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Ep.120 AI Overload and The Mind-blowing Ways Artificial Intelligence Elevates CX with Greg Weber

June 4, 2024

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Jump in as we have a very informative chat with Greg Weber of Tech Mahindra. Greg and I cover so many great areas of the tech stack and business problems as we talk about Customer Experience 2.0: How AI is Reshaping the CX Landscape! You’ll hear about some of the top asks Greg sees from customers, some of the tech mishaps and expectations customers are experiencing, and how Greg and the team deal with simplifying it down as we focus on MVP and the importance around that. Don’t miss this! There are so many great pieces of advice you’ll want to listen to this episode over and over. This episode is sponsored by Fortinet. Learn more about Fortinet here:

Hey, everybody, just finished up filming an amazing episode that you’re going to hear here in just a sec from Mr. Greg Weber, part of the Tech Mahindra group, as we talk about CX and AI, how that landscape is changing exciting stuff in there. Grab a pen, grab a paper, take some notes, get ready. Just wanted to remind you today, this episode that you’re about to see is sponsored by Fortinet. This is a name that you all know and trust. This is a Gartner leader. All the great products that Fortinet has. We love working with the Alliance partnership that we have. We’ve got NextGen Firewall, we’ve got got AI and ML enhanced offerings in there. We’ve just got these Fortinet edge devices everybody has. Oh my gosh, you’ve got access points. You’ve got a manager and an analyzer to really look at all of these logs. And I think as you as you pull that together, what you step back and realize is you’ve got a comprehensive global leader in security that allows the customers that you’re talking to a great platform to lean on. And so keep in mind within the delirious portfolio, you’ve got a multitude of options, take over existing management, sell net new, but what you got in there is you’ve got a lot of different options. So however you want to wedge in there, just remind yourself, you’ve got a great fit in there with Fortinet. So go check them out, work with some of our Alliance partners and providers that you know in the portfolio today. So buckle up, enjoy the episode. You’re going to find some great stuff that Greg and I get into. Enjoy. Welcome to the podcast that’s designed to fuel your success in selling technology solutions. I’m your host, JoshLupresto SVP of Sales Engineering atTelarus and this is Next Level BizTech.

Everybody welcome back. Today we’re talking about customer experience 2.0. And primarily we’re talking about how AI is reshaping CX. On with us today, we’ve got a well known name, long time friend, Greg Weber from the Tech Mahindra group. Greg, welcome on, man. Good to have you on. Thanks for having me. Looking forward to looking forward to the conversation.

Greg, so let’s let’s step back. You’ve got some phenomenal experience in this in this space. Just walk us walk us through origin story, how you got in, where the career started, anything windy path and juicy is helpful to Oh my gosh. Yeah. So if we were on all the way back, I was finishing up grad school in upstate New York, doing my electrical engineering and computer science degrees, which I’m not sure I really apply those things anymore. And made my way out to Denver and I figured I figured I needed to get a job. So I started actually working for communications company and started doing hardcore development. I was I started off doing a Japanese features for PBX is So, you know, I got I got to get the full full gabbages kind of thrown into the deep end to start. But then I kind of just kept progressing my career started a architecting a bunch of key solutions, patenting a lot of technologies.

And then working for kind of the office of the CTO kind of evangelizing kind of how this technology was going to drive different types of business outcomes. All around. So it’s it’s been quite the journey, but fortunate enough to have all those great foundations, but yet still be able to to talk to clients, customers and engage with you on stuff like this. I love it. So let’s talk about kind of where you’re at now. Right. I know we met you early on in the event stage. We did lots and lots of CX together. So so tell us about kind of the evolution here about the tech Mahindra and what you’re part of now. Sure. Yeah. So I know most of this community is part of event as I was the CTO of the event solutions group. You know, we were helping enterprises make their CX visions a reality. I know that sounds like a lot like marketing, but we kind of put together a lot of those kind of capabilities to be able to do it. Think about that consulting, you know, everyone’s seen Star Trek. Everyone’s seen Star Wars. They know the buzzwords, but a lot of organizations were struggling really codifying their visions, you know, really getting pen to paper of kind of building all of that stuff out. So we had kind of spent a lot of time kind of building that organization together. And once you have that North Star, how do you actually get there? PowerPoints look great and you know, graphics are awesome. But what does it really mean from a change management perspective? How do you navigate this vendor landscape? How do you blueprint solutions?

