Welcome, CX professionals and enthusiasts! We're excited to share our recent conversation with Andrew Kennedy, a seasoned customer experience expert with a wealth of knowledge to offer. In this interview, Andrew introduces us to the innovative concept of "elevation engineering" at KNAK AI. Drawing from his extensive background in workforce management and customer support, Andrew shares valuable insights on integrating AI with human empathy to transform customer service. Get ready for an enlightening discussion that challenges conventional thinking and offers fresh perspectives on enhancing customer experiences.

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Video Transcript:

Sophie: Hello, Andrew or aka AK. Thank you so much for joining. I'm super excited to chat about customer success and AI and innovation and all the cool stuff. But yeah, thank you so much for joining.

Andrew: Thanks for having me. I've been excited to have this conversation for a while. And I don't know if I'm a little bit nervous or not. I think it's just my nerdiness. I love talking about this stuff.

Sophie: Totally, yeah, it's just so exciting. It's AI, it's making customer service experiences better. What could be more exciting than that? So yeah, let's definitely just geek out for the next little bit.

Andrew: Yeah, all the buzzwords, right?

Sophie: Exactly, as many buzz words as you can possibly fit in. That's really the goal of this meeting of minds. But yeah, we were kind of coming up with questions to ask you, and one of the cool things I learned about you is that you coined the term “elevation engineering” at KNAK AI.

Sophie: And it's a very niche coining and I would love to know if you would be willing to share a little bit about what that means, how it's kind of changing the game for customer experience.

Andrew: Yeah, yeah, I'd love to talk about it.

Man, I definitely have like a storied background in like workforce and workforce management, right? So my roles have been very fortunate and blessed to just navigate through all kinds of crazy gig economy scenarios. And really my background in college, took some, I was an economy major and I was like, this is bland, let's switch. So my whole point is I've always been like fascinated by like macro and microeconomics. So looking at these trends and the impact of the gig economy, I really started to get fascinated in my time.

and into it and really, you know, like the TurboTax like experts on demand and you know, this is the time of really Uber just being this massive unicorn and all this really cool stuff. But then there's all these like social constructs that don't fit, right? And it kind of comes back to this just time old or age old timeless phenomenon where technology and innovation always outpaces the workforce, right? You have the printing press, you have the automobile and you think about the jobs that are displaced.

So, elevation engineering is really like a twofold way to tackle a very like systematic and like generational problem in just our natural evolution and technology and to really have a place and a space to protect and arm the workforce for the future of technology. And this is something that I think in our hearts throughout all of all of time, we never wanted this place, the workforce, but it just happens. Right. So for elevation engineering, really it's this ideal of saying the current workforce within, especially customer support and the customer experience place, have all kinds of systems, especially economic forces that require optimization, they require optimization to stay cost effective and to stay competitive. And honestly, for businesses to keep running and make profits and maintain market share, they're backed into corners, right? So I have a lot of empathy and sympathy for the business marketplace. But I don't think it justifies some of the inequality that we have with wages. 

So at the end of the day, this is a way to elevate the workforce, to be able to partner with automation, to partner with AI, in a way that extracts and focuses on human centricity. So using AI as a tool to pinpoint human connections and empathy. And in some ways, these are like the most empathetic warriors to advocate for the customer experience and, know, frontline agents know your customers better than anyone else. And my background being a customer support agent for years, being a supervisor, having operational roles, living and breathing these conversations day in and day out. You know, I've seen, I just felt all of those pain points. 

So, we're arming basically customer service agents with the tools to take on this new hybrid role where it can be a more technical role and branch into, like to say is kind of making them almost like business system analysts or mini product managers. And I think that they empower them to give us the insights that we need to craft awesome experiences. So instead of saying, Hey, I've gotten 10 calls that this thing isn't routing the right way, or I got this bill, you know, marketing set out this promotion. Like they hear this stuff and now we have speech analytics. We have powerful technologies that can take this unstructured data and quantify it. So we now have the nuance and we're able to empower these really tangible experiences that agents are going through and level them up into what we're calling elevation engineers, where they are engineering experiences and taking the ordinary and elevating them into extraordinary. 

