Digital Transformation: Optimizing the Customer Experience with AI and Automation | Digital CxO Summit

Hello, and welcome back to today's digital C X O summit. I hope you've been learning a lot and you have something to take back to your businesses. Meanwhile, I'm excited to be here with two amazing individuals and speakers.

We have Car Tag Leone, he's the Chief Data Officer of Insight. And John Arnold, a speaker and analyst, and the principal of Jay Arnold and Associates. How are y'all doing today?

We're doing wonderfully, and thank you for having us back today. Yep. Excited to be here today to talk about a very important topic, which is optimizing the customer experience with AI and automation tools.

And with that, let's get started. So the first question I have is, when it comes to this topic of enhancing the customer experience from your standpoints, what should the main areas of focus be here for the enterprise? Or to rephrase, what are some of the key pain points that business leaders are attempting to solve?

Well, I guess I'll go first. Um, so when I think about it, um, I think about maybe some of the opportunities first. Um, and the first one I would think is personalization.

So using AI to be able to create a better personalized experience for the consumer is really one key area. And we know with generative techniques, it's, we have the ability to create these really engaging kinds of personal experiences. And I think for organizations that are able to take advantage of the data assets that they have and the experiences that they've already been able to provide, and understanding the kinds of things that have worked with, um, their particular customers, as they think about the experience, they can continue to use those and to drive additional engagement with their customers.

And so I, I think that's one key way to do this, but they can also use it to, um, grow the, uh, their experience and reach out in different ways by leveraging some of the AI techniques that are available to them as well. And we can talk about how to do that in, um, in a little bit. But, um, maybe I'll pass it off and see if we have some additional commentary.

John. Yeah, thanks Amanda. Um, excuse me.

Yeah, Carmel, I'll call and build on that a little bit. So as an analyst, uh, our, our little tribe of people who follow the trends in the industry, we are very close to all, you know, all of what the vendors are doing. And we look at those bigger picture trends, right?

So when you talk Amanda about pain points, uh, as a kind of a strategy guy, I like to work backwards from that. And so what are those issues? And you know, Carmen raised a big car.

You raised a big one about personalization. And I think that ties to this idea that expectations, right from today's customers are, have certainly gotten greater in the past few years. And, you know, you have to really point to the pandemic as a starting point for when people were pushed into this new environment of being isolated, working from home, et cetera, et cetera.

And it forced a people to have, um, more communication because they couldn't go in person. And also with the accelerated the adoption of cloud. And that was a big driver, especially in contact center because when they start going to cloud, they have the capabilities now to address some of these pain points, uh, uh, as Carm says, with personalization is a big one.

And also just providing more, you know, connected, empathetic experiences. And, and what I mean by that is customers have all kinds of issues. And a lot of it's because when they've come to the contact center, they've generally tried a lot of other options.

They've probably gone onto the website, they might have tried possibly a chat tool or something else, and by the time they come to the contact center, it's like, I, I'm at wit's end. I need your help and how are you gonna help solve my problem? And when an agent takes that call, they often don't know why the customer is calling what those issues are, and they're kind of put into this on the spot mode.

And that's a real pain point for the contact center because the, if the agent doesn't have the right information and the right tools to engage with the customer, it goes downhill really fast, right? So if the customer comes in on a chat and you've got really weak chat tools and you say, do you mind, I'd rather talk to you than go on chat. And the customer says, well, no, I, I can't do that right now.

I'm in a noisy place. You've got a problem right off the start. So that pain point, whatever it is that they have coming into the call all of a sudden gets exacerbated because now they have an additional problem that they can't communicate the way they want to communicate.

So a lot of things compound when you have a legitimate issue that the customer needs addressed. And if you're kind of hamstrung with, with technologies that can't adapt, right, um, then you've got a bigger problem on your hands. And that again, ties back to the cloud movement that was really pushed ahead with the pandemic to get these tools.

But a lot of contact centers really, I think you'd agree, Carm, a lot of 'em haven't got the first base yet with this stuff. Yeah. So there's still a gap right, between what the customer needs and wants as opposed to what the contact center is ready to do, like right now.

