Name: The CX Architects: Designing the Future of Customer Experience
Uploaded: 2026-08-19T17:16:26.283Z
Duration: 57 min 8 s
Description: The CX Architects: Designing the Future of Customer Experience

Transcript for "The CX Architects: Designing the Future of Customer Experience": Hello everybody, welcome welcome. I see a lot of people active in the chat already, so excited that you are here. My name is Deeka Pila, I will be your host for today. I'll get started in just a moment with introductions to our fantastic panelists and a bit of a preview on what we're gonna be doing over the next forty five minutes. But first, I'd love to hear from everyone in the chat. What city are you joining us from? I saw Tina earlier talk about howdy from Texas. I'm joining from Austin as well. Hello. Hello. We've got folks from Ukraine, Arizona, Georgia, San Francisco, Chicago, some more Austinites. Hello. London, DC, Zambia, amazing. Bentonville, love it. It's been a while since I've been there. Denver, also. Well, that's you, Chris. Love it. Amazing. We're so excited to have you all here. We'll get started in just another moment. If you're just joining us, we'd love to hear from you in the chat. What city you're joining us from? Seattle. Oh, I love Bogota. I was just there a little while ago. Argentina also. Awesome. Great representation here across the world. Welcome, everybody. Now I'm trying to calculate in my brain what time it is for everybody. Oslo, Philippines. Oh, I'm not gonna get any of the timings right now. I don't even know what time it is here in Austin. It's 11AM. We'll get started in just a minute. Welcome from Barcelona, Toronto. What an incredible turnout. And if you're just joining us, welcome. I'm Dee Kapila, your host. We'll get started in just a minute. Please join us in the chat and tell us what city you're joining us from. Got my little, welcome voice on. It becomes a little more natural as we get into the topic today with our panelists. I promise. Oh, Cairo. Welcome. Ukraine, Arizona. Welcome. Philly. Welcome. Some more Oslo, welcome. Alright. We're two minutes past the hour. I'll go ahead and get us all started. So excited to be with all of you here today. I'm Dee Kapila. I'm the senior director of customer success here at Fin. I lead teams across customer success, customer education, voice of the customer, community programs. And so I have the privilege of spending my time with our customers and really following them through and helping them through the transformation that we are here to talk about today. Before we introduce our panelists, I do wanna talk a little bit about, what we're gonna cover. I think over the last several years, we've all, been running various aspects of customer success. We've got running we've got folks running support, success, and running CX Teams used to be a pretty steady job. We had playbooks to follow. We had people who'd already walked the path ahead of us, but it's changed in the last several years. I think we're all feeling that in the chat. Do let me know if you've been going through a transformation. I'd love to hear from you in the chat throughout, but it's been quite different. No more playbooks. And so I'm joined today by three people who have not just gone through this transformation, but frankly have left and been really pioneering in the CX space when it comes to transforming with AI. And so these are folks that are representing three completely different businesses. They have three totally different role roles, excuse me, and then they're all also at different scales of transformation. So I'm really excited to dig into their experiences. But what you'll hear from them is that they're going through some things that are quite similar. And so these may help all of you as you go through the transformations ahead of you as well. And I think that the point here really for today is that the shift is happening in every company no matter the size, no matter the industry, no matter the shape. And we're here today to compare notes really. This is a very practical conversation, totally unscripted other than my own intro which I read from a piece of paper because it's always good to have that nailed down. But I'm really excited to learn from these, leaders, and I hope you all get a lot from this conversation. Would love for this to be interactive. So if you've got questions, there is a q and a box that you should see on your screen. Please submit questions through there. We'll chat with our leaders here for about thirty minutes, and then I'll start to take questions from the audience. And in terms of other housekeeping items, we are recording this session. We will share this with all of you after, so feel free to share it within your organizations as well. And, let's get into it. So let's start with some introductions. I'd love for all of you, our fabulous panelists, to introduce yourself. Chris, do you wanna kick us off? Yeah. Hi, everyone. My name is Chris Rule. I am the vice president of customer experience at Terno, where I lead our all of our customer facing roles from sales to onboarding to customer success to, customer support and operations. I've been in sort of the the customer success, sales space and tech for about the last fifteen years. And before that, I actually started my career. Did about ten years in hospitality and hospitality management. So hotels, restaurants, things along those lines. And I was, like, incredibly excited to be here. Love it. Thank you, Chris. Amy, we'll go to you next. Awesome. Thanks, Dee. Hi everyone. I'm Amy Harvey. I'm the Chief Customer Experience Officer at TrueCommerce. And TrueCommerce is a global supply chain integration platform. So we connect, global businesses to their trading partners, suppliers, marketplaces, through a combination of things like EDI, API connectivity, and ERP integrations. My role is really overseeing the entire post sale journey for our customers globally, including onboarding, implementation, support, long term value delivery. So I've been with TrueCommerce twice now actually over the last fifteen years. And the majority of my career, I've spent at tech companies leading post sale journey. So really happy and excited to be here today. Thanks for joining us, Amy, and I'll hand it over to you, Kayla. I'm Kayla Art. I'm the director of member support and operations for Prize Picks. We are a daily fantasy sports gaming app, and we're also, now into the prediction markets world, which is endlessly fascinating. Prior to, PrizeFix, I led support and sales support and operations departments and teams, for, several different b to b organizations in in the b to b space. Led a lot of automation efforts there. And this new endeavor, being on the b to c side