So work a lot on kind of crafting those actionable plans. Then I think everyone would agree it always comes down to people process technology. You know, we had a lot of, you know, those architects and developers and administrators and configurators, you know, building these CCAS CRM solutions.

Empowering the workforce, using WFM, QM, knowledge, CSO, all that stuff. Like how do you get the workforce using that technology? And then obviously, how do you support it? How do you make sure it’s actually driving your original business cases?

And we ended up being pretty successful. So we got we the event, the solution group got acquired by the tech manager group about three years ago and we became kind of that technology lead for CX. Worldwide, we went from, you know, millions of dollars to billions of dollars overnight. Now, part of a huge conglomerate, but you still get that really that niche, that attention that a small company can can give you. And how do you feel a little bit of a commercial here, right? When you guys are are engaged in these and you’re working with customers, what are some of the things that you feel that you do now in this role as part of the tech group to to stand apart from the competition? What do you see yourselves doing differently? Yeah, I think I think there’s a couple different dimensions to that. You know, I think number one is how we put together our teams has truly changed. You know, it’s not just about is someone certified on a platform or can someone program this language? It’s really about everybody being aligned to a true orchestrated customer experience. And we have a lot more flexibility to kind of bring cross functional consultants together on these teams. So when we’re finally kind of, you know, understanding what those objectives are from a business, it’s not just, hey, hand it off and someone behind a curtain does something, it can put these teams together to really drive the right outcomes. I think number two is that we do both professional services and managed services. And listen, some organizations want us to do both, some people don’t. But the point is, is our professional services becomes a lot better because we have the day two in mind. We have the managed services in mind because everyone knows they need to support these things. The next dimension is really CX is way beyond CX. You know, it’s gone way beyond it. And most organizations that really just did CX or just currently do CX.

You know, we talk about knocking down those silos, but every time you go outside of that, you know, where do you get those resources from? And being part of the tech manager family, I have the ability to go be the biggest partner for almost anything that CX related and even beyond. So I have the ability to pull resources, my reporting and analytics can pull from sources that no one expected to be part of it. And the last part is, is I have full flexibility to do different types of engagements. You know, as I said, we went from millions to billions. So our balance sheet allows us to, yeah, we can do fixed price deals, TNN, game shares, sharing models, all the way through. So, you know, I think that those four areas really separate us, you know, from the pack. I like that too. And part of it, what we’ve seen, right, you know, you mentioned going from millions to billions, you’ve got resources. And I think translating that to, you know, from a tech stack perspective, there’s not an integration or a CRM or a piece of an application that you don’t have somebody go, oh, yeah, yeah, I can pull them in. Yeah, we’ve got six people that do that. Right. So there’s a lot of confidence, I think, in that as we get into some of these engagements from what I’m saying. Yeah, and I think we’ll get into it a little bit today, you know, how we construct those modernized contact center frameworks or those modern CX frameworks. And it’s not just about pulling in those resources. I think that’s a big part of it. Don’t get me wrong. But I think it’s also how to negotiate on how these components are going to orchestrate together. And that’s where a lot of these kind of initiatives sometimes go sideways is, yeah, you bring on resources. But who brings that? And orchestrates it together. And we can kind of help with a lot of that now. So so let’s talk about the challenge here. You know, title, this is talking about CX and an AI, right, how that’s changing the CX landscape. But, you know, we’re, we’re looking at this from a broad brush of AI in cloud AI in CX AI in all these areas. And we just see so much product ization happening in CX from an AI perspective. So why do you think we got to AI productizing in CX? So quick before any other areas? Well, listen, we’ve worked together for years and that acceleration. I mean, I don’t think I’ve ever felt anything like this. I mean, there’s been big paradigm shifts. There’s been, you know, new tech that’s become interesting. But the acceleration that, you know, you mean all all of its partners and everybody’s been on is going crazy fast.

You know, I think, you know, if you take a big step back of what people were trying to solve, I think that’s kind of answers the question of why AI is accelerating adoption and and productizing and what have you. So if you think about kind of the new world of customers, okay. They have a bunch of different paradigms that we’re not used to. Number one, their expectations have drastically changed. You know, I, I always kind of, you know, tell a little story about my daughter. We were driving up to the mountains one day, my wife, myself, my daughter, and you know, my daughter is in the backseat of the car and she goes, you know, I want to watch Episode Two, Season Three of Mako mermaids. And like my wife and I are laughing each other. We’re like, we used to play the license plate game.

And now and now it’s like, and what’s funny is number one, she wasn’t talking about she was talking to her iPad. And number two, she was expecting it right then, like instantly.

