Earley AI Podcast
In this podcast hosts Seth Earley invites a broad array of thought leaders and practitioners to talk about what's possible in artificial intelligence as well as what is practical in the space as we move toward a world where AI is embedded in all aspects of our personal and professional lives. They explore what's emerging in technology, data science, and enterprise applications for artificial intelligence and machine learning and how to get from early-stage AI projects to fully mature applications. Seth is founder & CEO of Earley Information Science and the award-winning author of "The AI Powered Enterprise."
Earley AI Podcast
Earley AI Podcast Ep 77: Leading Through Digital Transformation: Data, Change, and AI in Retail
Use Left/Right to seek, Home/End to jump to start or end. Hold shift to jump forward or backward.
This episode of the Earley AI Podcast features Betsy Mello, a seasoned retail executive whose career includes leadership roles at Dorel Home, Levi’s, Old Navy, Sears, and several retail startups. With deep expertise in merchandising, inventory management, and eCommerce strategy, Betsy brings a pragmatic perspective shaped by years of leading digital transformation and managing major marketplace relationships with Amazon, Bed Bath & Beyond, and others.
Host Seth Earley talks with Betsy about how retail leaders can navigate the ongoing shift from traditional operations to AI-driven business models. Their discussion explores how structured data, process discipline, and organizational alignment form the foundation of successful digital and AI initiatives—and why the fundamentals still matter, even in the age of automation.
Key Takeaways:
- How the move from brick-and-mortar to digital commerce has transformed consumer expectations and the pace of retail innovation.
- Why marketplaces are data supply chains—and how brands must adapt content, taxonomy, and product positioning across diverse channels.
- The importance of clean data, standardized terminology, and clear use cases before adopting AI solutions.
- Strategies for breaking down silos, aligning KPIs, and ensuring cross-functional collaboration around data and insights.
- Leading through change: the value of transparency, experimentation, and learning from failure during AI-driven transformation.
- What leaders often overlook when preparing for AI—and how to make foundational data work visible and measurable.
- The building blocks for sustainable AI success: information architecture, governance, and accountable data ownership.
Show Quotes:
"You need to have everything standard. You need to have clean data and very clear workflows and accountabilities... The key is having the team set in place and very clear defined processes and roles and responsibilities. It’s incredibly critical to make sure your foundation is correct. You need to be always starting at the basics." – Betsy Mello
"Supply chain is an information supply chain. And every time you have a new way of distributing your product, you have to think about, how do I distribute the data with that product?" - Seth Earley
Tune in to hear how retail and eCommerce leaders can turn complexity into clarity—and build the cultural and data foundations that make AI work.
Links
LinkedIn: https://www.linkedin.com/in/betsymello/
Website: https://www.dorelhome.com
Thanks to our sponsors:
This is the Early AI Podcast hosted by Seth Early. Join us as we delve deep into the passions, expertise, and experiences of thought leaders and practitioners to talk about what's possible with artificial intelligence. The Early AI Podcast is sponsored by Early Information Science, your digital transformation journey with design and deployment of innovative technology solutions, as well as Vector. Now enjoy the show.
SPEAKER_01Well, welcome to today's Early AI Podcast. I'm your host, Seth Early, and today we're going to be speaking with Betsy Mellow, who is a very seasoned retail executive who has deep expertise in merchandising, inventory management, e-commerce strategy. And she recently served as SVP of e-commerce sales at Durell Home. And she oversaw key accounts, including Amazon, Bed Bath and Beyond, and other marketplace partners. Marketplaces are so important these days. And she's also held other leadership roles at organizations like Levi Levi's and Old Navy, Sears, and other retail startups. So in this episode, we're going to talk about the role of structured knowledge, data, content, and organizational change when we talk about AI initiatives. And these are very similar to the other initiatives and the other transformations we've had in the past. So a lot of this stuff is kind of making sure that we're still doing the same blocking and tackling. What, you know, what do leaders need to understand and what do they need to prepare for? So, Betsy, welcome to the show. Thank you for being with us.