of the world, significantly different, much different expectations and a whole lot of transformation. That's incredible. And I know that's something that some of our our folks in the audience had messaged me about earlier, which is, wow, this is a very diverse panel. Three completely different businesses, three totally different transformations and roles. So I'm excited to dig into all of that with you all. Let's jump right in. Thanks for those intros. And, again, folks, keep it going in the chat. Ask questions in the q and a box, and we'll get your questions at the at the end. But let's dive into question number one. So I'd love to start with the job itself. If each of you could tell us about the role you're in today and how it's different from what you were doing maybe two or three years ago. I'd love to learn a little bit about that. And then if you saw any of this change coming, let us know that as well. Chris, let's start with you. Yeah. Absolutely. So, yeah, I mean, I I lead our I lead all of our post, you know, post sales as well as well as our sales and activation teams here. I I I neglected to tell you exactly what Turno is, but Turno, we we run the, the world's largest sort of, like, software and marketplace for short term rental turnovers. So think, you know, you have an Airbnb. You need, cleaners, maintenance, things along those lines. We run the software, component as well as the services aspect of that with our marketplace. And, yeah, I mean, I think that, you know, when I when I got to Terno, I I I implemented a lot of similar playbooks to to what we've done in the past, right, which is understanding customer health and and, you know, getting data into an an area in front of the teams and triggering workflows off of all of those different things. And, you know, that's sort of been the I don't know. I would call that sort of, like, the customer success or or or, customer experience sort of two point o version. Right? When I started at OpenTable, I don't know, about ten years ago, fifteen years ago working our our cuss building out our customer success function there. You know, CSPs or customer success platforms had just started to kick off, and so it was this really interesting idea of, like, oh, wow. We can use data to understand our customers better. That has sort of transformed over the last, I'd say, you know, eight to ten years into most businesses are doing things like that. And And so I came in and built a lot of those playbooks. And then in the last year, year and a half has been really interesting to see, okay. You know, that sort of very strict, you know, here's a data point, trigger something off of it, have become much more malleable with AI. Right? So being able to take multiple data points in multiple situations and multiple different, areas and leverage that to start to determine where our customers need us most and and to be much more proactive has been very, very interesting to me. We leverage a lot of different tools, including Fin, to do a lot of those things. So, you know, really understanding how we can deliver the right experience in front of the right person at the right time has been something that has always been this, like, long term, oh my gosh. I can't I I imagine a world where we can do that, and we've been able to step forward. And then this AI transformation, I think, has really led us to an area where we're getting really, really close to sort of that predictive, proactive outreach model that, is really important. And it's really important for a business like ours where, you know, our product is relatively human. Right? It's getting humans into properties, you know, physically. Right? And so there are a lot of situations where that goes wrong. You know, I'll you know? And we can detect when that stuff is happening early on now and actually put people against that problem so that we know, hey. There's not gonna be a problem with that turnover, and so there's not gonna be an issue with the guest. And, like, a lot of the AI pieces have helped us build out that functionality these days. So yeah. That's incredible. I I love what you said about proactive and also just thinking through, like, there's a world in which all of us were imagining some of these things would be possible, and now we're leading it. It's and it and doing it in practice. I think there's something fantastic about that, that's been that's been incredible. So excited to dig into that with all of you. Amy, how about you? Yeah. So, you know, my role in customer experience, as I mentioned earlier, is really about the full customer journey. Right? Getting a new customer onboarded to our platform, helping them, derive value out of our solution and improve the value you're getting out of our solution as they are a long term customer and then supporting them with any issues, concerns that they have. And I think what's changed is that before a lot of improving that experience for customers was doing things like looking at our processes, right? Like how can we change our processes internally or externally to make that experience better? And it might have been, you know, bringing in some new tools or things like that. And now, you know, with AI it's just gotten, two things. One, more innovative and more, you know, really bringing things to the customer that we can do way faster than we could before when we are just looking at process changes. It's become, you know, like, the speed of what we're doing, happens a lot faster. And I think, that, you know, what we're seeing because of that is that we're able to really optimize that customer experience, you know, exponentially from where before it might have taken us, you know, a year Yeah. to do some project that would bring, you know, good value to the to the customer. So the speed of of what I'm seeing is something that is changed dramatically. And, you know, I think we I saw something coming, but the the speed in which it's happened is something that I, you know, maybe wasn't predictive of. Yeah. And speed is such an interesting thing. Right? We've all had those speed and quality trade off discussions back in the day, and now, really, it's not the trade off that it used to in the past. So it's a very different world for teams, that we manage as well. I think that's a really interesting point. Kayla, over to you. So we we're at an interesting, space right now with our sort of AI transformation. And I I think what has been really, eye opening is, you know, we were in a world, you know, five years ago where, like we've all said, you know, having a data point and having an understanding of who your customer is and being able to take predictive repeatable action based on that customer, felt rather cutting edge at the