that maturity is by bringing pre-built solutions that are pre-connected to their systems. Don't expect to bring an empty platform and expect the customer to connect the dots, connect the systems and transform the organization on their own. That is not going to work. So verticalization is important because it brings— resolves part of the pain, which is bringing the systems together, bringing the knowledge together. And for that, that is not just about integrations, APIs. It's not about instructions. This is also about having people on our side, specialists in each of those verticals that we can talk about. We know your market, we know how you operate. This is how this orchestration should work. This is what we have been seeing in other companies within your market. This is what works, this is what doesn't work. This is what it's worth to invest. That differentiation for us is fundamental, and we invest a lot in technology and in people that know those verticals. So the whole goal is to have people, process, and product that help customers to reach higher levels of maturity. Makes sense?
Bob Laliberte
>> Yeah, absolutely does. Yeah, Pedro.
Zeus Kerravala
>> Yeah, and I'll finish up talking about the way customers can think about CXA as a way of transforming their organization. So one of the interesting things about CXA is that it's extended AI beyond just using it for inbound services and answering calls quicker, right? And you do things like proactive interactions such as cart recovery, loan prequalification, collections, customer outreach, things that we historically didn't think of as a part of the service organization. So when you think about that vision, how do you think this changed the organization? Does the contact center become revenue generating or does it merge with the CX organization? And talk about that.
Pedro Andrade
>> Yeah, so absolutely, it is a change. It is a change and it is happening now, especially because we and the customers are seeing this transformation, not at the lenses of only automating one use case, but automating the whole journey. And guess what? The majority of the journeys are not just inbound. Inbound is kind of the last piece of a journey is when everything breaks, people call in. So when you start talking about CXA automation orchestration, you start uncovering those journeys. Come on, let's talk about that journey. Where does it start? What is the trigger of this? Oh, people are calling because they want to schedule to get their car serviced. Hold on a second. Why is that? You know when the car needs to get serviced. You know when AC equipment needs to get serviced. Why don't you— what is that part of the journey? And that's when it starts, you start, you almost hear the gears changing and moving in the customer's brain when they start thinking, oh yeah, the reality is that is in another place somewhere in the organization. All right, let's bring them in. And then that's when you start automating the whole journey. So instead of just waiting for the call when someone wants to get their car or the AC serviced, you have an AI agent that automatically verifies periodically what are the customers today that I need to contact that are going to get their car serviced or their AC serviced in the next X amount of time. And then they start outbounding those messages or phone calls depending on the strategy. And then they may not pick up the call. They may call you back like 10 minutes later. But because you start the journey, the customer is already on that journey. So when you pick up the call, you know what this is all about because the context is shared across these multiple agents that are taking care of that. So it comes naturally when you start looking at use cases that are not isolated. And that's where it pains me when the majority of the AI thinking is about, oh, I have this issue, I'm going to put a chatbot here, it's going to answer those questions. No, man, there's a reason why that is happening. Look at the whole spectrum, look at the whole journey and put your journey all on paper and now start thinking about automating the whole journey instead of just having a one-point solution that takes you nowhere.
Bob Laliberte
>> That's a great explanation. Thank you so much. And this has been an awesome discussion. Unfortunately, we are running out of time. So Pedro, Thank you so much for joining us.
Pedro Andrade
>> It was a great pleasure, Zeus Kerravala and Bob. Thank you so much for inviting me.
Zeus Kerravala
>> Yeah, thanks, Pedro.
Bob Laliberte
>> Yeah, absolutely. So clearly the key takeaway is that the next phase of AI and customer experience won't be defined simply by how many interactions it can automate. It's really going to depend on whether AI can securely and reliably coordinate people, data, and enterprise systems to resolve customer needs and deliver measurable business outcomes. Now, Talkdesk's evolution towards customer experience automation reflects that broader shift from isolated bots and copilots toward governed multi-agent systems capable of supporting the entire customer journey. Zeus Kerravala, thanks again for co-hosting, and thank you to everyone for watching this segment of the CX Summit.
>> AI is rapidly becoming part of the customer experience environment, but many organizations are still using it to automate isolated interactions or assist individual agents. Now, Talkdesk is taking a broader approach through customer experience automation, or CXA, which uses multiple AI agents, enterprise data, and cross-system orchestration to resolve customer needs from beginning to end. Hey, welcome everyone to the CX Summit. I'm Bob Laliberte, Principal Analyst, joined by Zeus Kerravala, Principal Analyst and Founder of ZK Research. Welcome, Zeus Kerravala.
