Ep.129 The Gen AI Revolution: Expanding Customer & Agent Experience! Pt. 2/3 with Michael Roche
Welcome to the podcast designed to fuel your success in selling technology solutions. I’m your host, Josh Lupresto SVP of Sales Engineering at Telarus and this is Next Level BizTech.
Hey everybody, welcome back. We are on talking about the hottest topic around we’re talking about AI. I know I know we’re gonna get into some good stuff though. We got some meat here. We’ve got MichaelRoche on with us today AI subject matter expert fromFive9 Michael, welcome on. Well, thanks so much. Appreciate it. Nice to be here. And I appreciate you asking me to join you today. So so we got a lot of good stuff here. So so the title of today’s track is the generative AI revolution, expanding customer and agent experience. So that’s going to be the theme as we go through this. But before we get into that, walk us through just a little bit about your backstory, how you got into this field, anything winding or wild along that way is cool.
I don’t know if it’s winding or wild, but maybe a little pedestrian. I think I probably follow the same course that most of us do, right. I went to went to college and got a degree in communications. And then my first job had nothing to do with that, you know, that great off. So probably pretty, pretty standard and pretty powerful, the course. I actually worked for the Commonwealth of Massachusetts as director of audit for the Massachusetts Department of Revenue, oddly enough, and I did that for quite some time. And then I made a move over into the telco space, where I was doing operations, and contact center work really cut my teeth in the contact center there spent a substantial number of years there, which was great.
That contact center experience has been really valuable as I’ve been along. And then I was lured away from that by a friend who was in a startup company in the in the world of AI. That this was back to when AI wasn’t sexy like it is now. It was just a little bit more mundane. And you know, we’re talking, you know, 20 years ago or so. But he showed me some stuff that I thought was groundbreaking. And I resigned my position within two weeks and jumped into AI. And I was again, that was about 20 years ago. And I’ve been doing it ever since just kind of following along as things have evolved and changed. Now I’m at Five9 Love it been here for just a couple of years. But I came actually for the same reason that I probably moved into AI in the first place, which isFive9 was kind of doing some new stuff and really investing heavily in AI, which is which is where I want to be kind of at the tip of the iceberg. Love it. Is there any wild but legally allowable stories you’re able to tell us from crazy audits? Or any? Is there any like wild stories there that we need to know? Like,
no, that if I told you I would have to we’d have to delete it. We can I can’t really speak to those. All right, so so pay your bills and be honest. All right, lesson learned. I get it. Alright, so so yes, obviously, great partnership here withFive9 we love how muchFive9 is investing. You know, obviously, just just someone in, you know, they’re having a title that you have outlays, how much investing is clearly happening. And so talk to us a little bit about for anybody that maybe hasn’t done business, you know, for our partners out there that haven’t really done business withFive9 tell us aboutFive9 the differences, you know, how you guys stand out some of the products, things like that. Let’s start on that a little bit. Sure. I’ll start from the kind of the beginning cloud based seacast company for those that don’t know, seacast contact centers and service company. This company originated in the cloud. So we’ve been cloud from day one. Well predating me, the company just created a rock solid foundation, right? Contact center technologies that include outbound inbound voice, digital traffic, agent desktop, supervisor, desktop, workforce management, all kind of typical contact center solutions start to finish really best in class just kind of as good as it gets. But in terms of differentiators, and probably some of the reasons why I came over was really that that investment in in AI. They saw the need to invest in AI really early in the game. So they felt like the contact center technologies were robust and they continue to add features and so forth. But AI brought kind of a new set of tools to the business and they’ve been going straight out with that ever since. They also saw the writing on the wall, I think, in terms of giving the customer keys. What I mean by that is, you know, I work for other companies where, you know, if you were a customer of that company, you wanted to make a change, you had to reach out and you know, put a change order in place and then the software development happened and then it had to be put into production and tested and then UAT and you get the whole idea. And customers really started clamoring for hey, we want some of those keys, we don’t want to have to reach out to you every single time we want to make a change. And 5.9 was kind of ahead of the game there. So as a differentiator that they’ve kind of been following that pattern all the way along. Probably the biggest differentiator, at least the one that I really gravitated to is they have a, we have an agnostic approach in terms of AI. What I mean by that is there are a ton of different engines out there, AI engines, and the platform is built in such a way that as new technologies emerge, we can either swap out old technology and bring in new technology or augment that which we have. And that’s critical, right? I think you would agree that things are happening so fast and new stuff is coming out all the time. If you kind of lock yourself into a single technology for a long period of time, you’re going to miss out on some of the new stuff that emerges. So that was a huge differentiator for me as I was contemplating making the move to 5.9. And then I guess the last thing I’ll comment on in terms of differentiator and probably the thing that, you know, so this conversation is a technical conversation. And I’m going to walk away from technology for a second. Love it.