That's right. Um, I would totally agree with that, John. Good and good points.

And I, I think the area of problem solving, I think is an important one. Um, 'cause when you think about, is the information available to the, um, agent, let's say when, um, you know, they're interacting and in a lot of times, um, that's the hard part, right? So the challenge is collecting the appropriate information to be able to respond to the customer that may be coming in.

Um, and that can be an issue, of course. Um, but with AI now, and I'll call it the democratization of generative ai, we have the ability now to collect that knowledge and to be able to, um, create a more automated or productive experience now for not only the consumer, but also it enables the, say, the agent who may be responding to be able to get to the information faster so we can increase their productivity as well. So I think along the, along those lines, we're starting to create this mechanism for speed to, um, ex speed to better experience.

How about that? I'll coin a term there. Um, but that, that kind of is a thing that, um, I think we'll start to see a lot more of in the future, especially with these AI techniques and capabilities.

So now that we've discussed some of the key points and issues that businesses are trying to solve, what are some key strategies for implementing these digital transformation initiatives as they relate to the customer experience improvement? Well, you know, Amanda, you're, um, you, you're mixing about three different questions in that one, Right? And I, I'm gonna focus here, and Carm can pick up in a minute, um, on digital transformation, uh, I talked, you know, as I was saying before about the cloud adoption accelerating, that's kind of a, a very important building block for businesses to have that digital transformation strategy in place because they have to go down this path, whether they like it or not.

And some businesses are already there. Some have a very long way to go with a lot of analog, you know, artifacts and processes, et cetera, that are gonna take quite a long time. The big problem when we talk about customer experience here, Amanda, is customers again, are, they're already there.

Most customers, especially in what we call digital natives, uh, they have re-embraced digital technologies like a hundred percent. And contact centers are generally way behind that curve for digital transformation. And that's really the root of why they have a real shortfall in being able to kind of meet the customer where they are, you know, according to their preferences and expectations, et cetera.

So when you talk digital transformation strategy, to me, it comes back to your first question about pain point. The reason why your customer has a pain point is because your business itself hasn't evolved enough digitally yet. And the contact center is really just one business unit within an organization.

And so it's this, the, the word strategy here to me is the big one because it's really a business level transformation that the organization is going through, of which contact center is kind of like the, the, the leading edge of the wedge, so to speak. They're like the front door of your business now. So if you're a very brand and customer-centric kind of business, and that's kind of what you wanna hang your hat on, your contact center has gotta be front of line for digital transformation capabilities because they need it real bad.

And if that's what's important to your business success, customer satisfaction, and almost everyone is, you have to prioritize that. Yeah. And I'll, um, and I, I love that, John, 'cause I think it, it really sort of hits to this part, this point around, um, are you ready for digital transformation?

And you know, of course being a C D O, my focus is on data. So do you have the data that it will enable you to be able to transform? Um, but also probably more importantly, is it accessible, right?

So I think we, you talk about the contact center and how do we increase the experience if you do want to transform maybe to make the experience better for your customers, how is it that you will take advantage of the data assets that you already have available? Or if you don't have them available, um, how do you, um, create them? And so I think in a lot of ways that's where we're going today.

So digital, digital transformation is sort of, I'll call it writing on the back of, um, data-driven decision making processes and the ability to collect and use that data in the most effective way possible. And it's not, you know, I don't want to get into the, uh, concept of, and maybe we could talk about this a little bit too, but, you know, clearly we don't want to invade the privacy of our customers, um, as we think about look, you know, understanding data, but we can look for patterns or we can provide best case responses, you know, for questions that they might have or provide guidance in the right way. Um, or even use automation techniques that allow us to be able to, you brought this up earlier, John, which was problem solving.

So how do we get them to the answer that they might be looking for to increase that level of experience, not only to satisfy them, but also to, you know, guide them in the right direction so that they have a better experience overall from, you know, a product's perspective, for example, better use of the product, et cetera. So I think in a lot of ways, digital transformation, um, leverages these new technical capabilities, and it's really, I think, sort of the backbone of how we think about, um, transformation overall, but especially as it relates to the contact centers or customer experiences. Yeah, I'm with you on all of that, Carmen.

