Why AI Alone Won’t Transform Retail | STRIDE USA 2026

Hello, welcome to Calypso's 2026 Retail Research. My name is Joshua Young.

And I'm Alison Coderre.

Thank you. We are both from the industry. We are consultants now, but we both have a long experience within brands. I was with Nike for 20 years and then I consulted for 5 years and then I was with VF Corporation for 4 years. And then now consulting again. So a lot of the experience that you may all have, I have as well. And it was fun this year doing the retail research and being able to speak with all of you. So Alison, would you like to introduce yourself?

Hi, I'm Alison Coderre. I am also with Calypso. Came from industry, spent some time in inventory management, merchandising, product development and sourcing before making the leap to consulting about 5 years ago. So excited to share with you what we are hearing, where we think we're going, and what's next.

Great. Great. Yes, we had a lot of great conversations this year. And again, I would encourage all of you, if you would like to be participants next year, please do let us know. One of the things that we heard quite overwhelmingly this year is about AI being extremely disruptive. Even in the previous panels, we've even heard the word scared. Of what's coming with AI. And so it seems to me like we are going through one of those big eras of change. We've seen it before, the Industrial Revolution, the Renaissance, the internet age. We are now in, whether we like it or not, in the AI age. And it got me thinking, what can we learn from ages of the past that we might be able to take away and use in our daily lives now to deal with what we're going through with AI? And so I thought of an example during the internet age. A company called Cosmo.com. Who, by show of hands, how many of you are old enough to remember Cosmo.com? But pretty much nobody. All right, great. Well, I think I saw a few hands back there. But so Cosmo.com was an internet startup, e-commerce startup. And their idea was they sold small grocery goods that they could deliver to you. Within about an hour or less. And the trick was that they would do it without a delivery fee and without a minimum charge. So if you think about it, that means that you could just order, you know, a Snickers bar, which— and I actually did this, they had it here in Portland— you could order a Snickers bar, had it delivered to your home, and that was it. And, you know, that's, uh, and it was super convenient. And so everyone thought, oh wow, this is great, this is like the the e-commerce of the future. But the problem was, is that they were losing money on every single delivery. Because think about the time that that was in. That was the late '90s. Yeah, late '90s, early 2000s. There were no iPhones. There was barely any GPS. There was any of the modern, you know, routing, delivery routing services that you have today. None of that infrastructure had been built. So they basically just brute-forced this. And so if you can imagine, if someone— like in the middle of winter, in the New York City Manhattan winter, all of these delivery riders on their bicycles with paper maps trying to get people their Snickers bar in these massive high-rises. It was absolute mayhem. And so the reason why is because they had a great product. People wanted that. And they had a great technology, which was the internet. But they didn't have anything— they didn't have a system. They didn't have anything in between that. And so it was all about brute forcing it with the people. Now, there was another company, a bookstore e-commerce company that was basically on— going at the same time. They thought about all of these things. They built the infrastructure. They thought about how the deliveries needed to happen. They started small and grew over time to the point where they could support that entire system that they had cultivated. And be successful. So when the bubble burst in whatever it was, 2001, 2002, Cosmo.com immediately went under. They blew through $250 million of investments and then quickly went out of business. The other company we probably all know today is Amazon, the world's— does anyone remember the world's largest book— or words— yeah, world's largest bookstore? That was their initial tagline. There you go. I've got someone in the audience that remembers. And so the takeaway is that very similar, I think, to what we're seeing now is that with all of the shiny objects flying at us, you have to think about not just, ooh, that's an exciting technology or that's an exciting technology. You have to be thinking around, OK, how can I use these capabilities within the industry to build a system that is going to work for my business? Because otherwise, you're just chasing you're just chasing technology. You're going to end up like a Cosmo.com, and you don't want to be a Cosmo.com. So anyway, it's a cautionary tale, and I think it really fits what we heard from all of the brands that we talked to, is that they are trying to think more than any time— more than any time else, right now, they're trying to think in terms of building systems, not just grasping at technology. So with that, why don't we go ahead and dive in?