It’s really about the people, right? So, I mean, you know, you’re a partner of ours, and we are careful about those who we partner with. And as I talk to customers day in and day out, sometimes the conversations are hey, the technology that we had or that we bought a couple of years ago, doesn’t work the way I wanted it to. But more often than not, it’s about the partnerships that I just, we can’t get anything done, we’re frustrated with the partnership, etc, etc. So when I came to 5.9, there was a lot of talk about the culture and the people here. And the NPS scores are off the charts in terms of our customer feedback on services. But I don’t need an NPS score to tell me what I know as I’m standing in the trenches next to people. It’s just a great group of folks. And that makes a big difference for customers as they think about purchasing technology. Remember, you make up spend, that spend is going to last five to 10 years. And you really need to make the right right pick there in terms of partnerships. Yeah, it’s a commitment. It’s a long term relationship. So all of those things certainly factor in, right? So no good, good, good call outs there. Love those. So let’s, let’s talk about this before we get a little bit into into the topic at hand, give me one more. I always love to ask this one. Over the last, you know, 10, 15, however many years, good lesson that you’ve learned, something you stubbed your toe on, or maybe something you’ve learned from a mentor early on.
Yeah, well, I’ve stubbed every toe. So there’s probably a bunch of those. I guess, in terms of the things that I’ve learned, probably the thing that stands out the most is that, you know, the companies that that I like to work with, and that means both kind of the companies I work for as well as the ones I like to work with partnerships, are those that are continuing to evolve and innovate, right? And you think about the best companies in the world, it’s companies from a technology side that that just continue to innovate, they’re looking for the next best thing, they’re not resting on their laurels, and they’re evolving to his technologies change, or his operations models change, business models change, you know, you have to evolve as things go, you know, kind of a softball example of that, if you will, is this idea of managed service, right, there’s value to managed service, but I think I mentioned a minute ago about giving the keys, right, you have to kind of accept the fact that customers need those keys. And if you’re not able to make that evolution, and benefit customers in that way, you’re going to get left behind, right? So things like that. And, and obviously, Gen AI is produced, you know, in terms of evolution, and being able to roll with it, and being able to adopt new technologies, Gen AI has forced many hands now when it comes to that. So those are probably the things when I think about, you know, what I’ve learned, you keep on your toes.
Yeah, you know, it’s a good one. If anything, all of this is accelerated a determination, a determination around product market fit. You know, you used to be able to just say, All right, we’ve got a pretty good consensus, we know the market, the market’s going to stay the same for a little while, let’s build this thing. And now, I think if you if you read any of these great, you know, I love to read stories from previous great entrepreneurs, great founders, right, everybody from, you know, Rockefeller, all the way back to Charlemagne. And there’s this common theme of as you get into the more modernization of products that the product that you set out to build is one thing, but the one that ultimately becomes the most successful or the one that people gravitate on sometimes isn’t that initial product, right? And so if you’re if you’re understanding and if you’re being open minded to where you know, we want the keys, we want the customization, we just want to build it. If you have the mindset that no, we’re not going to do that. Okay, cool, you’re gonna lose the customers and you’re never gonna know why. Well, you kind of know why, but you’re not going to admit to knowing why. So, you know, kudos to you guys for the flexibility, the modularity that you’ve given, I think along the way. And so I love, love that story. Good example. Let’s talk about all right, let’s let’s jump in here. Let’s let’s talk about, as we get into CX here, we’re going to talk about the agents. And then we’re going to talk about, you know, the actual customers themselves. So let’s, let’s talk about when, when customers are coming to you, you know, our partners are bringing you into customers. What are they coming to you for with regard to AI? Like, what are the demands for the agent experience?
