Good morning, everyone. Thank you all for being with us this morning. We appreciate you spending some time here with us this morning. We know the weather wasn't the most cooperative, but thank you all for getting here, and we're really excited to be here with you today. My name is Andy Szanger, Director of Strategic Industries at CDW. Thank you for joining us for the session: Technology Innovations: Transforming the Customer Experience, What to Know and How to Get There. I'm thrilled to be joined today by three of my great colleagues, to talk to you about the latest technology trends and how to build a plan of to get where you are today and where you wanna be in the future, while adding value to your organization along the way.
I'm joined today by Gina Cox, Senior Industry Advisor from CDW, David Dobson, Global Industry Director for Retail, and Jill Klein, Industry Lead, Head of Emerging Tech and IoT. So, Gina, you wanna take a quick moment and, give a little bit about your background?
Yeah. Thank you for joining us. I'm so excited to talk to you. Retail is my favorite, favorite thing. I am a senior retail advisor, so my role at CDW is to work with customers to kinda talk about their strategy going forward, take advantage of new technology, and just discuss what CDW can bring to them, and so fun. I love it. I have 25 years of experience in retail. I do have a computer science degree, but, oh, my gosh, imagine how excited I was that I got to combine my love of computers—Sorry, didn't mean to touch that. Wake everybody up, with retail. And I've held several leadership positions, from director up to IT VP, on great customer-centric brands, Nordstrom. So like I said, being able to work at Nordstrom with computers, amazing, amazing company.
I also have been with REI, the outdoor retailer. Fantastic company. Most recently, I was with Blue Nile, which is a jeweler that is part of the Signet family. So excited to be here, so passionate about the customer and just bringing these great new strategies to you. Thanks.
Thanks, Gina. David, you wanna tell the audience a little bit about yourself?
Oh, I feel a little bit jealous. I've never worked in retail. Can you believe that? So, yeah, my name is David Dobson. I'm the Global Industry Director and heading up the Center of Excellence, for Retail at Intel Corporation. I've worked in retail now for more than 30 years, in several large corporate organizations, but also building, developing solutions. I guess, it's my geek in my history.
Yeah.
In fact, my degree, I did applied statistics and computing, which was-
Uh.
These days, everyone would be talking about being AI, but back in the day, it was definitely a way, way geeky degree to do. So yeah, really happy to be here again with my good friends from CDW. So thanks for inviting me.
Pleasure to have you here, David. And Jill Klein.
Thanks for coming. My name is Jill Klein. I'm the Head of Emerging Tech and IoT for CDW. I've been with CDW about 17 years through acquisition. I also sit on a couple of advisory councils. I just rolled off of the chair of the CompTIA IoT Advisory Council. I'm now on a data monetization council for them. I'm also on an emerging tech council for TD SYNNEX, so.
Thanks, Jill. As I mentioned, my name is Andy Szanger, Director of Strategic Industries. I've been with CDW for 29 years now, but prior to being at CDW, I was an assistant store manager in charge of operations for a big box retailer. That gave me a foundation of understanding of the retail industry, and over my career with CDW, helping retailers understand how technology can be used to innovate with their business and do amazing things. Currently, I lead the team that's responsible for overseeing our retail strategy, helping to identify, develop, and scale new industry-specific solutions and services, and provide industry expertise to our clients. I'm thrilled to be up here with you today.
The last couple of years, probably the last four or five years in a row, David and I have been on stage to talk a little bit about what we're seeing in the industry, what happened in the previous year, and taking a look forward to what we think is gonna happen in the next year. So David, I thought we'd just take a quick moment to take a look back.
I don't wanna spend too much time on what's happened in the past, but last year, when we were here at the session, we, you know, we were talking about things like experiential shopping, removing friction from the checkout process, and having more seamless experiences for customers, retailers having to do more with less due to labor shortage issues, and of course, loss prevention, which, you know, continues to be a huge headache, and that probably turned out to be one of the biggest ones that, I think we're seeing challenges with. At Intel, how did you see the year playing out?
Yeah, it was really great, actually, given the previous years that we've had, for us to have a more, balanced, year, right? So we definitely... The things that we thought were gonna happen, I think played out pretty much. I think one topic that everyone was talking about last year, Metaverse, I've not really heard very much about that. I don't think we talked about it, Andy, but, I think pretty much those themes that you've laid out there, are the major discussions we've had with retailers. I think frictionless, you know, from an operations perspective, but also from the customer perspective, I think that's been a big theme, and I think that's highly linked to the loss prevention, right?