That's what really captures the brand essence. That in an experience economy is what's so important. And that's why there's this really, it's like a necessity for a paradigm shift to go away from a transactional cost efficiency economy to where consumers want, they want experiences. Like Amazon is giving us the price. I want it, I want it tomorrow for cheaper. Like I want that experience. You know I would pay more to have a better experience. So now when that standard, when that expectation is so high that now a customer can churn just because they had one bad experience, because you have five competitors that look, well, I can get it for the same price or I'll even pay a little bit more. The old business model is really fragile at the moment. So this is a way of helping, especially businesses, fill that white space where they don't have the internal resourcing, where our company can come in and from a consulting standpoint, be that fractional CX leadership and strategically guide them and transform them into an experience -based, outcome -based company, very data -driven, while also up -leveling their workforce so that as we're automating interactions, we're understanding the unforeseen consequences. We have the feedback loops and we're empowering the frontline employees to do something about it and take action.

Sophie: I love that. And I think that actually aligns well with conversations we've had in the past of really imparting, I guess, empathy as much as you can, whether it's with a customer or with people on the customer service kind of side. How do you feel with the focus on AI and automating more and more kind of customer service experiences, we can inject more empathy and make sure that those experiences are overwhelmingly positive rather than, you know, trying to scale and AI bot too fast.

Andrew: Totally. Yeah. I think the simplest way to start answering that is to have an analogy of, I point a lot of things back to AWS as you'll see. The current state is kind of like, cool. AI is this awesome tool. And it's like the equivalent of saying awesome, AWS is great, but we just use S3. Cool. Cost -effects, like we can really store data really well. It's like, well, we have all these other services. So for us, when we're looking at like KNAK AI solutions, that's automation intelligence, not artificial intelligence because AI in its sense right now really is a tool. It's the next printing press, right? The analogy I draw for that is like cool AI can help us write create content. So it's like yeah, you can create content and it can be automated but it still needs the human touch. Like you don't get a best -selling book by taking a typewriter and chucking it into a bookstore and saying yeah.

It's a best seller. No, it's like unleashing a bull in a China shop. Right. So for us, it's a way of unpacking and getting more like clear delineation between what is AI and what's the purpose of it what's the role that it plays in customer experience as a tool, especially to empower the personalization of experience for the sake of the customer experience itself and not for revenue or profit generation in the marketing sense. Right. Like that's where hyper personalization is really focused is like, well, yeah, let's use AI to sell harder to help our top of funnel. And at the end of the day, there's just this really big reverse disconnect of like, well, we'll just outpace our acquisition against our churn and our customer churn. it's like, Whoa, like, why isn't it the opposite? Like if you have a day zero company, why not fight to keep every single customer? 

Because if every single customer feels like that, they're going to tell their friends. And it doesn't matter if maybe company B goes and gets more funding, builds a better product faster. If they don't outpace their product with their customer experience, their competitors will. So we're that competitive advantage to allow you really to connect with the role of, well, AI can help you accelerate really your product development because we can funnel these insights. We can funnel the voice of the customer. We can structure this thing. We can help it automate even our own processes to make things more cost efficient that are just distractions from getting back to that like human centricity. 

So I see really AI, the balance of being like it oversteps its bounds when it's putting some friction in human connection and human centricity. Even if it's just a gut feeling of like, really want a human. There's just any time you have that feeling that everybody listening to this, I'm sure like you feel that like, when was the last time you felt like, the last 10 times I reached out the customer support, it was awesome. Like, why isn't it that way? Why isn't it the exception that, man, I didn't get the right person. It's like, you're paying the bills. You're the customer. Why aren't you know, so these are questions that we're trying to answer with AI, be powered by AI in a healthy way. We're really better understanding those unintended consequences. And that's, I think, the blind spot that we just got to tackle before it's like a black swan that like, you know, your competitors are going to get to it before you if you don't pay attention. So one way or another, it's going to bite you.

Sophie: 100%. Yeah, I think, you know, on our side, when we're talking to our customers or, you know, amongst ourselves, totally geeking out, we bring up a lot of statistics and research that has been done on how fragile your customers tend to interact. It's fragile how the relationship has kind of grown between customers and your customer service, you know, like 80 to 90 % of customers will leave if they have one negative bad experience, which is pretty crazy. On the customer side though, when you're working with either, I guess, customers or your customers, which is organizations, what data are they using to drive decision making when it comes to their kind of CX strategies or what's kind of, if there is a common thread, what would that be?

Andrew: Unfortunately, like in best case scenarios, it comes back to like CSAT. That's still one of the best metrics that we gravitate to, but it's still at best a directional metric. And this is something that time and time again, continues to fall short. And this was a very big, like when I was deep in like speech analytics and interaction analytics, in my previous roles, there was, it was hard to correlate data, right? You want to be able to say, well, the sentiment on this call was really good. They talked to the agent, it was great experience, but why'd they give like a one CSAT? Well, because they were unhappy with the experience. 

