3214: AI-Enabled CX: Unlocking an $860 Billion Opportunity with Isabelle Zdatny from Qualtrics XM Institute

[00:00:03] Welcome back to the Tech Talks Daily podcast coming to you from the X4 Summit in Salt Lake City, Utah, where the world's brightest minds in experience management, AI and customer insights are all coming together to share ideas, breakthroughs and real-world applications.

[00:00:21] And today we're going to be diving deep into the AI-enabled customer experience, a space where opportunity meets challenge and where businesses that get it right, they stand to unlock the 860 billion or maybe even 1.3 trillion in value.

[00:00:40] But while AI is clearly transforming customer interactions, personalisation and business operations, the reality is that many companies are still stuck in pilot purgatory, experimenting with AI but struggling to scale and integrate it across their organisation. So to break it down today, I'm joined by Isabel Zdatny, Head of Thought Leadership at Qualtrics.

[00:01:08] And her work is all about building the category of experience management, helping organisations bridge the gap between AI potential and real-world execution. So today we're going to unpack the latest research, explore the disconnect between AI ambition and strategy, and discuss how businesses can move beyond isolated AI pilots to drive real measurable impact.

[00:01:36] But enough from me. Time for me to beam your ears all the way to the show floor here at X4, where you can join me and Isabel in conversation. So thank you for joining me on the podcast today. Could you tell everyone listening a little about who you are and what you do? Absolutely. So thank you so much for having me. My name is Isabel Zdatny. I'm Head of Thought Leadership here with Qualtrics XM Institute. And XM Institute is almost like a little think tank inside Qualtrics.

[00:02:02] So we are focused on building the category of experience management rather than helping people use the product per se. And so my job is really helping people have the insights and the frameworks and the data and the best practices that they need to be successful in their role and build a successful, sustainable CX or EX program. Well, thank you for joining me at the X4 Summit here in Salt Lake. And we met briefly yesterday. We were talking about the report and your research with McKinsey.

[00:02:30] There's a great stat in there. You found that AI-enabled customer experience represents an $860 billion opportunity. What are the key areas where businesses can unlock this value, do you think? Absolutely. And it could go all the way up to $1.3 trillion. So that was the conservative estimate. Yeah. So the value is expected to be unlocked in three ways. So first of all, through productivity gain.

[00:02:55] So about $420 billion of that $860 billion opportunity is expected to come from using AI to help augment and automate employees' work. So freeing them up to focus on the more higher value activities. Another $240 billion is expected to come from revenue growth. So using AI to transform how you're acquiring and growing your customers through things like intelligent targeting and personalized messaging.

[00:03:21] And then the third one is $180 billion is expected through process improvement. So using AI to help optimize your operations and lower the cost to serve customers. Wow. Some phenomenal figures there. And many organizations recognize AI's potential for CX transformation. Yet one of the things that stood out was few have a company-wide AI strategy in place.

[00:03:45] So interesting. Yes. So we found that 72% of executives believe that AI will fundamentally transform their approach to customer experience within the next three years. And many of them understand theoretically the high-level potential of AI for their operations. But only 12% of organizations have a centralized CX strategy, centralized coordination and ownership.

[00:04:14] And we found, though, that when they do have those in place, they are 2.3 times more likely to report gaining market share compared to other organizations with more limited and uncoordinated actions. So few people are doing it, but it's worth it. Why does that disconnect exist, do you think? What steps should anybody listening, especially if they're a business leader, what should they be doing to maybe close that gap?

[00:04:38] Yeah. So I think part of the disconnect is coming from the fact that everyone, again, kind of theoretically looking, long-term gets that AI is going to be quite transformative if you look to the horizon. I think nearer term, a lot of people remain unclear about the exact value it's going to create for their organizations and what investments they need to make to generate that value. And so I think a lot of them aren't quite sure where to get started.

