We are recording live at Qualtrics X4 in Salt Lake City and seeing and hearing all about how to create and enable amazing customer experiences.
Today we’re going to talk about enabling and accelerating customer experience success by augmenting your teams and processes with AI.
To help me discuss this topic, I’d like to welcome Isabelle Zdatny, Head of Thought Leadership for XM Institute at Qualtrics.
Resources
Qualtrics: https://www.qualtrics.com
Qualtrics Report: https://www.qualtrics.com/news/qualtrics-report-executives-are-hesitant-to-lead-in-ai-transformation-putting-up-to-1-3-trillion-at-risk/
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Transcript
Greg Kihlstrom:
We are recording live at Qualtrics X4 in Salt Lake City and seeing and hearing all about how to create amazing customer and employee experiences as AI and AI agents increasingly play a role. With a potential $1.3 trillion in revenue gains and cost savings on the table, Organizations across industries stand to gain a lot through strategic investments in AI. Today, we’re going to talk about enabling and accelerating customer experience success by augmenting your teams and processes with artificial intelligence-based tools. To help me discuss this topic, I’d like to welcome back to the show Isabel Zdatny, head of thought leadership for XM Institute at Qualtrics. Isabel, welcome back to the show.
Isabelle Zdatny: It’s fantastic to be here. Thank you for having me again.
Greg Kihlstrom: Yeah, absolutely. Good to see you in person here.
Isabelle Zdatny: It’s wonderful.
Greg Kihlstrom: I do so many of these remotely. It’s always nice to be in person.
Isabelle Zdatny: It’s my favorite part of X4.
Greg Kihlstrom: Yeah, absolutely. So for those that didn’t catch you when you were on the show last fall, why don’t you give a little background on yourself and your role at Qualtrics.
Isabelle Zdatny: Yes, so I lead thought leadership for Qualtrics XM Institute, which is almost like a little think tank inside Qualtrics. And we’re really dedicated to building the category of experience management. So less focused on the product and more how do we equip CX and EX and XM professionals with the tools and the insights and the data and the best practices and the frameworks that they need to feel confident and successful in their role. I produce our content, I advise companies on the design and execution of their program, speak on topics, design training, and I got started back in 2013 at Temkin Group and then joined Qualtrics when we were acquired in 2019.
Greg Kihlstrom: Great, great, wow. So yeah, you’re doing a lot here. So let’s, a lot of, and yeah, I want to talk primarily about some research that was recently published that I know Qualtrics and McKinsey worked on. So I want to start there. And you know, we certainly talk about AI a lot on this show. I think literally every conversation somehow goes into AI. It’s not starting that way. But your research, as I mentioned at the top, top of the show shows that businesses are standing to gain anywhere between $860 billion to $1.3 trillion by making strategic AI investments. So, let’s start at the beginning. Again, that’s predicated on them making the right investments. So, where are businesses maybe going wrong as they plan and strategize?
Isabelle Zdatny: Yeah, so I would say I’m seeing two big mistakes that organizations are making in their approach to AI. First, I would say is that they are implementing AI for the sake of AI. There’s the allure of the shiny new feature or executives are putting pressure. And so they’re just like, we need to stand up these technologies rather than starting with what is the outcome that we are trying to achieve? And then how could we possibly bring AI into the mix to help us get there more quickly and economically? And then the second big one I’m seeing is more at an organizational level as opposed to a CX, specifically that they’re struggling to get out of pilot purgatory. and invest in the types of systemic changes that are really necessary for unlocking the full value of AI. So they start small, they do very expensive experiments, and then they don’t really scale them more broadly across the organization, which is not creating the value that AI can create.
Greg Kihlstrom: Yeah, and I mean, that said, I mean, pilots is a good way to start, but what do you think is kind of, what’s the leap they need to make from pilots?
Isabelle Zdatny: Yeah, I think you need to move from doing AI in silos to kind of system-wide organization and management. I actually just saw an Axios article yesterday that I think 71% of executives are concerned about how much AI development is happening within silos at organizations. And so I think a lot of teams are looking at, here are the solutions we’re doing that work for us, and there’s not a lot of up-leveling yet. And that requires some fundamental changes to your data and technical infrastructure, too. And change management, right? Scaling can be quite difficult.