So you're dinging this rep who actually showed empathy, was a good transactional experience in that moment, but you don't even have insight into that because your brand is burnt. And at that point actually doesn't even matter. Like the customer's not satisfied. So something's wrong. So at least you have some compass to say that is wrong. That flow always has a bad CSAT. That agent always has a bad CSAT. Oh, let's coach the agent. Oh let's look at the workflow. Right? So at least we're like there, we're like at the tip of the iceberg, but there's so much under that that I think is missing. 

So when we're looking at the data, a big piece of what we're trying to unpack as we're analyzing interactions and understanding and quantifying like the problems that we want to solve for our customers, because at the end of the day, we're advocating for agents and customers and the brand is the third party. Like, yes, you mentioned they're our client, but you're not anything without agents and customers. So it's almost kind of like we're incognito here. Like, Hey, we're going to keep you in check so you'll be successful. But getting back to it, it's like building trust. And we don't really have trust metrics. We don't have transparency. We have publicly listed companies that, oh, now you're public, you're on the stock exchange, you have to go through your audits, you get exposed. But it's still, I mean, there's so many things. There's so many tricks, even best practices of just how you manage the finances that you cover up expenses.

I mean, we see the scandals out there. There's still not trust from the consumers. Like, you know, people at the end of the day understand we have to maximize profit at any cost. I feel like that's finally dialed down a little bit with some of the downturn in VC funding. Realize, hey, you can't just throw an idea that we throw two million at. You got to get traction. You got to get data. So I think we're moving in the right direction. I think that's good, but we're still missing some of those fundamental ways of like measuring what is empathy? Like how do you quantify that? And like the best measure we have right now, CSAT and sentiment scoring. It's so much more complex than that, right? Because people are different.

So I've been playing around with this concept. I haven't really talked to anyone yet, so we might as well just bring it up now, but around like empathetic translation, right? People speak different languages. People are empaths in different ways. At the end of day, I perceive things. My perception changes. In fact, my perception of a company changes with each interaction. So consumers have a choice. Brands have a choice. Each time they interact, whether it's on the website, whether it's with Chatbot, whether it's calling in, was it good or bad, right?

So I'm almost looking at like, okay, we're trying to flip the script. How do we move from NPS, Net Promoter Score to an NFS, Net Frustration Score? How do we start correlating friction and pain points to churn rates? So it's metrics like that where we can start to better quantify another dimension of saying, okay, cool. Like the CSAT wasn't great. Maybe they still love the company. They just had a bad experience. Maybe they're frustrated with this thing. Cause if you're capturing the frustrating, that's where, you know, when you're acknowledging the negativity, that's where you really get the brand loyalty. 

So for us, that's a very powerful way of, I guess, to wrap up that question is like, how do we get better data at understanding where trust is broken? And how do we start applying frameworks where we can systematically measure and influence the perception of brand trust? And for companies that are in a bad spot, how do we save and rescue them? For companies that are just starting, how do we align a business model for them that actually gets their unique selling proposition position, their product market fit that ties in their business model that's linked directly to the performance of customer satisfaction and trust and frustration and these more emotional metrics, if you will, because it's very close equivalent to like, well, everybody was like IQ IQ. Well, now there's EQ and now there's these other things and people feel differently about stuff because of the personal experiences they have. Customer experience is the most intimate version of that. And to just reference an abbreviated story, like in the call center.

A chunk of my time was taking escalations as the supervisor. So I heard the sob stories, why I can't pay my bill, what I'm going through, the hardships. I mean, there's some people that just...

They go to their nine to five, they come home, their cables turned off, their internet's gone, because they can't pay the bill. And it's like, you get the brunt of that. So a big piece of these, like, I know this isn't personal, but I'm giving you the space to make you feel, and that relationship building can even repair the fact where, you know, you kind of have like a come to Jesus, the thing that they call, like, I'm so sorry, I was just frustrated. Like my boyfriend just dumped me or this thing happened or whatever. And you get to understand the person. That's to me, the real true customer relationship management happens is you're taking time to connect.