[00:05:07] We're seeing a lot of organizations stuck in pilot purgatory where they have these siloed, isolated, very expensive experiments, but they're not scaling those across the organization. And so in order to do that, I do think that to some degree, AI needs to be top down and it needs to be outcome focused. So rather than starting with, we just want to do AI for the sake of AI, what are the outcomes that you are trying to achieve?

[00:05:35] So as an organization, what are the business results that you're looking for? Are you trying to get customers to spend more? Are you trying to increase average contract value? What are you trying to do? What are your business and brand objectives? And then based on that, what is your organization-wide AI strategy? And then you need things like the governance and organization in place to help coordinate and manage the execution of that strategy and AI implementation across the organization.

[00:06:03] You also need things like centralized risks and ethics guidelines, right, to make sure that you're being compliant and not delivering off-brand, possibly brand-damaging experiences. So, yeah, there's a lot of, I think, centralized activities that can happen that organizations should be looking to right now. Obviously, we've been talking about AI for a few years now, but it's no longer just AI.

[00:06:30] It's now agentic, analytic and creative, as you mentioned yesterday. So can you explain how each different plays a different role in improving that customer experience? So the three categories that I tend to use are analytical, generative and agentic. So analytical kicked off about 15 years ago with major breakthroughs in things like natural language processing and speech recognition and computer vision.

[00:06:57] And these technologies are helping organizations understand their customers at scale. It's able to process massive amounts of unstructured data, uncover hidden patterns, predict future behaviors. This is things like sentiment analysis and churn prediction. I think once generative AI hit, a lot of people were calling out that things were AI that had always been AI, but they weren't necessarily drawing attention to. And then obviously late 2022, generative AI burst onto the scene with the release of ChatGPT.

[00:07:27] These technologies allow organizations to create brand new content and power natural language conversations. So now we're not only able to understand customers' experiences across millions of interactions, we're actually able to engage individual customers in meaningful one-on-one dialogue at scale. And I will say a huge percentage of that $860 billion opportunity came from capabilities in that category.

[00:07:55] And then the third one, which everyone is talking about now, is agentic AI. So unlike analytical and generative AI, which excel at specific tasks but still require human coordination, agentic AI is able to independently orchestrate multiple capabilities across complex workflows. So it's not just assisting humans. They're able to drive these complete processes all by themselves, like closing the loop with customers and taking actions on them.

[00:08:22] So it's almost like having a fleet of automated project managers on hand who are able to coordinate people and resources and tools, including other AI systems, in order to accomplish a goal. So they're more independent than previous iterations. And one of the things I always try and do with my guests on this daily podcast is give them an opportunity to bust a few myths or misconceptions. So what are the biggest misconceptions executives might have about implementing AI in CX?

[00:08:51] And how can businesses avoid some common pitfalls? You probably hear a lot of stories. Oh, my gosh. Yeah. Yeah. I would say myths for that it's like easy and is just, you know, turning on existing features and functions. Again, I think often in CX, you get pressure from executives. You're like, what are you doing in AI? As opposed to, again, starting with the end in mind. So, yeah, other myths.

[00:09:20] One of the ones I actually thought was most interesting, I interviewed one of the companies I interviewed for this report was ServiceNow. So I talked to their head of AI and chief analytics officer. And one of the things that they told me was most organizations think they need to get their whole data house in order and have clean data to get started with AI. And they just started putting AI models on top of the data that they had.

[00:09:45] And what that allowed them to do was very quickly identify where there are gaps in their data, where they needed to go collect more, connect more systems. They were able to see what employees were searching for or when they were, you know, a chat bot was wrong. It was getting flagged. And so this wasn't externally facing to clients. But I think a big myth is like that you need perfect data in order to get started and actually just getting started and then quickly learning and iterating and improving is the way to go.

