Yum! Brands, the World’s Largest Restaurant Company, Advances AI Adoption – Ep. 254

0:00:16 Hello, and welcome to the NVIDIA AI podcast. I’m your host, Noah Kravitz. We’re here in
0:00:21 GTC 2025 in San Jose, California, where I have the pleasure of speaking with Joe Park.
0:00:26 Joe is Chief Digital and Technology Officer, Yum Brands Incorporated, and President Byte
0:00:31 by Yum, where he oversees digital experiences, AI-driven products, and much more for brands
0:00:37 like KFC, Taco Bell, Pizza Hut, and Habit Burger and Grill. Joe’s here to talk about how technology
0:00:42 is transforming the quick-service restaurant industry, including how Yum Brands is using
0:00:47 AI across their own ecosystem. Joe, thank you so much for taking the time, especially during GTC,
0:00:51 to join the AI podcast. Thanks for having me, Noah. Excited to be here.
0:00:55 So maybe we can start with you telling us a little bit about your own background,
0:01:00 what brought you to Yum Brands, and how your journey’s been with the company to get to the
0:01:05 position you’re at now. Yeah, so sure. Just a boy from Queens, New York, who happened to grow up loving
0:01:12 technology and ended up going to get my undergrad in IT, went to my first job in technology, and kind
0:01:16 of made my way through. So one of those lucky guys that’s been able to stay in technology knowing I loved
0:01:21 it and continued to be there. Right. You know, it started with a career with General Electric
0:01:26 at the time. Oh, okay. Spent about 12 years working for GE. I have to ask, where were you?
0:01:31 Oh my gosh, all over. All over, okay. I grew up in Schenectady or outside Schenectady, which is why I ask,
0:01:34 yeah. Well, so I went to Rensselaer Polytechnic Institute. Yeah, okay.
0:01:39 RPI to get my undergrad. And so that’s right in Troy, New York. Yep. And, you know, it was a
0:01:45 talent pipeline for GE. Right. And so my very first job, first internship was in Schenectady,
0:01:49 New York, working for GE Power. Yeah, yeah, yeah. And so, you know, GE’s a huge company,
0:01:58 many different industries, and obviously played a big part in shaping my career. So being in GE’s energy
0:02:03 business, their healthcare business, their aviation business, financial services, I mean,
0:02:06 the list goes on and on. Everything, yeah. Everything. And being in their leadership
0:02:10 development program early on, I got a chance to rotate through different parts of the business.
0:02:14 Oh, cool. Looking at technology, looking at finance. And so that played a really big part
0:02:17 in how I think about technology as a leader. It certainly played a part in seeing different
0:02:22 patterns and how relevant they were, no matter if you were in the credit card industry or an
0:02:26 aircraft engine or a healthcare device. There are a lot of commonalities. Yeah, yeah,
0:02:29 interesting. Everyone shares the same challenges. So enjoyed my stint through GE. Following that,
0:02:33 I wanted to get a little more faster paced and try out B2C, enjoying,
0:02:38 Walmart, moving to Bentonville, Arkansas, where I spent about three amazing years. My family,
0:02:44 we love Bentonville, Arkansas, and got a chance to work for, you know, the Fortune One retail company,
0:02:49 understanding how data at scale, technology at scale, people at scale really works.
0:02:54 And along the way, I had a wonderful opportunity to come to my attention with Yum Brands.
0:03:05 You know, I grew up as a heavy duty customer of Pizza Hut as that ’80s kid with the Book It program and eating a ton of Pizza Hut.
0:03:22 There’s also Taco Bell, which got me through college. And KFC, which is a staple in our family. So when Yum reached out to me to say, “Hey Joe, we want to create this Chief Innovation Officer role where we’d love to look into AI, robotics, and other emerging technologies.” I thought, “You know what? Pizza Hut,
0:03:34 KFC, Taco Bell kind of still look the same as I did when I was a kid. If I could be part of the team that changes what it looks like for my kids when they go to one, this is a once-in-a-lifetime opportunity.” And so that’s what brought me to Yum.
0:03:49 Amazing. So the term quick service restaurant, right? The word restaurant, I hear it, a lot of people hear it, we think food. Technology played a big part in the industry, especially now going forwards. Can you talk a little bit about how technology and getting into AI in particular are transforming quick service?