And we're uniquely positioned to talk about this. We've been doing this research for 10 years now. We started in 2016. Looking at product development in general, gauging what we were doing differently at that point, which was 2016, and we saw it grow throughout the years. So in 2019, we started talking about DPC. So originally we would talk about product development systems and technology. DPC became really big in that timeframe, so we reframed our survey in terms of DPC. So what is the industry doing to serve DPC? What are the technologies, people, process, et cetera? In 2023 to 2024, we actually saw another shift. We saw that people had matured what they wanted to be in DPC. Everyone had some type of program going, but the nuance at that point was in scale. So what are you doing to achieve scale? We couldn't deploy the same kind of quantitative metrics we'd done in the past, so we engaged in conversations with brands to understand why So we, sorry, backing up a little bit, we rated the brands on a maturity of 1 to 5 in various capabilities. And at that point, everyone was about a 4. They were pretty mature, but they hadn't fully scaled. So we dove into conversations to understand the nuance between being mature and being able to deploy it at scale. And that was some interesting conversations, but we're seeing another sea change today. So in 2026, our survey today, we reframed it again away from DPC into retail research because the DPC of 2019 is very different than what we're seeing in 2026. So in order to develop this survey, in late 2025, we engaged with brand leaders with a worldwide reach. Some of them are sitting in this room. Thank you to those who participated and had conversations where we talked about both the qualitative, where are we ranking ourselves and So you have the quantitative and the qualitative, how are we getting there? So we're moving into a new foundation again. To Josh's point, there were things that you needed to build in '99, and there are things we need to be building here in 2025. So, oh, we moved to digital thread. So we framed our research, and honestly everything we do at Calypso, in terms of the digital thread. This is a high-level illustration of that digital thread, showing how we bring product to market from discovering So the strategy, the concept, through to creating it, eventually making it, the manufacturing that we talked about earlier with Keen, and then moving it, getting it to the customer, selling it, and then at the end of the day, also bringing it back and what does that look like when you repair things, et cetera. So we talked our interviewees through this digital thread and asked them questions, and we really saw 9 key themes shake out. So we're gonna talk through those themes in, oh, there they are, in just a moment. We structured them in people, process, and tech. You heard me say that once already, I'm sure you've heard it a million times. Pillars of project management, pillars of how we attack every transformation. We're not gonna dive into all 9 today. We're actually gonna focus on 3, one in people, one in process, one in tech. We're happy to talk you through all of them if you would like to talk through more. We've also published a paper, there'll be a QR code at the end. Where you can download it. I believe it's published today. So this is our first day. This is our sneak peek going live. So Josh is going to take us through the themes.