time. Right? And and it was such a a part of, establishing forward thinking, you know, support teams and and operations teams. It's it it really was know your customer, define that journey, define that repeatable outcome, and then find a way, you know, to automate that and and make that more efficient. And and that's where we saw ourselves as as CX leaders kind of coming into our organizations and and being the experts at here's how to create a repeatable, outcome. And now, it's interesting because with AI, we're able to, differentiate ourselves, not in our ability to to create a repeatable outcome. Right? Creating that widget, that that low, defect experience. And rather than that being our our differentiator and our desirable outcome, it's much more so now, you know, here's how we are an integrated consultant to the business to say, here's what I know about my customer, about our shared customer that you may not know. And here's how we can leverage that and tap into it. And we're able to identify friction in the journey and address it and sort of bring that forward so much faster. We're able to respond to a shift or a pivot or even an AB test in instance where, you know, before we would have to pull hundreds of agents off and do a retraining and have manual trackers and make sure you're notating. All of that is is so much more efficient now. And in some ways, isn't that great. And in the other ways, now it challenges us to say, what are we gonna do in that space? Are we gonna just lie on, you know, the race to being the most efficient and the most cost effective? Or are we going to say, now what do we do with this data? And how do we then advocate within the organization to not just respond from the business to the customer faster, but also from the customer to the business faster? And that's where, there's been a massive shift, in our organization. I love that. Thanks for walking us through that. I think there's so many points in there that resonate, and we'll dig into those in a And, also, Meg, thanks for your comment. It sounds like this is something that's resonating for everybody. The role of CX and CS in the business has evolved. You're really an advisor across your entire business, as well as your customers, which I think is interesting. Alright. Thank you all for sharing that. So we talked a little bit about before and after. The thing that I always love to ask all of the leaders that I talk to is about when was that moment, right, for you. So we've all been through the various media cycles of AI. It was a threat, and then, it's a buzzword. And there's some fear, and then there's some excitement. And, I mean, we've been through all of the emotions here. I'd love to understand when did it really become real for you? Like, when did you decide, oh, I have got to lean in. This is my moment. Chris, we'll start with you. Awesome. Yeah. I think there was I think there was two. It came in the version one and then version two. For me, version one was, just our first time experimenting with with Fin. And this is probably two plus years ago. Right? And we we were AI was still relatively new. There was a lot of trust that we needed to build with it, and we did a lot of testing with a specific role. So one of the things we did here at Turner was we we elevated a customer support agent to be our our AI expert. Right? Reading every single ticket. Right? Making sure that all of our documentation is is correct. Making sure that the the the bot wasn't hallucinating. Right? We we really cared a lot about the customer experience and wanted to make sure we weren't degrading any piece of that. And so our first moment came when we were just going through these. We would do sort of two meetings a week. where this role would you know, we'd go through multiple tickets. We talk through what it looks like. We would do them live. We would, see exactly where there was opportunities. And almost every time, like, well over 99% of the time, it was an issue with our documentation, not an issue with the bot. Right? And so it was very clear that our ability to scale this was limited only to our ability to make sure that our documentation was accurate. And so we, we continue to lean in, and that was our first moment, and it continued to grow and grow and grow. I'd say the second moment is, something that I think someone mentioned a little bit in here, which is, like, escalations. Right? Like, we again, our product is very human. Right? So there's a lot there's, like, two there's, like, two types of problems that we get within our support, world, which is one is a a sort of software question, right, which is well documented. Fin can do a fantastic job of walking a lot of people through what that looks like. And then there's a very human problem, which is like, oh, this cleaner did a very poor job, or this cleaner hasn't shown up yet. Right? And, an AI agent can, do a great job of initiating that conversation and starting that, you know, initial conversation, but it needs to be escalated very quickly to a human in order to work through exactly what's going on and how to best solve that problem. Maybe at some point, we'll be able to, you know, get, AI to do to fix this. But today, that's what happens for us. And so our ability to actually push a 100% of our customers outside of what I call, like, the traditional phone treeing, which is like, what is your problem? Click here, and then click here, and then click here, and then and then it and then make some decisions based off of that versus allowing Finn to read what the problem is and escalate to the proper place, whether it's answering that question immediately or sending it directly to different inboxes depending on the problem, all the way up to our our our highest, most talented, like, support agents to handle our most complex, you know, human led problems was the second moment that really made me very confident in our ability to not only, you know, make the the the customer experience more efficient and better with the conversations that were easy to answer, but also be able to handle all of those really complex conversations actually much faster and be able to spend a heck of a lot more time with the existing resources we had on the team. And so those two things were my massive moments with with AI, and it started with Fin. We we leverage AI across multiple different work streams across customer experience, but it all started with with customer support and those two moments. I love that. And I just have to say, like, that's resonating in the chat. So Christopher pointed out that that's exactly the transformation their teams have been going through, and people are definitely feeling the documentation note that you made. And on a