So a lot of the discussions we've had, now we've opened up, made it easier for people to transact with us. That has generated some of the issues around loss prevention, and so, you know, every great innovation obviously comes with its own issues and problems. I think also loss prevention has evolved through the year. I think what we heard in the middle of the year was really cause of loss prevention has been refined as we've gone through the year now. And I don't think personally, although I work for a tech company, it's not all about tech in that space. There's a lot of other things that need to happen for us to really crack that issue of shrinkage in retail stores.
But yeah, no, I think they're all big themes, and I think we did a pretty good job last year, to be honest.
Yeah, absolutely. And Gina, so last year at this time, you actually weren't at CDW. You were actually on the customer side. You were working for some great retailers. How did you see the year playing out at being on, in the industry?
I think even with the economic times, I think there is great news out there just based on these surveys we hear of where we can go in the next couple of years. Forty-six percent of people are willing to work with chatbots. I mean, how many of you remember when you get on the phone and you get an automated voice, and you're just like, "Zero, zero, zero, please get me a human?" But now people are more willing to work with these chatbots. Huge saving for retailers. Great news. The next is my favorite, in-store experiences. People are returning to the stores. We were so worried about COVID. Would they come back? Would they stay online? But they are returning to the stores. They want that digital experience in the stores, but they totally love coming back.
I mean, 70%, as stated by Scandit, is a huge number. Unfortunately, we're still having problems hiring people. The market is tough, tough, tough. 36% are still facing that, so the more automation you get out there, of course, that can help with that. And then the last stat, I think, is music to every retailer's ears. The, according to IHL Group, 2 times sales growth for those companies using AI and machine learning. That's huge. Who doesn't want that, right? An easy win for all retailers.
Yeah, for sure, and, and I think there's no question, although we talked about a lot of great things last year when David and I were up on stage, but at the end of the day, it's, it feels like the only thing everyone wanted to talk about was AI, and, and what is AI? How can we use AI? What is it gonna mean for my business, and how do I get on the train, right?
Yeah.
You know, and we saw retailers using, you know, AI for everything, or looking to use AI at least, you know, for things like task automation, checkout modernization, demand forecasting, dynamic pricing. You know, and I think it's across every area of the business, and at the end of the day, retail is one of the top industries that's really looking to AI to transform their business. You know, David, I know you and I have talked a lot about AI, so, you know, what, what's your thoughts on what you're seeing today?
Yeah, pretty much. I mean, we've started at Intel talking about AI everywhere, and I think that fits perfectly into, you know, the retail narrative. And probably 90% of the conversations I've had during the last two days, and I'm sure for the rest of today, will be in or around AI. We kinda think about it as like old AI, which was the analytics, some of the stuff around even computer vision that a lot of people were really super excited about a few years ago is almost... That's in the old bucket. And then we've got all the generative AI and all of the new, machine learning, deep learning, all of those new technologies.
So as the market and the technologies are maturing, I think retailers are finding some great new use cases, and we're seeing it impacting everywhere. You know, it's not just in terms of how they're interacting and understanding their customers, but it's also a huge benefit for them in the supply chain. You know, I love that statistic that you quoted from IHL Group, right? 2x increase in sales. I think it's. A lot of what we see in terms of figures is how much people are investing, but what that's showing is why they're investing and what the value is to them in those spaces.
Yeah, I think people forget sometimes that AI is actually not new, right? You know, it's been around for, you know, probably more than 20 or 30 years or so. It's just been called different things, right? So, you know, obviously, one of the challenges is, you know, where do you start? How do you get there? I mean, I think what we're seeing today is that, you know, not everyone's thinking about it, but not everyone's gotten off the ground yet, right? I know Intel has been doing some research on that.
Yeah. I mean, the research that you guys could see on the screen was something that we did, not specifically for retailers, but we think it applies, you know, pretty well in, in this space as well. But you could see a lot of people in that early understanding of the technology and how the technology can apply. Now, clearly, you need to start with what problems are you trying to solve? So it's understanding where your, what state your data's in. It's understanding, where the pressures are on the business, and then understanding how the technology can support and help you in those spaces. So people are on various levels of the journey.
