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AS Watson is approaching AI as an enterprise transformation—not a series of IT pilots. Its CEO-led, cross-functional approach embeds AI in workflows, decision-making, and performance management across the business, rather than leaving adoption to a single technology team. To support this effort, the company has appointed 31 global strategic technology partners to accelerate its AI-powered online and offline retail transformation.

In this one-on-one conversation, Group CEO Malina Ngai discusses what this shift means in practice—from smarter warehouse operations and more connected customer experiences to the leadership choices required to make AI integral to the way the business operates. How can companies turn AI investment into compounding value? And what must leaders be willing to change to achieve it? 

Malina Ngai, Group Chief Executive Officer, AS Watson Group 

From Efficiencies to Reinvention: Navigating the AI Transformation

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Thank you, Andrew. Doctor Molina and I, thank you for joining us at Leaders Forum. In this session, we're going to discuss AS Watson's approach to AI transformation. And how this 185 year old company is reconfiguring for the intelligent age. But before we go into the details, um, I want to start at the beginning. Was there an aha moment for you? Was there a moment of realization when it became apparent that AI wasn't just another technology, but had the power to reshape your business, your entire industry, and therefore, it would require an entirely different approach? What did that moment look like? Well, as you said, we are 185 years old. So imagine that uh back in 1841, there's no electricity, no telephones, no internet, and certainly no AI. Uh, and, uh, over the past 185 years, well, basically it wasn't is as old as organized retail, I think. So we went through all the changes, you know, from an independent store to a chain store, and then the chain store becomes a category special. List, and then you from a local retailer becomes an international retailer. And then when the digital age came, and then we have to transform ourselves so that we have we have an Opus model which is offline, uh uh integrated with online and giving that customer experience. The interesting thing is, for all those changes over the two centuries, the change is mainly about like where you sell and how you sell. But then AI is very different. AI is very different because When you think about retail, it's a lot of processes. Oh gosh. You have to manage all the supply chain, move thousands and thousands of products, you have to manage promotions, you have to, uh, execute a lot of, uh, procedures in the company to get it running. In the past it's 10 hours a day, now it's 24/7. Now with AI. It's the first time we are able to look into how we reinvent the model so that we don't just focus on processes, OK. AI can do that, but we focus more on customers experience and problem solving. Now this morning, for example, when you wake up, You told me you run, right? When you wake up, you wouldn't be thinking about that. I need a bottle of vitamins. You think about, ah, today I need to be very energetic, right? On a good day. And when I wake up, when a customer wake up, they wouldn't be thinking about, I need a skincare product. They will be thinking about, oh, I need a healthy Younger looking skin. So that's what customers want. They don't want thousands of products. They just want retailer who understand them and being able to provide the right solutions. So with AI this is the first time that I told my team, this is the first time that we can look beyond just efficiency. It's about reinvention, how we relocate the way we serve customers. So that's when we think AI is transformational. So there wasn't necessarily one moment of friction. It was a kind of overall realization of this how expansive the influence of artificial intelligence would be. Um, so let's talk about how you're doing that. You've made the case that, that leadership, culture, organizational design is going to be what determines the success of implementing AI at scale. Um, and therefore, you've said in past remarks, it must be CEO led. So tell us about AS Watson's enterprise mandate around AI and what CEO ownership really looks like. When, um, decades ago when I led the digital transformation at the time, meaning that we have besides our physical retail experience, we have to build online. And at the time when we integrate technology, uh, either CRM, uh, e-commerce, and a lot of different types of technology. I will call it is vertical, it's top down. So we, we were in the boardroom, we decided we need to have go for this strategy. This is the investment, and then we pass that on to IT and pass that on to HR for example, for training. So then you go function by function and market by market. So that's what I call is vertical. But for AI what I What I see is my vision is that the whole organization needs to move together. So I would call the AI is horizontal. I can't afford the time to let a silo department or one department or a couple of departments to lead it and then try to roll out market by market and function by function. So then I started to um Uh, chair all the meetings. I talked to every market. I have 30 markets, and then I, I talked to every function to start everyone to understand and give them the transparency. There's still some companies where, um, the, the people who work in the company, they are They hesitate whether they should use AI or not use AI. But in AS Watson, we have announced our strategy, uh, last year is a human AI, uh, partnership strategy. And everyone, you should bring the AI assistant with you to work, help you to think faster and smarter. So that's step one. That's why I call it. COLED meaning that I, I would have to lead it because this is not the time to delegate. If you look at AI, I, I know we talk about AI like opportunities, but at the same time you look at AI, it's like crisis. Crisis meaning that technology develops so quickly, both from uh customer and consumer adoption and also all the companies. So if you, you don't do it in the right way quickly, it could be a crisis. But every crisis has opportunities. And at the same time, it allows