Yeah, great, great. And by the way, if you had coffee at any point during the day, you probably saw the QR code on the coffee cups. That's our QR code, and that will bring you to the retail research as well, OK, in a lot more detail than what you're going to see today. So like Alison said, we're going to be going through these 3 themes, and the first one is around people. So this is kind of the same thing that we talked about with that change fatigue that they were seeing in the '90s. And I think everyone's going through that right now. I think what a lot of— in fact, I think it was even in one of the previous roundtables or the panels that they were talking about how much frustration is in with a lot of people have within companies right now because there's so much change going on, whether it's AI or DPC or PLM or any other 3-letter acronym. That's flying at you, it's very hard for people to keep up. And so the problem I think that a lot of the brands were saying to us is that how do we get out of that loop of technology hype and get into a more people-centered approach so that we're really thinking not just about the technology, but we're starting with people and how they work, and then also thinking about what does the process need to look like and how will people look work within that process. And so, it's moving from what has been traditionally a technology-led conversation around transformation, more to a people and process-led transformation. And I think that's really what is now becoming important to brands. And so, how do you reduce decision-making friction? So how do you ease decision-making by reducing friction? How do you magnify those decision points so that the decisions that you make at each point along the process becomes more sticky, more solid. And how do you empower people to make better decisions and work better within that atmosphere? And I think it's ironic that companies right now, in a time when everything is around, oh, you're going to lose your job to AI, or you're going to lose your job to someone using AI, or some other terrifying thing, that companies are realizing that is causing pressure on people, and they're rearranging it and say, how do we assist our team members in being successful? And I think that's a really important message that we're hearing from brands. So the next one is igniting DPC with AI. And I think this is probably one of the best environments to be able to talk about DPC. Now, you may be wondering, this is a technology. Why is it in the process section? Well, that's because— and this might be a little bit controversial to some, to some of you— DPC is not a tool. Let me say that again. DPC is not a tool. Now, what do I mean by that? It is an approach. It is a methodology. So DPC, if you, if you try to think of DPC as a single technology like 3D or AI, it limits you in terms of what how you can approach changing the way that you work. Where if you think about DPC as a system, where you're creating product digitally in a way that's much more efficient and effective than using physical product, then you have access to any number of different tools. So you could use 2D, you could use 3D, you could use AI, you could use something that maybe we don't even know about yet. It's more about How do we take advantage of the economies of scale and the, the, what, the allowing us to be able to make decisions earlier in our process? How do we take advantage of that using these digital, these digital technologies? So let me give you an example. So in the past, DPC was really mainly about digital prototyping or digital sampling. It's not really about that anymore, is it? It's about a designer concepting, iterating with something like AI to be able to get to a concept that's accurate in their mind so they can present it either to their product team or they can present it to manufacturing or present it to sales. In the past, it was, I'm doing 3D to get a digital sample and that's it. And so if you defined the way you work by that one tool, if that tool doesn't become popular, if it doesn't work, then you're sort of out of a job. Whereas if you define DPC as a methodology and a way of working that can solve a number of problems across your enterprise, it unlocks a lot of doors. And that's one reason why we at Calypso encourage people to think about DPC as a problem-solving methodology and not as a single tool. And it was what we heard from our respondents as well.

And when we see brands, leaders building roadmaps that address this question— sorry, if you don't mind clicking back.

Oh, sure.

There's some questions that we were hearing. So they're focused on these questions that we have on the side. So are they freeing designers to focus on intent, not tools? Are the digital assets making better decisions? Is it proving its value? And is the operating model built to use DPC as it's evolving rather than what it's been.

Yeah. All right. So the last piece is around technology. And again, this one may surprise you. Companies are— when they're thinking about technology and how they're going to be managing technology in this current age, yes, the conversations around AI are there. But there is one thing that you need to do to be able to really affect effectively make use of broad AI at scale, and that's making sure that the data that you have, that your enterprise has access to, is in a high-quality state. So that could be any number of things. Number one, is the data accurate? That's probably where it needs to start. Number two, is the data that I have in my system, uh, is it timely? So are you able to get real-time data? A lot of companies in the past have had to have just focused on legacy data or historical data. Real-time data is becoming much more important. Is the data in the right place? So do the people who are making the decisions have access to it? A lot of times the answer to that question is no. People are looking through multiple systems, they're storing it in spreadsheets. It's really important to have that data online if you're going to be able to make accurate decisions with it. The last piece that is probably the most important is, do your team members trust that data? Because if they don't trust the information, if they are using, let's say, AI to bring insights from that information and they don't trust it, then likely they're not going to trust the results from the AI either. How many of you heard the expression GIGO? So garbage in, garbage out. So if your data is garbage, then the output of the AI is going to be garbage. So it's really important for companies. And this is, again, some things that the respondents talked about is that their companies are focusing on getting— making sure that their data is ready for the internet edge.

And we've talked about getting your house in order in years past. This isn't brand new. But today, it has a much stronger, robust meaning than it has in years past. Years past, it was a nice-to-have. In the future, you absolutely need data governed, managed, and shared across your business to enable the AI and technology of the future that you may want to deploy.