So if we can make space for businesses to have to be able to have that space to prioritize that space to have time, that's where it just leaps and bounds will go ahead. So that's where this concept of, you know, predictive instead of this traditional reactive proactive, how do we actually start to predict and anticipate where these frustration points are going to be? And that's where it dials back kind of conclusion of my data story is implementing product analytics very specifically into customer experience like as a product, as a service, treat it like that where you're measuring and dialing in on A -B testing. Well, we're getting frustration, friction points. What do we do here? Do we need to offer a chat flow? Do we need to do a chat bot? Like what does that individual need? And as we have a building cohort of people that respond in that way, then it justifies, cool, we need a whole support channel of that. And then taking that approach instead of like, well, I need to spin up a CRM with chat support, chat bot, all this. I need my knowledge articles. I don't have the time to build my knowledge articles.

Enter Sophie on your white seat. That's, mean, you know, coming back to like, that's where it's really starting to be transformable for us, because now you guys are dialed in on that problem, right? So now we can just collaborate and build the right things on the right knowledge base that just unlock so much.

Sophie: 100%. Yeah, I actually really relate on the, you know, if you give customers space, how much they open up in some cases. I remember my first, one of my first customer service jobs, I was getting people opening up about like deaths in the family and like totally breaking down. And it was a lot, it took up a lot of time. But I think when you're focusing on only efficiency and keeping calls as short as possible and it rips away a lot of that empathy. You miss out on a lot of not only happy customers, but these were the customers that were posting on social media about how much they loved the company and it just built so much brand loyalty where if the next time they call, they didn't have such a good experience, I highly doubt that that would kind of break that experience and that relationship. So it's almost like when you're having those empathic moments with customers, you're putting money in this bank that potentially in the future you'll withdraw from if they have a negative experience. But if everyone starts out at ground zero, you want to fill that up as soon as possible. And that's one of the easiest ways to do it.

Andrew: Yeah, it's like a savings plan, right? It pays dividends. And I learned this lesson so early on in my career, because I started in the call center about when I was like 20. And I was in the call center for seven years. So like that was my duty in the trenches. So I was literally on the call, like on the phones full time for about three and a half years. So I've fielded thousands of calls. So I'm not just pulling this out, right? Like I've talked and it was in upstate New York. So I'm talking like East Coast. I don't have time for your nonsense and I don't have emotions. You messed this up. You had to fix. I'm going to drive your house with a baseball bat. Literally, I've had that plenty of times threatened. I'm like, I'm sorry that I can't control you paying your bill, but I'm going to find a place and I'm going to make that space. We're going to figure it out. What's really the issue here? And you kind of take on a little bit of a therapeutic thing, which I think is a beautiful thing, because people don't just in your day to day, you don't have time for that. 

So a really touching story from that that really resonated to kind of, you know, making that time and space. There's an elderly gentleman that you know, older, maybe had some dementia, whatever, clearly lonely. We were out troubleshooting a call for like an hour, 90 minutes, trying to get his wifi to work or something. He was in there doing all kinds of stuff, reconfiguring his DNS stuff on his router. I'm like, sir, we don't need to do this. And it got clear at a point where it's like, okay, he's obviously stalling and he just wants to talk. And we just kind of put the technical stuff aside and he started on a story and I just played into it and it turned into like a two, two and a half hour call. Then next week, so and so called back, he wants Andrew, I saw you had notes on the accounts, he really insists to talk to you, and I built a personal relationship with you. Did it jack my stats up? Sure, but. You know what? I was able to allow it to not impact me. So it's like, yeah, I can afford to have this longer call. It doesn't affect my after call and the note taking and you know, while he's talking, maybe I'm doing other tasks on the side. Right. So I've found ways to make it work. And that for me really actually helped me get out of a rut because it helped me in some ways, gamify like this call after call after call grind, just getting beat up. I was like, okay, you know what? How do I just make every call happy? No matter what, no matter how upset they are, even if I had to talk to him for an hour.

That's my focus and that helped motivate me, right? So that was a big learning lesson early on is understanding people's motivation. So if you can understand your customers' motivation, you understand your agents' motivation, you can build very, very powerful, inspiring cultures where people really can enter into this like servitude mentality of like, I'm here to serve the customers and I actually get personal fulfillment. And you see that naturally happening with some of the best customer service reps. I mean, some of my mentors, when I first started, they were doing it 20, 25 years. They just love talking to people and they found their niche and you know, they could close when they needed to, upsell when they needed to, or just talk to somebody, keep their stats in line. So, you know, that was just a really cool, like I'm very fortunate for being exposed to that type of stuff because it's a very quick downward spiral mentally in the service industry. 

