Amit Mathradas, Five9 | The AI ROI in Contact Center Summit

Artificial intelligence has quickly become one of the most significant technology shifts enterprises have seen in decades. While many organizations are still exploring how best to apply AI across their business, customer experience has emerged as one of the earliest and most successful use cases. Today's contact centers are evolving well beyond traditional automation. AI agents are beginning to augment and in some cases perform complex customer interactions, helping organizations improve satisfaction while reducing operational costs and enabling employees to focus on higher value work. Hello everyone and welcome to the cube research contact center summit. I'm Bob La Liberte, principal analyst at the cube research and I'm here with my co-host Zas Caravala, founder and principal analyst of ZK research. Joining us today is Amit Maas, CEO of Five Nines. Amit, welcome. >> Well, thank you. Thank you for having me. >> Yeah, I'm gonna actually start the questions off here. You know, when we talk with um either IT leaders or business leaders, there's not one today that doesn't have AI at the top of the agenda. In fact, we always talk about how the 20 26 is the year that we put uh you know, went from AI pilots to production. But when I ask them to focus on where they're going to be applying AI, the first place they seem to always bring up is uh is customer experience. And so why do you think customer experience has been such a compelling use case for AI? >> Well, thank you Zeus and and really good question. Look, as you all know, AI is now being used in multiple practices across organizations. But it doesn't surprise you know me or anyone that that AI's first use case or the most prevalent use case is in contact centers for two key reasons right first it started off as as if effectively if you look at it you know nine out of the $10 that go into opex into a contact center is labor it's humans so when I think about where you want to use new tools like AI that seem like a natural first starting point right how do I actually uh optimize how do I make my unit costs better of what comes out of a contact center. What is strangely happening is but as AI has gotten better uh you're actually finding that it is not just removing cost but it is actually revolutionizing how companies are leveraging uh contact centers and the experience that they are now providing right today's new AI agentic AI is effectively saying hey I can take all my agents and learn from all of them you know before when we did quality management it was like let's listen to 5% of calls or 10% % and make a you know extended understanding of what's happening in the rest of the contact center. Today with AI you can listen to 100% of the calls and not just you know make corrections but also route to specific human beings solving specific problems versus saying I'm going to send it to one part of my contact center and hopefully it gets solved there. So I think these are the two big fundamental shifts that are happening. Uh it started with you know with labor but now it's moved to how do we drive better experiences and lo and behold what we're already seeing is you know interactions are starting to go up because as unit costs of serving a customer in the contact center come down you can serve more and more interactions more and more of your customers and that's leading to you know much greater outcomes for for our customers at the end of it >> a one of the things that I've been fascinating by and following is something I refer to as the time to comfort with AI. Are you seeing that organizations are becoming a lot more comfortable trusting AI with their customer interactions as well? >> Yeah, for sure. Look, AI, you know, even in the even in the span of the last 6 months, a year, a lot of it has moved from PC's or proof of concepts into actual deployments. Two things are happening there as well. One, the new generation of AI and and the new generation of the models that are powering AI have only gotten better. So latency is you know is improving hallucinations are down complexity of of answering that you can get to is up. Uh so that is one right the capability the foundational capabilities have gotten better and you can serve more and more complex use cases. The second thing that we're also noticing is organizations have become better uh adapted organizing around AI, right? Security, governance, uh better management, actual thought process of how do you deploy it. It's not just let's throw it on the wall and hope it sticks and we get an outcome. It's actually now being planned. There are there are use cases with, you know, very specific outcomes in mind. Uh those two things coming together is really kind of helping uh this transition accelerate. Got it. Got it. So, I know there's a lot of organizations jumping into it. Zas mentioned about the whole pilot to production this year and so forth, but we also know there's there's still a number of organizations that haven't started yet. They're still on the sidelines. What advice would you give to organizations that are just getting started on that AI journey in the contact center? >> Look, the the first thing anytime I talk to a customer or I get, you know, requested on this is focus on what is it you're trying to solve. A lot of people rush in saying AI can solve this. So I'm just going to throw AI