You know, if I had my mother in law calling in and she was on a half an hour wait, she’s like, thank goodness, it wasn’t an hour. I’m okay. It’s just the expectations have drastically changed.

The second one is, is how customers and consumers are using technology. Right, you know, and like it’s, you know, I was dropping off my daughter at school. I think I’ve told you this story before I was dropping her off at school and she was talking to mom and said, hang up the phone. You’re not going to be late on my watch. You know, she gets out of the car and rather than hugging me to go to school, she goes, why is it called hang up? You know, she’s she’s 16 years old and has never seen a phone hang up. And we talk about how fast that change, you know, it’s like my father invented the 1-800 number of my daughter’s never heard dial tone. Like, think about how fast that has to happen of how do you use that technology and then what people value, you know, is it time? Is it random gems? Is it awards? Is it money? The value proposition is changing. And every one of our clients, it doesn’t matter if you’re a mom and pop of the largest enterprise, you’re no longer getting compared to just your peers. You’re getting compared to everybody across any vertical and they expect you to be the best of the best of the best. And most organizations are like, oh, my gosh, this has just changed so drastically. So from how we integrate with customers, they’re dying for those solutions that are going to attack those three dimensions.

There’s there’s a second part of it. You know, it has kind of tried to integrate these stacks together for a while. And they’ve been running into some some issues. You know, the big issue is, is the if you ever do need to get to an agent, that agent needed to be the glue of a process. It wasn’t like automated, it wasn’t, you know, these are old processes. And then that ad hoc nature made it really hard to scale and solve problems that the business was driving for. So AI is the first, I don’t know if you see the AI is the first tech that I’ve seen for a while. That’s really hitting on all those dimensions. Yeah, yeah, it’s, I know we’ve done the Moore’s law thing. And we’ve talked about all that and the pace of innovation. But, you know, I like to look at this thing the same frame way you look at, you know, investing, right? Don’t get caught in the now. Zoom out. And in things like this leverage hindsight to go, all right, if this is five years from now, what do I really think this is going to propel us? And you can usually get pretty predictive with that. Yeah, things are slowly iterative. This is mind blowing every day, the new piece I wake up to and I watch and I go play and I test and I’m like, holy crap, that works. And it worked really good.

Yeah, mind blowing. But I love I love your points in there that you drive home about customer expectations. I love that. I think it’s a and then so so down to kind of very specifically answer your question of why it why it’s kind of accelerating and being productizes because it hits so many different areas that it can help on in different ways. It doesn’t solve everything. Not everything’s just perfect in this. But like, there’s a lot of things. And I love your idea of kind of like when you zoom out. So when you zoom out, you think about the five macro areas for CX that AI is affecting is it’s affecting the journey. Right. So think about the journey around like, you know, how you make clients aware of your services, how they acquire your services, how they consume it, how they get help or get assisted on it. And then how do you build trust with the brand? Like AI is affecting all those dimensions within that kind of that customer journey. And then typically, when we talk about CX, we’re interacting, right? So let’s let’s take that interaction part, two types of basic interactions we have automated. And we have it when you’re talking to agents or reps or whatever term we want to kind of put on there. Right. So when you’re talking about those automated services, you know, everyone’s kind of like, how do I initially have a dialogue with somebody like figure out who they are and what the heck they want? You know, how do I find the best resources and the best processes to be able to serve that client? If I’m going to do it automated, like, how do I do it with a nonlinear approach? How do I actually create new use cases that I could have never dreamt of before?

And then if I’m gonna get to an agent, how do I deliver this in a contextual way? Right? Because I want a very smooth, I want it to feel like one common journey. And then once I get to the agents, AI is affecting it in so many dimensions. How do you figure out what someone really wants to the next level? You know, how do you ascertain what the best next steps are? How do you engage with them without memorizing training? How do you summarize a call and wrap it up? And how do you handle it? So we have that automation, and then we have kind of our agents to drive those things we were talking about before. Once you not to get too preachy, but once you have agents, you got to develop those agents. How do you recruit them? How do you select them? How do you upskill them? How do you onboard? How do you how do you retain them? Like, you know, so so AI is hitting a bunch of those. And then obviously, are you driving outcomes? You know, that bucket of performance management, for lack of a better word, right? You know, it’s like, how am I making sure I have the right people at the right places at the right time, make sure I’m measuring things well, I’m doing all my operational stuff to make sure that AI is kind of giving me the outputs that I want. So again, a long way to say it, but like, it’s worth seeing the acceleration because it solves those things we talked about. And it has so many use cases.