personal note, I mean, that was that was my moment too. I was a customer of Fin before I joined the team, and and that was the exact, sort of moment that I had as well. And we are not explicitly asking you guys to say, like, Fin was the moment, but I love to hear it when that is the genuine truth. So thanks for sharing that, Chris. Amy, over to you. Yeah. Well, similar to Chris, I mean, Fin was an moment. That was probably the first moment for us. You know, we had been a long time customer with Intercom and had been using that platform for knowledge base and helping our customers with issues. But, you know, when we had FIN turned on for the first time and we saw, like, the what what it could do, the capabilities it had to to resolve those frontline support requests, and just how fast customers were responding to that. And we were getting, like, great feedback from our customers. Like, wow. Like, the bot actually really answered a complicated question. Like, I was like, okay. Yes. This is really coming. And then the second thing is we actually attended one of the events, the Pioneer Conference. And that was really impactful to us as a business because we were hearing, not only, you know, specifically about FIN, the product, but what others were doing in the industry and where things are headed. And so there's some great speakers, myself and and one of my other leaders attended. And that really was like, okay. This is really coming and it's coming fast. So those were, you know, a couple of the key moments for us. I love that. And it's like I'll I'll let everyone know, Pioneer is open for registration. So unplanned plug, but there you go. Over to you, Kayla. I'd love to hear from from you. What was your moment? Yeah. I I think for so many of us, it's been, quite the evolution in how we have looked at this. I I have kinda three phases to that. We I implemented my sort of first pass at AI in a prior organization. The our documentation was a mess transparently. We had, like, 1,600 different articles. Everything was so complicated and layered, and there were multiple, you know, SOPs for the same thing. Right? And so we implemented, an an AI bot that was just agent facing, purely internal only, just to help sort of get an answer out of the knowledge base. It was an awful experience. It was super disruptive, and and it it really all came down to, a lack of discipline and organization in documentation. But my attitude at the time was, see, I proved it. AI is not gonna work. I still have a job. Yay. Not the right attitude. So then taking a sort of second pass at it, we started our, our journey with, you know, ramping up Fin, similarly after Pioneer, and took a vastly different approach. We wanted to be as streamlined as possible and be really strategic about not only timing of when we implemented at at sort of peak volume with, low complexity, because we are tied to events. We sort of kicked off on NFL Sundays. We know this is when our volume comes in. We know this is our most predictable volume, our most repeatable volume. And we took an approach of really making sure that we were very thoughtful in how Fin was gonna speak to our customers because our customers have a really specific tone. We, our our go to market is very, very sort of honed in on on our brand messaging. We are casual. We're humorous. We're informal. It's a game. Right? So we were gung ho on getting that voice right. Worked with marketing, worked with engineering, making sure that we really were zeroed in on what that needed to sound like. So we launched Fin for our first NFL Sunday with guidance around having a humorous tone. To which it responded by talking like a pirate for about eight hours to figure out how to stop doing that. And there was a lovely screenshot that is still taped up in the office here where, it told one of our customers, that it would not tell them to walk the plank. So we learned a lot very quickly. And rather than sort of throw our hands up and say, well, see, it doesn't work, we were really pragmatic, and have since built, a lot of internal discipline. We are are currently formalizing a structure around AI operations. We have a team that is reviewing conversations and guidance on a daily basis. But then also, we're looking at each procedure that we have established where we're actually making calls into our back end to review, customer data so that we're really customizing those journeys. The piece that has been a real evolution and a real unlock has been, reviewing those conversations and finding, a challenge internally to say, how do we make this procedure 1% better every two weeks? And we have a two week cycle of let's make this at least 1% better at actually getting to, a resolve. And and that sort of, internal discipline and and having an elevation of skill set within our support team, has been, a real night and day game changer. We're, you know, we're creating internal expertise. We're creating a vastly different career path for folks. So they're not just, you know, our our escalation path, our our sort of internal SMEs, but now they are AI operations experts. And we've gone through, some really cool certification courses through MIT, to get folks certified in, AgenTic AI for organizational transformation, an investment in them and in their professional development, but then also, an additional unlock within the organization where we're sort of now being looked at as the team to pull on for tips and tricks on implementing AI across the org. So it definitely has been an evolution, and a lot of learning by failing quickly. I I love this. There's so many gold bits in what you just said, a couple that I'll point out. The mindset shift, I think, is just so real. Right? I and everyone talks about this. Like, we all kind of came in with some preconceived notions, and then you get in, you kick the tires, and you're like, wait a minute. Things are actually a lot more different, or more more so than I thought. So I love that. And and I and I think, like, we're we're all leading from a place of, this hasn't been done before. Hence, pioneers is the term that we use here. So how do you can keep that open mind, and how do you really tap put the system to the test? So I love what you said, but I think it's, like, two week sprints where you're trying to make it 1% better. Was it one week sprints where you're trying to make it 2% better? The former? Two weeks, that's 1% better. Better. Low stakes. yeah. I love it. And that's and that's really what it's all about. Right? And I think it resonated in in in the audience as well. So thank you for sharing that. And I think the development piece, the ongoing development and learning and, agentic workflows and how you're getting your team upskilled