My brother's in the restaurant industry. So I've seen that side. I've done my own like busboy stuff and things like that. So it's in every element of the service. So, you know, I tell my wife all the time we talk about it. like, you know everybody needs at least one or two years in the service industry, kind of like how some countries do. Like, well, you got to serve the military. No, you need to do your time in the service to just be able to be like, this is what the raw, just in a professional sense, needing to deal with personal issues. And it just prepares you so much for your professional life and journey. So yeah, very fortunate for that.

Sophie: 100%. I saw a standup comedian actually literally say that he was saying that like millennials will use service industry as you know, like, were you in front of house back of house like, my gosh, you were at Denny’s is like you were really through it, you know, like you kind of all understand what those experiences are. But you know, like he said, they can bring really profound experiences, you know, you're really are just out there connecting with people in kind of vulnerable moments, right? This is something you're finding people in the middle of their day or in the middle of their life where they're frustrated and something's going wrong and you just kind of have to work them through it. Yeah, no, I think that's a really good insight. And as we kind of start wrapping up, I have one last question. So as we're talking about empathy and AI and how we can kind of make sure that both of those things can exist at the same time, where do you feel like AI has the most to do in certain areas? Like, what are they missing? What gap are you waiting for AI to kind of fill in making sure that those experiences are as good as possible?

Andrew: Non-creative monotonous repetitive tasks.

Anything that perpetuates social disconnection. So if I'm stuck in a back office as an accountant going through auditing stuff, I'm not connecting with customers. I'm not, you know, it's very transactional. Like that's something that AI can easily do. It can analyze, right? So it's really about understanding where and how to capitalize on AI. And then in tandem, making it truly a partnership and treating AI like a partner and even having empathy for the fact that like, this is only good as we make it.

And we are accountable for the outcomes and we need ways to hold ourselves accountable. So we need more transparency. So the really a big piece of like AI and the solution is the framework is having a framework. So for us, the end of the day, the KNAK AI solutions, our framework is what our IP is going to be. It's like, okay, we want to handle customer experience as a true service, as a true product. We need a framework that allows us to partner with AI and to even hold ourselves accountable that we're able to dial it in, that if something's off, it's calling us out on it and we're able to calibrate because you want to surface those misalignments. You want to do it quickly and you want to do it proactively, especially before, you know, something happens, right? Classic examples are with outages, right? With, okay, cool. Snowstorms happen all the time in New York. Power lines would go out. Well, you don't have power on your block. I can't watch my TV. I'm going to call the cable company. Well, can you turn on your TV? No. So, you know, what do we have to do that when those outages happen? We put up a header, we, send out emails, we alert our customers. It's, it's reactive, but it's also kind of being proactive. 

So we're like, Hey, we know there's a problem. We're working on it. Don't know the solution yet, but something's going on. And that's how you start to build these pieces of trust. So when you can harness AI to identify and pinpoint those things, such as like, you know, like the chaos monkey for Netflix example, right? Like how do you load tests and how do you, how do you like throw it? So for us, it's almost like, how do you throw that at the customer experience? How do we have our framework be a bot that crawls through IVRs or calls through chats and brings back this audit that says like, Hey, I know you have metrics that tell you your whole time is this or this is this, but this is what the experience is like. These are some clips. This is what it's like talking to your agents and getting those verbatims, especially when it's the, I can'ts, I don't know. The system won't let me, or just hold on. Let me pull that up. Right. Like those are tangible things that like you're actually helping tackle because it's like when you have access to knowledge, when they can quickly find that and be informed, they can continue to focus on the customer.

So that's, think a little bit more into the practical solution of AI is when AI is being a co-pilot, right? As everyone overused that term, but really understanding what it means to be a co-pilot and how do you measure the trust that that builds? Because the agent needs to trust the co-pilot too. I remember so many times I'd get a call, said, cool, recommend this discount package to this customer. So then you do it and then you're like, wait a minute, they're not eligible. Why did the system tell me to do that? And now you have an upset customer that's been with you for 10 months that you've just offered a new promo that's only for new customers. 

