Earley AI Podcast — Episode 75: Eric Rehl

Guest: Eric Rehl, Vice President of Digital Customer Experience in North America at Schneider Electric

Host: Seth Earley, CEO at Earley Information Science

Published on: September 19, 2025

In this episode of the Earley AI Podcast, host Seth Earley welcomes Eric Rehl, Vice President of Digital Customer Experience in North America at Schneider Electric. With over 25 years of expertise in digital strategy and customer experience, Eric has guided global organizations through complex digital transformations, always keeping business outcomes and customer needs at the core. Drawing on his deep industry knowledge, Eric shares how large enterprises can move beyond buzzwords like “digital transformation” and “AI,” instead choosing a pragmatic, data-driven approach to drive real business value.

Join Seth and Eric as they discuss the evolving role of digital capabilities in business strategy, the foundational importance of high-quality data, the unique challenges faced by B2B organizations, and how AI can power truly personalized customer experiences—from the ground up.

Key Takeaways:

• Digital transformation should be rooted in business outcomes, not technology hype; focus on the “so what” for your customer and organization.

• Strong, clean, accessible data is critical for scaling digital experiences and enabling AI-driven personalization—without it, even the best tools will fail.

• B2B companies often lag in digital maturity due to legacy data architectures and complex customer relationships, but can catch up by investing strategically in foundational capabilities.

• A robust digital journey relies on operationalizing and continually improving product and customer data, rather than one-off fixes.

• Maturity in B2B digital experiences evolves from simply “doing no harm,” to enabling ease of business, and ultimately leveraging digital platforms for growth and commercial impact.

• AI’s promise lies in moving from segmented personalization to real-time, dynamic customer engagement powered by integrated data and knowledge.

• Preparing for AI-driven customer discovery means syndicating high-quality, semantically-structured content across channels—both on and off your own domain.

• The next frontier is operationalizing knowledge (not just product or customer data) to fuel AI tools for differentiation and problem-solving.

• Continuous experimentation and responsible opportunism allow organizations to discover new outcomes and business value.

Insightful Quotes:

"I think as you start building maturity, you're learning how to orchestrate those pieces. You're getting more of that harmonization of organizing principles across those disparate departments, across knowledge and content and customer experience and product information. And so that becomes kind of the holistic journey that you're thinking about." - Seth Earley

“We always start with the outcome. Like, why are we talking about capabilities here? Why are we talking about AI? What are we actually going to do with it to get to what the business outcome we’re trying to drive or the experience outcome we’re trying to drive?” - Eric Rehl

"Having very sound practices around acquiring data, structuring it, and cleaning it in a way that it is going to specifically support outcomes you need, and investing in the talent to be able to do that and scale that, I think is the journey we all need to be on." - Eric Rehl

Don’t miss this in-depth conversation packed with practical advice and forward-looking insights for anyone leading or navigating digital transformation initiatives in the AI era.

Links

LinkedIn: https://www.linkedin.com/in/ericrehl/

Website: https://www.se.com/us/en/

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Podcast Transcript: Building Data Foundations for AI-Driven B2B Digital Experiences

Transcript introduction

This transcript captures a conversation between Seth Earley and Eric Rehl on moving beyond digital transformation buzzwords to focus on business outcomes, data quality, and customer experience in B2B enterprises. Topics include the evolution of digital capabilities in large organizations, the unique data challenges faced by B2B manufacturers, building maturity in personalization, and balancing foundational work with experimental approaches to AI.

Transcript

Seth Earley:
Perfect. Well, welcome to today's Earley AI Podcast. I'm your host, Seth Earley, and in this episode, we're going to be talking about how enterprises can move beyond the hype of digital transformation. Digital transformation has become a catch-all term. It's lost its meaning. And then you add AI in that, and you say, AI is so broad and ambiguous and entails so many different things. Of course, people think of AI as generative AI these days. But it's more than that. But people think of AI as generative AI, and then they think of digital transformation as this big, amorphous, ambiguous thing. What we're trying to do is get past that and really talk about the fundamentals of how we can drive results and not just focus on the broad, ambiguous catchphrases, right? We're going to take an ambiguous approach, and we're going to add another ambiguous approach to that, and then try to get specific results. So we're going to try to get beyond that. So, real success comes from building on the foundation of data, on a process, an understanding of customer needs. And so today, we're being joined by… and Eric, I'm sorry, I should have asked you, is it Rehl? Is that the way you pronounce your...