at the problem and we'll figure out if it works. My my you know strongest recommendation is first figure out what is the problem. Where is the the longest you know pole in the tent that you are trying to solve with your customer experience and then kind of consider does AI actually solve that and how do you actually plan around the implementation there. That's that's number one. Number two is once you've decided that how are you organizing for the outcome right it's not just you know you have to now have teams that are uh kind of specialized or or specific with how do you drive that outcome how do you measure that outcome how do you deploy that outcome so think through not just the the the the capabilities of the technology but the tooling that goes behind it and how you get the best of breed uh the third one for me and and this is you know as I think about AI in in the contact center. What how are you actually choosing the product or the vendor or the capability that that utilizes the infrastructure you have today? I think that is a big one because very often customers will go in and then they'll come back and be like, you know, oh snap, I got to actually change another whole set of tools for what I am I'm actually working with, you know, with this vendor to go solve. So, who can actually solve it with the infrastructure you have today? and who can actually give you an open platform to go drive those those connection points I think is a is a really key piece. Um and I think that if you can if you can nail those three things and go in cleareyed I think you will end up with with um you know clear outcomes don't bo the ocean. Don't go say I'm solving everything with AI. Pick a use case get it right move to the next move to the next and and you will have a pretty robust outcome. Yeah, you know, it's interesting you bring that up because within the CX ocean, I guess to use your analogy, there's a lot of things you could do, right? There's agent assist, there's genic agents, there's not taking things like that. And so, do you find when you talk to customers, they're trying to do too much? And if so, when you talk to them, where do you recommend they start to be able to demonstrate that value quickly and get some uh some good wins there? Yeah, look, if it's a if it's a customer who hasn't had experience with AI before, you know, before they go into voice agents, which I think is the most complex, you know, to deploy and and get right and tune, I always recommend, hey, start with something like an agent assist like get the most out of your entire organization that is sitting there today. It is easier to deploy. You actually get better outcomes with your agents, you know, getting a whisper in their ear telling them, hey, you're missing this, so you can add this. maybe move to Agente quality management that can actually lift all boats across your organization and give you uh you know better servicing metrics on hey who's performing who's not and then once you're comfortable with how you actually deploy these then I would recommend hey go into voice AI there is tremendous outcome there there's a tremendous lift but it also requires a lot more uh you know a little bit more of engineering might a little bit more it's not out of the box you have to fine-tune you have to think about the use cases there's you know very often forward deployed engineers working with you on your infrastructure to get this right. So that is generally the steps but you know if you are if you are comfortable you've gone through the first one or two you're seeing the outcome you know start moving to voice you will see the next big lift that that's coming from it. >> Yeah. So on the topic of agents uh that's really the topic dour at every event you know we go to today u and really every CIO conversation I have from your perspective how do you think about it? How do you define it and how does that differ from a lot of the chat bots and virtual agents that you've frankly been using for for years, right? >> Yeah. Look, the the the prior generation, right, the the new generation is agentic, you know, AI agents. The you know, the prior generation was very much it was predominantly built on deterministic, right? And what I mean by that is it was a decision tree like effectively you went in and you you you programmed your bot. Yeah. It was rules based. say if then statements. You know, Zeus is asking for do you have a credit card? Yes, it takes you down one track. No, it takes you down another. So, it was effectively a decision tree, you know, masked with with voice capabilities and would take you down that. The new the new generation of agentic is probabilistic and what that means is it will contemplate all different probabilities of what you are asking and then take you down to based on the knowledge that it has gathered from your organization or knowledge it has from from the entire market right how you've trained and tuned the bots and so effectively what that allows you to do is get into more complex solutioning it can actually understand what you're asking for it can prob you know go through a probabilistic understanding and saying based on this it actually wants you know the customer is asking for that and I can take you down this route or transfer to you to a human or transfer you to a content site all those things are is what's in the new generation of agentic AI and it is truly you