And it goes back to this whole broken trust of like, what the heck? There's no reward for my loyalty. I've been here for 10 years and it's not worth a $20 a month discount. It's not worth $500 a year for the 10,000 in LTV to protect 20, 30, 40K in future revenue. And that's the metric. We have to look forward to what the revenue loss is if we don't retain this customer. We're so focused on, well, we've only had them three months, we churned, we lost 10K whatever it's like, well, how many six figure customers have you lost just because, you know, and that's the, that's the whole crux of the SMB business and like it's ignored too, which is why we're also passionate about the startups, the small scrappy stuff where you don't have the resources to build the CX. And that's where we're really passionate to like bring this together in a much more accessible way so that any company has the access to world-class customer experience and moving with data, with AI to partner together into predictive type customer experiences where we're.

like staying ahead of the game and even doing things that are counterintuitive to say, well, you you're not using all of your bandwidth on your, you know, everyone's starting to get these data caps on their internet users, right? We actually suggest you downgrade, builds trust. They're probably going to stick around longer than if a customer gets a mailer for a competitor. Well, I'm going to switch that's cheaper. No, I'm going actually stay because I know if I'm not using it, they're going to tell me and actually going to save me money. And then that positions more upsell cross sell and LTV. Right? So it's just very basic fundamental things that we just somehow have crossed wires on and not prioritized. So we're hoping to bring that alive. And that's, just see so many use cases for AI to understand those, the cohorts, the data, the personalization, dialing in those experiences at a very individual level, while as much as possible, funneling it into like predictive proactive human engagement where like I'm now calling you up or I'm texting you as a human and using my human capital to have these really powerful connections to just further cement the relationship.

Sophie: 100%. Yeah, I think AI is kind the motor and then the car is all the infrastructure and strategy that needs to go into CX operations. And I think a lot of companies are just buying the motor and then not having anything else around that. And it's just so important that it all is going to work together. If you just buy a motor and then phone it in everywhere else, it's not really going to work.

Andrew: Yeah, there's no seatbelt, there's no windshield, you know, you just, it's, I think that's a great, that's a perfect analogy. I like that, yeah.

Sophie: Totally. But yeah, no, I can't believe it's already been half an hour. That was a lot of geeking out. And I loved every minute of it. That was awesome. Is there any parting words to any CX leaders or anyone that's kind of thinking about investing in AI or maybe a consultancy or trying to shake things up a little bit?

Andrew: Yeah. I think you really have to start with a lot of humility because it's easy to fast track in AI. There's a lot of buzzwords, there's a lot of quick wins. There's so many, I mean, you know, you can go into ChatGPT and ask you to do anything now and you can blindly follow it. So I challenge anyone. if you're not, if you don't have experience, if you're trying to get into it, experiment, be, be intentional, be purposeful with it, go into it with an intention of what am I trying to learn? What's the outcome I'm trying to get and how is AI getting me there?

Pay closer attention to the journey of like how you're working with it and how that impacts the outcome. And whenever possible, always try to come back of like, well, where's the human element of this? How does this like, you know, what's the fallout? Like, how do we maintain this? How do we scale this? You know, really take time to contemplate on these unintended consequences because that I think is the framework that's truly missing from our society. We've gotten so good at recorded events and capturing data and be able to look back and explain things down to the microsecond, with logging and everything. But, you know, we don't use that to truly spend time because we don't have time. So you have to make that space for the sake of your customers and in some ways for the sake of humanity, right? Like we've all seen the sky now, we've all seen the movies, right? So we're in the age where we get to actually be thoughtful, build these frameworks, find ways to actually make like elevate the human experience, right? And so elevation engineering is a path to that. It's a way for us to bring forward an academy where we're not just training on the technical aspects, but really the ethical grounds and the empathy and really how to be empathetic and how to partner with AI to truly better understand not only yourself, but your customers and where those common grounds are and really building off the common ground.

Sophie: Amazing. Yeah, this is exactly why we wanted to bring you on and chat with so many words of wisdom. And thank you so much again, Andrew, for hopping on and just imparting those very helpful words about how to navigate the crazy world of AI and CX.

Andrew: It is a crazy world, especially if you just dive into it. You gotta go eyes wide open for sure, so don't be scared of it. But if you have questions, we're here to help.

Sophie: Absolutely. Yeah, and excited to continue working and yeah, thanks again for joining.

Andrew: Yeah, thanks so much, Sophie. It's been an absolute pleasure, as always. Talk to you soon. All right.

Sophie: Thanks, Andrew.

As we conclude our insightful chat with Andrew, he leaves us with some valuable takeaways. Andrew emphasizes the importance of approaching AI implementation thoughtfully and purposefully, always keeping the human element at the forefront. He encourages CX leaders to experiment with AI while carefully considering its impact and potential consequences. Andrew's parting wisdom revolves around developing frameworks that truly elevate the human experience in customer service. It's clear that his concept of elevation engineering offers an intriguing path forward in the evolving landscape of CX and AI integration. Thank you for joining us for this enlightening conversation!

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