Okay. All right. So next, we're going to dive into challenges and solutions. So as we had discussions with the brands, they would bring up challenges, and we would just talk about what those possible solutions could be. And so I'll start with the first one here. So again, back to the people. This is a really interesting idea, is that where a lot of brands right now have very legacy processes, they've built an operating model for a time that really doesn't exist anymore. And so they're trying to figure out how do we, how do we reorient, right, as, and as we change How do we make sure that as we do a program of transformation or as we implement a new system of capabilities, how do we make sure that we're getting the value out of that? And that's why we're calling something called value-led change management. So most of you probably think about change management in terms of, okay, when I'm going through a project, I need to make sure change management is about training, it's about documentation, it's about communication from leadership, and maybe it's about adoption, right? How many people adopt? But have you ever been on a project where you had 100% adoption, everyone trained, and you still failed? And so what's going on there, right? What's happening? Well, the problem typically is, is that they're not measuring value delivery. And so our idea is basically, and what we, again, heard from a lot of the respondents is, you want to be able— and as part of a project, you want to be able to find out what parts of your process, what activities, what deliverables are going to deliver the value that you expect. So in other words, what's going to make you faster to market, what is going to provide you those better decisions that you were looking for, and identify those expectations. And the change management team should be able to know about those. And as you're going through implementation, they should be seeing if you're getting those signals of value. And if not, it's not their job necessarily to do the course correction themselves, but they should be able to flag those issues to the delivery team to say, listen, we're not being successful with this change that we're going through. What changes do we need to make in order to get us back on track? So that's, I think, a key focus so that people are really about your success with transformation. If people aren't on board, if they're not— If they're not working in the new way that you expect, if they're not supporting that, no matter how much training and adoption you get, if the people aren't on board and if they're not delivering the value, then you're not gonna be successful.

I'm gonna steal the clicker now.

All right, there you go.

I don't know how to go back. One add just before we move on is this is really important when we talk about that hype fatigue. Things are changing every single development season for your teams. If they don't understand why, they don't get on board, you're gonna fail. So starting with your value, making sure they understand why you're doing it will really help with the massive amount of changes we're all going through. All right, second industry challenge. Josh kind of hit on it, but the way we work, the way we've been working is not the way we can work in the future. We have to change, we have to deploy the new technologies, et cetera. We're doing it, but we need to change the way we work to support those workflows. So solutions, build your op model to focus on where you're making decisions, where your inputs are, and the right time for those so that you can make a decision, stick with it, and truly change the way your teams work. We're seeing a ton of brands today going through op model transformations, really looking at their calendars, their org structure, et cetera. Making sure that when you are building your op model, you're centering it in when you need to make a decision, when you have the inputs to make that decision, and when you absolutely critically need to drop it and move on from it. So, what is the decision you need to make? Who owns it is a big one. Making sure you have accountability within your teams for these decisions so that you can go faster Reduce rework, reduce those decisions by committee where you're like, orange isn't it, it's gonna be maroon this year. Make a stand, trust your employees, trust your teams, but give them the insights to feel confident in those decisions. So arm them with why maroon is it. Our fan, I love fan, Keen called it a fan, I love that. Maybe the early insights and data are telling us that International Orange isn't it, it's gonna be maroon. Let's feed that to our team so that when they're making the decisions, they have that at their fingertips. It could be color, that's an easy one. Could be buys, this could apply to your planning teams, apply to your sourcing teams, this applies across the board. Making sure that everyone has insights and analysis so that they can make their decisions and you can stand behind them. Final challenge. Just to be clear, these are not the only challenges we heard. These are the 3 challenges we are featuring today. Our third challenge is around disconnected tools and fragmented data. So we talked about change readiness, organizations not being ready. We talked about changing the way you work. Now we need to change the tools that we have at our fingertips. So we talked about this a little bit already, but let's talk about it in detail. In order to enable AI, which is the buzzword that almost everyone, I'm not gonna say everyone 'cause there are some brands that are choosing not to invest in AI, AI is everywhere. It is the buzzword, it is the NFT of 2026, if you will allow that. But in order to do it, you need to have the data there. You need to have the master data management in place. You need to have the governance. You need to have the data models that can communicate between systems. Because if your PLM says one thing, but your CAD tool can't get access to it, doesn't matter what PLM says if they're not connected. You also need to use those, that data to drive insights. Josh said garbage in, garbage out. It applies here too. If your insights are wrong, they're probably built on the wrong data. That's something we know about AI, right guys? It's only as good as the data you give it. So make sure that you have the right data to enable those decisions and enable your team. Just as important is maintaining a single source of the truth. When we have connected tools, we can point to one place and say, "That's the right answer." If they're not, you have 5 right answers, so none of them are right. This ties back to the op model and making decisions, but they all go hand in hand. You can't have one of these without the other. They're super important. So as you look towards your transformation, make sure you're looking at your data. That should be— it's not the, not the greatest thing, but it should be top of mind if you are looking to deploy any type of AI. But I would say the number one thing in a transformation roadmap, like in looking into '26 that we've heard from brands, is op model. So these others are foundational, but op model should be top of that transformation roadmap. So you change the way you work instead of plugging in a new point solution. Looking forward.