I, I'd also just mention too, when we talk, uh, digital transformation, I, I still think of it as kind of an organization wide thing, right? It's not just contact center. And to me, if I'm a contact center, decision maker, leader, influencer, whatever, I would be wanting to view this as a prime opportunity to kind of bridge the, from the analog to the digital world.

Because a lot of contact centers are really kind of hide bound by their existing deployments of premise-based, mostly legacy technologies that aren't that easy to unpack, uh, unpack or, uh, basically migrate quickly to cloud. It's, some businesses are gonna have a really hard time with that. So they need strategies to say, okay, our, our capability to move is kind of limited, but if we're going to cloud now, all right, we can bridge, uh, our capabilities with the, what we have in place that we can't get rid of right away, but start adding some of these more digitally native tools and, and capabilities to start going down that path.

Because if contact centers don't kind of get to the leading edge with digital transformation, they're gonna kind of lose, they're gonna lose some ground strategically within the organization, because kind of old line thinking amongst senior management, Amanda, you know, a lot of it is they just view the contact center as kind of someplace over there. It's not part of our everyday business, and it's largely thought of as a cost center, right? So it's just a, it's just a line item.

They don't really think too much about it. That mentality doesn't cut it today, but there's still a lot of that thinking there. So this is a, i i see view this as a real good window for contact centers to kind of like, you know, reimagine, reposition themselves within the organization as driving value that they're more than just a cost center, right?

As I said earlier, they're the front door of the business now with so much, you know, being e-commerce and online people aren't coming into stores anymore so much. So that technical ability to connect to the customer with all these tools is way more important now than it used to be. Yeah, I think the, um, you know, the key focus area that you're talking about, John, is it's, um, technology enabled, at least a lot of this is technology enabled as we think through it.

Um, you know, whether it's a contact center or other kinds of personalization experiences we might have. Um, so in, in the grand scheme of things, it's the, we've seen over the course of the last di well probably more than a decade now, as democratization of the these technologies. So the cloud, the one that you mentioned is, is an, um, an easy one.

That was one that sort of came around and people were reluctant to move the data center to the cloud, but then we moved to the cloud and realized that it does provide us acceleration in terms of being able to create, you know, durable and available and scalable types of solutions. But it also allowed us to be able to do things, um, faster as it related to, I don't have to rack and stack equipment, for example. So that made things a lot easier.

And I think we're seeing the same phenomenon now with AI and data, uh, data-centric technologies. So, um, democratization of AI allows us to be able to increase the personalization experiences for our end users and be able to, you know, sort of drive toward creating, um, not, not only a better experience, but you can get to the answers faster when you need them, but also, um, just understand general behavior, um, by analyzing the data assets that you may already have. So I've seen this over the last decade, um, and I was working with AI before I was really popular before the AI winter, but now that you can see it being used in so many ways, it, it really helps us to transform from what was a, you know, remember when we used to do sort of the contact centers or call centers that were off, you know, offshore and, you know, really wasn't a good personalization experience, um, because of the fact that it's like they couldn't really, customers couldn't relate to the people answering their questions or helping them through these challenges.

But it's amazing the, um, the, um, the way that the AI techniques today can respond in very human-like fashion, um, or well human-like I'll say, but in, in the fashion that the consumers are expecting to be responded to, um, which is really just something that I think is, um, a key area of customization that allows us to be able to create that, you know, I keep saying great experience, but it's true, um, a much better experience, and it does feel like it's, um, an actual human that's responding to you, um, in this particular way. So I think we'll see evolution over time. But, you know, some of the things I know, um, Amanda, you kept asking the questions around what are some of the challenges and roadblocks, and I, I think those challenges and roadblocks, uh, really stem from, you know, responsible ai trusting ai, collecting the data, is the data accurate?

Are there biases, et cetera. So it's not all just smooth skating here, but I think if we look at it, there's so much opportunity potential if we focus in the right areas that we can really transform the way that we think about, um, the customer experience Overall. Let's think about for a moment, the key performance indicators and metrics.