Eric Rehl:
Rehl, like in railroad. Eric Rehl. I should have… that's one of the things we usually ask right before we start.

Seth Earley:
Vice President of Digital Customer Experience in North America, Schneider Electric. He has extensive experience in digital strategy and customer experience. He's helped global organizations really transform the way that they engage with customers and leverage technology for value creation. So, Eric, welcome to the show.

Eric Rehl:
Thank you, Seth. Thanks for having me here. I look forward to this discussion. It's certainly a target-rich topic, so, thanks for having me.

Seth Earley:
It sure is, and there's so many things that are changing it so quickly. So you've been around this space for the past 25 years, around digital strategy, around customer experience. How did that journey kind of shape your philosophy on how organizations really need to approach this writ large? What is your thinking, and how has that kind of evolved? And of course, in the last few years, a lot of things have made that much more complex, but tell me a little bit about your philosophy and how you think of transformation and experience, customer experience.

Eric Rehl:
Yeah, I think that, you know, over that 25-year period, I've watched the space, sort of the digital space evolved, and the place it occupies within these companies, within big companies like Schneider. You know, it for years, it was something we have to do, right? Or, in the sense that, well, we just need to be present there, right? And so it was kind of off to the side in terms of, let's have a website and a few other things. But it really didn't—it wasn't kind of an organic part of kind of the company's operating model, right? And so, what you've seen happen over the years is it go from, you know, just something they did to something that, okay, now we can really see how these kinds of capabilities can enable some parts of our business, right, where they can kind of extend our presence in the market, right? We can get amplification if we're part of this digital ecosystem, we have some stuff out there that people can consume on their own. To where it is now, which is really, it's not—especially in companies like Schneider, it isn't THE business strategy, but it is a key enabler of the business strategy. But only… and you made a really good point in your opening comments, Seth, that digital transformation is a buzzword. It is business transformation, right? That is digitally enabled. But none of that matters unless it results in customer-driven outcomes. Right? So it's not transformation for transformation's sake, it's transformation to realize some business or customer experience outcome, right? So the space—and we turned to digital capabilities, and now more increasingly to data, to figure out how can we get to that result in a way that is going to not just meet our customer expectations, align to our business strategy and create impact, but also differentiate us. Right? So how that shaped my philosophy is that we always start with the outcome. Like, why are we talking about capabilities here? Why are we talking about AI? What are we actually going to do with it to get to what the business outcome we're trying to drive, or the experience outcome we're trying to drive? So, that's how we always approach it, and so I'd say that's kind of my philosophical approach to what is now just business activation, you know?

Seth Earley:
And it's regardless of the technology, it's regardless of what the digitization might be, or the digital strategy, it's saying, what is it? It's the so what. I always say, so what? If we have this information, so what? If we provide this capability, so what? What's it gonna… how's it going to impact people? How's it going to impact how the customer does business, how's it going to impact what they do, their efficiency, their effectiveness, internal, etc. So I totally agree with you. It has to be really brought down to that detail of what's gonna happen on the ground when people are interacting with our organization. And with that, you know, you talk about data, and I'll tell you a quick story. I was talking to… and B2B has been laggy, right? B2B has kind of lagged behind. It's based on, you know, long relationships, and kind of embedded products, and product design wins, and relationships with large, other larger enterprises, and so it's been slow to evolve, and of course, the data has always been challenging. The other day, I was talking to a B2B company, and we were talking to their digital team about the need to resolve their data. They've grown through acquisitions, they have lots of different catalogs, a broad range of products, and their data was a mess, right? And their hierarchy was a mess, their product information was inconsistent, user experience is inconsistent, and so we talked a lot about that, and talked about how to remediate that and how that would then enable things like, you know, conversational access, and, you know, chatbots, and different ways of retrieving information. And then I got a note from them a couple weeks later saying, oh, we've decided to go in a different direction. I said, oh, what are you doing? He said, we're gonna build a bot. It's like, okay, you're gonna build a bot. Okay, how likely is that going to be to solve their problem, it's not, right? Unless you're saying the bot is kind of the Trojan horse, and really we're going to be fixing data, right? Because you cannot do that without the data. So let's talk about the data. You know, when you think about the role of data in that experience, it's all based on data. The whole digital experience is comprised of data. And so, how do companies think about, you know, consuming and owning and orchestrating data to create that personalized experience, right? Because many times they're getting data from suppliers, or they're getting data from upstream processes, they're getting data from their customer behaviors, they're getting data from lots of different systems and touchpoints. Yet, many times this is an area that's been under-invested in.