know in my view revolutionizing what is happening in the in the contact center and and serving a lot more complex use cases >> no that sounds really fascinating one of the things that you always get is that you know what's the right balance between humans and the agent so forth. Where are you seeing customers? How do they determine what that balance is between leveraging the agents and automation and human engagement? >> Look, I we you know 5'9 you know have a very very strong belief that the world the future world is what we are calling human right and humanic is the combination of humans and agentic sitting in your in your contact center. I think there in from my perception from my perspective I think there is a misconception that all humans are going away in the contact center that is not true. Talking to our customers that you know deal in in complex and regulated industries there are three very clear use cases complexity value and vulnerability. When you are facing one of these three use cases you want a human involved. Whether it is your highest value customer, whether it is someone in a healthcare environment may have just you know you know be dealing with the death of a partner or a family member and is dealing with an insurance case. Uh or it's highly complex and you want to understand like you know what's happening with all my stocks and trades and someone needs to you know kind of show you the different optionality. So for us I think this combination of how does AI solve a lot of the base cases the high volume cases the two the two areas we see you know AI solving is high volume password resets what's my bank balance things like that or when I need to be available 24/7 right I can call in the middle of the night someone will take my call and and transfer it in and it's in in a high uh you know kind of desiraability code where where you need someone available and where humans are going to be is in this complexity. And so as as we think about the world, Bob, we are building a world for this humanic era where architecturally our voice AI agent is connected to our contact center as a service platform so that you can get low latency. You can get humans to actually jump into an AI call and take it over if there is an issue. Uh you can get these levels of service that drive the next level of containment uh you know across the board. I'll close it with this one thing. From the research we have done, we found one stat really amazing. 99% of business practitioners, companies who are deploying think that their their contact centers and their serviceability has gotten better. Only 66% of actual users think the contact center has gotten better. That means a third, you know, a third of all your customers are actually saying the experience is worse and more than 50% of them are saying the reason is I want to access a human and I don't get that. So just you know put that into perspective and that's that's you know what what's in the back of our minds as we build. >> Excellent. Yeah. And it's you know it's interesting as these deployments occur a lot of people are so focused on the technology you brought up before it's also about people and process that need to be involved as well. Um, so we know the technology alone doesn't determine success, but you know a lot of times that implementation speed, integration, change management can really help make the difference. So how does 59 help customers move from their pilot projects to production and get to that point where they're realizing business value quickly? >> Yeah, look, our heritage, we have, you know, over 20 years of of experience in in being, you know, voice ccentric. We know that this is where the complexity in the ecosystem is. You know, like our name suggests, you know, 59 of of uptime, you know, 180 countries, 3,500 customers, uh nearly 90 certifications and regulatory needs to to go get that going. So the way we with the way we help is we start putting and working with customers on two fronts. one our forward deployed engineers our capabilities around PS or professional services understanding the need starts with understanding the complexity you're dealing with and then how are you deploying building and going from there the second big one for me is is the open is the open platform and the open architecture you will never hear us at 59 saying you have to end this technology or toolkit that you're using for our capabilities to work about how do you actually open up and drive uh our solutioning on top of what you already have today. And as we get better and better and service you more, you will effectively start picking up you know greater pieces from us. That is traditionally what what customers want as they are deploying new technologies uh and a helping hand as they go through this go through this shift. >> Yeah. And I mean you mentioned that data and that's interesting because obviously if some customers are seeing value right there is value in it right and I think that's safe to say. So from the deployments that you've seen what are the the common characteristics of those deployments that make them successful? Yeah, you know, some of it is is a culmination of, you know, Zo what I've been saying. One is a lot of them start with an organization that is ready for AI and have actually thought through the use case and and how they want to deploy it. Two, a lot of them start with a base case. Uh even