SPEAKER_02Thank you for having me, Seth. I'm really happy to be here.
SPEAKER_01Perfect. So let's go ahead and jump in. Um you've had a very broad and diverse um career experience. And as you've gone through this, how did you get to digital transformation and how did how did your experiences inform your perspective on what digital transformation is in organizations today?
SPEAKER_02Yeah. So when I started my career off, I started in brick and mortar. So I started um in inventory management and merchandising at the Gap Bank at Old Navy, where I and then from there I moved over to Levi's, where we launched our outlet business from scratch. We acquired the um licensee back from Anchor Blue and really went overnight from 15 to 115 stores. So that was really my first, you know, step into transformation, but it was still at the brick and mortar level. However, you know, as the consumer has moved more digital, my career has followed us thus. So I, you know, consulted for retail startups. I moved over to Sears, where I oversaw kids and baby apparel and hard lines, and that included e-commerce. And then most recently, um the last eight years at Durell Home, overseeing our Amazon account as well as some other big accounts like Bed Bath and Beyond, Overstock on our marketplace accounts on these retailers. So I have followed in that direction. So it's great because I have the experience of being close to the consumer and really building assortments around the consumer, watching their behavior and building product around it. Whereas I've now moved into the digital piece of it, where you know you're really going into still what the consumer wants, but it's moving at such a quicker pace, right? And growth has been incredible the past five years in particular.
SPEAKER_01Absolutely. And you know, when you think about marketplaces, that that's a huge data play. How do you syndicate your data out? How are you not invisible? We can talk about that in a little bit, but I wanted to um kind of ask you wouldn't, you know, so what are the the with the biggest shifts you've seen uh as e-commerce has grown? What you know, what has really stood out for you in the past uh five or 10 years? I know there's lots of different changes in technology and the marketplace and customer behaviors and so on, but what are the big things that have been kind of, you know, large shifts uh that you've witnessed?
SPEAKER_02Yeah. Well, I think there's a lot more noise, right? The consumer has so many more places to shop than they did before. And, you know, it's it's increasing where it's not just with the retailer, right? You can also shop socials, you can shop so many places, you can shop on your phone. Like the transition has been incredible. And it really means that there's everything is moving quicker, but there's also so many more data points and it's just so much more complex. Because if you think about it, you're probably selling the same product, but you're selling it through multiple channels. Each retailer has slightly different requirements, terms, things like that. So you're really just dealing with so many different channels, and that can make it incredibly complex, even if you have a very simple product.
SPEAKER_01Yeah, and I think that uh people don't realize that you know, when you are uh a manufacturer and you're working through lots of different distribution channels, those are all channels for removing data, right? A physical supply chain is an information supply chain, and every time you have a new way of distributing your product, you have to think about how do I distribute the data with that product, right? It goes off into a big box retailer, Amazon, they're gonna have a different need than Target. Maybe they'll have different attributes, different values, different taxonomies, all of these different things. And then how do you differentiate while you're in that marketplace? How do you expose yourself in that marketplace? And uh, and then again, as you multiply these channels, uh, there are all nuances. So it really does become a very, very complex information landscape, especially when you're trying to take those signals back from the consumer and you're disintermediate disintermediated with uh a distributor, right? When you're selling through a big box or not, you don't have that direct customer data, but you still need to take the signals from the marketplace and you still need to take the aggregate signals coming from your distribution channels and somehow use those to change a behavior, to change a presentation of product, to change the data, to change however you're you're showing up in that digital ecosystem.
SPEAKER_02Exactly. And a lot of times, even if you're going back to selling a simplified product, because the requirements of different retailers and how they want to speak to their consumers, you actually may have to develop different product, you know, at different pricing. And it just makes it so much more complex. And that's what's really hard is you don't want to lose your brand values and you don't want to lose what you are trying to sell the consumer, especially in a B2B marketplace, that it just becomes so much more challenging. The margin requirements being vastly different between several retailers can really affect how you develop products and how you show up to the consumer. And that that's a big challenge.