Yeah, there's people who are way out and advanced in this space, and probably if they were in this room, would be saying: "But of course, David, we've been doing it for the last 5-10 years." But for the majority of people, it's really in that research space, in that early proof of concept or pilot type concept, building the demos to really show the value to the business, is really where the majority of the people are today.
Awesome. So, Jill, I know your team is spending a lot of time on AI as one of the many things that your organization's doing. Tell us, how are you thinking about AI?
So, let's start by saying that I think AI and Formula One race cars have a lot in common: speed, performance optimization, fuel dependency. All those things are critical to making both of those models work. And, you know, if you start with speed, in a Formula One, I'm sure this is exactly what it's like, because I don't know. You're speeding down the straightaway, you're hitting the brakes, you're turning the corner, you're looking for that apex, you come around, and you speed out of that corner. Sounds like I have a little experience there. I've been to driving school before. Yes, I still use what I learned in driving school, whether I'm in the Walgreens drive-through or a country road in the middle of Nebraska.
But anyway, the point is that both of them are dependent on that speed, and so the speed in your AI model is the same thing. So the speed to insights is critical to your business, it helps you reduce costs and create better scenarios for revenue generation. So those things are similar. Process optimization's the same thing. That AI model typically doesn't work out of the box, and so there's a lot of training has to be done, it has to be optimized. Cars are the same way, they gotta be optimized when they come through the pits. All those things are similar. You know, the next thing is fuel dependency.
I believe some guy, I think his name is Matthew McConaughey, he said that, "Your data is the new gold." I'm here to tell you that your data is the new fuel to power your AI model. The point is, the efficacy of your AI is dependent on that fuel, so you wouldn't put E85 in your Formula One, so it's all about putting good data into your model as well. So that's my opinion of AI and cars and why I think they're similar.
Yeah, no, I agree. You know, I think AI is obviously every place you're going around the show floor, people are talking about AI in some way. But I think if we really just simplify it and boil it down to what it really is, AI is essentially an engine that's taking data and inputs from different sources and ultimately making something out of it, right? Whether that's insights into your business, whether that's actual co-creative content through generative AI. At the end of the day, it's about taking something and data and making something out of it, and it does really, you know, start with that. You know, when we think about AI, though, you know, David touched on it a little bit, that it's really important to make sure we're thinking about what we're trying to solve for.
I think when there are new buzz around a new technology or a new way to use a technology, such as in AI's case, it's all too common to see leadership teams trying to pressure their IT teams to actually implement that technology and wanna know: How are we using that? How are we using AI? We saw it happen with cloud as well many years ago, and now that's happening with AI, and everyone wants to know, "AI is gonna solve all my world's problems. How are we using AI?
“What is our strategy?” And I think it's really a dangerous approach because when you start with the technology and try to force a business case for it or a way to use that technology, you're far less likely to achieve a true ROI than if you start with a business problem you're trying to solve for, and then finding the right technology to solve for that. And so, you know, Jill, I know that your team uses a lot of research from Gartner and other sources to look at what's happening out there, and one of the great things that Gartner releases every year are the Hype Cycles for retail technologies. And so, when we think about that, what's real today?
What can people be thinking about today to start their journey into AI or to accelerate that journey, that they can really use today and not just be a dream out in the cloud?
Right. You know, I think, I, I think the AI, if you look at the Hype Cycle for AI in and of itself, a lot of the solutions are still in the first hill. They're still climbing that first hill. The cool thing about the retail technologies is that they're past that hill, and they're coming down the trough, and they're going out into the plateau of productivity. Once solutions reach the bottom of that first trough, about 5% of organizations have adopted that technology. The second phase, when it reaches the very last plateau of productivity, it's about 20%-30% of businesses that are adopting that technology. So when I look at a Hype Cycle, I look at two things: one, where is it on the Hype Cycle? And two, what color is the dot?
That tells you how far out that technology is. One of the things that I see, especially in emerging tech, there's a lot of unicorn companies out there that are startups, and sometimes they don't make it. They've got a pretty high failure rate, so picking the right solution is important. So my advice is, you know, look at where it is and what color it is. But also, the cool thing about retail is there's a lot of things that are ready for real time, so assortment management applications, AI in retail, IoT-based store monitoring, workforce analytics, all of those things are prime and ready to implement today. So I would be looking at some of the solutions in those spaces if I was you.