me to bring in um a different meaning to agility, agility, because when we talk a lot about uh AI and everyone talks agility, we need to be very agile, the world is very uncertain. But agility is not speed. And this is what I told the team. I have a, I brought a video. OK, can I show a video? Yes, please. I think we have a video to demonstrate, uh, what Melina is talking about with regards to agility. Let's cue that. But Holland comes in for a pit stop. Time to refuel and change tires. Lou Moore himself changes the tires. Only 4 crew members, including the driver, are allowed to work on the car. It's a tense time. Holland stays in his seat, anxious to get away. Let's watch. Remember this 67 seconds. Mhm. Oh, just in case you didn't see it the first time. Yeah, this is the world record now is 2 1.8. So here we're looking at AI speed. This is not AI, this is real. Right? But I mean, this represents that, that agility. So, Normally when we see, even if you haven't been to F1, you have seen it on news, for example, you think, wow, Formula One is all about speed. Yeah. But in Formula One, when you have a car running 300 kilometers per hour, if you ask an engineer, Actually, the F1 engineer, the first thing they think about is not how to build a faster car, it's how to reduce the drag. So the weight of the car or the dirty air from the front and other car in the front, or they have uh some radio, uh they can't. clearly, so every millisecond decisions, it's like a drag if you don't do it right. So in an organization, if I can use this example, is that back in the 1950s, it's not, it's not slow, it's 67 seconds, but today it's 2 seconds. So when for an organization to look at AI it is not about how AI bring you speed. For us in AS Watson agility is acceleration minus friction. So, you've mentioned just now that you uh want to move from AI being a source of crisis to AI being a source of opportunity. You've given yourself as CEO the mandate to bring that together across your 30 markets, across your 17,000 stores um worldwide. So, let's talk about a specific Example of how you're doing that, the impact AI is making and and how you're measuring that impact as well. Can you give us a specific example of how you've reconfigured a workflow, a team, or maybe a whole business unit around AI and also the benefits you've seen as a result? Let's get into the weeds a little bit. Sure. Going back into that formula first, when I talked to the team, when I, I, I now see that every function like every, every two months. So the, the, the first question I ask them is not how you use AI. It's like, what friction have you identified? Because that is very important for us to take AI to the next level. And when you talk about ROI, a lot of people will think about AI investment is quite expensive, it's, it's, it's not cheap. So then what is ROI? Now if we focus just on AI efficiency, then we're trying to measure how many hours you save and so that will only just change the percentage. But what we are trying to do and think about is how to reinvent with the human and AI partnership so that we can change the equation. I brought you some equations, so share with you. I hold that up. So traditionally in, in, uh, in any organization. Then you have a, you have a perfect strategy in the boardroom which I pull up the image for Melina please which I would say is like you have a 100% perfect strategy but then as you move down the different levels of the Organization, every hands off, there's some discount to it because it's either uh communication scare people, how do they understand your strategy in the boardroom versus uh some inefficiencies or maybe capabilities. So if you look at the first number. is 50.4. Right. So meaning that if you, if you have a 100% perfect strategy in the boardroom, it could become 50. My customer can only experience like 50% of what we want to do. Now with AI you see the middle, this illustration is when you apply AI to organization, then it improves efficiency, right? And it could be even better than efficiency can cut off one of the process. So you see the formula actually have one less number in it and then that's nearly perfect. So then you get 99, quite close. So meaning that AI efficiency will help you to improve the percentages. But what we are trying to do in AS Watson is The human plus AI partnership because when you use AI to give us the speed, the scale, the intelligence, and the human brings, you know, that that context, we know how, what our customers need at what time. We, uh, we, the human also have that meaning and connection, how we provide a human touch to the surface. Then the formula is the equation completely changed. We can generate more value than just improving the percentage of every step. So this is how I explain this within the company. Agility is acceleration minus friction. You can't accelerate the pedal and then you have friction. So when we transform, we also need to look at the whole new way of doing every process and then focus much more on our customers. And what metrics are you looking at to measure the success of that? What we try to do, one, first of all, the first one, maybe a lot, maybe not a lot of companies do that. We measure the colleagues' engagement rate. So we, since we started um to promote human AI strategy in the company, we always do colleagues engagement rate, meaning that we, we ask the colleagues, you know, how they, whether they like work, what is the part they like, etc. But then the score has jumped up a lot in further to be in the uh several departments because the team leaders told us that when we started to use more AI. The colleagues love their job. They, they are able to have more interactions than facing the screen and that changes the engagement of our people working in the company to start with. And then we started to then put through into more customer front uh initiatives, then we measured customer love score. So for example, if we are able to free up more time for our store, our colleagues in the store to service the customer, then if you If you have shopped with Watsons, for example, then we will be asking you right after your shopping to give us your experience. How was that service? And we continue to see that customers love score improving. And this is uh not the usual way that people