All right, I'll take, I'll take that back. All right, so let's, uh, let's talk about a bit about AI right now. And I know it's all everyone, it's on everyone's top of mind. Um, what we were hearing from the, the brands, and we in fact we even had a specific part of our quiz, so to speak, of our— because we actually, we did take numerical data. So we had the— when the brand sat down with us, not only did we have a conversation, but they also filled out like a questionnaire. And so we were able to get empirical data from them. And one of the questions that we had around this was, what AI area of AI is the most important to you right now? And it was these, these three different areas. So assistive AI, applied AI, and agentic. And very, very clearly, we had sort of a stair-stepping where assistive AI was absolutely the most important thing that people were working at right now, that applied AI was the next most important thing. So basically what they said is this is the area where they're scaling their efforts. And then the last one, agentic AI, is where they're still trying to figure out, like, what does that look like? What does it mean for them? How does it work? How do we implement it? And so wanted to spend some time talking about these different areas of AI, what they are, and how you can maybe take advantage of them and what that looks like. So the first one, assistive AI, that's the one that probably most of you are already using. So that's Claude, that's ChatGPT, that's generative AI fits into that zone. Vibe coding is in the zone. So basically, it's any place where you are, the human is in control, and you're saying, I need this, and the AI gives you something back. And so that's an incredibly valuable, obviously it's an incredibly valuable area. And if we think about, but we, I think, as a society right now, are talking about it wrong. My— okay, granted, my opinion, but it's also again what we're hearing from a lot of the brands is that we're a lot of times, like, like I said at the beginning, people are saying, oh, if you don't learn AI, you know, someone with AI is going to take your job. We need to spin that narrative. We need to be thinking about that these AI technologies will help you be successful in your job. They'll help you be maybe more successful than you are. They'll help your business succeed. So we need to start thinking about it in a positive, because I, I don't know if all of you realize this, I mean, well, at least not yet, um, AI doesn't— isn't alive, right? It is a piece of software created, and it's a bunch of algorithms that give you a response based upon math. It's not, you know, it's not an entity. And I think if we continue to talk about it that way, I think we're going to have problems. Where if we talk about it like it's Siri or it's the little Microsoft paperclip. That's a bit more friendly of a way to approach it. And so the idea with assistive AI is that humans are in control and they're responding. It's a piece of software responding to help you provide content that you require.

I think it's funny when we talk about Clippy, people think AI is new. AI is not new. AI has been around for a real long time. It just has a new veneer in '25. That is making us talk about it in a different way. But it's been around. AI's been around for a real long time at this point.