is also really inspiring and I think something that a lot of our leaders continue to talk about. You know, our own support team is actually, serves as consultants across our organization as we think about identifying other workflows with customers. Right? So, like, our digital success organization, our skill organization, are working with our support teams to think about how that impacts this. So I love hearing that, from you, Kayla, and I know I've heard it from a lot of folks, that we work with as well. Thank you for sharing that. So I wanna get a little bit deeper into some of what you all started talking about, which is really the practical elements of the day to day job because that's the question mark that a lot of people who are at the beginning of the journey always have. Like, what does this actually look like day to day? It sounds great. We talk about mindset and everything. It's a bit abstract. Can each of you talk through, like, what the work actually looks like day to day now, and how far along in the journey, do you feel like you are? Because let's Yeah. I mean, the the day to day I think when you talk about sort of cost our our customer support and and our customer success and and all these other, areas, like, the day the day to day is really about with you. and we're not perfect. Again, I think that 1% better is exactly what we we strive for as well. It's like but what we really care about and that's the thing that I constantly talk about at our at our for our teams, in front of our teams is, you know, getting the right resource in front of the right customer at the right time. Right? And it's like, that is what we are truly trying to continue to improve on, which means, you know, if a customer just needs an answer to a question, great. Fin is fantastic for that. And how do we make sure that the, you know, the right documentation, that it's accurate, and that we have a a role that's looking at all of those things and looking at all of the CSATs for every one of those and spot checking every one of those constantly to make sure that flow is great, all the way up to you know, we have a very large customer and detecting those signals within their support as well as their usage and and all of those aspects to build out proper a agentic health profiles, utilizing other tools. And, you know, we use for example, we use HubSpot and Cloud as as our other sort of agentic tools, to marry a lot of this data for our customer success organization that isn't inside of, Intercom or Fin. And, you know, building out those, pieces to know where we put our CSMs, which are sort of our biggest and and most, you know, expensive resources to get them in front of our largest, most risky customers when they need us most and when they're signaling. Right? And so it's that's what the day to day looks like is, obviously, we still deal with escalations. Right? Those days still happen. Like, that doesn't go away. Like, you still have to deal with a lot of that stuff. You know, we still get a lot of inbound, you know, pieces, from our our customer with a lot of inbound tickets. You know, Fin solves a lot of that as well. But we make sure that we put the right resources in front of the right customer when they need us the most. Right? And and then on top of that, we then compile all of that feedback, into a a monthly voice of the customer, program where we bring all of that feedback back and say, hey. Look. Like, this is what we need the product to do in order to respond. And we're not just bringing it back in terms of, like, here's my opinion. Right? It's it's very clearly data driven. We have a bunch of quotes, and we're able to compile that amount of data so much easier now with AI to to truly pull out those insights that matter the most and attach, you know, really clear, like, here's how often this is happening, or here's the the the revenue that's at risk in in a way that we just have never been able to do before. So I I think that's what the day to day looks like truly for the teams, which is, you know, get it not just doing QBRs for QBRs' sakes. Like, really getting the team in front of the people who need us the most when they need us. Amazing. Thank you for sharing that. Amy, how about you and your team? Okay. So, like, I think we're kind of in a three phase process when it comes to what we're doing with customer experience and leveraging Fin and AI. And, really, the first phase was, I guess, your conversational chatbot. Right? So getting that out in front of your customers, helping our teams be able to focus on more of the consultative work so that, you know, the bot's helping the customer with those with that low hanging fruit. So we've had that deployed now for for quite a while both from a customer support perspective as well as, like, an onboarding assist for our customers. And then the next phase of what we're we've worked on is personalizing that experience more. And so really, like, having the agent access our data internally, everything we know about that customer, personalizing that experience as they're chatting with the bot, on things like, you know, we know what products they own. We know, what ERPs we're integrating that customer to. And so we can provide them with a lot more valuable insights than just answering general things. We can really get deep with that customer. So that's been the second part. And what we're working on now is really this third phase, which I will we've just more jet the agentic phase. And that is where, having that not only go one way. So we know about the customer and we can personalize, but also, allowing, us to actually access things within our products, and to actually get to the point where we can perform some actions for our customers if they so chooses to, to save them time and to better automate things for them. So and that's so that's this is more of the really exciting phase that we're getting into, and where we see like a lot of potential and like kind of the next hockey stick, call it, you know, part where we really think we're gonna optimize the experience. Because there's just a lot of things. And there's things that your internal teams do that you know are low hanging fruits that you can automate. But there's also tasks that the customer has, right, that they don't want to do manually. And I think you can by enabling this technology, we can do those things for our customers. Obviously, you wanna do them in a safe way where you're asking them to validate, and things like that. But, which make you know, you have to have to make sure that you're doing that. But, we really see that as the the next exciting phase for us. And I think will be something that, like, is, you know, hockey sticks that for our customers. I