SPEAKER_01Yeah, yeah. No, it's it sounds um uh that gets really complex. So at a recent industry event, we talked about the idea of activating data to get business value. So, you know, how are organizations and leadership teams, how are they thinking about data? And, you know, even if you take AI out of the mix, but the data is the foundation to all of this stuff, whether you're doing personalization, recommendation, search, you know, catalog operations, it doesn't matter what it is, right? AI is in there, but how are people looking at the data? Are they thinking more about uh the strategy or are they getting in the weeds? Are they rolling their sleeves up? Are they still giving it lip service and saying, yeah, data is important, yeah, nodding their heads. I mean, then when they see a budget, they go, oh, it's not that important, right? So what's your sense of what's happening in the in the leadership um suite?
SPEAKER_02That's a great question. I mean, I think it's interesting. Data is a big buzzword right now, right? Just like AI, you know, everybody wants to say they're using it. And in the in reality, we've always been using it, right? It's no different. It's just how you're using it now and the volume of it and whether it's good, clean data. That's that's the thing. You have to have a really clear view of what your end use is. What is your goal? What are you trying to solve? What is your use case? You don't want to let the data, just raw data, drive how you run your business. You need to understand what you're looking for and then be able to filter through and say, okay, what can I do in addition to what I'm doing now? What can I glean from this extra set of data? Or what is this telling us that we didn't tell us before? I think that's the big difference. And additionally, having experts that can truly understand it and tell the story for you because raw data doesn't help anybody, right? You need to have a team that's able to synthesize and analyze and tell the story from the data. That's truly what running a business is all about, right? So I think that that is a big piece that a lot of companies want to use the buzzword and say they're using it and use all these big fancy words. But the reality is, what are you trying to do?
SPEAKER_01What is your use? Knowledge graphs is another big uh buzz buzzword, but an AI is a buzzword, buzzword and machine learning and agent technology. But but I think what we've talked about before is the fact that many companies are still missing a very solid foundation uh standards and and so on.
SPEAKER_02Uh well, and I think the other challenge is is addition, how each team is using it. So you want to have, at least from leadership, a common set of goals and data that you want to use. So if you have different teams using different sets of data and it's coming from different sources and they may not match up, that's a challenge. You want to make sure that you're not working in silos and that everyone's working towards the same goal. I mean, in the end, right? I'm I'm in retail, it's sales. We all want sales, but how do we get there? What do we learn about the consumer from one end with marketing? What can we learn from our retailers and our sales teams from conversion and product development timelines and things like that? Like it still all counts, even if it's not direct from the consumer.
SPEAKER_01Well, sure. And and and of course, we still have the problem with data silos in different departments, owning and curating and managing their own data or being being territorial about it. What what are you seeing in terms of your, you know, when you would have to work with teams that would work in different uh channels and have different sets of needs? And how how were how were you able to uh break down or were you able to break down silos in terms of data? And how would you go about that type thing, type of thing?
SPEAKER_02Yeah, no, that's a you know, that's a really interesting question because the way um my current company, last company was set up was um sales teams were very siloed, which makes sense from a retailer perspective. But when you're all working towards the same goal, you want to be using the same KPI languages, right? So obviously with Amazon, it's very heavy marketing, traffic driven. It's gonna be different from a store account, right? But when, you know, leadership is hearing different terminologies and KPIs and goals, while the end goal is sales, you know, you want to be able to compare what's conversion like on us versus Wayfair versus Target, right? Like that makes sense. You want to understand across the channel where you're getting paid back for your marketing dollars. So getting aligned that consistently, it doesn't help. So I, you know, I was many times bringing to the table a lot of terminology that wasn't being used elsewhere because maybe it wasn't applicable or it just wasn't top of mind, which then just bounces around questions and causes a lot of, you know, could cause a lot of inadvertent spin when in reality we're still working towards the same goal.