I mean, when I was at Nike in the mid-2000s, we were using AI from Dessault to figure out how to make footwear outsoles lighter and faster. I mean, it's not a newer technology. It's just become very, very easy to use. And so we need to, I think we need to put it a little bit more in perspective. Than what we've currently done. So the next area, applied AI. So this is basically predictive AI. So in other words, you provide it a large amount of data. And in fact, you may have real-time data that's streaming in. And in this case, applied AI will monitor that information, identify trends, in that data and then offer up insights. So let's say that you are having a generative AI monitor— no, sorry, generative— an applied AI monitor your returns. It might say, oh, look, this particular shoe is having problems with delamination pretty consistently. So you're having a problem there. And it might even recommend that, oh, it's only on these particular models. And so it's, it's analyzing information and it's offering up insights to you that you can then act upon. Trend prediction, where AI is looking at the marketplace and seeing what's going on in the marketplace. There's even some AI technologies that will respond and provide you information back around how are people responding to your brand. So if you have a product launch, it'll, it'll go out and figure out what are people saying on the— I was going to say LinkedIn. They're not looking on LinkedIn talking about new product launches. They're on TikTok or on X or the other one that I can never remember, the one that my kid— Reddit. That's the one I'm thinking about. They'll be talking on there about what are the trends and how are people responding. And that can be really important from a consumer signal perspective. All right, so, um, the next one, agentic AI. This is— I guess this is the one that scares people because this is the one where it autonomously takes multiple steps. So if, if assistive AI was there to help you and applied AI was there to generate insights, agentic AI is here to take action. I guess that's the easiest way to look at it. So it can orchestrate multiple-step workflows and make decisions. So back in that same case of the returns, let's say that an Agentic AI is monitoring returns and sees that same problem with delamination. It might say, OK, here's some potential fixes for it. It might suggest a new product with a new construction method. It might send that to the development team. Once it hears back from the development team, it might take action to create a BOM and a tech pack. That's agentic AI. It's something that is performing actions for you. And ideally, in a lot of cases, and I think the most valuable place that people are looking at agentic AI is in the area of automation of complex processes that don't necessarily need to be done by humans. So in other words, taking the busywork out of the process and allowing humans to concentrate on the fun stuff. You know, so instead of worrying about, taking carryovers through a process. Give that to the agentic AI and let the team members focus on the brand new products in the pipeline. Now, there's some two issues with what I would say with this applied AI. Number one is that, or with, with the two here on the end here, is that applied AI, the risk here is it does optimization, but it does it, keeps doing it and keeps doing it and keeps doing it without innovation. So in other words, you keep going after a particular product and optimizing that product. You get to the point where, is it really worth optimization? Should you— is it better to jump to a new product? The problem with the Agentic AI is that those actions it's taking may not match your strategy. If you don't do, I don't know, high diving apparel or high diving footwear, you may— and the Agentic AI thinks, ooh, that's a great new product opportunity. You may want to be able to step in and say, no, no, no. So these are, I think, the risks that we need to be aware of as we move forward.

So don't click yet. One ad, sorry. Oh, OK. One ad here. The agentic AI, Josh touched on it. This is where people get scared. This is where people think it's coming for their job. So we recommend thinking about how people deploy their creative talent. AI is not creative, y'all. AI is a linear thinker. We are not. So how do we deploy our people to make the decisions, to be creative, and eliminate the busywork. So if you're hearing rumblings of people being scared about their jobs, that's how we like to think about it. Enable people to do what people do best and be creative. All right, one, our last insight as we talk about looking forward. So as we talk to brands, we really focused on early parts of the process. So how do you discover, create, and make, but one of the things that we are starting to build as an industry, some more ahead of this than others, is the idea of retail as one connected loop. So when your customers buy something and it breaks, some brands have begun offering return and repair. So you send it back in, they'll fix it for you. In order to do that, they need to know what it was originally. They need to have visibility to that BOM. You need to understand the tech specs, however it was made to begin with. So the idea of creating a connected ecosystem where your PLM contains data that can go all the way through to the DC so that you can enable return and repair and gather insights there, right? So we talked about insights fueling everything. You can get insights from what your customers send back, what is more important to them that they wanna wear it again. They're like, I'll fix it, please fix it. I wanna keep wearing it.