0:04:12 Yeah. You know, it’s interesting. When I joined Yum Brands, and I was going through the process, it was right as COVID started coming to the picture. And just for sheer numbers, I think in 2019, 19% was the digital sales mix. So 19% of transactions across Yum Brands was done digitally. Fast forward to today, it’s over 50%.
0:04:14 Right.
0:04:26 And so you’ve got this amazing curve that happened from that 19% to 50% where we’ve all seen it. Prior to COVID, if you wanted to get delivery, there was kind of a duopoly, I guess, with pizza and Chinese food.
0:04:27 Right.
0:04:29 Maybe Jimmy John’s, right? You could toss that in there.
0:04:31 There’s probably some geographical distribution.
0:04:40 Some geographic distribution, right? And now you look today and you think about the choices that customers have to be able to order any kind of food from any kind of restaurant today that you wish. Yeah.
0:04:42 That’s changed the QSR.
0:04:43 It’s huge, yeah.
0:04:48 Right? In addition to that, you know, consumers’ digital lives became a lot more digital over the time.
0:04:49 Right.
0:04:56 So there’s instant gratification, you know, the Netflix examples of the world, a lot more options and preferences to do and pick what you want.
0:05:05 And at the same time, we’ve got this big labor challenge that’s happening across the world where we need to make sure that jobs get easier as there’s more complexity being introduced.
0:05:05 Right.
0:05:11 So as we think about technology and QSR, the last four or five years has been transformative.
0:05:16 And it’s mirroring probably what I saw of big box retail at Walmart against Target.
0:05:20 It’s mirroring what’s been happening with some of the big tech companies over the past decades.
0:05:42 And so it’s a super exciting time to be in QSR right now, where as customers change and we’re reacting to it, it’s requiring technologies to help our people serve better customer experiences, better team member experiences, and especially with our franchisees who make up 98% of our stores’ ownership, making sure they’ve got easier jobs running their businesses too.
0:05:43 Right, right, right, right.
0:05:46 And so how did you wind up coming to work with NVIDIA?
0:05:54 You know, it started when I took this job about a little over a year ago as the chief digital technology officer for Yum Brands.
0:06:02 Part of taking on the job from my predecessor, Clay Johnson, who continues to be my mentor, good friend, we said, look, we’ve got to go AI first.
0:06:04 We see how fast the world is changing.
0:06:09 We know that AI is going to be such an important part of how a restaurant is going to run.
0:06:17 And so sitting down with the top leaders across Yum Brands, including our CEO, we said, you know what, we don’t just need to have a vendor.
0:06:19 We don’t need to just have a partner.
0:06:22 We need to have a long-term strategic partnership.
0:06:24 Who would make sense to do that?
0:06:30 And we looked across the tech industry, and there’s only one clear answer for us at the time, and that was with NVIDIA.
0:06:40 And so that sparked the series of conversations, visits to the headquarters, many calls, as usually goes with these sort of things.
0:06:43 And we all just felt, wow, what a great cultural fit.
0:06:45 What a great strategic fit.
0:06:51 And, you know, through that journey, we’ve been very pleased with how the partnership’s been evolving.
0:06:52 That’s great.
0:06:58 So can we dig into some use cases and talk a little bit about some of the applications that you’re using within the Yum family?
0:07:10 Yeah. So, you know, over the last five years, Yum’s digital technology strategy has been to build and/or acquire technologies that we think could give differentiating capabilities to our franchisees.
0:07:11 Right.
0:07:14 And so we’ve always been working with AI.
0:07:27 To give some examples of them, you know, for us, when we think about our data science team and the use cases that really stand out that helped us, is we started applying machine learning to sales, forecasts, and inventory.
0:07:37 One of the common challenges we’ve had in QSR is when you try to predict how many sales we’ll have this week versus next week versus the week after.
0:07:41 It can get tough when you’re ordering inventory as a restaurant general manager.
0:07:46 If you order too much, that’s going to hit your wallet because now you’ve got food you’ve wasted.
0:07:50 And if you order too little, customers are unhappy and they might not come back.
0:07:59 And so we’ve been excited about use cases in this area where we’ve rolled this out across most of our KFC U.S. stores.
0:08:02 It’s going out to the majority of our Taco Bell U.S. stores.
0:08:07 And we’ve seen AI really play a great role in making the math really easy.
0:08:08 Yeah.
0:08:17 Our favorite point is in some of our regions and stores, like for KFC, we’ve seen a 90% reduction in the number of times that they’ve run out of stock.
0:08:18 90%.