SPEAKER_01Sure. And I think it's it's getting alignment in terms of the goals, the objectives to say, how are we getting, how are we aligning these goals? Are these the same goals? Are they similar goals? Are they adjacent? Are they dependent? And then how are we measuring these? So can we get aligned on the objective and the measurement and and look at the greater good, so to speak? I like to say there's no budget for the greater good, but there's you know, incentive for overarching growth and and all of the other strategic initiatives. So in order to get data working correctly, we need people aligned around a goal that that data supports, right? They have to understand well, why is this important to me? How is this going to meet my objective? And then how do I compare apples to apples for for my leadership and really start to show that these things are moving a baseline, right? We're impacting some metrics, some ROI. So when companies are overlooking new technologies, or I'm sorry, are examining, exploring new technologies, what do you find is frequently overlooked in terms of the preparation for those things, whether it's AI or whether it's personalization or any other type of initiative when you start technology, what do people miss?
SPEAKER_02I think it originally they just missed was what will this work with your current systems and teams? How does this work? It's not just always a plug-and-play situation. It's normally make, you know, you have to go through some some due diligence with whatever you're plugging in to ensure it's going to speak to what you do. A lot of time, I feel like the thing that I see the most across the board and hear from a lot of peers in my situation is, you know, you have that overpromise under deliver, and you need to make sure that you're ready and that you know what you're solving. So again, going back to what is your use case and what you what problems you need to solve, it needs to be incredibly clear how this will work.
SPEAKER_01Yeah. And I think in that situation, you know, when you are preparing or looking at vendors, you have to give them your data and your use case to solve the problem, to show me with, you know, here's my data, here's my use case, show me how this works, right? Because that is where it's the road.
SPEAKER_02Yeah. And making sure you have the right teams in place and that you have someone who's going to be a subject matter expert who can actually run this. It's it's that's I think the hard piece is a lot of a lot of times you think this may solve headcount issues, or oh, this will just take care of this entire side of the business. You need to have someone who's really the expert there and able to implement and use it and drive it.
SPEAKER_01Drive it. Yeah.
SPEAKER_02Right. It seems simple, but that actually tends to be a pretty big downfall.
SPEAKER_01Yeah, yeah. And no, of course. And and I think the other is that, you know, when when many times organizations will think that they have a tool problem when in fact they don't have a tool problem. They have may they may have a uh a uh uh a process problem or a structural problem or a policy problem or a data problem. But they but the tool is the attractive answer because it seems to provide, you know, the answer and it and it provides a contrived, you know, set of use cases and environment. Whoa, wouldn't this be great if you had this, this, and all these great things? And like, yeah, we want that. Well, buying that tool may sound great and you may be able to do those things, but that's not where your problem is today. And so many times skipping ahead, buying the tool or or acquiring a technology before you have that fundamental understanding of what the problems are, how they can be solved at their root, right? Not after the fact. When you when you install a new technology and then you load all your data and then you try to fix it later, it's a factor of 10 more uh 10 times more costly to do that. So I think that's really when you mentioned use cases. I think use cases can't be overemphasized. The use cases are saying what is it gonna do at the end of the day, and being really clear on what your objectives are, you as a leadership team, you as a functional manager, you as the person who's going to own this technology, what do you need out of this, right? What's going to be really clear with your vendor uh about that? That's great. Now, you've also led but you know, teams and navigated uh reorganizations and structures, and you've been through that many times in your career. But what do you think? And if we're AI, just more disruption and more uh uh change. It's it's kind of the same old story in a lot of ways, right? Especially when it comes to change management. From your perspective, what is leading through that uh that disruption and that change uh mean and what is so important uh uh to make that successful?