Zeus Kerravala
>> Bob, thanks. This event's been great.
Bob Laliberte
>> So yeah, looking forward to another great session here. And joining us to explain Talkdesk strategy and its latest innovations and how organizations can really translate AI investments into measurable business results is Pedro Andrade, VP of AI at Talkdesk. Pedro, welcome to the CX Summit.
Pedro Andrade
>> Hi, thank you so much, Bob. It's a great pleasure being here with you.
Bob Laliberte
>> Absolutely. So this is going to be a fun session, and I wanted to kick things off, and obviously I mentioned this in the opening, but you've been positioning yourselves as this customer experience automation company rather than, simply just a CCaaS provider. What does CXA mean and how is it different from the AI capabilities enterprises may already have in their contact centers?
Pedro Andrade
>> Yeah, well, it's a great question to start. So, Customer Experience Automation, CXA, is what we defined as an operating model. Okay, so it's not a category of technology. It's the system that coordinates a hybrid workforce of AI and human employees. Then connecting the systems, connecting knowledge, and also connecting the workflows. So, when the customer calls in, reaches out to a brand, they get what they need resolved end to end, independent of how many systems you need to get into and put together to resolve that issue. So for customers, this is not acquiring a technology just alone. It's an operating model shift. Okay. It's not a tool purchase. Majority of the enterprises already have some sort of technology about either generative AI or scripted AI. And so Talkdesk launched recently a survey on that with great interesting data. So around 74% of the enterprises already have some sort of generative AI. That's okay. That means that gets the work started. You can have some answers to your questions. It can eventually draft responses for agents. It can follow a script. But the problem is that that situation alone without having this operating model is that it stops the moment where you need a judgment or an action that needs to cross multiple systems. Okay, so basically then you need to have a human to take the data from place A to place B so things continue working. So that's the change. CXA is a layer that keeps that work moving across different systems. And so you get your work done across systems, knowledge, humans, and machines.
Zeus Kerravala
>> Yeah, thanks. Pedro, thanks for the update on what CXA is, and it's a great pivot for Talkdesk. Now Talkdesk is though by and large one of the leaders in the contact center industry, right? And you scored very well on all the different rankings. And so talk about the relationship of CXA to the contact center platform? Is it a replacement for it, does it sit on top? How does Talkdesk think about the relationship with the two?
Pedro Andrade
>> All right. So different from other situations or other companies, other providers that sell technology infused in the contact center. So we hear that term very, very often about infused AI. Actually, Talkdesk, when we decided many years ago that we want to move from a human picking up an interaction, a phone call or an SMS or an email, and try to do that manually, we understood that other contact centers also have the same problem. And so instead of building a platform fully infused in the contact center. We built CXA as a platform, as a different offer. So Talkdesk has two offers, CXA and CCaaS, and the CXA platform can be offered on top of other contact centers beyond Talkdesk Contact Center. Because the reality, Zeus Kerravala, is that the majority of the seats are still on-prem. And those customers that are still on-prem for multiple reasons, contract reasons, because moving the contact center to the cloud is still a big project, they can't afford to miss the opportunity of AI. So CXA works as well as a standalone on top of other contact centers to help relieve that pressure for the customers that are still on-prem that cannot get the latest and the greatest of AI on their on-prem system. But to be able to, again, to use CXA to relieve that pain and start gaining those productivity gains on top of an existing contact center.
Zeus Kerravala
>> Okay, thanks for that explanation. And I think the ability to work with the legacy ones actually, it really helps customers modernize now. You mentioned some of the research that Talkdesk has done. We actually looked through it in preparing for this and we saw an interesting data point that 98% of companies have deployed AI somewhere, and that's probably going to be 100% pretty soon, somewhere on the customer journey, but only 15% combine agentic AI with cross-departmental orchestration. So why do you think that gap is so big right now?