Greg Kihlstrom: Yeah. Are there particular, I don’t know if it’s industries or use cases where benefits of AI are particularly clear or maybe potential? I mean, obviously, that’s a big number of potential, but where are you seeing some of the biggest potential?
Isabelle Zdatny: Yeah. So just to give a little bit of background about this research report. So we partnered with McKinsey. Again, the goal here is really to put some hard numbers around the opportunity that AI can present to organizations who use AI to improve customers’ experiences. Our source of insights, I would say, for this research were, one, Qualtrics conducted an executive study of 1,500 global executives in Q4 of last year, asking them about their expectations and efforts around both customer experience and AI, which yielded some really interesting insights. And then McKinsey performed a detailed value analysis sizing the opportunity, the potential impact AI could have across 19 different industries. So that’s where that $860 billion number comes from. And then we also interviewed a number of innovative clients to understand how they were using AI to better understand and serve their customers. So for McKinsey’s value analysis of that, so AI enabled customer experience is expected to drive an estimated $860 billion and possibly all the way up to $1.3 trillion in annual value. And it’s expected to do this in three ways. So the biggest opportunity that they found was in productivity gain. So 400 And $20 billion is expected to come from productivity gains using AI to augment and automate work. $260 billion is expected from revenue growth, so using AI to transform how you acquire and grow customer relationships like intelligent targeting and personalized messaging. And then another $180 billion from process improvements, so using it to optimize your operations, lower cost to serve customers. So they also broke that number down across those 19 industries. And the biggest opportunities were B2B, I think it was $420 billion opportunity. The biggest was business and professional services, which was $150 billion opportunity on average. And that was through things like personalized marketing and I believe optimized operations. Also on the B2B side, commercial insurance was $70 billion opportunity and SMB banking was $60 billion opportunity. And both of those are from things like automated processes and risk assessments and things. On the B2C side, both retail and retail banking were $100 billion. And then there’s a whole host of other B2C ones that were pretty close, like hospitality, airlines, QSR. So yeah, it’s definitely the nature and scale of the opportunity varies a lot, but it crosses everything. Yeah, there’s something for everybody. Yeah, exactly.
Greg Kihlstrom: So, I mean, you mentioned, I mean, obviously there’s several different categories here, but to kind of go to the process thing and back to your point about silos are what often get in the way. You know, this is where, you know, I think software and platforms have a huge role to play, but so does the change management part. And so how do you, you know, for those executives that are, they’re getting pressure to like do AI, whatever that means, but like, they’re still getting pressure to do it. How do they put the right focus on the software or the platform part as well as process?
Isabelle Zdatny: It’s such a good question. And I’ll say one of the things I heard the people I interviewed over and over is the technology is not actually the hard part. It’s getting people to use the technology effectively and feel comfortable and confident using it. That change management piece is actually, I think, long-term going to be much trickier. So yeah, so I would say, again, start with what is your organization kind of AI vision and strategy and value. So what are the outcomes you’re trying to drive? And that will be different by different businesses across different industries, right? You might want to increase customer spend or average contract value. So starting with a central organization wide vision, having governance structures in place like a centralized governance council, some way to monitor all the different AI use cases and make sure that they’re getting implemented effectively. You need common risk and ethics guidelines. And that should help with the culture change, right? Starting with this is the vision that we’re all trying to move towards. And this is why we are asking you to change your behaviors and adopt these new technologies. And then making sure that your workforce is ready for AI, that they have the skills they need. You do a capabilities gap where can you upskill people? Where do you need to bring other people in? One of my favorite stories from this report was when I talked to ServiceNow to get change management, get people comfortable using AI, they have a central, they call it an AI control tower, so a central dashboard that has all of their different AI use cases across the company. It was like 350 when I talked to them. Everyone can see it. Here’s who owns it. Here’s what value it’s driving. And then they selected certain roles across the company. They looked at those roles and they identified ideal workflows. They talked to people, what are your tasks like? And then matched, here’s the parts of your job that you don’t love, that you don’t find engaging, that could be automated based on the AI solutions we have, or augmented through synthesis or prediction. And so taking that type of role-based approach of like, this is specifically how this is going to help you do your job better, was really influential for getting that change management and buy-in across the company. It’s not here to replace you, it’s here to help you.