Eric Rehl:
Yeah. And now, you know, to various degrees, organizations are saying, hmm, maybe this is something that requires a little more investment, but it's usually they have to fail first before they really come to that point. But what are your thoughts there about how that's kind of evolved, and what's happened over the last several years, so maybe you could just riff on that for a little bit.

Seth Earley:
Yeah, I mean, I've been wrestling with, kind of the notion and the constraints around data in large B2B enterprises for some time now, and you know, I… when I think about…

Eric Rehl:
Not a problem. Not a new problem.

Seth Earley:
Certainly not. I was gonna say, when I think about… and when I think about… especially with, like, manufacturers, when I think about their progression through this very long journey that we've all been a part of, they started behind the eight ball when we started talking about leveraging data for customer experience and for digital engagement and things like that, because if you think about, you know, 20, 30 years ago, what kind of the data architecture for a large manufacturer was, it's I gotta build a product, and I gotta ship it to a site, not to a person, to a dock, alright, or to a door, and I have to sell this through a person that is going to then enter the sale, and, you know, and the name on that sale is very likely not to be the name of the person I sold it to. Right? It's the procurement manager, or somebody who's responsible for processing the transaction and making payment. So, that entire architecture was great when you are, you know, kind of selling face-to-face, and you didn't have to think about, how am I going to extend this customer relationship beyond the sale, or how am I going to digitally enable it to be more personalized? So, you know, the entire kind of back-end data architecture was misaligned to… not on purpose, but it was not aligned to what we need today out of that kind of data. So, you know, that's why I think these companies tend to lag, and they kind of had to completely turn their thinking upside down in terms of how they manage customer data, right? And what customer data really means to them.

And so, now, fast forward to today, and your example around the bot. I mean, I don't… I discourage anybody from doing a bot, unless it can really help, you know? I mean, how frustrating do we all find it when you're interacting with a bot, and it only can take you one step into the conversation, and it asks you, you know, was this helpful? And, you know, it's like, I just want to talk to somebody, please, you're not helping me. So having very sound practices around acquiring data, structuring it, and cleaning it in a way that it is going to specifically support outcomes you need, and investing in the talent to be able to do that and scale that, I think is the journey we all need to be on, right? Because I'm a consumer of data. I own product data, I don't own customer data. But I have extreme dependencies on good customer data for me to be good at my job, right? And this is something that—and deliver the outcomes I need to for my company and for our customers. And this is a journey that I don't know that we're ever going to be done with, right? I mean, there's always going to be a new use for this stuff, a new opportunity. And I don't know that anyone's totally cracked the nut on it, you know? So I think you kind of have to take it, align it to your business strategy and your customer expectations, and that's where you start.

Seth Earley:
And all of that is a moving target, right? All of that is constantly evolving. You know, customer needs are evolving, business strategies are evolving, technologies are evolving. So you can't just say, okay, we've got our data right, we're done. It's a continuous process. And I think that's one of the challenges that organizations face, is thinking of this as a one-time project rather than an ongoing operational capability. And so, when you think about, you know, the progression of digital maturity in B2B organizations, what are the stages that you've seen companies go through? And where do you see most organizations today?

Eric Rehl:
Yeah, I mean, I think there are kind of three stages that I've observed. The first stage is what I call "doing no harm," right? Which is, you know, we have a digital presence, we're not actively hurting the customer experience, but we're not really adding a lot of value either. We're just kind of there. The second stage is "enabling ease of business," where digital capabilities are actually making it easier for customers to do business with us. They can find information, they can transact, they can get support, all of those things are enabled digitally. And then the third stage is what I call "driving growth and commercial impact," where digital is actually a driver of new business, new opportunities, new ways of engaging with customers that create value for both the customer and the company.