Yeah. David, how about you? Like, you know, being at Intel, obviously, you're using a lot of the same information and constantly speaking to clients and doing research on where they can start and what's real today.
Yeah. I think very similar to what Jill's just described. I think, you know, and some of the things on the slide really resonate with me. The workforce analytics, you know, we all know the pressures of hiring people, the turnover of staff, the ability for people to onboard and manage that workforce at scale is a huge problem. And that's definitely maturing and the technology in that space. And actually, the use of AI, some really interesting use cases that AI can help in that space. Just generic AI in retail all up, whether it's.
I hesitate to say optimizing price because that's probably some of the research I've been looking at, probably the least value, because I think retailers are pretty good on pricing, and it's probably something that they can't really control themselves. It's more dependent on whether people will purchase at that price, so it's a bit more of a fine art. But in terms of supply chain execution, inventory optimization, there's so many different areas, as you mentioned earlier, where AI has relevance. I think the other thing I would add is, as you go through that maturity, you know, the software matures, the knowledge in your business of how to leverage that software matures, and understanding the impact of some of those technologies on your business and how to manage them and introduce them, you start to get better at doing all of that.
All of that is part of why that you go through that maturity curve, and that's an important factor that we're seeing in the maturity in this space.
Absolutely. So David, you know, obviously there's a lot of great stories about what this looks like out in the real world, and you know, why don't you talk a little bit about that?
Yeah, sure. I mean, we always get. I work for a company that has an ingredient in a device that many people use, and as a brand, we've done a great job of helping people understand the value that Intel can deliver out in the marketplace. But it's solutions that really people are looking for, and we worked with this company over the last 12 months. Actually, we first met them here at NRF last year, got really excited about the solution. What they do is help you select the clothing that fits or best suits you, and you can curate that clothing. You can decide that you wanna wear loose-fitting clothes or tight-fitting clothes. You can kind of curate.
It's tailored to you and your personal preferences and choices. But what it does is it scans your body. It doesn't keep an image of that, of your body. It keeps a digital avatar of your body, a digital twin, if you like, and then it compares that to the clothing that they have on stock and makes the recommendations for you based upon your personal preference. And that does a couple of things. You know, we all know part of the problems with selecting and buying clothing and apparel, if you're not in a store environment, is, well, is it the right color? Is it the right size? Does it, how does it look when I put it on me? And this start...
So what people then do is they say, "Well, I'll buy both the medium and the large 'cause I'm not sure which one fits me," knowing that you're gonna return items, and that's obviously a huge issue for the industry, and actually, in some cases, causes damage, which means it's not a very sustainable model either. So really, this AI solution is gonna tackle some real issues that retailers have in the, in the industry, which is making the right recommendation, minimizing the refunds, being a more sustainable operations. So it hits a lot of different sweet spots, and it is a great way where this AI technology that we all talk about shows how it can provide new value to retailers, both in terms of new revenue opportunities, but also optimizing their operations.
I love this example, David. I am so glad you brought this up because, two sides on this: What retailer that sells online is not talking about what to do about returns? It's costing a lot of money, and they are even throwing out, can they charge the customer a return fee? And nobody wants to do that. That is not very customer-friendly. I don't think the customers are gonna be happy about that. And the other side that people don't talk about, as a customer, when I buy something online, I want it to fit me. I wanna be so happy when it comes because I chose the best thing for me, and it looks good, and it fits. I don't really wanna return it. So I love this example of how you can use AI to...
It's also good for the climate, reduce those returns, and in the end, make your customers happy.
Yeah, no, it's a great point because there's so much data out there that shows the impact that return policies have on the purchase decision, especially if it's a new brand that a customer may not be as familiar with how the fit will be. So this is absolutely a great example of how technology can be a game changer. You know, Jill, obviously, you know, we talked a lot about how AI can be used, what are some of the use cases, some examples, you know. But I think obviously right now, people certainly need to justify their spend. I think there's a pragmatic approach coming to how do I invest, where do I invest?
And so it's more important than ever to understand where to place your bets and how to, you know, how to prioritize what you're looking at. And we talked about, obviously, you know, looking at not using AI or any other technology for that matter, just throwing it at something, you know, because you wanna use it, but really starting with that business objective. So how does your team look at prioritizing those projects, and how do you decide on what business objectives and outcomes to really focus on?