0:08:19 90%.
0:08:20 Wow.
0:08:24 Because now we can accurately forecast the amount of product and inventory they need at a store-by-store level.
0:08:25 Yeah, right.
0:08:35 And the pain point they often have is if you’re a store manager and you’re running low, they’ll spend up to four hours a month calling different stores to say, “Hey, I’m low on chicken.
0:08:38 Can you send me some chicken or can you send me some potatoes?”
0:08:40 And they barter, right?
0:08:43 I’m laughing, but I can only imagine how stressful it is.
0:08:44 Can you imagine how stressful it is, right?
0:08:45 Yeah.
0:08:47 And the cost and the mental cognitive load.
0:08:48 Yeah.
0:08:50 And so for AI to come in, I think that’s been a wonderful example.
0:08:51 Yeah.
0:08:55 The other example that we get excited about, too, is kitchen management.
0:09:04 For me, I’ve done my training inside of a Pizza Hut, and let me tell you, there’s nothing more stressful than being at a Pizza Hut on a Friday dinnertime rush at peak.
0:09:05 Right, yeah.
0:09:09 I’m getting childhood flashbacks now to the Pizza Hut I went to, and yeah, but forgive me.
0:09:10 Yeah, you can imagine.
0:09:21 Even your local pizzeria, just imagine getting dozens of calls in a short window of time, in 30 minutes, and trying to mentally figuring out which one do I make first?
0:09:22 Right.
0:09:25 How do I get the pizza out to the customer hot and fresh and tasty?
0:09:36 And so we use AI to solve for that in understanding how we can collect all kinds of different inputs and apply machine learning to figure out which customer’s pizza should you make now versus later?
0:09:39 Which one should be picked up by the delivery driver?
0:09:43 And just orchestrating all that, it’s kind of like we make AI our air traffic controller.
0:09:44 Right, right, right.
0:09:48 And so we’ve been very pleased with that, where customers are getting hotter, fresher, tastier pizza as a result.
0:09:49 Yeah, amazing.
0:09:51 And what about computer vision?
0:09:55 Is computer vision part of the kitchen management system, or how is YUM using computer vision?
0:09:58 Yeah, so computer vision’s an exciting space.
0:10:09 I came into YUM about five years ago, and was part of the team that started seriously looking into it and applying different pilots and prototypes across our YUM system.
0:10:13 YUM’s in over 155 different countries across the world.
0:10:14 Right.
0:10:15 We have one of our four brands.
0:10:17 Do you have an idea of how many stores total?
0:10:19 We have over 61,000 restaurants.
0:10:20 Okay, wow.
0:10:21 Under our YUM brands.
0:10:29 And if you think about that, I feel fortunate in that we almost have the world’s, it’s like saying we have the world’s largest R&D center for QSR.
0:10:30 Sure, yeah.
0:10:32 Where some markets are more advanced than others.
0:10:39 Take a YUM China, which at many times feels like they’re in the future, to some other markets that are still developing as countries.
0:10:43 And so we have different degrees of technologies depending on where we go.
0:10:44 Right, right, right.
0:10:53 You know, early on, we’ve used computer vision to help us with compliance during COVID to see if team members were wearing gloves and sanitizing their hands and so forth.
0:10:56 So there’s a food and safety use case for it.
0:11:00 Other end of the spectrum, we’ve used it to test for order accuracy.
0:11:12 And, you know, the challenge we saw there was that sometimes our team members are so fast and quick with their hands, we couldn’t always prevent the wrong order from going out or the wrong toppings from being put in.
0:11:13 Right, right.
0:11:22 For example, we use, it’s like that movie, Minority Report, if you’ve ever seen that film, where you can see when the crime is going to happen, but can you get there before the crime takes place?
0:11:25 And like, I see the anchovies going on the pizza, but we can’t stop them.
0:11:26 That’s right.
0:11:30 For me, I’m lactose intolerant, so it’s always been one of the things for me of saying, hey, no cheese on the taco.
0:11:32 And so could you prevent it?
0:11:34 So order accuracy was a great use case for us.
0:11:35 Yeah.
0:11:37 It continues to be one we test out.
0:11:49 The other one that we continue to test and we think has applicability is the fact that computer vision can really help us at the drive-thru with the speed of service when we test and count how many cars are there.
0:11:50 Right.
0:11:56 So typical QSR restaurant will know if a car is at the drive-thru waiting, you know, and there’s a customer about to place an order.