Greg Kihlstrom: Right. Right. Yeah. And I mean, that’s that’s I think that’s such an important point is the visibility on it. It’s it’s not so much the having these siloed tools that do individual tasks. It’s kind of and that’s that’s a lot. You know what I heard, you know, this morning at the keynotes, you know, having that almost that operating system that allows you and not only the executives, but the front line employees and everybody in between.
Isabelle Zdatny: Yeah. I mean, you really need all the data, right, to be living. You need to be moving towards some type of like enterprise data warehouse or something where you can, yeah, be applying these models on top of using them. AI compounds the garbage in, garbage out issue. So making sure it’s all clean and good for powering your LLMs. It’s a big undertaking.
Greg Kihlstrom: Yeah. So what about the customers? We’ve talked a lot about the enterprise level and leadership and even employees, but what do customers want out of this? How are they responding to greater AI integration? What have you seen as some of the best customer responses to AI?
Isabelle Zdatny: Yeah, so we saw as part of our 2025 consumer trends that consumer comfort with AI dropped 11 points year over year, which I think shows we’re kind of in this trough of disillusionment part of the hype cycle right now. So I think one of the things from that was like, again, rather than organizations were telling customers like, we’re doing AI, your experiences are now AI. Customers don’t care about that. What they care about is being able to achieve what they want to achieve, you know, quickly, effectively, enjoyably. So I would say some of the best use cases I’ve seen are using AI like virtual assistants to help customers complete complicated tasks, a big one. One of the companies I talked to for this report used it for their claims process, which historically is like a very long, complicated process. You don’t know what on my receipt is eligible, what’s ineligible. And then it would take, on the back end, someone multiple days, right, sometimes to review the information. inserted properly. So they had a virtual assistant help walk people through. They could take a picture of their receipt, even like a CVS receipt that’s really long and filled with things, and it would automatically fill out that process. And it moved from being, I think, two days to like under two minutes for 60% of the interaction. So that’s good for customers, right? They have something they have to do that’s not fun. How can we use AI to help them achieve their goals better?
Greg Kihlstrom: Yeah, definitely. And I mean, that that’s where I mean, I think kind of what you were referring to earlier is that lots of companies were throwing AI at the wall to see what’s like the chatbot thing that isn’t really smart and it gets you into the like doom loop.
Isabelle Zdatny: Yes.
Greg Kihlstrom: Voice, you know, calls sometimes still do. But I think agentic AI, kind of what you’re referring to here, it really does kind of bridge that gap. And so, I mean, do you, are you seeing that consumers are, I mean, obviously there’s a, there’s a little bit of a trust gap, it sounds like right now. I think in the last year, according to like Forrester and some others, there was a dip in CX in general. So like, Maybe that’s related to what you’re saying too, but do you think we’re going to kind of cross that?
Isabelle Zdatny: I hope so. I think usually if we look at like, right, new technology adoption, you have something comes out, people get very overexcited. They start applying it to things they shouldn’t. So chatbots are a great example where, again, garbage in, garbage out. They started training chatbots on bad internal data, old knowledge-based articles. The technology wasn’t there, so people were having really bad experiences. So then they’re like, well, this isn’t living up to the hype. What then often happens is that companies get much more buttoned down and understand these are the best use cases for these specific tools and start improving that. And consumer perception is a lagging indicator behind that, but eventually kind of comes back and they’re like, oh, But I think it’s all about like what’s in it for them, how is it helping them not, they probably, the less they know that they’re engaging with AI, and that’s like even in their thought process, probably the more enjoyable the experience is.
Greg Kihlstrom: Yeah, and I mean, I think eventually, I mean, I’ve heard other people have said the same, like I think in five years, we may not even use the term AI, it’ll just be doing stuff, but.
Isabelle Zdatny: Well, because it’s also so kind of ethereal now for people, yeah. don’t distinguish between analytical and generative.
Greg Kihlstrom: But the promise of this, and I think what’s really powerful is you’ve got the, and someone characterized it this way, you’ve been listening, you have a lot of data already, generative AI helps kind of make sense of all of that data. Now, agentic actually can tie the pieces together. And not every organization is there just yet, but that promise of, one-to-one, I’ve been talking about one-to-one personalization for years, but never felt like it was possible until a year or two ago. So how close are we to that for brands?