Sure. We use this model... We've used this for probably the last eight years. We presented it to some of our partners and vendors, and they said, "You know, you're a little bit unique in how you guys transform these, but it's fantastic." So the key here is that we start with the business folks. This is a business first, technology second. We see a lot of people start, stumble, and stop because they pick a technology, and they have someone on their team build a pilot, but it never gets to production. IoT has a pretty high, or a pretty low pilot-to-production rate. It's like 16% of projects make it from pilot to production. So we looked at those factors. We looked at why people stumble.
We tried to build it back into this workshop, and so we start with the outer ring. We start with their business goals. What are you solving for? So we get a group of 10 people from our clients, and they're business people. They're typically not IT people. We talk about business goals. We've had goals like, "I wanna become a market maker," and I'm like, "That is a good goal.
Uh-huh.
So we start with 4 goals. We look at value drivers. The 10 folks that are in the room, we ask them, you know, "How do you think you can meet these goals? What are some of the ideas you have?" We then take those, we look for duplicates, we put them through a funnel. For the top 12, we then do further analysis. So we look at what business process is associated with this idea, what business systems are tied to this idea. We then start looking at the data itself. Data is often a stumbling block for folks, and so we say: How much of this data that you're now sourcing is relevant? You need to peel it off at the edge. How much of it is revenue? How much could you sell to your clients or make products out of it?
We then also look at systems of systems. Are there other things outside of your existing ecosystem that could help your solution, such as the Weather Channel data or Google Maps? We then do a 12-point technology gap analysis to say, for this particular idea, do you need a new sensor? Do you need to improve networking? Do you need analytics? What does that gap look like? We also look for IoT technology and then security in that ring as well. The next phase is we look at, if this project was to go live in year one, what's the ROI impact in year one? Are you creating a cost avoidance scenario, or are you creating a new revenue stream, and how big is that for your company?
Last time we did this, for a client, we had 80 different ideas that we came up with. Once we boiled it down to the top 12, it was $10.5 million in year 1 if they would've implemented all the projects, returned to the client. So those are some of the things that we saw clients stumble with. And so we kind of put it together in a workshop. It takes us a day and a half of the client time, then probably a week to turn it around, so it's a micro service for us, but we found it to be very successful. So that's how we help clients with products transformations, with business transformations, with operational transformations.
Excellent. Gina, obviously, you know, again, as we mentioned earlier, in the past, you've been with some amazing brands on the IT leadership side, with brands like Nordstrom, REI, Blue Nile, and you've actually been in charge of doing a lot of this work. Tell us, when you were on that side, you know, when you were with those retail brands, how did you look at, you know, getting started and building that plan, and how'd you get going?
Yeah. And as Andy said earlier, it is overwhelming 'cause AI is the buzz, and you just hear: "We have to do AI. Please show AI. Put it in our quarterly reports. Let's talk about what we're doing with AI." But it doesn't have to be overwhelming. Don't get caught up in the hype. I think Jill showed a great chart earlier, and we've been saying it again and again. Start with your strategy. Start with knowing what problem are you trying to solve? David's example is a great one, that they're trying to solve the return problem that's popping up, and that's hot right now. You can't solve everything at one time, so focus. Know what's important for your business to move forward, what's important to your customers, what's important to your employees. Start with that strategy and your business problem. Otherwise, you will start way too much.
Then the next thing, my favorite slide, I gotta reference it again, is that race car, Formula One. Very cool. Wish that was my slide. But, understand your data. Again, so overwhelming. We've been collecting data for so long, and every retailer has so much data, and you're thinking: "Oh, my goodness, is my customer data right? Is my product data right? I switched systems. I have data all over the place." Once you understand your problem, then only look at the data that is relevant. If you're trying to clean all your data, everything's sludge. It's gonna take you a long time, and it's very overwhelming. Start small. Identify your business problem, identify the data that is needed for that, and then turn that into the oil. That's where you wanna focus. Then finally, if you're struggling, go get help.
There's companies out there, like CDW, but there's others that have done this before, that can help you get started, who follow processes to make it easier for you. Because it is... When you just hear AI, everyone's talking AI, it is overwhelming, but I think just breaking it down, going smaller, understand what your target is, working on that, you can go get this. And then you can be part of those retailers that we're seeing that experience 2 times the sales growth, right? Who doesn't wanna be in there?