Yeah, those are two separate questions to let’s take the first one, just kind of what what are they coming to us with respect to AI? And this is a really an interesting, a bit of a loaded but interesting question. I think that answer, the answer to that question has changed. Really with JNI, it’s produced a whole new set of folks coming to us and in a very unique way. What do I mean by that? So typically, in the past, you’d be meeting customers and they would have a particular set of problems that they would come come to you about. And that was fine. You kind of walk through those problems and then back into your technology and say, Do I have a piece of technology or a solution for that? Do we have something that we can offer the customer that will help alleviate some of those problems? That still happens. There’s still plenty of that today. But you get from that into the spectrum all the way to, hey, I would like to implement AI, I don’t know where and I don’t know what that means. Like, you know, mandates for individuals, right, for operations folks or for, you know, individuals within a company to say, you know, from their from their leadership team to say go, we want to jump in this pool, go find us where we start. So you know, we literally have customers that don’t really know where to begin. And that’s where they start just where do I begin and, and there is a ton of noise out there. I was at a contact center a couple weeks ago, contacts in a week. And I mean, every vendor, every everybody that you talk to is an AI whisperer. Everybody does it and has been doing it forever. And that just creates noise and a really difficult atmosphere for customers to navigate through as they’re trying to figure out what their own individual AI journey will look like. That’s, that’s challenging, I think. Ideally, they want to all figure out how they can use AI in a way that, you know, has some sort of positive impact on their business. Ultimately, that depends on the issues that they’re trying to, to solve. But you know, I think one of the keys that we talk about when I talk to people is just try to avoid the shiny object syndrome, right? I mean, really, let’s focus on problems, not not products. Products will work themselves out, AI will work itself out. Let’s go back to basics. What are the problems? I guess the other thing I’ll say is, maybe more than I’ve seen in the past, there are a large number of companies that walk into the table with the defined AI strategy. So you have everything from, I don’t know, I’m supposed to do something, but I’m not sure where to, hey, we have a full blown AI strategy, here’s what we’re going to implement here, the tools that we want to, we want to purchase, what do you have, that kind of thing. So while that’s not the norm, I would say, you know, kind of we see that more, more often than we had in the past. And then the other question you asked is not kind of agent experience and kind of what people come into us. You know, for me, what’s interesting about that question, and we’ll talk about the technology in a minute. But the fact that you asked that question gives me great joy, right? Because, you know, having somebody used to work in a contact center, we didn’t ask that question. And we weren’t asked that question. And as somebody who has been selling software, you know, for a long time, those were not questions asked by companies that that was kind of a given that agent, they’re in the background, they’re doing their work, brutal work, but they’re doing it. But I would say, the vast majority of calls that I have with customers, this topic comes up, because agents are so crucial to the business. They’re hard to come by, they’re, they’re hard to retain. And anything that we could do to make their lives a little easier is the better off we are. So I would say, kind of, that’s one thing that I that I really enjoy in terms of a shift in the market, that we talk a lot more about that. Yeah, I was with that, you’re right. Also coming from the contact center, you know, back in the day doing doing tech support, nobody ever asked us anything about what we wanted, right. And so I did the fact that you guys are able to offer so many different things with regard to how you can improve the agent experience. It goes back to what one of our guys always says, right, as we try to learn the different technologies that are out there and help our partners show, don’t don’t show me the market texture, right? Show me the architecture. And really, let’s see. And I think as you, as you start to sniff this out, the productization for folks like yourself is very apparent and very easy to show. It’s cool, let’s put it into a PLC and I’ll show you how it works. We actually have that we can do that right now. You guys have such power in that. And obviously, that’s kudos to the investments that you guys did early on and continue to do. That’s got to open up so many doors to continue that conversation.