Isabelle Zdatny: Yeah, I mean, I think Ethan Mollick, who’s a Wharton professor and AI researcher, describes the jagged frontier of AI gets really good at some things and is very far out in front, and then it’s really strangely bad at other things. So I think, right, we’re still, some things are moving forward at a much faster rate. I think that we’re closer than people think we are. It’s like, it’s a cliche now, but that old Hemingway quote about going bankrupt, it happens slowly than all at once. I think a lot of the foundations have been built, that organizations are ready to start deploying these for prime time. Like I think the foundation’s stronger than it was before. So I do think it’s coming soon.
Greg Kihlstrom: So what would your recommendation be then for those organizations that are, they know this, they agree with the sentiment here, but maybe not sure where to start in getting things moving. What should they be thinking about?
Isabelle Zdatny: Yeah, so I would say, again, first of all, starting with the outcomes that you’re trying to achieve. So if you’re a CX professional, what are the metrics you’re trying to move? What are the experiences you want to deliver? What’s an initiative on your roadmap that you are trying to implement? And then how can you bring AI in to help you execute on that? So again, rather than getting distracted, using it as a tool to help you do your job better. And then the other thing I would say is start using it yourself. I think there’s a big difference between people who don’t use these tools at all and use them a lot, they’re not going to be good at everything. You don’t know what they’re good at, what they’re bad at, until you start playing around with them. And I think a lot of people are going to be surprised when, you know, agentic AI becomes mainstream and mature. And I don’t think as UX professionals, we want to be one of those surprise people. I think we want to be on the leading edge and be able to come to our organization where the closest to customer needs and opportunities and our work should be spanning a lot of the functions across the organization. So I think we should have some AI expertise that we can bring to the table to help our organization implement these.
Greg Kihlstrom: And the research, we didn’t talk a lot about the employee experience specifically, but the research touched on there’s huge opportunity in CX, but also with EX. What’s the relationship between those two or how should leaders be thinking about that?
Isabelle Zdatny: Yeah, it’s a good question. And we actually, I should say, for the executive study, we also asked about plans and ambitions around employee experience as well. And it was very similar to what we found on the CX front. I think on the short term, as I said earlier, the biggest value looks like it is going to come from those productivity gains from helping augment employees’ work. So I think that’s where a lot of organizations are starting as helping employees. It’s also a little bit lower risk, right, internally. But I think the more you can free employees up and take away some of the routine, repetitive administrative tasks so that they have the bandwidth to make those genuine human connections with customers and do their jobs better, that then you’re going to see the customer experience improve as a result of improving the employee experience.
Greg Kihlstrom: Yeah, I mean the employee’s happier, doing more valuable work, and the customer can only stand to benefit, right?
Isabelle Zdatny: Yeah, it’s an interesting dynamic though, like customers, employees, and executives of employees do sit right at that intersection point where they have to use all the tools internally, but they’re also noticing how it’s helping them. Like they, as part of our EX Trends 2025, they were actually more, I think, confident in AI than customers were. because they’ve seen the applications within their roles.
Greg Kihlstrom: Well, as we wrap up here, just a couple things for you here. I know X4 is not done yet, but I wanted to get a sense of what’s been a highlight so far of your experience here.
Isabelle Zdatny: Oh, it’s such a good question. I have to say, I just love seeing everyone. I work remotely, so talk to people a lot all the time and the opportunity to actually get to spend face time with people, just connect with clients to one-on-one and hear those kind of side stories and things that you wouldn’t get in like more official conversations about what they’re doing and thinking about is always really exciting to me.
Greg Kihlstrom: Great, great. Love it. And last question for you. I know you were on the show, so I asked this to you before, but I’m going to ask you again. We’ll see if we can compare answers here, but that’s a research project of its own. What do you do to stay agile in your role, and how do you find a way to do it consistently?
Isabelle Zdatny: Oh, I do think I’m going to answer this the same way. I think for me, it’s continuous learning and staying curious, especially if you think about something like AI, right, that’s changing all the time. The ability to continuously be taking in information, synthesizing it, thinking about it, and just making sure that I, you know, head of thought leadership, I need to be aware of where we’re going to help kind of prepare people. Yeah, exactly. That doesn’t sound kind of fascistic at all. But yeah, so I would say just continuous learning and staying very curious.