Pedro Andrade
>> Well, the reality is that adoption is easy. The orchestration is the hardest part. So adoption is easy. You can get to a provider, buy a chatbot, put it to run, and it's easy. The problem that you are mentioning and the reason why only 15% use agentic cross-departmental orchestration in these journeys is specifically for two reasons. Number one is that Agentic orchestration requires AI to maintain a context across all those systems. A solution is not just get my balance, which is only one place. You are going to resolve a problem of a situation with one of your transactions that may require multiple interactions across multiple systems to get that problem solved. The majority of these steps, are steps that require an interaction with a human. Someone is going to approve something. It's a whole journey that embeds multiple systems, multiple humans involved, machines involved. And today that situation is fragmented. Those workflows are fragmented. They are not linked and connected to each other. So orchestration is really the hardest part. Currently, you know this, right? We still have companies that have information in silos. And when you ask them to solve this end-to-end, you will need to orchestrate across these multiple systems. There are those, this is the reason why this requires the connections between multiple systems it's a blocker when you reach customers that are still working with systems in isolation and then expect humans to connect the dots and to transport the data from one place to the other. And the reality is that, for example, in that report, 64% of the organizations, they are already running some sort of AI agents for specific functions, for example, billing or for example, identity, right? It may sound like it's coordination, but the reality is that this handoff between those agents still requires someone to go there and move the data from one place to the other. And I told you there are two reasons for that gap. So from the 98 to the 15, the second reason is governance. Okay, governance connects and connecting to that report. It is more related to an enterprise readiness than actually just an AI capability. And if you take a look at the 4 top reasons why people— to justify that difference of adoption is because number 1, compliance, taking 50% of all the reasons. For that adoption cross-departmental and this orchestration. Second, with 48% of responses, is about security. Disconnected systems takes third place with 45% of the responses pointing to that as the issue. And with 44% are legacy infrastructure, systems that don't talk, systems that are legacy, still on-prem. And so that was the four reasons Zeus Kerravala, to answer your question, is you need to orchestrate that. But beyond the orchestration, governance, including compliance, security, disconnected systems and silos, is being the top barriers. And that differentiates those customers that are part of the 98% that deploy something versus the ones that are able to actually harness those agents into one experience that goes cross-departmental and cross-system.
Zeus Kerravala
>> All right.
Zeus Kerravala
>> Thanks for your thoughts on that.
Bob Laliberte
>> Yeah, Pedro, I'm interested in this as well because it sounds like some organizations are a little bit more mature. Clearly, you've seen a lot of this activity, these organizations, what works, what doesn't work. How are you at Talkdesk helping customers integrate those AI agents with all those systems of records, right? Whether it be CRM or ERP or others, and helping them to get over this bottleneck, get over these challenges that they're having and accelerate that time to value.
Pedro Andrade
>> All right, so over time, Talkdesk created a gazillion sets of integrationsbecause automation, Bob, it's not new, right? We already have automation on IVR. Press 1, press 2, and you still get your balance. You still file your claim. And the fundamental piece that was required to do that level of automation is that one way or the other, some customers needed to put some of these solutions in place. Some issues, right, sometimes is that building an integration requires a whole project that takes months. With Talkdesk specifically in industries, we relieve that pain because all those integrations that you require to have an integration, let's say with Epic, right, that requires integrations with specific healthcare protocols, or with majority of the banking systems. Talkdesk offers that out of the box. You have those integrations out of the box and allows you to, with minimum configurations and setup, allows you to have these agentic systems ready to connect to those data sources with quality, reliability, with security that is fundamental. So over the years we created those connections, we created that relationship with those partners with those providers that now we are reusing, especially then on our verticals to accelerate the adoption and the time to value.
Bob Laliberte
>> Excellent. Yeah, that makes a lot of sense. And especially for those specific verticals where you've got those tight integrations to be able to accelerate that. And it leads me to another question I have for you because we know there's a lot of the CX leaders, other business leaders, they're all under a lot of pressure to show that AI is creating value. And in this space, it's about not just call deflection and maybe even headcount reduction. When you're thinking about CXA, what business outcomes should organizations expect? And are there new measurements and new metrics that they should use to measure them?