Let’s talk about let’s talk about lessons learned. So you know, flashback as far as you want to flashback here. But give me give me something that you’ve learned along the way that you’ve applied hard lesson learned because some ways you stubbed your toe, something from a great mentor that you’ve had as you come into the space, you know, what do you got there?

Oh, that’s a tricky one. Um, I guess I would answer like this. Um, what MVP means, you know, I think, you know, every kind of project, every kind of engagement, you know, the words MVP come out and you know, when I when I was growing up, MVP was Lawrence Taylor and the New York Giants, like the most valuable player, you know, and then all all of a sudden, somewhere in the last,

10 15 years, like MVP became a goal. Right. And I think the biggest lesson learned, you know, that I have, or you know, when I push on my team is, if you’re going to do MVP, really make sure you don’t forget about the viable part. I think too many times,

things devolved to MP or minimal product rather than, you know, a viable product and and how you approach MVP is really important, especially when things are going so fast the way they are now that acceleration. We just talked about and those things. So I would say it’s if you’re going to do MVP and you’re going to try to start getting those quick wins and in your ROI, which are all really good things.

Make sure you’re building for today, but designing for tomorrow. And where a lot of organizations get stuck is they either become so overly focused with like an MVP or something that’s just really just, you know, not driving every single outcome that they wanted. They just needed to get to the next gate, but then enhance it over time. You know, it becomes a impossible task. It becomes you got a road until the entire system in order to kind of get the next thing and and that maybe worked with our old world together. Right. Where it’s like, hey, something new came out three years ago. You know, you were just talking about it’s daily, if not hourly, like you can’t keep restarting. So we when you when you think through kind of MVP or what you’re what you’re building and what you’re constructing for organizations, it’s it’s lay that foundation.

You got to train it. You got to keep making it more optimal over time, but it can’t be a road until you got to have those really great foundational designs and architectures and frameworks in place. In order to be able to hit that acceleration. Otherwise, what was it? The Wayne Gretzky? Where’s the puck? You know, do you pass it to where they are or where they’re going? And you can’t put it too far ahead. Otherwise, you’re going to lose the puck. So you got to find that nice range. But if you hit too close, you know, you’re not going to be able to go. I think that’s probably my biggest lesson and most applicable today. Today’s role. I like that. Maybe a harp on that a little bit here.

You know, as you get into these engagements with customers, I suppose you have, when you think about MVP, it is a place to get to, to prove that what we’re saying you can do works and to get your buy in all these things that it accomplishes, right? Psychologically, technically. But you’ve also probably got a lot of biases to watch out there for from a customer perspective, you’ve got, you got your your naysayers of your confirmation bias that this thing isn’t going to work. And you just, I think if you to your point, I love that example. Because if you do focus on the viable part, it’s data that tells the story. And they can envision their self in this house and you know, all those things. Totally there. I mean, again, I don’t want to confuse proof of concepts or proof of value with MVPs. You know, I do think that those are different things. But to your point, you know, a lot of these technologies now can can bring it in your ROI. They can they can sit there and change things that, you know, those years and months are really weeks and days now, to be able to do it. The key though is, is having that foundation in place, you know, and that’ll allow you to keep accelerating. Because listen, we’re going to talk about a lot of different case studies, we’re going to talk about examples today, and, you know, and beyond. But I can tell you right now, you know, all those people that are doing those five year visions. Interesting. But shrink that in, like even just trying to get into a year, you know, it’s hard. So, you know, we got to make sure we’re kind of building on those right principles to be able to accelerate. So let’s talk about that. Let’s talk about a, let’s talk about a quick surprising win. So maybe you walked in, you got brought into an engagement and just something where the customer expected, I just need this and everybody was surprised with kind of how it ended up. Give us an example of something like that.