0:12:01 But there’s two, three, four, five, six, seven cars behind them that’s hidden from the team members.
0:12:02 You don’t have a view of that, okay.
0:12:05 But as a computer vision can count all those cars.
0:12:15 And if you count them, what that does from a business perspective is then we can start being able to tell team members, hey, if you want to speed up the line, you can’t now.
0:12:17 You might want to suggest things that are quicker to make.
0:12:18 Right, okay.
0:12:19 Right?
0:12:20 Yeah.
0:12:22 So we can get to second, third, fourth customers, especially during a busy lunch or dinner period.
0:12:23 Right.
0:12:36 So kind of along those lines, any big hurdles that you’ve overcome or particularly interesting challenges that popped to mind during the, and whether it was designing the apps or deploying them or tweaking them after the fact?
0:12:37 Yeah.
0:12:37 Yeah.
0:12:40 You know, in our partnership with NVIDIA, we get excited about voice AI.
0:12:41 Yeah.
0:12:42 Voice AI for order taking.
0:12:49 And I can tell you that had we gone at it alone, we’d still be tweaking.
0:12:50 We’d still be optimizing.
0:12:52 We’d still be learning our way.
0:12:52 Right.
0:13:02 I think due to this partnership, I think in less than four months, we’ve been able to accelerate those learnings, get to viable products sooner.
0:13:13 Good examples for us is when it comes to NVIDIA’s products like NIMS and with Reva, we have some really wonky terms that just don’t exist in the English language.
0:13:14 Right?
0:13:21 You know, if you think about like a Baja Blast, if you think about a Chalupa, if you think about a Gordita, words that aren’t found in Webster’s English dictionary.
0:13:22 Right, right.
0:13:32 Hardenering with our teams, with NVIDIA, has helped us a tremendous amount because now we’ve been able to train these models with our own custom words.
0:13:33 Right.
0:13:34 The vernacular, yeah.
0:13:35 The vernacular.
0:13:46 And bring that in so that when we’re training our models and customers are coming to drive-through, it increases the accuracy, it increases the speed, it increases the likelihood that it can make a team member’s job easier, which is the ultimate goal.
0:13:47 Which is the point, yeah.
0:14:00 At the scale that you’re working at, any specific, or specific, but, you know, challenges or, I don’t know, creative solutions you came up with, just the process of scaling to the level that Yum Brands is at worldwide, what was that like?
0:14:07 Yeah, I mean, when it comes to scaling the technology, it’s just interesting because for us, we are the world’s largest restaurant company.
0:14:08 Mm-hmm.
0:14:16 And as a result, if you think about the 155 markets out there, even in the US, if you think about the 50 states and the different dialects.
0:14:17 Yeah.
0:14:22 And you think about the different colloquial terms or vocabulary, it’s a lot of data we’re trying to train on.
0:14:23 Right, right.
0:14:26 And so for us, we know this is going to be a marathon and not a sprint.
0:14:27 Right.
0:14:45 The benefit is we’ve got all this data, we have the ability to train the models, but at the same time, we know that it’s going to be a long-term strategy for us to get right as we partner with NVIDIA to make sure state by state, in some cases I’m guessing it’s zip code by zip code, making sure that, you know, we’re providing the best possible experience for our customers and for our team members.
0:14:49 So as I mentioned in the intro, Joe, you’ve got two hats.
0:14:52 We probably have more than two hats, I would imagine, with what the two are.
0:14:59 But the chief digital technology officer on the Yum brand side, but then also your role as president of Byte by Yum.
0:15:02 So what is Byte by Yum? What’s kind of the difference?
0:15:07 And then maybe we can get into some of the Byte-specific technologies and applications you’re working on.
0:15:13 Yeah. So, you know, exciting times for us right now as we officially announced Byte by Yum a couple months ago.
0:15:19 With Byte by Yum, it’s really a proprietary platform for restaurant technology that’s powered by AI.
0:15:20 Right. Okay.
0:15:28 What that’s made up of is when you think about a restaurant and all the technology inside, you’ve got a point-of-sale system to record transactions.
0:15:35 You have the mobile app, the website, some of your kitchen management tools, and some mobile tools that help productivity for team members.
0:15:36 Okay. Right.
0:15:43 And so when you think about all those different products, you know, over the last five years, we’ve built some of them, we’ve acquired many of them.
0:15:47 And with Byte by Yum, it’s really our way of saying we’re going to start integrating them all as a full suite.