How can business leaders ensure or see or measure how their digital transformation initiatives are playing out as far as the customer experience? That's a, that's an interesting one and, and a tough one to answer because, um, if it was as easy as just identifying, um, the, a particular set of metrics, then we probably would do it effectively. But I think in general, um, I would look at it more from an R o I perspective.

So if, uh, one thing that I work with my customers on a lot is to think about if you're going to leverage technology to be able to help to transform your business, what are your, what are your expectations? Like, what is the actual business impact that you're looking to measure? And then try to quantify that as much as possible.

We've been talking today a lot about customer experiences and, and that that's hard to measure. You can use things like N P SS scores, et cetera, but when you're talking about business transformation, you can measure specific productivity gains, for example, or maybe even, um, increased sales, et cetera. So you can start to identify more quantitative metrics that allow you to be able to say, did that particular transformation impact my business in a positive way?

And that's measurable. And I think in, in those ways, I work with customers to help to identify what are those key metrics that you can, that you can, uh, specify and then monitor those and work toward them. And then as you start to, you know, improve or adjust your particular, um, sets of activities, you can then, um, ensure that you're at least heading in the right direction.

John, can you weigh in on that? Yeah. So it, it's an important question for contact center in, in my view, because no other space in an organization is more governed by metrics than contact center, right?

All the KPIs that have been traditionally used to measure agent performance, right? You know, first call resolution, time to answer hold time, all of those conventional contact center metrics, they're all strictly operational, and they don't, they've never measured, uh, customer experience. You know, the, the, the link between those metrics and the, the accepted KPIs for customers like N P S and csat, it's hard to make a direct correlation there.

But the idea is that, you know, contact centers are used to having those kind of metrics, but for digital transformation, it's a far more abstract thing. You can track, like car Carm said some operational metrics maybe reflected in higher sales. I was thinking more about potentially reduced cost because you're moving from, you know, hardware to software to SaaS model.

So there can be some quantifiable savings that you can show from digital transformation, possibly in headcount, right? Um, but also also, I, I think, again, where carbon brought up earlier, some of the AI related things, you'll get better with digital transformation. You'll have not just access to more data, but you'll have more accurate, uh, forms of data.

And what that will translate into is probably less repetition or duplication of effort, wasted time due to all kinds of miscommunications. There are things that you could kind of measure that are a result of having a more digital organization, but there won't be like one number that you point to and say, ah, digital transformation was a success. So it's a little, uh, it's a little misleading to say, well, we do it for our agents as KPIs.

Yeah. But digital transformation, I'm just gonna say DT from now on, by the way. Uh, it just takes, it's, it's a harder thing to quantify and you shouldn't get so hung up about it.

Like, i i, with karma. I mean, it's an inevitable thing you have to do. Um, yes, you gotta show some kind of an R o I, but I, I think you have to look at it more as like an indirect effect rather than direct outcomes of doing digital.

You know, when that customer sat number goes up, that's good, but you know what is even better for the contact center if that translates into better retention of agents, that's a big win. And, and that you can quantify, right? Because when you start layering AI into things, you shorten the training time for new agents, you make your training more effective so they get onboarded more quickly.

Those are, those are huge things for contact centers. 'cause it's one space that is really hard to find people to hire and keep. The turnover is so high, it's so problematic for all kinds of reasons.

And if you can cut that down, you'll be a hero and they won't ask you any more about digital transformation if you can solve that problem, Right? Kar, you're smiling. Yeah, no, totally.

I completely agree with you. Uh, yeah, and I, I think that it's a, um, you know, as you think about like, like you're talking about, um, it's the media labor tasks, I think that, you know, those are the things that are most exhausting, let's say, for the, you know, for the service agents that might be working with people every day in the, in the contact centers. So it's those kinds of things where you can automate those activities.

Um, you can definitely increase the productivity of the, of the, of those reps, but you can also, I think, increase their sat their own satisfaction levels, not just productivity, but its satisfaction levels, and then it, when they do, um, need to talk to customers directly, um, just think about how much better that experience will be for the customer. 'cause you know, instead of having to worry about like, yeah, I've been answering all these mundane questions all day, um, now you can actually focus on the ones that really do require their expertise. So I think, you know, I've been seeing this too in a lot of different areas where the, when you are freed from some of those, I, I hate to use the term media labor, but it kind of is, it's like sort of those, those, um, um, lack of productivity style things that you would typically do, and you use automation to help you to be able to do it more effectively, um, that allows you to be able to focus on the things that are really important and providing value add.