It does, you’re absolutely right. I mean, and some of the tools in this space in particular, from an agent, agent experience perspective are kind of really handy and really helpful. So we’ll talk about a couple of them. So Agent Assist is the product that we have. But within that product, there are a couple of pieces of AI that really make a big difference for agents. I’ll talk about three, two briefly, and one a little bit, maybe I’ll dive into a little bit more. But the first is agent checklist. So, you know, from an AI perspective, you’re listening to the call between the caller and the agent at all times. And you have a preset list of kind of a checklist of things that you want to make sure the agent is doing to give it like a mini QA. So as the as the conversation is happening, these checkboxes are happening kind of reminders to the agent, for example, greet the caller, right? Well, when you read the caller, a checkbox shows up. So it’s a great little tool to keep agents on their toes and aware of the things that are really important in terms of each of the calls. Those things can slip by in the in a call where you forget about things. So that’s a nice reminder. guidance cards or next best action kind of cards. Again, AI listening in the background, and as the customer says something, pop the pop the agent with a card, some sort of a helpful card, either with instructions or links to places that they need to go to service the customer. So example, let’s say the caller was saying, Okay, I’d like to make a payment. Great payment card pops up. And the agent has right at their fingertips in front of them, how the customer needs to go about making the making the payment really kind of helpful tools on the fly real time. And then I think, you know, one of the things we’re here to talk about today is Gen AI, right? And so one of the newer tools in the tool bag, if you will, is leveraging large language models to auto summarize or disposition the call when it’s over. So you’ve been in contact centers before, you know, that’s the bane of a contact center agents exists, right? Like I have the calls over, it was a 10 minute call. Now I have to disposition the column. That happens in a whole bunch of different ways. One of those ways is to use some sort of a disposition code, historically unreliable, right? Another way is to actually freeform or text kind of write what happened on the call. Very time consuming. Typically, you’re after call work, maybe you a lot anyway from 15 seconds to two minutes to allow the agents to do that and take a little breather before the next before the next call comes in. Well, with Gen AI, basically what’s happening is you’re you’re you’re producing a transcript, not with Gen AI, but you’re producing a transcript of the call. And then you send that transcript through Gen AI. And poof, it summarizes the call. So you could take a 10 minute call, and summarize that into just a few paragraphs. That is an immediate ROI. So on 100% of the calls, whatever that disposition time is, on average, it just goes away. Take that disposition stuff into the CRM. And bam, you really have a win right off the bat, quick, easy to implement. low risk in terms of the trust model from an AI perspective. So yeah, there are a couple of really cool technologies out there that that we’re seeing have dramatic impact. Well, and I remember too, it seems like there was a big disconnect always between how long the manager thinks an agent should stay in after call time and how long the agent believes they need for after call time. And either way, there’s always the argument. And that’s just such a I love hearing that because that’s such a you know, AI tends to be this nebulous term that we don’t always get to quantify in quick conversations and that for the partners listening out there, that’s a really great, quick productization distills it down. And what I want the partners to be able to take away from this is hey, as you’re talking to customers, prospects that have these opportunities, and you’re looking for ways to tap into them from a CX perspective, or to see if you can help them. Are you currently using these things that Michael just talked about? Like, have you considered this? Do you know the ROI on this? Well, as you’re shopping for a new platform, as you’re looking to embed something in, we can help you with that. And so people are in this efficiency improvement phase, they’re in a technology modernization phase, the checks both. And so, you know, sometimes with those costs, I can imagine, if you’ve got a lot of agents, you got a lot of talk time, to your point, it’s got to be some magical ROI, and you’re not going to pay a lot for this. It’s not that big of a net increase.
No, it’s a it’s a really quick win. And again, we’re here, you know, this this topic came up under the umbrella of agent experience. Well, it just takes that right off the plate. They don’t have to do it anymore. And that is a huge because nobody likes likes to do it. You can get you can get dinged for it. Nobody appreciates the work that’s done, because it’s so hard to document a 10 minute call with a customer and get all the details right. I mean, that’s that’s a lot to ask of anybody. Yeah, fair.
All right, let’s, let’s shift this to the customer experience. So let’s, let’s talk about now, what kind of struggles are you hearing, you know, from from the end customers themselves with regard to the customer experience?
Yeah. So I there’s probably that some of these problems that I’ll talk about, I guess, things that you and I probably have experienced from the day we walked into this this industry.
But some are probably a little bit newer. So, you know, so I guess one of the problems that we’re trying to solve for our customers is first and foremost, kind of things like self service, right, allowing the customer to be the fuller or partial self service, right, allowing their customers an avenue to do so, both digitally and via voice, right, meeting them where they want to be met in terms of, of self service.
That’s probably the I would say that’s the front door, right? That’s what the most obvious place for problem resolution for businesses, right, take that’s that low hanging fruit off the table, but also provide customers with a way to do that.
But what we’re seeing and what we hear from customers is really they want they want it done quickly. They want it done with ease, they want it to be conversational. And when it’s not appropriate for automation, they want to get to an agent, right? So there’s this idea that, you know, automation is for everything, but it’s really not right. I think we both know that there are many use cases that are best left in the hands of a skilled agent, and getting a customer finding out that that issue, and not not dancing, not playing a dance with a customer, but moving them right to somebody that can help is just as important as being able to provide a customer a safe space to do some sort of self service, you know, quickly and so they can be be in and out with ease.