Sure. You know, I guess the way I would attack this one is merging together technology and operations. I think that too often either things are business led or IT led and they’re not led jointly together. They’re not kind of thought of together. And you mentioned biases before everybody’s coming from their biases of what they experienced. And even though you talk about AI or seek ads, or you talk about some of the other kind of, you know, key buzzwords or what have you. Even though they know there’s a paradigm shift, even though they know it’s different, they’re not ready for those paradigm shifts. They sit there and they still think it’s like in the old world, the systems that we were constructing, you know, we were working on an environment, working with a client about, you know, a little north of about 20,000 agents. And by the way, that’s a big cut down from where they were, you know, with that kind of stuff is and you know, the original project plans and what they were going to build. I mean, this was a, you know, when I say multi year, I’m not talking about a couple years. I mean, talking about long term thing. They had no idea that once you embed with the right foundation, data driven models, bring AI in the right places, that you could literally start rolling on hundreds, if not thousands of agents a week of a blink of an eye. You know, you can roll on new self service capabilities that are that are shifting the containment rates and deflection rates to numbers that no one thought was going to be even close. And they’re rolling it out with, you know, fully tested, ready to go and be an operational effect. Are if it was the number one thing that was surprising, everyone was used to setting up these like eight, nine hour war bridges, you know, like we’re launching, you know, what do we do? And you know, after the first two, we’re like 45 minutes in, everyone’s like, okay, can we go home now? Like, there was nothing else to do. So the expectations of that. And then I would I would kind of build on to that is nobody really, people make business cases. Okay. And everyone’s like, okay, and everyone puts their little deltas in there, and they sold it on this, but they think it’s going to be this. But in general, like when you look at some of these, these big leaps that people are embedding all this technology in an appropriate, operationally efficient way, all their standard KPIs are, are getting better, like the, I mean, better, like handle time and FCR and all that kind of stuff. Like that kind of stuff is like, just like, like, they never seen numbers like that. But yet not sacrificing CSAT, not sacrificing MPS, not sacrificing those, and the cost went down. So so very rarely do we see these win, win, wins all the way across. And, you know, I loved it that, you know, for that one, it was very operationally, like, just, they were, they were amazed by if you have the right foundation, build it in the right way. And think about operations, while you’re embedding this AI technology and embedding it, you can hit these really fast. Awesome. Let’s talk about customers out there, right? As you’re talking to customers, you see, you see people in the business side, the tech side, small orgs, giant orgs and everything in between.

What are some of these things that, you know, you’re seeing people are demanding of AI or just common threads that you keep seeing in these customer discussions?

So, um, so I’m gonna answer this in a little bit of weird way. So let’s start off with customers being the end customers and my clients first, let’s talk about that first. And then we’ll talk about my customers or our partners customers. You know, I think the, the first thing is, is what, where do I get stuck with AI? When AI is forced in the wrong way, the end clients, then consumers, they get pretty frustrated. You know, trying to make a voice channel, I don’t care how much you embed AI into it and how many cool nonlinear cases you can solve. If it’s the wrong UI to solve a problem, it’s the wrong UI to solve a problem. You know, how do you kind of think beyond it? So when people are forcing some of that AI technology into a channel or into a UI that doesn’t make sense for the outcomes that there’s frustration starts building. I think the second big area is what processes are exposed. People are not typically needing to engage with a UI bot, a channel bot or an automated bot just to get frequently asked questions. You know, Google was good enough for some of those. But it’s not that good. It’s not that good for some of those items, right? So the question is, is how many processes are exposed? So we talked about CX beyond CCAS. You know, part of that means how do we take antiquated processes and number one, expose them into a world that has a good framework to be able to interact with it. Number two is how do you make those processes more modernized? And that’s really hard for organizations. You know, organizations have invested millions and millions of dollars and hours and hours of time. In fact, they don’t even have people in the organization understand the processes anymore. But how do you actually make those modern, you know, for the world? So I think there’s frustration of, hey, I might have a great UI and might have something that’s, you know, interacting with me well, but if I can’t really get the outcomes I’m looking for, you know, that’s a problem. I think number three is we still have a large part of the population that’s looking for empathy.

And having bots trying to be empathetic. I’m not that great. I’m figuring out exactly where all that’s going to go. But I hear a lot of complaints about it. You know, if you’re working with a bot, get my work done. You know, I’m sure there’s a community that likes just gets fulfilled, you know, interacting with bots, but it’s faking empathy, you know, is a tough one that people are having trouble digesting. And then I guess the last area is how do I get out of the loop? You know, if you if you look at AI, and again, we’ll dive into all those cases, but there are some parts of AI that were directly exposing to those end clients.