Pedro Andrade
>> Oh, absolutely, yes. And if I had $1 every time that I get this question, I could retire myself today. So Bob, the majority of the times I get this question is, is AI going to help me to reduce my headcount? People are just so, so crazy about this and nothing wrong about that. But let me share some additional data that we also got in that survey. And I think that that kind of needs to be used to change the way you need to think when you look at adopting AI. If you are just thinking about reducing headcount and do these savings, you are missing part of the story. Okay, so I'm going to give another example. So let me give some data and some examples here. In our report, we created 4 categories of different customers depending on their maturity level, or their maturity to adopt AI. The top ones are what we call CXA Leaders compared to the ones that are right before these ones, right below these ones. They are getting much more results in terms of NPS gains than the ones that we call Agentic Scalers. So we have the Agentic Scalers and on the top, the CXA Leaders in terms of NPS. The difference is crazy. It's about 4 times. So you have 5% better NPS scores for the ones that are Agentic Scalers. So they have some level of maturity. They are running agentic AI in their contact center. They are deploying it in production. But they are just not like the leaders that they are harnessing those agents to resolve end-to-end. The difference between those two is 4 times, 22% for the CXA Leaders in terms of NPS gains versus 5% for the Agentic Scalers in that KPI. So what is interesting is that the cost per contact also improves but not as dramatically as NPS, for example. So for the CXA Leaders, you get 57% improvement on costs per contact versus 48%, which is a moderate reduction in terms of your— between the Agentic Scalers versus the CXA Leaders. So that suggests that Savings alone don't tell the whole story. They understate the value that you can get with AI. So the other way then to look at that is to look at retention and revenue. And let me give you some numbers. So for example, the customers that are using CXA to run predictive modeling, they are getting way better results if they are on the top tier of this adoption. 51% better results in predictive churn modeling versus the 28% that the group of customers, the Agentic Scalers, right, that they are seeing. Also personalized recommendation. All right? 44% of the companies that are running personalized recommendation, they are seeing gains if they are part of the CXA Leaders versus 19%. So again, it's almost double of the gains if you are increasing your maturity of your AI adoption. And again, that's using it as a harnessing system that connects those agents, humans, systems, and knowledge. So the summary is that if you took a look at all the spectrum of companies that are adopting AI, it's true that only 5% can say that, oh, I have a clear way of measuring impact. But reality is that 46% of all CXA Leaders, they have the impact, they have measured, and it is a good impact across metrics that are just not cost saving. They are reduction of churn and increase their revenue through for example, personalized recommendations.
Zeus Kerravala
>> Yeah, Pedro, I'm glad you're actually focused on a lot of the revenue generating type of metrics, right? We see a lot of cost cutting and I'm curious within the customer base, what's the typical time frame for these customers to start seeing meaningful ROI? Because I think a lot of companies really aren't— they want to invest, but they're not sure of when to start, when they'll start seeing the upside.
Pedro Andrade
>> Well, that answer is less of a pattern. Right? It's going to depend a lot on, where your current pains are. You may have a solution that you can spin up and put to run in a few weeks, maybe 2 weeks. You can put it to run in two, three weeks. And if you, for example, have a problem of optimizing your assignment of the right people, imagine that you are a company, you are an insurance company, you are in a season of renewal of policies. The biggest problem that you have is that you need to guarantee for a specific scenario for customer, you need to have the right person to do that because it's sales. It requires a touch, a personal touch. The AI job here is to connect the right people. So for example, intelligent decision routing is one of the typical use cases that may affect a customer in an insurance company, you can put them to run in a few weeks. Understand the business rules, understanding their business, put that into an intelligent system and make guarantee that the user is not forced to press 1, press 2, press 5, and hopefully it gets to someone that is going to help to renew their policy. Or in case you are doing outbound, you need to make sure that you connect the right people depending on the right customer profile. So other scenarios may take a little bit more time depending on what is the pressure point. You can run for 2 weeks to 4 weeks If it goes more than 2 months, maybe you are trying to boil the ocean. There's so much to do that you should break the problems in smaller pieces and maybe you are not attacking the right pain point there and understanding. So understanding first what is your journey, where the friction of your journey is the first thing that we do when we interact with a customer doing consulting service, trying to understand where we can help them. That is the first thing that we do. Instead of answering, yes, we can do whatever the customer asks, we do the questions first. We try to diagnose the pain points, the friction points, and try to see where we can automate. Sometimes it's not a voice system, it's not a bot. Sometimes it's an operation on the back office that is breaking the whole experience that you have. This is where we start. So basically, to answer directly your question, Zeus Kerravala, this is a matter of weeks, not a multi-month project. Something is going to be really wrong if you need to spend multiple months doing a project in the CX space. Yeah.