[00:10:14] So many great points there. And the report also emphasized that AI creates compounding advantages over time. And there's a big debate around ROI of any AI project at the moment. So with that in mind, how can companies ensure that they are early adopters rather than result in falling behind their competitors? Yeah, I mean, again, I think you need to make the investment. You need to be smart about where you are making the investment. But you kind of have to get moving.

[00:10:43] You need to be decisive, again, responsible. I think there's, as CX professionals, frankly, one of the things I struggled with in this report is that there's only so much you can do within CX. It's that it does need to kind of come from the C-suite. You need someone like your CIO or CTO often to be leading this and building the technology. But I think just getting started using what you can. If you do have technologies or vendors in place in your CX tech stack, what's available?

[00:11:13] How can you start building use cases around there? Start small, build momentum to demonstrate its value and hope it picks up steam across the rest of the organization. 100% with you there. And one of the great things about this summit is so many real-world examples that we're seeing. Because you hear about AI, but how's it going to impact me? How's it going to impact my business? So KFC was a great example today.

[00:11:35] But what would you say are some real-world examples of a company that has successfully leveraged AI to drive personalization, reduce churn, or just improve operational efficiency? Any examples speak to mind? One question. Yes, I have a number, but I'll share my favorite one. So a few years ago, Fiserv, who is a global leader in financial services technology, they set out to transform how they were able to predict and prevent customer churn.

[00:12:04] And so their VOC team took all of their customer feedback data, all that experience data, and they started combining it with a wide variety of demographic and firmographic and operational data. So everything from their client's business type to ownership profiles, like was it veteran-owned or women-owned, billing pattern, service history, and then applied machine learning on top of that to build out predictive models that were able to say,

[00:12:32] these are the exact series of events and circumstances that are likely to lead to customer churn. So they knew if these six things happen, we are going to lose an account. So as soon as thing four happens, we need to step in and intervene. And they automated these interventions. So they made them proactive and personalized. They're tailored to each client's unique risk score and their individual context.

[00:12:58] They studied the VOC customer team, studied other customers who'd faced similar challenges and stayed. And then based on these learnings, they used generative AI to automatically generate prescriptive guidance for the account teams of these at-risk customers and sometimes draft initial communications. And what this has allowed them to do is even the most junior employees on the team now know how to handle these very complex, naughty situations. And they were so successful.

[00:13:25] This is one of my favorites because I feel like everyone's obsessed with stopping customer churn. But they actually also use these predictive models to identify opportunities to activate their promoters. And so like automatically connecting them with a referral program where they would earn money by recommending FISERV to other businesses. And they recently expanded into sales. So they were able to use AI to create these tailored, they call them recipes for success, that they're able to give sales teams based on their happiest clients' accounts and how they were set up.

[00:13:55] And so, again, that enables every client relationship to start out on the strongest foot possible. So I think that's just a very good example of how, you know, they're using it to listen and understand and act on that information and personalize it and optimize, you know, their operations and help their employees. And they've seen, you know, productivity gains, increased revenue and decreased costs. So hitting all those value points. Incredibly cool.

[00:14:20] Of course, on the flip side, the rapid adoption of AI has raised concerns around ethical use and data privacy, all those things. So how can businesses strike a balance between AI-driven CX innovation and responsible AI governance? There's possibly an episode all on that, really. Yes, I think you need, again, those kind of centralized risks and ethics guidelines in place. Often you'll have teams like compliance or security or legal coming in and help shape those.

[00:14:49] One of the things I saw a lot talking to organizations is their central AI governance council was like co-opted from previous councils that were reviewing new software implementations or products. And so they already had a process in place for making sure that they were rolling things out responsibly and using data responsibly. We've had customer data for a while.

[00:15:13] And so I think figuring out where there are already those type of processes in the organization. And again, as a CX person, we can help define what are acceptable uses and quality standards for governance or for data that we're sharing customer data that we're sharing back. How are we communicating to customers when they're using AI and being transparent about it?