And I like to, I the term co-pilot is being overused, but assistant maybe is more appropriate. And I, and I think in a lot of ways, the more that we can provide assistance, um, through these digital transformation tech techniques and concepts, the, the more effective we can be as a business. And like you said, John, those things are measurable.

So you can then stay, you can say the productivity has increased, you can say, you know, response times have, um, decreased. And, and so it just becomes, um, a better experience for everyone overall, including the business. Yeah.

You know, a, a big one, uh, Amanda, and for the audience out there that a lot of the vendors that's touting now would be, uh, automated call summaries. And if you really wanna get kind of people excited about what the possibilities are here, again, it's all building blocks. If you have, when you have cloud, you can have ai.

When you can have ai, you can do these new things. And so call summaries are important on two levels. One, it's one of those manual tasks that not just agents go through, but sales guys have to go through at the end of that call, they've gotta put, pull all the salient points together.

So not only does it automate it, and by the way, it does it right away. And regardless of what language you're speaking, you can get it pretty much in any language you want. Um, it's accurate, right?

So, and it's objective, so it captures what was there. So otherwise the agent, if they're compiling those notes manually first, they have to kind of have recall about what it was they got. Secondly, there's lots of room for them to be a little subjective about things that might make them look bad.

They might kinda rephrase a few things, right? But AI, automated summaries is, does, doesn't look that way. They just, they just report what was there.

So it's a more accurate form that supervisors can use to evaluate performance. But that's like, just level one with this, when you really have the good ai, AI working for you, not only do you get those just automated summaries, which you basically transcripts, right? But you can, you can get more intelligence out of that because you can now, um, you can, you can, you can like focus on keywords or phrases or things a customer might have said, and that can tie into what other agents are doing with customers in similar situations.

Now you can start building best practices because now the, uh, coming back to Carmen's point about data, AI is all about data. The more you have, the more utility it has and value. So now you start picking up patterns that with customers in situation X, well, the high performing agents, when they did X, Y, and Z and these particular calls, they got the good outcomes.

So imagine criminals call summaries. You capture that across, you know, thousands of interactions with customers. Now you've got some data based, uh, insights that you couldn't really have gotten manually.

When you think about what supervisors have to do manually, they can maybe listen to, maybe I hear, I think it's like 3% of call recordings. Well, that's such a small snapshot of what you have. Imagine now, oh, I can capture a hundred percent of a hundred percent of calls that the richness of data that you now have is just exponentially better.

And you know, how, how would you not want that? Right? Hmm, that's right.

It's a, um, that's a great point. Yeah, the collection of data, but it's, it's enabling it and using it, I think is the key to your point, John, um, oh, for sure. Because Those are the things where it's like, I mean, how many times do you work with customers where they, they collect all the data, but nobody, like I said, 3% of managers are able to review the data.

So yeah, that's a problem. I, I, the one thing that I, um, is really fascinating at least of late is, um, this concept of transfer learning and, and you alluded to it, but it basically is like when you start to develop knowledge within the contact center, within any environment, um, that you might be working, sharing that knowledge usually requires a, a degree of training in order to be able to understand it. But with AI systems, there's this concept of transfer learning, so you can transfer knowledge, um, and use it in other ways.

And so we think about it, like, if we understand patterns for, let's say, whatever call center tracking, um, or a meeting res, uh, meeting, um, summarization and action items, for example, or takeaways, um, those kinds of things can be done consistently. And then learnings from it can, can be compared to the knowledge base that we might've collected in a semantic database or into, you know, uh, one of a large language model wherever it might be. Um, so that actually opens up the door to so many different ways to accelerate our ability to not only take advantage of the data assets, but to learn from it.

And then to apply that effectively without necessarily expecting, you know, people to become experts in the area. You know, the 10,000 hour rule, well, not everybody has to spend 10,000 hours becoming the expert. Um, so now you can actually accelerate that much quicker to being productive.