There’s other AI that we’re using behind the scenes, you know, so think about like, how to search and, you know, better information for an agent or how to figure out next specs action or how to alert a supervisor that’s behind the scenes. But all the front end stuff, you know, we’re still growing, you know, many new use cases, I never thought we’d actually kind of automate sometimes, we’re getting there. But there’s a lot of people who are like, I’ve already done my tier one tier two support. I’ve already done that. You know, you know, you’re calling in for computer help, of course, my computer’s plugged in, come on, let’s get going. You know, so it’s like, how do they get out of the loop? How do they how do they figure that out? So I think those are kind of some of the areas that as we’re constructing these AI solutions, that we got to be careful of what we’re pushing all the way out to our other clients. If I use the word customer, to mean our customers, you know, you know, so many customers to our agent population and our partners, you know, those customers are struggling, I guess they were going from the paradigm that AI was a competitive advantage. And they’re realizing it’s now table stakes. So so so they’re getting stuck up, like, how do I harness this really fast? And and they’re struggling on boiling the ocean. As opposed to figuring out exactly what areas you want to focus on first, you know, picking two to three data points and pain points should be it to start. But they’re getting overwhelmed by vendors and products of saying, I can solve this and solve this. And if you don’t pay attention to this, you’re going to lose this. And like, there seems to be way too much, you know, pressure to say, implement AI, or people added dot AI at the end of their names.

That that’s where our customers they don’t, they’re not, it’s not clear to them how to harness it in an effective way. Sorry, I didn’t know how you know, no, it’s, those are a couple, those are a couple good points to I mean, it is I mean, it’s, it’s, we’re all guilty of it to its shiny object syndrome, because it’s really exciting. Shiny object syndrome. There’s so much good tech, you feel compelled to investigate just a little bit of like, well, I don’t know, I didn’t need this yesterday. But maybe this is the thing I can’t live without, right? And I think that’s where we have to balance everybody out to your point, just billing on some of the other thoughts that you had before. Great, great color there.

Let’s talk about case study. So walk us through, walk us through an example here, customer you walked into simple, complicated, large, small, whatever, and kind of what were the components of their tech stack? What was the business problem as you peel that onion back? And then what did it look like after? Got it. Um, yeah, so so I’m gonna stay away from exact client names and exact partners. Let me just talk about the case study. Let me talk about one of our case sites. And we can talk about other ones if you like. Um, I guess I’ll start off with, um,

let’s, uh, I’ll use a utility company, um, you know, international utility company, and they were a little bit behind the times in terms of their tech stack. And they were kind of sitting here going, Do I kind of rip and replace to kind of get to a modernized world? And that those projects and the way the contracts are working was not as simple as it sounds, right? So there were questions like, how do I start bringing on kind of the, you know, AI in a way that’s gonna really help me kind of hit those business outcomes I was looking at. And this one, you know, is around kind of a conversational AI bot, right? You know, with the typical metrics of like, how can I truly contain and deflect all those operational and billing interactions?

They were getting flooded, you know, for them, you know, they were at the about the 2 million interactions a year that were getting hit, you know, obviously, we have partners that are bigger than that, some of their smaller, but just picking one right here, they had five different service lines, they had like financial care and help desk and retail, you know, customer service, I can’t remember all five off the top of my head. But what they really want to do is they wanted to enhance the voice channel, they wanted to get the voice channel, because they had a large population that was still calling in a voice. How do you start kind of bringing those out, but they wanted to really open up those digital channels and be able to solve problems. And, you know, we digital channels been wrong for a long time. I mean, that’s not like a new thing. But when you start thinking about the use cases, you can solve in those digital channels. And then how do you create kind of a coherent, cohesive experience across of it that on the channel feel like those

still aren’t all solved. So that’s what we kind of brought to them, you know, a little bit, you know, with that. So so we took, you know, obviously expanded out the voice channels to bring in those those digital channels, we brought in all you know, the WhatsApp and the WeChat and Twitter or and, you know, web chat across all their their different service lines. We then kind of looked at all their intents. Okay. And we kind of were very realistic of what we could actually solve very effectively, very fast for them for that beginning of the MVP. You know, and as I said, the game, like we did kind of that billing intents and you know, they were utility water related kind of intense.