And I think most B2B organizations today are somewhere between stage one and stage two. They've got the basics in place, they're working on making things easier for customers, but they haven't yet gotten to the point where digital is really driving new growth and new opportunities. And I think that's where AI comes in, is helping organizations make that leap from stage two to stage three.

Seth Earley:
Right, and that's where, you know, when you think about this with AI, AI has the promise to take personalization, because this is what you need to do at scale, right? If you had to hire one salesperson for every customer, you could have a nice personalized experience. You have to have the digital equivalent of that, so how do you see AI as being able to provide that more dynamic and real-time customer engagement compared to, sort of, segment-based approaches that a lot of organizations rely on today. So talk a little bit about how you see that scaling up and being operationalized and being done at that level that we need to achieve. And I imagine there's various stages of maturity around that in and of itself.

Eric Rehl:
Yeah, absolutely. So I… you know, where I think this is all going, at least my kind of conceptual vision of all this, and I don't think this is particularly unique to me, I think this is… everybody's kind of thinking about the same thing, is that you know, we're gonna get to a place at some point where the experience that a customer has on our website, alright, or in any given kind of digital engagement we have with them, will be entirely personalized, right? It'll be created on the fly. It will build and personalize—the immersiveness of that personalization will increase the more we… the deeper that engagement goes, the more we interact with people, to the point where no customer, no user actually has the same experience on our website, and the whole notion of, like, a homepage or a landing page goes away, and upon receiving that, upon getting that engagement, everything is just kind of constructed on the fly by AI.

Right? And, but the requirement for that is good data, so the ability to kind of understand the individual, who they are, where they are, are they an existing customer? Are they a potential new customer? What is their interest? What is their intent? Firmographic, demographic, personal, you know, all that stuff, layered with, or combined with, the knowledge that you're talking about, right? So, if I start to build some sense of who this individual is and what they're, you know, what might be of interest to them, I need an entire knowledge set and language model that is going to be able to very quickly analyze and understand that and respond to it, right? And the immersiveness of that response is going to be based solely on the quality of the data that we understand about this person, and the quality of the content that we have to deliver back to them, right? But in the middle is an engine that is always on, that is looking at who we're engaging with and saying, alright, what is the—it's almost—think of it like what you have, like, customer experience software for customer support we see out there, next best action kind of stuff, right? You know, and some of that, you know, we're going to start by prescribing some of that, right? So it's a little bit of a combination between what you said earlier of kind of segment-based approach with pure personalization, right? You need to orchestrate a little bit of that, but as we get more effective, it's going to be more agentic, and it's going to be more conversational, and it's going to be more real-time.

Seth Earley:
Yeah, that's a great point, and I like to say you have to have the handcrafted artisan experience at first. Right? Because you need a framework, you need a model, you need, you know, a template to say, this is how we need to interact with these customers. So you frame that, and you build that, and it is hand-crafted, right? It's based on human judgment, human insight, knowledge of the customer, knowledge of the solution. And then, once we have those parameters, that's when we can start to riff on that. That's when we start to turn this over to multivariant testing, right? And the ability to vary those different parameters and those different experiences, and come up with those next best actions, the next best piece of content, next best product, whatever it might be. But you made a really good point to say, so we're not just invoking AI, right, or generative AI, we're invoking it based on our differentiated knowledge and expertise, right? It's like taking your very best engineer and saying, I'm gonna give my very best engineer to this customer to help them solve that problem. That is great. Well, you can't afford to hire an engineer for every customer, but you can build that expertise from your knowledge base, from your technical specs, from those documents that are being created in engineering upstream, but then don't always make it out to the user experience, right? Because you can't anticipate everything. You may have attributes in your technical specifications that nobody anticipated that a customer's going to need a facet based on that, or an attribute, or a bullet point, and you can't just inundate everybody with everything, right? So you have to be selective and then say, what's a procurement manager care about versus a design engineer versus a maintenance engineer versus somebody who's troubleshooting something. So that all is based on that organizational expertise and that knowledge of customers, solutions, engineering, designs, all of that, which is proprietary, right? You don't want that out in the public domain. And that's where things like retrieval augmented generation come in, right? Where you're saying, I have repositories of content that are curated, that are structured, that are componentized, that an agent can access and then surface to the customer in the context of all these other things. So that is a really wonderful vision, right? But we're still kind of far from reality on that because of all the problems that organizations are having. Everybody kind of feels like generative AI and RAG are a solved problem, but they're not. It's not. And so, you know, what are your thoughts on, you know, how do we get from where we are today to that vision that you just articulated?