Good, good stuff. All right, let’s, let’s jump into a win. So so walk me through customer example, a deal you were brought in, you know, let’s do the what was the tech stack or the business problem before, and then ultimately, after theFive9 stuff gets layered in, what did that you know, what did we uncover and what did that environment look like after?
Sure. So, you know, it’s funny, the first thing that comes to mind is a little bit of a different direction. Do you mind if I go slightly different? It’s your show, man. Let’s roll. I’m gonna take a left turn, I think so. We I was with the customer too long, it was a few weeks ago, and we had implemented agent assist. So you and I just talked about agent assist. So it was a full gamut of agent assisted had these these kind of task lists that had a guidance cards and it had these auto summaries. So you know, we get through the process, we implement it and they’re running it for a few months. And now I’m talking to the customer, I said, I think I think we’re going to be commissioning. Really? I’m kind of surprised. They said, Yeah, we’re, we’re not seeing the benefit that we had talked about. Really? I just had such a hard time kind of wrapping my head around. So we just had a long conversation about us. Well, talk me through your invitation, kind of what you’re seeing and what? So lo and behold, what they did was they implemented it and in these dispositions. But they didn’t change the after after call work time. So they had a lot of the agents two minutes at the end of every call to do the wrap up work, they implemented agent assist with the dispositions should take off about 45 seconds. But they didn’t adjust that after call work time debt. So they weren’t seeing a benefit. The problem wasn’t with the customer. The problem we didn’t communicate, hey, by the way, when you do this, there has to be an effect after the case. So the lesson learned and kind of what I know when I think of it as a win, now it’s a win, they’re going to stay with it, they’re gonna, they’re gonna keep it. But right. But for a moment, it almost wasn’t a win. And for all of us in this business, that’s we do anything to avoid that when, when you gain the trust of a customer, when you bring them on board, and you’re implementing a solution. The last thing you want to hear is they’re going to take that solution out because it’s not working the way they want, right? I mean, that’s just, you know, tough news to hear. Part of this is about AI isn’t just a magic wand. I mean, it’s not just a, you don’t just throw it in there, all of a sudden, it works. This after work that has to get done, and there’s a cause and effect, right? So it was a great lesson to learn. And of course, we communicated back through the folks that were doing the implementations like, hey, don’t forget, you need to remind customers that there’s there’s work that needs to be done once they implement these solutions. So it works. So I don’t know if that’s where you wanted to go. But no, no, no, no, it’s good. Well, it’s a good call out. Because I think some of these new solutions that are being implemented that are out there, and all the things that I was going to bring it up earlier, when you were talking about, okay, well, we’ll interrupt with if the customer has this tech stack or this tech stack, you know, I think people are looking at a lot of different technology right now. Because you have we did an episode a number of weeks ago that talked about the the tens of millions that are slated to flow into this space that are already productized companies that have gone through product market fit in y combinator, and are really entering in. And so I think this, this, this future for the partners becomes so so much more important of helping the customers really distill down all these things that are helping having the partners help the customers to sell down all these things that the end customers are looking at and shopping against because before it might have been, well, I’m just looking at replacing this prem platform with this cloud for platform or this cloud platform with this cloud platform. Well, now it’s, I’m looking at moving to this cloud, this thisFive9 platform, but I’m looking at this AI product, this AI product, this AI product, will it all work together and they all kind of look the same. So what does what do you think is going to be the best way to get to this cloud platform? And so I think that’s our, that’s our role going forward. And I think to your point, you know, there’s as much of a pre sales component of that as there is, what I’d love to tell partners of stay with the deal, stay with the deal after it closes, right, because you’re just going to find other things and we’re going to make sure there’s a level of satisfaction there. And so I think this is a new definitely it’s a great call out because it sparks. This is a new frontier. There’s so
many guys, guys, obviously, the big boys dumping piles of cash. And so we’re back to this, find a product market fit and you better find it fast, you know, kind of thing. And so I actually love that call. I think it brings up a great point.