Zeus Kerravala
>> Now I suspected your answer would be it depends, which it sounds like it is, but I'm glad you time-bound that within a couple of months because I think that gives businesses some sort of frame of reference to work with. Now, I want to shift gears a little bit here, Pedro, into the workforce and Talkdesk recently introduced your CXA Operations Center to manage both AI and human agents. And I know workforce is really, a hot topic right now. And so when you look ahead with what CXA is, what's that operating model that CX organizations will work with as AI agents assume more responsibility, but we still have to rely on our humans.
Pedro Andrade
>> Yeah, it's a great point. And this is one of the strongest beliefs that we have at Talkdesk. We believe in the hybrid workforce. And so the CX operation manager is a role that emerges from that. When you have, when you have, imagine this, imagine a conveyor belt. You are going to place machines and humans as the work passes through the conveyor belt. This harnessing is the most important thing. That is what optimizes your operation. So the place where that shift is from having people that before they were doing the job, now they are monitoring. So the supervision, for example, shifts from building scripts into behavior monitoring. How is your harnessing, your agent, your machinery, your hybrid workforce working? Are you seeing problems when an AI hands off to a human? Are we losing something here? And what about when a human engages with an AI to complete part of the job? Are we losing something? So that behavior monitoring, it's important. And exactly what it means, it means basically 3 things. Guarantee that your non-human workforce is ready to go before you launch them into production. What is the quality of this skill that you are about to launch as part of your team? It's like recruiting, exactly the same thing. You do interviews. Well, in AI world, you are doing evaluations and that's a job for the CX operation manager. After you hire that agent, not a human agent, but an AI agent. The second thing that you are going to do is exactly what you do with human agents. You are doing evaluations of performance. You are going to do observability. You are going to understand how it is performing. What are the errors? What is the error rate that they are doing? So, do you do course correction? You do training, you do an optimization of an instruction. That is again the job for the operation manager. It's not a technical skill, but it's a behavior changing or a behavior monitoring skill. And finally, the last piece of this skill of this emerging role is understanding how your hybrid team is operating and how this is affecting your business KPIs. It shifts. Now you don't measure just average handle time. You are going to measure how much time it takes from opening of a problem until it gets closed. Not because average handle time just measures the time of an agent, but what about the rest of the process? Or what about the other systems? What about the people in the back office? They are not counted traditionally in those KPIs. But now that you have a hybrid workforce, you need to measure the efficiency of the whole process from the beginning until the end, even if it takes 300 interactions to get solved.
Bob Laliberte
>> Pedro, this is really fascinating for me because, developing these new skills and in a lot of cases I often look at AI and refer to it as the time to get comfortable with the technology and so forth. And now you're talking about a kind of almost a cultural shift of these supervisors having to manage these hybrid environments, learning new skills, So how is Talkdesk helping them make that shift? Are you actually offering some guidance, classes, things like that to help them accelerate and understand this is gonna be their new role and these are the new skills that they're going to need?
Pedro Andrade
>> Bob, at the beginning, I thought that it will be much, much more difficult. Reality shows that when you talk about bringing AI into the contact center, a first reaction that you get is scare. People get scared, people get fear. That fear is the first reaction is this is going to mess up big time because I see ChatGPT messing up big time as well. So the question is how do I guarantee that this is not going to create a problem for me? So the answer to your question comes very naturally. It's a need. People didn't even know that they need a role in the contact center that is the CXA Operations Manager. The reality is when you present it as that is the response to their fears. So the adoption and the reorganization internally to move people from previous roles into this role comes very naturally. They, from day zero, they are involved in the designing of the solution. So Bob, this is not like a traditional SaaS sale where you install the product and then here's the video, here's the training team. Actually, the customers are involved from the very beginning in co-development. So we work with them and as we work, We present these tools, we present here's what, here's the agent that we're just deploying, here's the quality provided by this eval report. And after the launch, we keep monitoring and they have access to all of that data. So for them, it's a fundamental part of their journeys where you don't need to have a specific action on that because it comes very naturally. The customers are involved with those tools and in this role of operation manager from the very beginning.
Bob Laliberte
>> Excellent. Now that's great. And I also— this next question I wanted to ask you is kind of a follow-up. We've talked about it a little bit earlier on and you've talked about the value of it, but I know you've done a lot of specialized capabilities for verticals, whether it be healthcare, financial, insurance, retail, etc. Why do you believe that vertical specialization is going to be essential for that successful agentic AI adoption? Yeah.