[00:15:37] So I think CX actually has a big role to play in ensuring the shaping and delivering of those risks and ethics guidelines. Love it. And if I was to ask you to look into my virtual crystal ball, look into the future, how do you see AI further reshaping the future of the customer experience? And what should organizations be prioritizing this year and beyond? Big question. That's a big one.

[00:16:00] And I'll say this was something I was thinking a lot about last year of if we look ahead like 10 years or something, I think the challenge in front of organizations is not necessarily the technology. It's an imagination. Because right now we're seeing a lot of companies apply AI to automate processes, improve productivity.

[00:16:25] Ethan Malik, a Wharton professor and AI researcher, described it like just using a smartphone for placing phone calls. Like it can do a lot of other things that organizations haven't really yet explored. And so I think there's opportunities now for fundamentally rethinking how we're creating and delivering value for customers instead of just, you know, doing what we've always done but better and faster. It reminds me of the Henry Ford quote, right?

[00:16:52] If I would have asked the people what they wanted, they would have said faster horses. So how are we not just creating faster horses but actually creating new experiences and capabilities that have never been possible before? I would like to see organizations thinking about that. Before I let you go, you're here at the summit this week, back-to-back interviews, speaking with customers, keynotes galore, walking around the show floor, soaking everything up. What are you going to be reflecting on on that plane ride home?

[00:17:21] Yeah, I feel this year talking to customers that some corner has been turned. Not even just with AI, I feel like people have more buy-in at an organizational level. Another one of our findings actually from the executive study was that 77% of executives consider customer experience a significant or critical priority for their organization.

[00:17:48] So I think there's broader buy-in for CX teams. They're having to spend less time on stakeholder, you know, convincing people that CX is worthwhile. I think they're having fewer problems connecting data sources.

[00:18:04] So I feel like we've hit some higher level of maturity just on average of the clients I've been talking to this year, which has been really fantastic to see and is going to make me think about, all right, well, if they're, you know, now getting a little bit more omni-channel and centralized and have support, like what else does XM Institute need to be producing to support them along that journey? Awesome. For anybody listening, maybe they want to dig a little bit deeper on anything we talked about today, the report or anything.

[00:18:33] Anywhere in particular you'd like to point everyone listening? Yes, you can find me on LinkedIn. I am very easy to find because my last name is quite unique. So send me a note. I will post the report on there. You can download it from Qualtrics.com. I also encourage you to check out XMInstitute.com. We have over 800 resources, how-to guides, blog posts, reports, videos, a very robust library aimed at helping CX and EX professionals do their jobs better.

[00:19:03] So lots of places to come find helpful things. Well, I will add links to everything to make that nice and easy for everyone. And just going back to when we very first started, the AI-enabled customer experience represents an $860 billion opportunity, possibly more, because I know you're passionate about that figure. But more than anything, just thank you for stopping by today. Yeah, thank you so much for having me. This was so much fun. Wow. What an insightful conversation.

[00:19:32] Now, I think the AI-enabled customer experience represents a massive opportunity. But as Isabel pointed out, it's not just about technology. It's about strategy. And companies that take a structured, outcome-driven approach to AI adoption, rather than just experimenting in silos, these are the organisations that are already seeing huge competitive advantages. But the question still remains, of course,

[00:20:00] Will your business act fast enough to move from AI pilot projects to enterprise-wide AI transformation? Or will you run the risk of falling behind in a world where AI-driven customer expectations are rapidly evolving and setting new standards? I'd love to hear your thoughts on how you see AI changing the way you interact with brands.

[00:20:25] You can email me directly, techblogwriteroutlook.com, LinkedIn, X, Instagram, just at Neil C. Hughes. So, a big thank you to Isabel for joining me on the show floor today. A fantastic discussion there. And an even bigger thank you to each and every one of you for joining me. And if you enjoyed yourself as much as I did, I will beam your ears all the way back to that show floor tomorrow. I'll speak with you all then. Bye for now.