love that. I know that, like, when one of our customers, that I'm a customer of, used Finvision to give immediate, like, refunds, I was just like, wow. Like, bringing all of this to life. Like, that's such a great use case for for having the agent take, steps for you. So love that that's that's a part of y'all's journey already. Kayla, over to you. So we are, evolving quite a bit in the day to day. Much of the day to day now is it's less about sort of being in the weeds and putting out fires than it's it's much more about how do we then leverage this information that we have to go advocate better for the member, but also, really having a much more in-depth understanding of where we have opportunities to create custom and memorable experiences. Right? Where instead of thinking about every conversation as, you know, we want this to always match the template of how this needs to look at the end, Rather saying, for this specific member who is on this specific journey, where do they need to be met and how do we best optimize the outcome for them? Because we in our in our specific industry, we we have kind of some interesting challenges, that that are challenging both our definition of of a good outcome, but also I think even, even Finn's definition within CX score. So for example, there are things that we have to be really careful of for regulation, for, you know, being, thoughtful and responsible about how we speak to our customer because it is gaming and we wanna encourage responsible gaming and that is a core tenant for us. So there are things that we have to be really careful of. Right? If if, someone were to, chat with us, we know there are some markers in their behavior that could indicate we may have, a concern with this person around responsible gaming. And how do we talk to them about that in a way that still encourages them to play in a responsible way, but also doesn't encourage irresponsible behavior, and doesn't sort of harp on that? So we're leveraging, a lot of, the data that we have right now to be more proactive in some of those flags and behavior that we know, right, that that start to indicate some irresponsible behavior or escalating behavior and customize their journey based on that. So we're leveraging AI both in the conversation, but then also on the back end to sort of intake all of those triggers and help us to make, some different decisions in how that journey happens. And that's one microcosm of, you know, a million different scenarios that that we're looking at. But the day to day has has really shifted out of that firefighting mode and much more into, understanding the customer, mapping their journey, and thinking differently about how we engage in those conversations, where we're able to say, okay. There are, you know, certain conversations we know we need to have with Finn. There are certain conversations with certain members we know, need to happen at a human level. And then, transparently, there are some conversations where we don't want to waste a human or Finn's time, and let's contain those into a workflow with sort of more antiquated chatbot, but make that a less desirable experience because it is a user that has a less desirable sort of value to the business. And it's allowed us to really look differently at how we approach architecting those journeys. And so, our day to day is looking constantly at that, at that journey map, at data coming out of Amplitude or FullStory. A lot of the tools and data coming from product. And then us looking at, okay, now how can we look differently at how we're going about things? So we're doing a lot of AB testing in our journeys and looking at how each of those shifts then impact long term retention. And so we become much more sort of data scientists and design thinkers, as opposed to, a team that was looked at before as like, okay, here's some humans that can just get stuff done. And it is a significant difference in how we go about structuring, the day, week, months. And then to your point, Chris, our our QBRs and our voice of the user meetings are now, people are beating down the door trying to get an invite as opposed to before where it was like, oh, let's sit and look at a couple of slides. I I I love that. I think that's incredible. And I also just love that all of y'all's descriptions are showing the clear transformation of our roles into something that is both art and science. Right? I think we maybe leaned a little bit heavily on the art. In the past, we knew there needed to be something a little bit more scientific about our approach. We were moving in that direction, but the technology wasn't there yet. And now a lot of the tech stack and processes that you all have, transformed and and described here point to you know, you're having conversations with engineering. You're having conversations with your r and d and product teams. I mean, that is exactly what I think this transformation, is about. So thank you all for sharing. And ask one more question, which is like a hybrid of a question that we, that I wanted to ask you as well as something that's coming through the chat, and then we'll start taking questions from our our audience. So audience, please do use the q and a box to get your questions in. I don't think we'll have time to go through all of them, but I think the panelists are open to sticking around for another fifteen, twenty minutes, and we'll we'll try to get through as much as we can. So hybrid question here. All three of you have created roles on your teams that didn't exist a few years ago. I'd love to understand a little bit more about who owns AI in your organization. How did you find and upskill those folks? Because there's a lot of questions in the chat about, opportunities here for career development. Let's start with you, Chris. Yeah. Who owns it? Who owns AI? I think that's just like by the way, there's no answer to that. Like, I I think I think every department is figuring out how to leverage, you know, AI in their own ways, our engineering department, our product teams. I'm seeing it all over the place. So, like, I I don't think that, at least at least where we're at, I don't think we have it figured out. I don't think there's like, oh, yes. Like, this is the AI team, and they do it all. Right? We each have our own sort of individual roles. So here at on our customer experience team, we've elevated two roles. We have one on our our support end that we had once we launched Fin from the get, which is our sort of, quality assurance and, innovative AI person that works with testing all of the new flows. Right? Like, you know, here's the problems. Here's what we wanna challenge. Working with, you know, she attends, like, almost every webinar that that, Intercom