So if you think through kind of like those water related intents, you know, after we analyze, you know, interactions for about three years, you know, we’re able to kind of get like reporting a leak, you know, oh my gosh, I don’t have any water. I’m not really a water guy, no pressure. You know, I have wastewater, whatever, you know, so we were able to kind of analyze and figure out what those head intents were and then be able to kind of build those other processes. I mean, that’s important because we actually kind of work with them. Using some, you know, intelligent automation to expose some of those processes so people could actually self serve their own water leaks. We could actually we started working on detection that they could run a detection to see if their meter was flowing and it was so that we’re able to kind of bring together those new workflows and move it into that UI. From a billing perspective, billing things are, you know, the easy ones are I want to pay my bill. Okay, like everybody can do those things and it’s a little bit harder internationally, but you know, I want to pay my bill. But when you start getting questions like my bill increased, you know, or I want to, you know, add someone else that’s an authority to my account and they can make changes or, you know, all there are a lot of complex billing types of intents. So we were able to kind of take about the top 11 of those and then we were able to kind of create this really high containment rate, you know, between their billing cues and there we were able to, you know, get to, you know, one of them was in the mid 70s, one in mid 80s. And again, I’m not talking about just like pay a credit card where those are in the 90s, but like these are hard complex going through there and they got year one benefits.

Yeah, it’s not in US dollars. So I’m gonna probably get the conversions wrong, you know, but they were at a million or two of savings, you know, in their first year, you know, and I can tell you right now the project itself didn’t cost that much. So so they were able to kind of get back those things. And then we’ve now since identified, remember that MVP we talked about as a foundation, we’re able to identify kind of a whole new range of intents. We then also took a lot of the ways we were solving those new sub intents and we built in agent assist for them. So now they had this core, they couldn’t just change. We added a lot of a lot of conversational AI capabilities. And as we were starting to expose new services, we then also made our agents more efficient. In the meantime, so and ironically, the the CSAT went up. People people sometimes want an empathy, but sometimes people just wanted their problem solved. Yeah, I love it. Awesome example. Love the detail on that one. So so many different areas. Good, good stuff there.

Okay, so so final couple questions here is we start thinking about this. So framing this up, right, you know, we’ve got hundreds of suppliers in the TSD portfolio, each of these suppliers has maybe one OEM that they have access to maybe 25 OEMs that they have access to, right. So as that productizes over time, you know, AI and components of AI and large language models and all of those things are going to bleed into that. So we have what the channel does now. And then we also have kind of what is let’s call it semi outside the channel. So I’m just, you know, advice for anybody that’s listening or kind of Greg’s opinion here. How can the channel do better, knowing that we’re going to have, you know, Microsoft and co pilot and all the things that they’re going to do. And you got to open AI doing all the things that they’re going to do. How can the channel do better? And how can we do better at product a productization of this over time?

Um, yeah, I think I think there’s a couple different motions that we need to kind of get to. I think the first motion is deciding how what business outcomes are really trying to go for it. You we really shouldn’t be doing AI for the sake of AI. Like really mapping what those business outcomes are And helping kind of construct that and using some of the vendors to help your own consulting services to help but really honing in on those business cases. I think right now we’re getting flooded and inundated with it, you know, and everyone says this is AI enabled and you’re like, okay, like looks like the same thing it did before, you know, but what are the outcomes. So really working on kind of those those understanding and those methodologies to be able to map the technology. To the business outcomes is is really important. So that way you can sell it and to to businesses at the same time. I think the the second part is is Understanding what parts of AI are being used in these solutions. I know you just brought up large language models and everyone loves to talk about it and you know stuff like that. You know, I would argue that You really don’t want to do everything through Gen AI. You really don’t want to listen. There’s a great place and there’s a lot of utilization, but that’s where it goes back to those business cases of can you solve these use Gen AI. To train things use Gen AI to build it up use some part. But if you do everything through Gen AI your business cases are going to look a lot differently. And, you know, those tokens are going to be flying as opposed to kind of figuring out, you know, what parts of AI you want to harness for the outcomes that you’re trying to do. The last part is is, you know, there are some hyperscalers in the industry. I think you mentioned a couple. I think there’s some other ones and you know there’s some people are still kind of in that range. I think it’s going to be really interesting to see which types of AI are getting put into the platforms themselves and which ones become very specific for industry or use cases. And I think the trend when you kind of mentioned, I sorry, I forgot which ones you mentioned. I think you said chat GPT and co-pilot. I forgot which one to talk about. But the point is, is a lot of those tend to be a little bit more general

As opposed to, you know, being very specific for a brand, a vertical and industry. So understanding where what again what problems you’re trying to solve Is where you’re going to kind of start bringing in that technology. So the more you understand about that stuff, the more that you you you you experience it. I think the better off we are. I think the other thing is, is with your vendors every single vendor, you know, really hold their feet to the fire. When they when they start talking about AI, no one’s going to tell you what’s happening in the black box, but really make them explain why it’s different and what outcomes it’s trying to drive. I think that’s going to fundamentally change the channel. The last thing I will say, especially for, you know, let’s talk about C-CAS for a second.