if they start directly with voice AI agents, they will start with one simple case, build it, get it right, expand to the next department, the next department. And the and the last one is you know the ability for them to really pick and and and drive the new shifts with the ecosystem and infrastructure that they have today. I think that is another key key reason why they are successful and what you know kind of drives back that they're not forced to make changes on the whole entire backend stack for their AI to work. They can kind of build it on top of what's available today and and go drive and test from there. >> All right. Well, that being said, can you give me a customer example that demonstrates the business outcome they were hoping to achieve and um you know and then the result that the deployment had? Look, there are there are a lot of them and you know the the one I will probably cite is there's a large uh moving in in logistics company that that you know effectively and storage company you know been in business for 25 years uh you know has been a customer of ours for for a long time came to us first when when AI became real started to deploy AI agents started to deploy AQM pretty recently they moved to our new uh AI you know agent take voicebot and you know over time have now you know we're on track by the end of this year to handle about 100,000 uh calls a year for them you know running through our agent agentic stack and what this has led to is not just the improvements around the overall agent ecosystem with agent assist and AQM but even with our AI bots now they have got you know over 50% containment on a on a on their specific use case uh it's moving towards 53 it's higher than what the original you know point was the seesat has gotten better and strangely enough the agent churn rate has come down even in the in the time because you've taken all the the manual work out and and moved a lot of the you know the grunt work to to AI agents. So that's just one example of you know how we're seeing customers use the full stack uh and keep maturing along with it. >> Yeah, that's a great I I love to hear those customer examples, right? It really brings it home to a lot of people who are watching. Um, you know, clearly we're still in the early stages of people adopting AI and really understanding how they're going to get all the value from it. So, I'm wondering I like as I as I wrap up, I like to look at, you know, looking out a couple of years, how do you see AI transforming customer engagement? What should organizations be doing today to prepare for what's coming next? >> Look, I have a huge belief that the contact center 5 years from now is not going to look anything like the contact center it does today, right? And that is that and I hope a lot of your listeners are really grasping that and saying well yes humans will be around but what they do how they service you know there's a whole new world of of customer memory that is that is coming to light let me give you an example uh you know tomorrow with the capabilities of of an AI agent being able to listen to every single call you can now start capturing customer sentiment which is the biggest part of the interaction layer right if you have called in and we both have bought the same pair of shoes from the same vendor, same size, a CRM will capture that. The sentiment will capture, did I have a great experience with that agent when I was talking to them or did I not? Uh, you know, and and that can serve as the next engage when you call back the next time. I can open it up and say, you know, I'm sorry you were talking to Amit. He completely sucked. I'm going to give you a $20 coupon. Right? Well, these new these new capabilities around around the platform uh around how contact centers are going to evolve is new. So that's one area it is going to pivot. The next big thing is around how these systems of record all work together to actually enable the next shift that is coming. Right? If the interaction layer, digital, voice, AI coming together is going to create these new experiences. Well, you have to be able to connect to be connected to the right systems of record to enable the orchestration to take place like what are you driving with these outcomes. So, orchestration is the next big layer that that I think a lot of uh you know customers should be thinking about and companies like us are thinking about in in terms of where where it needs to go. >> I think I think those are really valid points. Makes a lot of sense. Amit, thank you so much for joining us today. Zeus, thank you for co-hosting. >> Thank you both. Really enjoyable. >> Absolutely. Well, you know, it's really clear that AI is moving beyond experimentation, beginning to deliver measurable business outcomes across customer experience. The organizations that combine the right technology with the thoughtful implementation and to focus on business outcomes are likely to realize value much faster than those approaching AI simply another automation project. So I want to thank everyone for watching. Uh if you enjoyed the conversation, be sure to explore the rest of the sessions from the cube research contact center summit where we're talking with industry leaders about how AI is transforming customer engagement, operations, and enterprise technology. for Zas Garavala. I'm Bob La Liberte. Thanks for joining us and we'll see you next time.