SPEAKER_02Yeah. I mean, I think it's it's a really interesting subject for me because leading through ambiguity is really tough. It is, because a lot of time you you don't know. You don't know what's gonna happen. Like right now, the market's crazy. Nobody knows what's gonna happen. You know, there's a lot of, to your point, angst and nervousness around AI and what that's gonna mean for people's roles, what it's gonna mean for people's businesses. And the thing that I've learned the most is to just be as transparent as you can, especially with your team, so that there's confidence in you as a leader because they need to find a touch point right now that they can look to, that they can believe in, that they, you know, it helps, you know, it's really difficult to work through high anxiety situations and knowing if you're gonna have a job at the end of the day or knowing what's gonna happen in the business or the market or what's gonna happen with tariffs. A lot of these things are out of our control. But what you can control is how you lead your team through it. And I've found that transparency is really important. Obviously it's a to and agree, but I found sitting down with my team and saying, listen, this is hard. And I don't have all the answers, but I am gonna share what I can. And here's what I know, and here's what we can control, and here's what we can do. And I think that that's received the best feedback and the best performance and the best, you know, to drive through something like that. Yeah, because it's it's hard. And especially when you're going through right now, there's so many companies they're going through layoffs or they're so much restructuring. Um, I think the point is to focus on hey, what are your talents and what are you good at? Maybe your role right now will change. And that's okay. It's time to try something new. Um, this gives you an opportunity to grow or really show that you're able to pivot and you're able to react and show that you can do that and show that you're a leader. And so I've really tried to focus on those pieces of it when it comes to transitioning to you.
SPEAKER_01Yeah, I think you're right in terms, especially when there's uncertainty and the problem with transparency and authenticity is that um many times leaders are under a lot of pressure to produce. So sometimes that uh pressure to produce results may lead to some positioning of results that are maybe a little bit ahead of where you actually are, right? Because you want you want to be um aggressive in your your your your achievements and trying to get there. But many times aggressive timelines don't always work. And you have to look at what the real drivers are and what the real deadlines are. And and if you can't make those, then people need to understand. But many times the leadership is putting leaders under so much pressure that they kind of have to, you know, toe the line and say, Oh yeah, this is going great when it's not going as well as it could be. And that's where you start to lose credibility with team members, because team members are like, yeah, no, this isn't going well. And you're telling the executives it's going well. So I think it's hearing that bad news and it's communicating bad news sooner rather than later, not hoping you can make up for your past losses, right, with extra acts of heroics, but but being realistic and that that will engender trust with the team when you you can have those hard conversations with your leaders about the reality of a project. Because every project, no project is perfect, every project has bumps in the road, right? So you have to be able to address those, supporting your team members, right, through those transitions, making sure that they have what they need in terms of support and air cover, right? Because again, there's going to be when there's pressure and there's change and there are mistakes. I think the other is to make sure that you're building uh a culture that will uh be able to experiment, right? Because when you're doing you have to try different stuff. What's your experience been there?
SPEAKER_02You know, it's it's really interesting. I think testing is always important. Experimenting is usually fun for a team, right? It's you don't want to be doing the same thing every day, all the time. You have to have processes and have it work. But hey, what if we could do this? And what if we could try this? And a lot of it is how you spin it because it could end up being something that doesn't work and we lost some time there, but hey, we tried and we now know. But I think it's a really important thing to do, especially with how quickly um everything's changing and how quickly um technology changes, how quickly the consumer is changing. It's just moving so much faster than it ever did in the past. So it's critical. You have to be able to test and learn. And whether that be in a small way or in a bigger way, it's something that you need to have in your skill set regardless in this place. You just, you just do.
SPEAKER_01Absolutely. Absolutely. And then people have to have the room to do that. And I think you have to kind of budget it or be intentional about it to say here's our path. Here's some areas where we're going to do some experimentation, explore, exploration, try some different approaches. Much of this may have to be thrown away, right? But we hope this conclusion. But you have to be able to build that into the process.
SPEAKER_02And I think that's important because it also teaches that failure is not a bad thing, right? We're not going to learn unless we try some things. And there's no way they're all going to work. We all know that. But if you make that part of your culture, it also, I just think it's a really important thing for a team to learn an appearance skill set to have, and especially as a leader, to be able to say, hey, I tried this, I failed, it didn't work. But here's what I learned from it.