Great. Yeah, it’s interesting with the things that we’re uncovering as we work with customers and deploying some of these newer technologies. And some of them are kind of obvious things to learn and others are not so obvious, you don’t pick it up, you actually do those implementations, you hear back from customers. And, you know, when I think about the agent assist product, at least that we deployed as an example, and this is typical the wayFive9 works. And so we implemented agent assist, and it was just a raw summary. It was just, you know, maybe a five, six, seven paragraph summary of a 10 minute transcript or 10 minute call. But we got some feedback from customers that hey, we’d love to be able to edit these. Maybe we want to add something in or maybe we want to take a look at it in the way. So we listened to the customers and we made that available. So now they can edit those summaries before they hit submit, it pops into the CRM. So then we were listening to more customers who were implementing more of these things. And it became clear that the next iteration should be, hey, can we, can we structure these summaries? They were just unstructured the way the call was. Now can we structure them? Can we add definitions to them and say, I want the summary to
be the customer name, I want the first sentence to be the customer name, I want the second sentence to be the product that was talked about, I want the third to be an emoji on sentiment, how did the call go, and I want the third to be a three sentence paragraph only of the whole call. That like those kinds of things are now possible, right? But that’s an evolution we talked about the outside of the call. Yeah. Companies that are evolving, listening to customers, take
as they, as you get customer feedback, evolve them so they meet the customer needs, you know, and it’s been fun to watch really. Well, and it’s such a it’s such a data story anymore. I think what large language models are giving people the ability to do is to really get back and have, you know, let whoever needs to leverage their data leverage it, but also get the power of that data back to really kind of tell the whole story, right? So the next, the next iteration of that is, yeah, but also feed that back to me in this format, so I can get it into my custom building LLM that is going to help my sales reps out on the street, you know, that are hearing what customers are saying and support so they can front of line talk to, you know, what we’re doing new, what’s next, blah, blah, blah. And so it’s just get the information structured in the right way and into the right hands of the right people. And these models just seem to do it in a better way than just a simplistic database ever did before. So it’s just, it’s wild to see what products are going to come out of this, you know, yeah, it is.
Okay, so if you think about, you know, as we get to kind of these last couple thoughts here, if I’m a partner, and I’m listening to this, I want to get a little bit deeper, CX AI, some of these virtual agents, I mean, I want to go deeper with my customers and my prospects, what’s your advice? Where do you steer them? How do I stay in tune with it as this thing evolves?
Yeah, so a couple of things, I guess, from a partner perspective, I think, you know, really developing that really tight relationship with companies likeFive9 or whoever you’re kind of partnered with, and make sure that you’re aligned in terms of the messaging, the products, where the roadmap is going. You know, a lot of this, a lot of times what we hear is customers have a particular problem that they want to solve. And maybe we don’t have that solution today, but it’s three months away. And that’s fine. That the customer is okay with that. They don’t want to, they don’t need it now, but they’d like to know what’s on the roadmap. They’d like to know you’re thinking about it, those kinds of things. So really kind of making sure those relationships are really strong and there’s visibility into what each company is doing in terms of growing their AI portfolio. I think things like you’re doing here, right, this podcast, you’re probably being exposed. And, you know, as the extent to which other people listen to things like this, like this podcast and others, you know, we’re all learning more and more about how people are using AI, what their fear, you know, one thing you and I haven’t talked about, and I don’t know if we have time today, but the fear of AI, there’s this, there’s a ton of fear out there. So I think one of the roles of the partner, I think you had said it earlier is to help with that noise. But it’s also the kind of waylay some of that fear, like, talk to customers about good use cases that are, you know, low in the trust, I keep using the word trust, like, you know, low on that, that kind of scale of trust, where you don’t really have to worry about it, like summaries. And that’s a really important role for the partners, because you are that you are that trusted partner with customers, they’re leaning on you kind of to do just that, right. And I think one of the other things I, you know, probably be remiss to not mention is that from a customer perspective, one of the things that I’m seeing out there, I don’t know if you’re seeing this as well, but I’m seeing customers take this opportunity in terms of the way the AI is changing over time here. And we’re seeing AI councils being created all over the place. And I really encourage that because I hear all the time customers talking about the silos within their organization, all running in different directions from an AI perspective. And as a partner, or as aFive9 to be able to go in and have conversations with customers about trying to put that all kind of within some sort of a hub and AI Council, some governance of that is critical because we’ve been to this dance before in not an AI, but in other software perspectives, when silos go off on their own, trying to stitch that all together down the road is going to be really challenging.