or Fin has. Like, we we, and she's really responsible. Her day to day is all about, like, figuring out how we can increase, or or sort of increase the customer experience leveraging the tools that we have, and even some external tools. And should and then the other half of it is, like, QA ing. Right? Going through all of our negative CSATs, spot checking chats, making sure that the bot is performing as we expect it to. And then we have another AI role that we've created, which is on our operations side. So we have a lot of, like, back end operations things where we're going through, you know, cleaner host reviewing cleaners and cleaners responding to those things and, you know, making sure that those aren't don't have any inappropriate information on them, you know, going through applications and background checks and things like that that we have to do on our contractor side. So there's a lot of, like, those, pieces that have traditionally been done by humans that we are beginning to, figure out how to leverage AI to make that process more efficient while still including humans at the most important and impactful pieces of those processes. And that one's kind of interesting because the real what we've really sort of figured out is then our best partnership with that particular role is actually our product and engineering teams because a lot of what we build on the back end to do those things then get slated into the front end to then just only escalate truly need, which is really cool to see sort of that collaboration becoming a thing where it's like, we have these really strong prompts and workflows, with our AI agents that we've built that we can now just sorta slide over because they're trusted and they're built. And then I think the the last thing around, like, you know, people are asking, like, what what what kind of skill sets? How do you leverage your AI? What what programs, things like that? I don't think that there is anything that's, like, truly out of the box, which is like, oh, yeah. Go do this course, and everyone will be good at AI. I think that there are different levers, layers of responsibility within that. Right? So a tier one support agent, I don't actually need to be super engaged or, like, knowledgeable on AI unless they really want to. Right? Like, they just need to be able to handle the things that the AI is sending to them. At the same time, Alright. like, on our customer success organization, I want them to be leveraging AI to understand how these triggers work and be able to build out workflows and and be curious. And so we have a we have an internal, a Slack channel. It's for all of our organization, but it's it's sort of like, I built this cool thing with AI. Right? And so, like, people just show their flows. They do Loom videos, and they talk about their agents. And we all sort of learn from each other, I think. It's probably the best program you can have, which is just play around with it. Play around with Claude. Play around with the tools you have existing. Every almost you know, our CRM is launching a bunch of AI stuff. Like, play around with those and see what that looks like and and how we can solve problems utilizing AI and, you know, whether or not that gets sort of scaled into an actual workflow or it's just sort of like this, oh, wow. That was cool. Either way, we're learning from each other in how we we do this. Right? And so being much more collaborative internally, I think, is the biggest thing that I can say is is, like, beyond the roles, beyond the, you know, programs or or classes or anything like that, just, like, just do it. Right? Just execute. Play around with it. Right? Do it in a safe way, but, like, just start. Right? You can't that that's what I have found to be the most impactful for me and all of the the individuals on my team and throughout the organization. I love that. Such a great I'll underscore the, like, do the thing aspect of your advice, because theory won't get you too far. Amy, over to you. Yeah. So, yeah, I'll echo Chris. Like, not nobody owns AI I think yet. I think everybody's leveraging it. Right? At TrueCommerce, we we have a team that I think would we would call the governance team. Right? They're like governing what tools we are using and how they're able to access the various data sources within our organization. But within CX and within many other teams, at TrueCommerce, we have built out, you know, groups of folks who like and who are the experts in their area. Right? So in my team, these are people that understand what we do for customers from a support perspective and from an implementation perspective who wanted to learn, who wanted to be creative. And we have created, roles called CX Automation Engineers. And these folks really like they blend technical capability obviously, with the the tech itself but also the business knowledge of, like, what we need to do for our customers and how we can better improve the experience. And that's really important, I think, because this is not just about a technical role. When it comes to AI, you really want, you know, to understand what problem you're trying to solve for the customer and make that experience better. So I love the stories because our entire CX automation team is all people from within the team who have wanted to move into this newly created role, that we've had. And you might say, well, how do we do that? Right. How did you get support to build this? And, you know, how did you create these new roles? Because that can sometimes be hard. And it was really, you know, like trying, picking a couple of use cases that are low hanging fruit, experimenting with the tools, finding something that you can streamline, show the business outcome, and then it just starts to snowball. And so that's really what's happened within our organization. And I think it's like a great, a great success story that I, I talked to my peers that's happening in many places incredible. today. Kayla, how about you? Could not echo more. There is no single training class. There is no single certification. And there's no single owner, of AI. I think a big piece, for us was, you know, pushing people to to use it and and pushing folks to, you know, issue each other some challenges. So what is one repeatable task that you do that you could go to, Cloud or ChatGPT and and automate? Like, what is one thing you could go to, you know, co work that you could make some automation in your day to day? And we started sharing those in team meetings and staff meetings and Slack channels, to get folks a little bit more comfortable. But then there have also been some, like, small things that that we have done to try to, help reduce the fear for people. And and one of those was