Pedro Andrade
>> So it's fundamental because when you look at industries, their level of maturity differs a lot from one to the other. Retail is the most mature vertical, where for example around 24% of all the retail organizations reach the top tier of maturity. For example, comparing to what the average in terms of maturity is of 15%. So this tells you that your adoption, the way you adopt is going to be very close to your level of maturity of those industries. The way to accelerate that maturity is by bringing pre-built solutions that are pre-connected to their systems. Don't expect to bring an empty platform and expect the customer to connect the dots, connect the systems and transform the organization on their own. That is not going to work. So verticalization is important because it brings— resolves part of the pain, which is bringing the systems together, bringing the knowledge together. And for that, that is not just about integrations, APIs. It's not about instructions. This is also about having people on our side, specialists in each of those verticals that we can talk about. We know your market, we know how you operate. This is how this orchestration should work. This is what we have been seeing in other companies within your market. This is what works, this is what doesn't work. This is what it's worth to invest. That differentiation for us is fundamental, and we invest a lot in technology and in people that know those verticals. So the whole goal is to have people, process, and product that help customers to reach higher levels of maturity. Makes sense?
Bob Laliberte
>> Yeah, absolutely does. Yeah, Pedro.
Zeus Kerravala
>> Yeah, and I'll finish up talking about the way customers can think about CXA as a way of transforming their organization. So one of the interesting things about CXA is that it's extended AI beyond just using it for inbound services and answering calls quicker, right? And you do things like proactive interactions such as cart recovery, loan prequalification, collections, customer outreach, things that we historically didn't think of as a part of the service organization. So when you think about that vision, how do you think this changed the organization? Does the contact center become revenue generating or does it merge with the CX organization? And talk about that.
Pedro Andrade
>> Yeah, so absolutely, it is a change. It is a change and it is happening now, especially because we and the customers are seeing this transformation, not at the lenses of only automating one use case, but automating the whole journey. And guess what? The majority of the journeys are not just inbound. Inbound is kind of the last piece of a journey is when everything breaks, people call in. So when you start talking about CXA automation orchestration, you start uncovering those journeys. Come on, let's talk about that journey. Where does it start? What is the trigger of this? Oh, people are calling because they want to schedule to get their car serviced. Hold on a second. Why is that? You know when the car needs to get serviced. You know when AC equipment needs to get serviced. Why don't you— what is that part of the journey? And that's when it starts, you start, you almost hear the gears changing and moving in the customer's brain when they start thinking, oh yeah, the reality is that is in another place somewhere in the organization. All right, let's bring them in. And then that's when you start automating the whole journey. So instead of just waiting for the call when someone wants to get their car or the AC serviced, you have an AI agent that automatically verifies periodically what are the customers today that I need to contact that are going to get their car serviced or their AC serviced in the next X amount of time. And then they start outbounding those messages or phone calls depending on the strategy. And then they may not pick up the call. They may call you back like 10 minutes later. But because you start the journey, the customer is already on that journey. So when you pick up the call, you know what this is all about because the context is shared across these multiple agents that are taking care of that. So it comes naturally when you start looking at use cases that are not isolated. And that's where it pains me when the majority of the AI thinking is about, oh, I have this issue, I'm going to put a chatbot here, it's going to answer those questions. No, man, there's a reason why that is happening. Look at the whole spectrum, look at the whole journey and put your journey all on paper and now start thinking about automating the whole journey instead of just having a one-point solution that takes you nowhere.
Bob Laliberte
>> That's a great explanation. Thank you so much. And this has been an awesome discussion. Unfortunately, we are running out of time. So Pedro, Thank you so much for joining us.
Pedro Andrade
>> It was a great pleasure, Zeus Kerravala and Bob. Thank you so much for inviting me.
Zeus Kerravala
>> Yeah, thanks, Pedro.
Bob Laliberte
>> Yeah, absolutely. So clearly the key takeaway is that the next phase of AI and customer experience won't be defined simply by how many interactions it can automate. It's really going to depend on whether AI can securely and reliably coordinate people, data, and enterprise systems to resolve customer needs and deliver measurable business outcomes. Now, Talkdesk's evolution towards customer experience automation reflects that broader shift from isolated bots and copilots toward governed multi-agent systems capable of supporting the entire customer journey. Zeus Kerravala, thanks again for co-hosting, and thank you to everyone for watching this segment of the CX Summit.
Bob Laliberte
Zeus Kerravala