So I think that's gonna transform just about all industries today as we think about this transfer learning concept, but also the collection and extraction of knowledge, um, directly from our own domains, which is, is pretty amazing. Data is the backbone of everything. It really, it really is important.

John, you had something else to add? Well, I'll just quickly tack onto that. Anybody out there in the audience who's wondering, what's a major in in college or someone who's looking for a career change, you know, this is the place to be, you know, data scientists, analytics people, right?

A AI is gonna be the currency of the future, and it's all about data. So to your point, car, how do you harness this information? So few people are trained, and that's a big gap in contact center in particular.

They don't have the expertise. And so there's a lot of, you know, vendors and third parties. Uh, it's a great opportunity actually for carriers to offer kind of a new, a new acronym today, das data as a service or data mining as a service, right?

Where you don't have to have the expertise, you just farm it out, but much better if you can have it yourself. Because as you mentioned, uh, Carm, when you touched on large language models, enterprises are gonna continue, are gonna keep going down this path to developing proprietary large language models. And that's gonna be a big, the customer service space is a big part of that because you wanna speak the customer's language and that can become a competitive differentiator for you.

So we're gonna, I hate to say it, yes, AI democratizes a lot of data, but it's also gonna lead to walled gardens. It's just human nature. Every tech cycle goes through this.

And, uh, the more you can kind of like leverage that data, it suddenly becomes strategic for you. So you want, you need that expertise in house. So you gotta find those analytics people and the data scientists, they're really, you know, they, they can write their ticket these days.

So that's your inspiration for what's to go do next for the next 10 years of your life, folks. There you go. Perfect And good, bringing that up.

Which brings me to my next question, actually, which is, um, the role of training and upskilling today. I know we have a skills gap, and you were bringing this up a little bit. So what role does employee training and upskilling play in ensuring the success of these digital transformation initiatives?

Well, I guess that one, um, hmm, I'll, I'll take that one first. So, so there's really, maybe there's two answers to this. So, on, on the one hand, um, a lot of it has to do with really just understanding the shift, if you will, in innovation within the organization.

Um, as you think about, um, the transformation process. So, you know, John was talking before about you shouldn't just focus on transformation for transformation's sake, but really just technology is a enabler. And so we will adapt technology and we should understand the technologies as they impact our business.

So really it's about transformation. Um, in that sense, or training for transformation really mostly has to do with understanding the technology capabilities and which capabilities can be applied to help you to move in the direction of where the organization wants to be. So learning specific techniques, whether they're productivity tools or understanding how to use, you know, like it used to be reporting, pretty soon it's going to be AI to understand how to use AI or large language model systems in order to be able to be productive.

Training really sort of fits into those areas. So I think we are seeing a bit of a transformation in terms of, um, the types of training that, um, people will need. So instead of being trained, let's say, on the, you know, the latest call center application or software or how to code macros, whatever, um, you are gonna be focusing more on AI will help us to do that more effectively.

So then we have to understand capabilities and then how to interact with the, with those systems in order to achieve the expected outcome or goals. So one of the answers I was gonna give earlier when we were talking about like, what skills do we really think people should have in the future, I think it really just comes down to business domain knowledge. And, um, I'll also say critical thinking.

So because the, um, the, when you look at what a large language model really is, it's really just a, um, it's a parrot, if you will. It takes, it has knowledge, it understands patterns, but it can't reason, it's not a reasoning, it's not a, a reasoning entity. So it really doesn't understand your intent.

It has no idea what your intent is. So that requires the human to be able to do that. So I, I think training starts to move more toward do you really understand the business?

What are your expected goals and outcomes? And then how do you actually get there, um, in, in terms of being able to understand how do you use the technology that is available to you today? So I think we're moving away from the, you know, yes, you have to learn these 17 systems and all the intricacies of how to use them, um, in order to be productive.

And we're shifting more toward how do I interact with these automated systems and really understand what the intent is, and then focusing more on the real goal of what your, um, job function is in order to be able to deliver, um, a good experience to your end user. John, do you have anything to add? Yeah, so I'll just e extend that to contact center and CX in particular where I, I would see training kind of opportunities in two ways.