SPEAKER_01Yep, absolutely. So, you know, a recurring theme that we have is uh talking about the fact that there's no AI without IA, there's no artificial intelligence without information architecture. It's pithy, people probably get sick of it, but uh many executives like that phrase and they use it, so I wouldn't shy away from it. Uh, from what you've seen, and again, think of it as data across any initiative, what do you see as the building blocks that organizations need to put into the place first?
SPEAKER_02Yeah, I mean, I think the key is having the team set in place and very clear defined processes and roles and responsibilities. It's like any other process you'd work through, but it's incredibly critical, I think, to make sure that your foundations first are correct. You need to be always starting at the basics. And I think a lot of companies and brands just move past that quickly. They're, you know, they're like, oh, this will solve this. But you need to have everything standard. You need to have clean data and very clear workflows and accountabilities.
SPEAKER_01Yeah, and I think that that's true. And it starts by by beginning to normalize the terminology, right? Have a dictionary of terms that are the official terms. You can have synonyms in there, you can have, you know, there could be code names for projects. You need to be able to resolve all that with a with a business glossary. And then from the business glossary, you can start building other pieces like control vocabularies and you know, things that are taxonomies and uh building out uh relationships between taxonomies, which become absolutely and I think you know, what you what you're saying about clear data ownership and accountability is is critically important and making sure that we're weaving this stuff into the workflow of the day-to-day environment. When people, it's like water going downhill, as soon as there's an obstacle to go around it, people will take the path of least resistance. So we need to make sure that whatever we're trying to enable, we're weaving it into their processes and not bolting it on. So, from your perspective, for the leaders who are being asked to move fast on AI, but feel like their organizations aren't ready, what might you advise?
SPEAKER_02It's a great question. You know, it's it's tough. That is a very tough question to answer because I think the expectation is AI is fast, so we need to move fast. Um, the challenge is that AI, you know, there's so much out there and what is right for you. And I really encourage to make sure you find what is right for your business and what fits you best. So to do, you know, to do your homework when looking out there and looking for shopping and what is going to have the biggest impact, I think is where I start. You know, where in your company does it make sense to hit immediately, whether that's with the consumer or it's just internal processes or how, you know, how your company is working, how your data is feeding internally. I think start with where it will have the impact. And then I really think it's important to make sure you have the right people and structure to use it because AI doesn't work without, you know, someone being able to determine what they need to prompt it, how they need to use it, and what the results should be from it. So you need to have very clear, transparent expectations and a true owner.
SPEAKER_01Yeah, that makes a lot of sense. I think also being able to look at um achievable use cases, coming back to use cases, right? What's going to be reasonable and making sure that we're we're educating people and enabling them and giving them the basics and getting them building some kind of AI literacy, right? To start with stuff and sharing sessions, lunch and learns, the things where people can show how they're using either the approved large language model or what they might be doing personally. But they're gonna do it anyway, right? They're gonna do it anyway. So we have to share that. And I think the other is there's a lot of work that needs to be done that is not necessarily visible, especially to executives.
SPEAKER_02Absolutely.
SPEAKER_01Because again, the complexity is, I mean, simplicity is hidden complexity. And I can say my cell phone is very simple to use, but there's a tremendous amount of complexity behind the scenes. But we need to surface that to executives so they know it's not pushing a button, installing a product, you know, assigning this change, whatever it is, that they understand that there's a lot of pieces under the cover. So you need an executive who will take a look at the weeds, even if it's briefly, right? You know, and help them understand that. So I think that's another piece of that. Yeah.
SPEAKER_02I think, and that's always, I think I find I find that's usually the biggest challenge because if, you know, your executives and leadership aren't using it, which they most likely are not, it's going to be hard for them to understand what it takes to get in there and then what your results are. Like obviously they want to see positive results, they want to hear how it's going, but how do you measure that as well? Like, how do you measure efficiency? That's hard. Like, so do you need to have a baseline of this used to take X amount of time and now it's this? Or hey, we're seeing an increase in conversion by X now that we've implemented this. Making sure it's measurable is really important.