0:15:48 Right.
0:15:50 And plugging them all in with one central AI model.
0:15:51 Okay.
0:15:56 Why we get excited about all that is that it’s never been harder to be in the restaurant industry than it is today.
0:16:02 And the average QSR restaurant probably has about 15 technology vendors that they’re trying to manage.
0:16:06 And if you were a restaurant manager, imagine that you’ve got 15 different technologies you’re trying to integrate.
0:16:10 The odds of 15 vendors talking to each other are slim to none.
0:16:11 So that’s where Byte comes in.
0:16:12 Got it.
0:16:13 And so it’s just a couple months old.
0:16:19 It’s a couple months old in terms of us laying out the fact that this is now our strategy moving forward.
0:16:20 Gotcha. Okay.
0:16:28 We have started going full steam into integrating all the different technologies we have and selecting certain third parties, plugging them into our ecosystem.
0:16:35 But really, as a part of Byte by Yum, it’s also our moment to definitively put out there that we’re going big into AI.
0:16:36 Right.
0:16:43 And this is where we think our partnership with NVIDIA will be so valuable in being able to fuel a lot of our AI-first ambitions.
0:16:52 Great. So as the restaurant manager, yeah, I don’t want to be dealing with 15 different technologies, vendors, you know, getting my master’s in CS just to be able to run the restaurant.
0:16:59 I would imagine the Byte platform is also helping out the team members. Any examples of that in action?
0:17:06 Yeah. So that’s our primary goal is to make sure that we’ve got the easiest experience for our customers, easy experience for our team members, and also for our franchisees.
0:17:07 Yeah.
0:17:13 So with Byte by Yum, I mean, one of my favorite examples is today at KFC US where we’re starting to roll out Byte’s point of sale system.
0:17:14 Okay.
0:17:18 We’ve completely rethought and reimagined how point of sale should look and feel for a team member.
0:17:30 What we’ve had previously with the old POS is that team members who were recently hired would come in and they would be intimidated by what looks like a 1980s mainframe green screen.
0:17:31 Yeah.
0:17:35 And it was so intimidating to the fact that training became difficult because they were afraid they would break something.
0:17:36 Mm-hmm.
0:17:36 Wow.
0:17:47 With the Byte by Yum point of sale, for starters, we’ve made it so user friendly with a big emphasis on UX and human centered design, where it looks and feels like an iPad.
0:17:48 Right.
0:17:54 You know, we’ve made the training time a fraction of what it originally was with the old system.
0:18:05 And by having that UI also replicate into other parts in our suite, we’re making so that when you hire a team member, they can not only go in and use point of sale, they can then also go into other products in our suite.
0:18:06 Yep.
0:18:09 And have just as easy of a time in doing the job that they are hired to do.
0:18:10 That’s great.
0:18:12 And again, these are all technologies.
0:18:16 They’re Byte technologies, Yum technologies, because you’ve developed or acquired.
0:18:17 That’s right.
0:18:18 Yep.
0:18:19 No, that’s great.
0:18:20 The familiarity in UI is such a big thing.
0:18:21 Yeah, it goes a long way.
0:18:22 It makes it easy, yeah.
0:18:26 So if we could look ahead as we start to wrap up here, can you speak at all?
0:18:31 I mean, there’s so much going on you’ve talked about already and the Byte platform is new.
0:18:32 What’s next though?
0:18:34 There’s always going forward.
0:18:35 What’s Yum working on?
0:18:36 What are you working on?
0:18:37 Are there innovations?
0:18:38 Are there big plans for AI?
0:18:41 What can you tell us about the future plans?
0:18:53 Yeah, you know, in addition to what we covered today, we do think voice AI is an area we get really excited about for order taking, whether it’s at the drive-thru or in a call center where some of our orders take place.
0:18:56 Computer vision will be one we continue to work at.
0:18:59 We think that could be the next emerging business case for us.
0:19:06 And then the one we haven’t talked about as much is really around being able to equip our restaurant general managers with intelligence.
0:19:17 If we think about some of the themes coming out of GTC and what Jensen’s talking about with agentic workflows and being able to apply them to business, we’re firm believers of that too.
0:19:34 We picture a world where if you were able to take customer surveys, sentiment, and feedback for your local Yum brand’s restaurant, take what a restaurant general manager knows in their head and is able to put into writing such that we can apply AI to it.
0:19:35 Right, right.