Eric Rehl:
Yeah, I think it's… it's a combination of things. I think, you know, first and foremost, organizations need to continue to invest in their foundational data capabilities. They need to get their data right. They need to have the right governance in place. They need to have the right talent. All of that foundational work still needs to happen. At the same time, I think organizations need to be experimenting. They need to be trying things out. They need to be learning what works and what doesn't work. And I think that balance between foundational work and experimentation is really key. Because if you only focus on the foundation, you're going to be too slow. If you only focus on experimentation, you're going to build on a shaky foundation that's not going to scale. So I think you need both. And I think that's one of the challenges that organizations face, is figuring out how to balance those two things. How do you invest in the long-term foundational work while also being agile and experimental and learning quickly? And I don't think there's a simple answer to that. I think it's something that organizations need to figure out based on their own context, their own capabilities, their own resources.

Seth Earley:
Right, and I think that's where having that outcome focus really helps, because it gives you a North Star to guide both the foundational work and the experimentation. You can say, okay, we're trying to achieve this outcome, what foundational capabilities do we need to get there? And what experiments can we run to learn and iterate and get closer to that outcome? And I think that helps bring those two things together in a way that makes sense.

Well, this has been great, you know, I've really enjoyed this conversation. You know, thanks very much for sharing your perspectives. Any final thoughts on organizations going down this path? Because it is changing priorities, it is changing how organizations think about their data, their customers, their content, their knowledge, and with all of this happening so quickly, what are your final thoughts for organizations going down this path?

Eric Rehl:
Yeah, what I'd say is, you know, I spent a lot of time talking about the basics and the importance of that, and that still rings true. I don't—I've never, kind of—I will always say that. But we all need now, in this, where we are now, with how quickly we're moving through technical innovation and the capabilities that are just in kind of their nascent stage of being able to deliver some impact. We need to balance our execution along foundational activities and experimentation. Like, we really need to, even if you, you know, I talked about being outcome-focused, even if you don't know what an outcome is, there's capabilities here now with AI that allow you to arrive at one by accident, almost. So, be opportunistic, I guess is what I would say, right? Responsibly opportunistic. You still need to start with an outcome in mind, but you might not know 100% what that might be, but I have an idea. So, I would say, you know, spend some time here experimenting, because the tools are there to do it, they're accessible. And you don't know what you're gonna get. I mean, there's some very opportunistic things out there now, and we're starting to tinker with some of that stuff, with the notion that we think we know where we're gonna get with this, we're not sure, but it's worth it to try, you know? And so every—the plate is there to be able to experiment and really find something, find some nuggets out there that nobody else is doing right now. So, interesting time.

Seth Earley:
That's such a great point. I think you have to still have some willingness to learn and experiment and fail. Absolutely. You can do that much faster and much more cheaply. I really want to thank you for joining us today. I mean, this is really great, you know, we've moved beyond the buzzwords, you know, to really talk about the business outcomes, and, you know, for our listeners, thanks for tuning in to the Earley AI podcast. Stay with us for more conversations on AI… how AI and data is shaping the future of business. We'll have Eric's contact information in the show notes. Rehl, that's E-R-I-C-R-E-H-L, on LinkedIn, and what's interesting is you don't have, like, Eric Rehl52, like, you've been on LinkedIn for a long time.

Eric Rehl:
For a long time, right?

Seth Earley:
But you got that name, so that's great. Eric, thank you so much for your time, and thank you to our listeners, and thank you, Carolyn, for handling things behind the scenes. And we'll see you next time on the next Earley AI podcast.

Eric Rehl:
Thank you, Seth. It's great being here.