One is with the agents, of course, 'cause they are kind of that direct point of contact with the customer. So for them, when you have the right tools in place and access to the right data, you don't get hung up on the, the nitty gritty stuff. You can focus more directly with the customer.

So the training there is twofold. One is to have a more natural, uh, authentic conversation directly with the customer that builds, you know, trust, uh, shows empathy, uh, and really kind of makes the customer feel valued. And, you know, you, you don't have to go the extra mile every time, but just enough that it sounds like you actually understand and care about their problems.

And if, and you can't do that if you're toggling between screens and applications and, you know, listening to your coaching from your supervisor and whatever. So the agent training piece is one, but you know, what, if AI kind of becomes the story, you also have to be training your chatbots because just as Asians have to sound authentic, chatbots have to sound, you know, humanistic too, right? So there is a whole specific realm of, you know, you know, you know, whatever you wanna call it.

But, but ai, AI-based training that has those chatbots be more what we call conversational, right? And so they, they, they do more than just have robotic closed-ended, you know, yes, no questions, which is what I, what I v r has always done. Now this is like next Gen I V R, but now it can you talk about intent karma, that's the whole thing.

Chatbots will be credible tools of engagement when it gets the intent reasonably, right? The customer knows they're talking to a bot, that's okay. 'cause five years from now, my bot as a customer will talk to your bot in the contact center and there won't be any humans, but we're not there yet.

But when you can have the bot, um, handle open-ended inquiries and actually have a reasonably accurate, you know, meaningful engagement, customers will open up and tell you more because now they start to trust the bot, right? So there's a lot of training and bots are very, all of AI is iterative, right? It gets better the more you use it.

So your first generation bots probably won't be that great, but they'll get better. And the most important takeaway to me for all of this, for the audience is, you know, your expectations around AI have to be kind of realistic. That you have to kind of take baby steps, have small deployments, try out a few chat bots with stuff that there's not a lot of downside if it goes wrong.

And you'll learn to trust it when it proves itself. Only at that point is it gonna be worthwhile? Because now you'll realize, oh, I can add more things, it can do more tasks, right?

It can integrate with more things, handle more languages, handle, handle more nuances of speech, right? All that stuff. It can handle slang, for example, and, you know, acronyms, those are big ones.

But once it's getting there, you say, oh, it's not bad. Okay, now let's have it do this task. And before you know it, you're automating more and more.

And that's kind of the, that's kind of the nirvana for these guys is because now automation is a key kind of use case for AI in the contact center. 'cause humans, the agents can't handle all the volume. You have to have forms of automation that can do the work, so they can actually be effective, you know, as the contact center.

Yeah. John, you gave a key takeaway. Km do you have a key takeaway for our audience today?

I do. Yeah. No, it's, I'll, I'll build on what John was talking about.

I, I think what, what he was describing there was reinforcement learning with human feedback. And, um, the thing to remember for all of us, you know, again, going to the AI side of the house is that, you know, while automation and leveraging these AI capabilities will become, um, sort of commonplace moving forward, I think we also have to remember that these are, um, they, they're assistance for us. They're, they're not replacing us.

And so we're using them to be able to, um, be able to remove some of the mundane, um, you know, productivity sucking things that we typically would do in the, in the past. And so if we look at it from that perspective, then, um, we can think about it as a mechanism to be able to let us do things better, more effectively. Um, but also to be able to train them to be, to do the right kinds of things.

And I, I talk about it an anthropomorphically by saying them, but it's, uh, but it, it kind of is the case. I mean, it, we, we tend to associate, you know, AI now with people or, or sort of, uh, sentient beings. But I don't think we, um, I don't think we have to worry too much yet about things like, you know, um, is, is the singularity coming, et cetera.

But I do think that there's a lot of value that is available to us by leveraging those data assets, by taking advantage of technology in digital transformation as we've been discussing. Um, and then, you know, putting it to good purpose. And so leveraging technology is, uh, really so important for us to increase our ability to deliver the kinds of experiences that we would like to give to our customers, um, or even, you know, bring to ourselves, right?