Not to get too preachy, but I think that there’s a huge paradigm ship coming almost every single vendor still has a core. This is how many seats we have. This is how many agents we have And right now our businesses that we work with our customers are starting to get a little skeptical going. Do I really want to do a massive effort to take X amount of agents and move to X prime amount of agents, maybe a little less or you know what have you and sign up for that for three years, five years, what have you. When we are seeing these drastic changes like people are talking about, I’m going to eliminate 70 80%.

So so really kind of understanding and pushing our vendors to say, what is your model, not just today, but going forward and make sure that’s aligned with how you’re selling to your clients. I think is a huge part of this. Love that. All right, so so if I’m a partner, I’m listening to this, you obviously helped me distill down where AI is how it’s evolving the CX space and other spaces.

And maybe I haven’t jumped into this space yet. Maybe I’m selling other products or you know other things in the portfolio and I want to expand my knowledge right at this rapidly changing world. Where do you steer me? What do you recommend? What should I do?

So I think you should call Tolaris and have them partner with me and I’ll go on a lot. No, um, you know, honestly, you know, most of my organization, it’s, it’s playing with it. You know, the barriers to entry to actually play with it have gone down precipitously. You know, really, even if it’s not in the CX space really playing with it, I think is probably the number one thing. Figure figure out how you play with it and then try to map it to a pain point and then map understand, you know, I’m listening. I’ve met so many of the partners like people are so good at knowing their clients and knowing a pain point. I would say pick one pain point and focus on researching on how AI would affect that. You know, you could read the large language model books and you can read about how CX is changing and they’re interesting and there’s things, but it’s, it’s really about researching one pain point and understanding the vendor landscape and how they’re attacking those types of problems. I think are the fastest ways to learn about it. Good. All right. Final thought. Let’s look into Greg’s, uh, miss Cleo crystal ball here. Uh, where do you see the customer experience heading in the future? I know we talked about, man, it’s hard to look out five years. Maybe we can’t even look up 12 months at this point, but what are, what are some of the innovations that you’re expecting to see? Um, yeah, I think, um, I think I’m going to give you a little bit of a boring answer. Actually, um, you know, I, I think we’ve spent most of our careers with this. This anytime, anywhere, any channel type of CX, um, uh, going through there. Make sure you can naturally escalate and have contextual experiences and, you know, be on a journey. I think the thing that I think people would love to see is that, um, uh, all of these things are actually, uh, are, are, uh, a, a, a, uh, a, a, a, a, that, uh, a, a, a, a, a, uh, a, a, a, a, a, a, a, a, a, uh, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a

future is really down to having productive interactions, predictive interactions, and pretty personalized interactions. And again, not mind blowing to anybody, but these things are now realities. I think that most organizations are gonna start looking at if I need, if there’s someone calling me for customer support, it is no longer gonna be just about how do I provide a good CSAT for that interaction that they needed support? But all the way up front to talking to product design going, why are people calling us? Why are people chatting with us? Why are they needing support at all? Your product is broken. Or if people cannot self serve, unless they’re looking for empathy or really high end clients,

I think a lot of organizations are looking and saying, my processes are broken.

They’ve been warping technology to fit processes that were designed in the 80s and the 90s. And it’s hurting the productive, predictive and personalized types of experiences.

So I think that those are gonna be kind of the three metrics that we start measuring everything on. And it’s no longer about just is CX better and CX and C-CAS or CX and the interaction, but it’s what is broken about my product? What’s broken about my service that I ever needed support?

And that’s where I think things are kind of heading and accelerating in that direction very quickly.

Well, a lot of questions, man. There’s so many good nuggets on this. Everybody needs to go back and listen to this 10 times. So Greg– – I hope I didn’t ramble too much. – No, no, no, there was zero rambling on this podcast. Lots of good stuff. Greg, thanks so much, man. I mean, you dropped so much knowledge on this thing. I really appreciate you coming on, buddy. – I appreciate it, Tom. I appreciate you, the Telarus family. And thanks so much for letting me share my thoughts. – All right, everybody. That wraps us up for today. Greg Weber from the Tech Mahindra Group. And as always, remember, wherever you’re coming in, wherever you’re listening, Spotify, Apple Music, go subscribe so you get these every Wednesday as they drop out. That wraps us up for today, Customer Experience 2.0, how AI is reshaping the landscape of Greg Weber. I’m your host, JoshLupresto SVP of Sales Engineering at Telarus.