Absolutely. A lot of great points there. David, what are your, what are your thoughts? You know, as we start to wrap up and kinda come to a close here on our time in a few minutes here, what are your thoughts on, you know, where should retailers go from here?
Yeah, I can't overemphasize the importance of data in this, right? So we've talked about this from the beginning here. Artificial intelligence is a set of technologies that builds insight from that data, and if your data is not in the right form or you don't have the right data, or if it's, you know, if it's not as clean as it needs to be, that's definitely, you know, the number one starting point. And if you don't figure this out before you start your project, you'll figure it out very quickly into the project. And I think that's one of the areas that's holding back a lot of these projects. Because the data's clean enough in most retailers for them to do what they need to do today.
... but it's not always clean enough for them to do what they're trying to try and do with AI. So that's definitely a big area for them to think about. And it also goes back to those things that we've also talked about in retail for many years, which a lot of that data is siloed, but you need to break those silos to really try and get the value out of the data that you're really trying to do. So that would be the second point that I would make. And then I think the other thing that we've seen is when it's just a technology pilot, when it's just trying to understand the technology, and you have to go through that phase as you start to scale up. We understand, but that's never gonna be the final stage point.
You have to start with the business problem, and it has to be a business problem that is significantly valuable for, for you as a brand or as a retailer, but also has the right data in support of your business. So I think this slide is really a great kind of checklist for, for what you need to do in terms of the next level. And in most of the times when we're having discussions with either our partners or our, our, our customers in this topic, that's always where we start.
So I know it's hard to believe, but in the retail space, there are some other exciting technologies and other things happening outside of AI. What's caught your eye so far at the show here, and what are you excited about for the upcoming year and beyond?
Who's gonna take that one first?
I'll take it.
Go for it.
You know, I think in 2024, I am most excited about video analytics. I think it's if you look at where it's at on the Gartner chart, it's at the 30%. So just out of curiosity, how many people are using video analytics in their environments today? How many plan to use them in 2024? There we go. Good. So I think video analytics are, is, is prime for adoption. I think audio analytics will be the next thing that we see to sense everything. So I think that's gonna be a hit in 2025 or 2026. But for next year, I think a lot of people are gonna start an AI project of some sort. I think we're all jumping in the pool together.
I don't know that we can all swim, but I think we're gonna jump in, so I, I think there'll be a lot of swimming lessons next, next year as well.
Yeah, I think that, we're certainly hearing from a lot of our clients and partners about the power that video and computer vision is starting to unlock. And I think one of the great things about that is it's an example of a technology that basically every retailer is invested in in some way, and in many cases now, I hate to go back to AI, but layering AI on top of that allows you to take that existing investment that you have. You don't necessarily need to replace all your cameras. You don't need to upgrade them. You don't need to change the placement in some cases.
It's just a matter of actually layering AI on top of that to gain more insights and use it in new ways, whether that's to understand your traffic flows, whether it's for loss prevention use cases, understanding your store productivity and optimal staffing levels. You know, we're seeing so many new use cases that are coming on board for ways of using it.
Yeah. And I'm, like, so excited. You can tell I'm like, "Let me go next. Let me go next," Andy. Sorry, David. I am so excited about RFID. I don't know. I am a big in-store shopper. You can tell by the brands I worked for. I'm all about that customer experience going in, and I love what I've seen here, where you pick up something off the shelf, and it changes your display. And this is a combination of RFID and computer vision. So exciting. And we've heard about going into... You take a children's store, where some mom is bringing or a dad, you're bringing your kids along, and can I have an easy checkout? We're looking at self-checkout in grocery stores. Exciting. Love it. But I just love that smart cart, smart bag, smart basket idea, and we're seeing that.
How nice, especially if your arms are full, that I don't have to hand every single item over to be scanned. It's like, here's my basket, scan it, get it in a bag, and let's go. Love it. Love it. And one more is the smart dressing rooms. Saw incredible examples of this, I hope you guys saw it, too, where I don't have to wait for a sales rep to come in to change a different size, right? Or different colors. A lot of people, we saw examples where they're putting it in the mirrors, where you can touch a tablet and just say, "Hey, here's what I want." This frees up your labor, which we heard was short, right? Everyone's having problems there. And it doesn't have to have someone come, keep coming through the dressing room and say, "Hey, can I help you?
“Can I help you?” And as a consumer, I don't have to wait for that interaction. So I'm just all excited about, like I said, the RFID, also computer vision.