SPEAKER_01Absolutely, couldn't agree with you more. And I think that is something that certainly the executives need to understand is what's the baseline and what are we improving? But then understand, well, what did it take to do that? Even if, again, you have to show them some scary diagrams, they may tune out, but they need to understand that there's a lot of work that needs to be done under the under the uh covers to do these things. So being able to see that and then being able to excuse me, see those results. Most executives don't look for their own information. They'll pick up the phone or they'll send an email or they're ask their assistant, right? They don't necessarily search and retreat. You have to start getting them to use this stuff. And if you can make it conversational, you can build a conversational assistant for the executive to run a report or to get certain things that they ask for over and over again. But again, that's kind of a tangible thing. It's visible progress, but then the metrics are super important and making that invisible work more visible. That's great. Looking ahead, as you see the next uh step in your career, what are you excited about when it comes to the future of AI or future of retail or future of e-commerce or the future of uh Betsy Mello? Yeah.
SPEAKER_02Oh, thank you. Um, so I think, you know, I obviously I'm excited to see what where AI continues to go. Because if you just take a step back and say, where were we five years ago versus where were we one year ago and six months ago, it's moving so fast. It's and people are learning how to use it better. It's I think it's what I I really love about it, it's is it's so helpful for the consumer. Like I'm using it all the time, personally, right? It's helping me do all kinds of things at home and in my business. But what I really love about it is that it's just become a becoming a widely known way to prompt and it's changing the way we think. And that's what I like is it's not just do this for me, but what do you need to give it context for? And I find that in business, context over my career has been so important because if you're thinking about the consumer, you know what you're looking for, but having that context and sharing why you're doing this or we think this new idea would be really important for X, Y, and Z reasons. I really love what AI is doing in general for that. And I think that's super exciting. I think the other piece I'm excited about is that, you know, we could the world just continues to become more global, right? Like as information grows and sharing, we have visibility to so many more things than we did a long time ago. And even recently, um, it's changing the way we source. It's I just think it's it's interesting because you're you're getting a lot more variety in what you can sell and what you can share in the United States and globally. Um, and I'm hoping that there's just a lot more alignment around that go forward. And I just, you know, I think that at some point the noise becomes too much and you start to focus down. And where do you want to shop? Where do you want to look? Where do you want to, you know, where's where's your life become? And I think that that's gonna be really interesting to see how people react to that. You know, I mean, obviously people have been going back to stores a lot more since COVID. They're changing the way that you experience stores and showrooming and just so much more interactive based when you're in there. It's just, it's not just product anymore. It's about the entire experience and how you can make yourself different. And I love that. I just love the way that we're continuing to show up to consumers differently.
SPEAKER_01That is the exciting part because it is, it is, as you say, it's not just about the product, it's about that experience, right? Who are you? What do you sell? What's the product for me? How do I buy it? How do I use it? How do I get the most value from it? And what's that entire customer life cycle and lifetime value? And then what we're trying to do is be able to anticipate those needs, just like a personal shopper would be, right? So we didn't even get into you know uh agents, but there will be shopping agents, and shopping agents should own your metadata and know who you are, yeah and not necessarily uh be freely shared, right? So there'll be a lot of interesting things. Have to touch on that another time. Betsy, this has been great.
SPEAKER_02Thank you.
SPEAKER_01Thank you again for joining us and sharing your experience and your perspective, your insights on how enterprise leaders can deal with this change is very, very important. And uh to our listeners, uh stay with us uh and we're come back and we'll continue to explore what it takes to make AI work in the enterprise. Thank you again, Betsy. Appreciate your time.
SPEAKER_02Loved being here. Thank you so much, Seth.
SPEAKER_01And thanks to our listeners, and thank you, Carolyn, for your work behind the scenes. We'll see you next time.
SPEAKER_00Thank you for joining us on another deep dive into AI innovation. Tune in next time when we introduce another industry expert and discuss how to maximize AI in your world. The Early AI Podcast is sponsored by Early Information Science and Vector. To learn more, visit early.com. That's E A R L E Y.com. Thank you for tuning in.