It.

Traceability is another big one here. We've talked about sustainability and traceability a lot throughout the years with the Product Passport. It's here. We have to be able to trace dirt to shirt in a lot of ways. So thinking about how there's one connected loop that goes from that early concept to tracing it all the way through to the end so you can reuse it and resale it at scale. So what does that traceability look like? Not just the making of it, but the end use of it as well. What is the entire lifecycle of that product is something that needs to live in the product development loop because again, it's a source of insights, it's a source of data, and it pulls from earlier parts of your product development lifecycle. Demand signals. So we all deal with this whenever we're doing any type of inventory management, but the idea that we, Thought it was gonna sell great in the Pacific Northwest, actually it's selling great in Texas. How do we move it? But before we move it, how do we have the information? How do we get the data, the insights once again? And how do we turn that into future insights? So these are really important pieces at the end of the lifecycle that we haven't invested as much in as an industry in the past that people are starting to talk about in a big way. Going forward. And the final one is returns data. This one we've all been talking about for a really long time. We want to understand why customers are returning it, how we feed that upstream to inform future product and planning decisions. So what we're seeing as we look to 2026 and beyond is really one connected loop that brings product from the original inception of it through to its end of life. And hopefully gives it multiple lives within that cycle. And enable it, and we enable that through tools and a modern pipeline, a modern connected digital thread that supports the operating model, supports the data, and responds to the customer need within this one connected loop. So while this wasn't a primary topic of our research this year, this came up a few times. We expect it to be a primary topic going forward. This is one where we see a lot of brands especially outdoor brands investing heavily at what end of life looks like just as much as they invest in what— well, not just as much, but as well as investing at what the beginning of life looks like. So that brings us to the end of what we are sharing with y'all today. We're just about out of time. But if you'd like more insights, more information, download the full report. We have QR codes available on the coffee cups or on our booth or here. If you'd like to talk to us about any of those other 6 insights that we didn't get to today, happy to do so. Come meet us at our booth, or you can shoot us an email. Our email addresses are up here. Do we have any questions? Yeah.

Any questions?

Maybe time for 1 or 2.

1 or 2?

Yeah.

If we can switch to the Slido screen.

I don't see any.

OK.

It's OK. Does anyone have any questions?

Any questions from the audience? We got like 2 minutes left, so—

If you want to shout it out at us, you can.

No? No? Yeah. One question?

Since what is a trend, can AI also predict what a trend cycle might be or detect when it's peaked right before it starts to fall?

Yes, I believe it absolutely can. One of the ways that it can do that, it can start taking a look. Let's say that to the thing that we were looking at earlier, footwear of a particular— say that everything is saying that a particular color and a particular style is what is trending. It can monitor sales within the entire marketplace, and this is what's so important to have at the source of, you know, at store-level data so you understand what is being sold in real time. And what it can do is it can start mapping, okay, how is the sales trending up? And the minute it starts to, that trend up starts to tail off, it can start sending signals back that says, oh, it looks like this trend is starting to level off. Maybe we should start pulling back on either the amount of new styles that we're producing or maybe we should slow down production so that we're not filling the channel full of that product.

Yeah, we are seeing solutions in the market that do both the customer insight part of that. So what's trending in Japan, for instance? I'm sorry, I keep thinking about the Keen presentation earlier. What's trending in Japan versus the US? What's downtrending? What's showing up on TikTok, et cetera? There's a lot of concept kind of creative insight tools that are doing that today. And then we are seeing AI embedded in a ton of planning solutions. Decisions. So when you need to get out of the inventory, when you need to buy more. So AI is really getting embedded along a lot of the technology today, as well as new point solutions coming in that serve that. But again, as good as the data you feed it and the prompt you give it.

Consumer signals are, I think, more and more becoming important, which is why it's becoming more and more important to get closer to market. All right.

Anyone else? Okay, we're out of time. So thank you all so much.

Thank you very much.

Thank you very much.