really sort of giving Finn some identity on the team. Making sure that we when we're, you know, from small things like when we produce dashboards or leaderboards or how are we performing, we include Fin as if it was an agent. Right? Like, this is how it stacks up against everyone else. That humanizes things a little bit. But then also, we have like an email address set up where you can share feedback for Fin from anyone in the organization. So if someone else sees something posted on social media as they're going through or if someone hears from a VIP member or if there's an escalation that comes through a channel outside of support, there's always a way to say, here's feedback for us, for Fin. And and that's gone a long way as well in in making sure that it's humanized. But I think so much of it is really, you know, getting folks accustomed to, the the way that we did things yesterday is not the way that we're gonna do things tomorrow. And we all have to sort of make peace with that and understand that and and not be afraid of that, but look at those as opportunities. And then, making sure that we are not oversimplifying the importance of using AI all the time. Right? It's it is a tool like anything else. It's not a a universal tool. I I always kinda joke with with my team that, like, yes, you could use a Swiss army knife to hammer in a nail, but that's probably not the most effective use. And so making sure that we're being thoughtful about let's since we are sort of the leading the curve on leveraging AI and we're putting AI in front of our customers, let's make sure that we're very thoughtful about how we're presenting things. Making sure that, you know, could you use AI to write all of your emails? Should you? Maybe not. Maybe we take the time to make that more human and leverage AI for informing you, educating you, researching, but then you put your thumbprint on it. And so we're sort of being careful about those two and finding ways to think very thoughtfully about where is the human element really impactful. We know that AI is built to give us the most pleasing outcome. Right? The thing that we're going to agree with the most. That's the way learning models are built. And so that has an impact on sentiment. Because if you get the answer from AI that you want to hear and the answer from a human that you don't want to hear and we're only using the human to say no or exception not granted, that has a major impact on sentiment. And it changes how we look at like where is the handoff to a human and where is a human needed and where are we gonna have that positive impact of sentiment rather than sort of, only looking at the human as a a decision maker. I think that's amazing. And thank you all for that. There's a couple of questions in the chat about, like, how to think about these things and how to approach it, and I think you all answered that in one way or another in your responses. We do have two minutes left. So I'm gonna take two questions from the chat, and I'll I'll I'll dispense with the roundtable. So if you're compelled to answer that question, jump in, and then I'll move on to the next question. So we've got someone here saying, I don't even know where to get started. How do I even start thinking about what to automate first? What advice do you have for, someone who is just at the very, very beginning of this journey? Panelists who go. I'll jump in. it. Yeah. You know, if you're on the support side, like, look for your your lowest your number one customer issue. Like, look at what the the volume is. Like, what does the volume tell you? If you're on, you know, if you're onboarding, like, what look for that low hanging fruit and something that, like, you know, if you do one little thing, you could have a a an impact and try it. Just try it. Like, you really just gotta have go try a few things. And it's not gonna be perfect the first time. But once you once you try a couple scenarios, you'll find that it gets easier to identify where these opportunities are. Awesome. Thank you for that. I'll say first sort of one of my first pitches was exactly that of, like, let's try and just test out AI on on this most common customer issue, and didn't get a whole lot of traction. But when I pivoted to say, what is the least desirable customer interaction and let's try AI there, I was able to to get much better, traction and sign off. So sometimes it's what's the conversation you don't wanna have that has no value to the business. So for us, it was like fraudulent, fraudulent customers, people that we knew were identified for fraud. We didn't wanna waste human effort having that conversation. So let's test out AI here where there's super low risk and frankly not a lot of reward, but it gives us a safe playground to try things in. So there there may be something in your, segment of volume that comes in for customers where, like, this isn't valuable to the business. So a heck of a lot less risky to to get inventive and and try something. Have a hypothesis and test it. I'll add one thing to that. I think number one, use your network. Like, there's I mean, by all means, like, you now know us. Like, find out send me send me a send me a LinkedIn. Right? Like, we could chat about it. And also use d teams, d's team. Like, they're fantastic. Like, the customer success team within Fin is awesome. We always run like, hey. We we wanna do this. What are your thoughts by them? And they've been awesome resources for us. So use your resources. Like, you know, you're not alone. Like, you don't have to do this by yourself. Like, there's plenty of people you can tap into that are more than we're spending an hour right now. Happy to to jump on a fifteen minute call and give you a little bit more tangible if you need it. I love that. That is just, like, the perfect place to end. Thank you all for sharing your expertise, rich examples, and all of the on the ground stuff that you've gone through in the transformation. Like, each of you has rolled up your sleeves and very much led from the front here. And we're just really fortunate to be able to learn from all of you. So thank you. I apologize that we weren't able to get to all, of the questions in the chat. Thank you, audience, for being so engaged and asking incredible questions. For community, we do have a community forum. Do join us there. This is a great way to kind of keep going and and and build community around what you're trying to do. We do office hours. We do all sorts of webinars and other ways for you to get involved. You can access all of that on academy.fin.ai as well. And with that, thank you everyone. Have a fantastic day. And panelists, you're incredible. Thank you so much. Bye, everybody. Thanks, everyone. Thank you.