Yeah. Well, I think it's another great example, though, of a technology that could be leveraged across multiple use cases, right? You know, we talked earlier about that pragmatic approach to investing in technology and retail and wanting to justify that ROI. And really, when you think about RFID, there's certainly an expense in getting started. There's, you know, the tagging of your items, if your partners are, are not actually doing that tagging for you already. Although, I think we're starting to see more and more manufacturing suppliers doing that work. You know, you see mandates from companies like Walmart and other places. In food safety, you're seeing the Food Safety Act coming online in 2026. So a lot of drivers about the adoption of using RFID. And, you know, it initially started obviously with inventory management.
Quickly realized that could be used for loss prevention as well, but now we're starting to see a lot more of those innovative use cases when we think about how do we bring the customer back to the store, create better brand engagement and customer loyalty, and creating those experiences and those moments that make a customer want to come to a store, as opposed to feeling like they need to come to the store to buy something. RFID really unlocks a lot of that with those immersive experiences, the connected fitting room, like you mentioned, and everyone's trying to solve for the labor shortage at checkout. And, you know, we're seeing customers rebelling against scanning.
I think there was a stat, and I apologize, I don't recall who, who made it, but I think it was one in five consumers in the U.S. have admitted to not scanning all of their items at checkout, which is staggering to think that one in five actually admit it. So how many are actually happening? And part of that is not even always on purpose. Sometimes it's their frustration with not being able to scan a barcode or find a barcode on their item. RFID solves for that by utilizing the RFID technology, dropping it into a scanning bin. Everything is scanned all at the same time, no delay, no fumbling, and they can move on and still actually achieve the reduction in staff. So I can go on and on, but, David, how about you?
What, any last thoughts before we wrap up here?
Yeah. So, and by the way, I think that's the coolest piece of in-store technology I've used, is RFID checkout. I think all the other stuff is great, and computer vision, I think, is an awesome set of technology. But just as a shopper, being able to just go in and just dump my bag in a box and a number come up and pay and go, it was just so cool. I was even in there with my family in a store in Europe, and yeah, all of them were going, "Dad, it's not that easy, is it? It's not that easy." So yeah, it's a really cool piece of technology.
I think the other thing that we haven't really talked about too much, but I think sustainability and the Circular Economy. I think that's another area we're gonna see a lot of take-off as people are more conscious, especially in the fashion space. I have a daughter who's 20 years old, and her older sister is all about the brands, and she's all about the, "I wanna buy reused, recycled clothes." So I think there's a generational shift where people are expecting more of a Circular Economy impact, lower power, less of an impact on them, on the future of the planet. I think there's gonna be a lot of... a lot more. And for retailers, of course, that also goes with new business opportunities and also reducing costs.
So I think there's gonna be some interest in play. And some of the technology I've seen into that space is very good, especially around food waste. How can you manage and minimize the food wastage-
Yeah.
is a big area.
Absolutely. I think food waste, we're seeing a lot of companies do amazing things with technologies to help with the environment and social issues. I know at Macy's, I saw, you know... I've been watching some of the work they're doing around how they're handling samples during their fashion seasons and digitizing that, and the amount of waste it reduces by using digital samples and still allowing people to actually not compromise on the actual review process. And there are just so many examples out there of how companies are using technology to help be a better global citizen. So with that, we're just about at time here.
I think we have a few minutes left, but we all recognize how difficult it is to get across the floor here in the show, and, and so we wanna make sure we get everyone out on time. So we're gonna start to wrap things up. We really appreciate you being here with us today and spending the time. We know you have a lot of options of things to go do today, and we appreciate you spending that time with us. If you're not familiar with CDW, we really didn't talk about CDW too much 'cause we're so passionate about talking about the technology and how it can help customers do amazing things. But if you're not familiar with us, we are a global systems integrator and services provider, helping retailers across the globe do amazing things.
And if you're not, you know, we'd love to have you stop by our booth, take a look at some of the services we're showcasing there. But also, if you are a customer of ours, thank you for that. We greatly appreciate your business. If you're interested in having a follow-up meeting with us, there is a QR code on the screen where you can request a free consultation or just stop by the booth. And if you are a CDW customer, of course, reach out to your account manager to set up a meeting, and you could also visit cdw.com/retail. So thank you again. We hope you enjoyed the session, and have a great show. Thank you.