0:19:44 You start getting this 360 degree view of a restaurant GM being able to go in and have a set of recommendations given to them on what the next best action could be.
0:19:45 Right.
0:19:49 It’s like, hey, RGM, your inventory stock might be going low.
0:19:52 Would you like me to place an order for you to replenish it?
0:19:58 Or, hey, restaurant general manager, your labor looks a little short in the next couple of weeks.
0:20:02 Do you want to help shift around some schedules so someone’s there?
0:20:02 Right.
0:20:05 And finally, oh, it looks like marketing is running a campaign.
0:20:08 Would you like to increase advertising in your local area?
0:20:09 Yeah.
0:20:13 So all these ways to help remove that cognitive load.
0:20:14 Yeah.
0:20:21 Being able to connect different work streams together and apply intelligence in a day-to-day way that RGM finds easy in a human language.
0:20:22 Yeah.
0:20:25 I think will go a long way in helping us bring out the best in our team members.
0:20:26 Absolutely.
0:20:34 For listeners who might be working in the food industry, maybe they’re in some completely other walk of life, but they want to start their AI journey.
0:20:43 They want to, you know, really take the plunge, get in, figure out not just what’s the tech about, but what can it do to help me solve problems, help me do whatever it is I’m doing better.
0:20:44 You’re on this journey.
0:20:45 You’ve been on it for a while.
0:20:47 Any advice you could give?
0:20:55 Yeah, you know, the best advice that I could give based on our experience and our journey is, you know, it’s like classic, you got to fall in love with the problem.
0:21:00 And everything we’ve talked about is mired in some business challenges today that we’re trying to address.
0:21:00 Right.
0:21:07 Whether it was order accuracy, which continues to be one of the biggest pain points for our customers in QSR.
0:21:10 It could be tied to speed of service at the drive-through, right?
0:21:13 Customers wanting to go in and out to get back to their busy lives.
0:21:14 And the list goes on.
0:21:15 You know, we always start there.
0:21:21 And you quickly realize that many of these challenges can be improved or addressed through technology.
0:21:22 And that’s where AI comes in.
0:21:23 Yeah.
0:21:35 For us, one of my favorite lessons that I learned is a great story is I remember visiting our Taco Bell stores in Dallas with our chief operating officer, Jason Kidd at Taco Bell.
0:21:36 Okay.
0:21:40 And as we started visiting several of them, you know, we were testing voice AI in several of them.
0:21:46 And the funniest thing is that some stores were better than others when it came to voice AI usage.
0:21:52 When we went to the store that had the best usage and the most accuracy, I just had to find out why.
0:21:53 Yeah.
0:21:54 I talked to the restaurant general manager, Ruth.
0:21:55 I said, Ruth, what’s going on?
0:21:58 How come you’re doing so much better than all your peers with AI?
0:22:02 And she said, Joe, look, you just got to give AI a chance.
0:22:10 You know, my peers, they’re more like helicopter parents who swoop in and stop the AI and say, oh, it made a mistake.
0:22:11 Right, right.
0:22:12 You know, stop it.
0:22:13 The customer’s going to be upset.
0:22:17 Whereas Ruth, what she would do is tell the customers, hey, now give this thing a chance.
0:22:18 Right.
0:22:19 It’s getting better.
0:22:20 Yeah.
0:22:21 It’s learning.
0:22:23 And same for her team members that she manages.
0:22:27 She says, hey, even if it takes an extra split second longer, just pause, let’s just see how it does.
0:22:38 And sure enough, that’s the kind of process, the culture and behavior you need to make AI successful through this iterative process of learning and training and improving over time.
0:22:39 Well said.
0:22:40 Joe, this has been great.
0:22:44 We don’t want to keep you for too much longer because it’s GTC week, plenty to do, but we appreciate you taking the time.
0:22:52 For listeners who want to find out more about Yum Brands, any of the AI and other initiatives we’ve been talking about, the website, the best place to go?
0:22:57 Yeah, you know, yum.com is where we put all the great work that the teams are doing across the world.
0:23:01 And it’s been a fun time seeing everything unfold, especially in the world of AI.
0:23:03 So I highly recommend folks to check it out.
0:23:04 Fantastic.
0:23:06 Well, again, thank you so much for joining the podcast.
0:23:12 Have a great GTC and all the best of luck on all the initiatives you are currently overseeing.
0:23:13 Great. Thank you, Noah.
0:24:03 Thank you, Noah.