Yeah, it’s a good, it’s a good lesson learned. There’s a great book out there that we follow called team of teams by say, it’s Gen. General Stanley McChrystal, if I’m getting it right. And the thought, the thought was that, you know, back in a number of years ago, his job was to pull together thousands of people in the army, thousands of people in the Navy, thousands of people in the Marines, right? And they all think my group’s better than your group, how dare you know, why, you know, his job was to unify that because he needed all the different groups out on the battlefield. And one of the most simplistic ways that he got that together was a team of teams, meaning, I’m going to take a handful of people from your group, put them in my weekly meeting, we’re going to take a handful of your group, put them in my this weekly meeting. And it’s something as simple as that, that can break down silos to your point. I love that in some of these customers in different departments, hey, let’s, let’s share what your needs are, let’s share what you’re hearing, let’s share what you’re seeing, because otherwise, to your point, our next problem that we’re going to talk about in the next year or two is going to be the podcast of AI toolset sprawl, and security risks, and all of those, those my data is leaked, and I don’t even know how it got out, you know, and those kind of things. So I’d love to great point. Great advice there, for sure. Yeah, I think, I think as again, back to this notion of trust a partner as a trusted partner, you know, as you’re seeing that kind of thing happen with other customers sharing that, you know, as, as, you know, you work with new customers, like, hey, we’re seeing other customers, and this is really working for them, they put a console together, and, and they’re trying to make sure, you know, that kind of advice is really important. And
I think it’s one of the something that you guys just great at. So and then, yeah, anyway, love it. All right, final thoughts here. So next couple years, next year to what innovations are you most looking forward to? And how do we all play a role in this?
Yeah, so that’s so I read a Gardner report was issued, I think, last, last month, maybe the month before. So it’s a recent one. And that report suggested that 64% of customers still prefer or would prefer that a company didn’t use AI. So for you and I, like, as I hear that, it really concerns me, right? And then in the two things that stood out the top of the list of things that they had for concerns, where one, they, they don’t want to have difficulty reaching an agent when they want to reach an agent, right? That’s a real problem where they get spun around in a cycle and can’t get to the agent when they need to get to it. So that’s creating a that creates fear, right? And then to that AI is going to displace all jobs, right, they’re just going to display. So that’s kind of this, this faux fear that’s out there today. So when you ask about kind of, first of all, kind of how can we help? That brings to mind, we have to tamp that down, right? Part of our job is to say, hey, listen, AI is these AI engines are just a small piece of a much larger thing. Like so for example, you look at CCAS, it is a massive kind of platform to support a contact center. The AI engines are just small pieces within that platform. And the Gen AI pieces are even smaller. They’re powerful. They’re really important. They’re a new tool in the tool bag. But they’re not they’re, they’re just a small piece of a much bigger picture, if that makes sense. So I think art of our job kind of our role is to, to weave through some of that and to, to get, to get customers thinking that way. And frankly, the easiest way to do that is by working with vendors and forcing the idea that contacts need to be easy, they need to be fast, they need to be conversational, right? The more we build trust with customers, the more that 64% goes down, right? And that’s by leveraging good tech. I think that there’s enough bad tech out there that, you know, it’s left a little bit of a bad taste in some cases, in folks minds. So I think that our job is to kind of really try to work, work through that. And in terms of the next, you know, kind of greatest evolutions of from a technology perspective,
I think they’re going to come in the form of generative technologies for sure. I think they’re going to probably be in the intersection of personalization, and what I would call real generative technology. What I mean by real is, you
know, after call emails or texts generated by a bot, not generated by a human, maybe moderated or supervised by a human, but not not generated, those kinds of things will become automatic and not using RPA kinds of technologies. But what’s right there today in terms of leveraging a generative summary and after call summary with next action for the customer, I mean, the world is the opportunities are endless in terms of that. And I think that’s probably the next exciting thing for me, at least that I’m looking forward to. Love it. It’s a good time to be in tech. Never a dull moment, lots of places to help. And obviously, we love the partnerships and hugely reliant on these relationships to make it all possible and give the partners what the customers need. So that that wraps us up, sir. MichaelRoche AI, subject matter expert. Thanks. Thanks for coming on today, man. Josh, thanks a ton. Appreciate it. Okay, everybody. As always, wherever you found us, Spotify, Apple Music, be sure to go subscribe, go like you get these every time every Wednesday morning when these these drop out, you’ll never miss any of these things. And so that’ll wrap us up for today. This is the Gen AI revolution expanding customer and agent experience MichaelRoche AI subject matter expert atFive9 I’m your host, JoshLupresto
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