Because we all want to enable, you know, even personally, um, we wanna make sure that we're taking advantage of these things for, you know, whether it's, um, say, organizing my calendar. I've been traveling a lot lately, so I'd love a, a bot or an agent to take care of all of my travel for me. But, um, it hasn't quite happened yet, so I have to work on that one.

But in general, I, I think we'll start to see these things, um, happening over time. So it's really embraced the technology, understand the technology, focus on, like we said, the, you know, the what are its capabilities, and then how do you leverage them to be able to really business forward and succeed. And, you know, certainly it's gonna be an exciting decade, I think, moving forward.

Last thoughts, do you have any last thoughts? For sure. Um, well, I guess since I was going on a tirade there, I'll, I'll maybe just give you the last, so I, I would say, um, so the, I, I know that what's coming right now is for a lot of folks, it's thinking about responsible ai.

Um, and so responsible ai, I'll just kind of end on that note. Um, I think we're going to think about ways to use it in the most appropriate way. So we didn't talk much about things like bias that might be in the data set, for example, or conversational agents that might, you know, maybe say things that might be inappropriate.

Um, but I, there is a lot of focus in that area. So I think for those, um, those of you that might be thinking, well, can we trust ai? Um, should we let our business, should we let it really represent us in our, in our businesses or even personally?

And I think that is an area to also, um, think a little bit about. So, you know, educate yourself on in terms of understanding what responsible AI really is and how it can be used. And so long as you keep an eye toward that, um, I think, you know, we can make sure that we create solutions that address the, you know, some of those challenges around bias and, and maybe, um, the appropriate use.

Um, so I, I would say, you know, as a, as a parting thought, think about the value of ai, but also think about, you know, what are some of the challenges that you might need to, um, at least keep in the back of your mind as you think about solutions within your own organization or using it, you know, personally. And, uh, we have a few minutes left, so John, if you have anything else to add, and if anyone has questions in our audience, now's the time to ask a question. We've got just a few minutes left.

Okay. Well, until a question comes, otherwise, uh, yeah, I, I echo everything Carmen's saying about AI and, you know, responsible use, how to fix all that. That's a whole other topic.

Um, the other parting thought I would leave particularly, again, our focus is on customer experience here. Um, the word, the, the term contact center has kind of been the standard for a very long time. Uh, it, it itself replaced the term call center, uh, which is from a time when almost all forms of engagement from, uh, from customers to an organization were done over the phone, right?

That's twice the call center. When we moved to more multi-channel model, it became the contact center. 'cause we had all these other ways of communicating.

Now though, um, what I, what is clearly happening is the term customer experience is not the same thing as customer service. And it's a, it's an expression of a broader concept of the relationship between customers and the brands and the companies that sell these, their products and services. And the takeaway there is that the traditional way of thinking of contact center, if you've got a problem from a customer, take it to the contact center.

We're not part of that. Well, if a cus if a company wants to become more customer centric and very brand focused, they have to think holistically that the whole organization's focus is to serve the customer. And that customer experience doesn't just happen in the contact center with digital channels, social with mobile, they're reaching out to your sales organizations, your marketing, your invoicing, your legal department, your r and d people, if they have a problem with a product, they don't, you know, anyway, there's a million ways they can engage and the contact center is only seeing part of that traffic.

So when Karma was saying earlier about, you know, it's a whole, there's a whole image you have to capture here if you wanna truly understand the customer. So if you, you need to kind of broaden your thinking, be beyond this idea of customer contact center equals customer service. No, your organization should be focused on customer experience.

That's a bigger ask and requires more of a, you know, a holistic approach. And that's where digital transformation really helps because of the entire organization is moving along that path. You have a pretty good chance that you can make the organization kind of nimble enough, transparent enough that all the data you're collecting from all these different lines of business can ultimately all be filtered to the contact center.

So they have a full set of information for the customer. That's what drives customer experience. When you have that one point of contact that solves all the customer's problems, that's a great place to be.

We're a long way from that, but these are the kind of tools we're talking about today that will help you get there. Absolutely. Well I wanna thank you John and Km, for coming on and sharing your insights today.

I think it was a great conversation.