would measure AI because score, yeah, customer love score and our people love score in the company. So, let's talk about that engagement among your staff. So, as we mentioned at the top, AS Watson this year is celebrating its 185th anniversary. Um, it's an incredible achievement, almost two centuries. So, how do you tool and retool for new technologies while still keeping that heritage, that culture, that DNA that has driven your success for almost two centuries? How do you get that balance right between um embracing what's new without throwing out what has gone before? We actually start with the purpose of the company. Um, back to a decade ago, and we, we are Asian-based international company. So in the early days, we don't, it's not like some of the, uh, maybe the European or US company where you have a vision statement and a mission statement. We're very focused on like generating profit. So then, but then a decade ago, I started to see that we, we needed to transform the, the company. And it's not just a company that is just in Hong Kong. We're in 30 markets in Asia, Europe, and the Middle East. So how do you link everyone together in terms of their focus without losing focus, whatever technology you put in a company. So then we, uh, I put in a team of young managers, not for a consult consultancy, but then we would brainstorm what what should be our purpose in AS Watson and in one hour they came out of the room and they told me with a flip chart at the time, sorry, still like 1010 years ago, a while ago they have flip chart and then a smiley, they said, Melina, I think the purpose of AS Watson. should be to put a smile on customers' faces. It's easy to understand what in whatever languages, it's easier to understand whether you are working in the office, you're working in the warehouse, or you work in the stores, and this is what we believe in. And within 30 minutes, we said this is going to be our purpose. Hence the customer love score. Exactly. So we don't have mystery shopper, it's customer love score, we just ask our customer. So we, we basically, we put our purpose in the center of everything we do. So here is AI when we even when we put in, we have to do supply chain optimization a couple of years ago when we start to have, have our Opus or model, etc. Then even you go to the warehouse, I, I talk about customer love, because if we are able to manage all the inventory on Time and being able to do the right delivery, we put a smile on customers' faces. And then the and then the the warehouse team start to understand what they do. They are not hiding somewhere in a very hot uh um premises to move products. They're actually putting a smile on customers' faces. Likewise, every function we go through that everyone knows this is the thing they do has a contribution to customer love. I, I believe when the company has a purpose. Whatever technology is not going to take away that focus. Absolutely. So customer satisfaction, customer love being a single-minded obsession that drives every decision you make, whether that's staffing, whether that's technology, and that being at its core. Um, I want to move on to talking about um employee love and, and engagement there. So you've talked before about AI as a tool for inclusion, which is interesting and, and counter. some oft-cited narratives around AI and how it could potentially um exacerbate existing human biases and so on. But at AS Watson, you've reported that AI powered automation at a warehouse in China has actually helped to increase the female workforce. You're, of course, on our most powerful women Asia list, uh, Melina, tell us about this and, and tell us the impact the tech has had. I was in Foshan two months ago. Uh, I went to our stores and then I usually try to make time to go to the warehouse. It's a bit remote, but then it's important. When I, I, I have read a lot of documents and videos and everything about the, the, the automation and the AI we put in the infantry forecasting, etc. I've seen that, but until the time that I was, I walked into the warehouse. I look around, it's not like you have many people moving boxes anymore. Everything is so orderly because we put in the different automation. And the thing is, the team told me that in the past, the warehouse, like a typical warehouse, we, we have 8 in in China and 80 around the world, but usually it's about 80% of our team would be males because we do require a lot of heavy lifting. We have big boxes. But now because of automation, uh, all, all, both males and females, they don't need to lift anything very heavy. We ensure that the robot can do that because it's for for our colleagues' wellness. So then in that warehouse, we have 62% of our colleagues, they are Now females and that was something that really positively surprised me of how technology can also bring more diversity in the company and actually in our technology team in AS Wilson we are our senior leaders 50% male and 50%. And females we have evolved that over the years uh for even for the technology team. We're here, Melina at the Fortune Leaders Forum. So I want to close by asking you a question on the leadership choices you've had to make enabled to do everything you've just been talking about. What has been the hardest leadership choices required to land that AI transformation? What must leaders be willing to change to get an ROI on AI? The hard decision for a lot of leaders, or maybe I'll start with the biggest risk of all the leaders with this type of AI, um, technology is We put AI to improve uh today's process. So basically you get a better version of yesterday. Uh, but we lose sight on how we can use AI, which is really transformative technology, to help us to reinvent how we serve our customers. Um, and the choices sometimes we will have to make is we focus a lot on the numbers, like how much we need to invest. But my starting point was actually focus a lot on human. What's the role of human going forward? Uh, is it really that we with AI we're going to cut out 30% of our workforce, or actually it's going, we are going to increase the quality of our people intelligence. Uh, in the past, it's just not possible to handle that amount of data within a quick speed and to analyze things and make decisions. But now with AI help people can actually play a much higher quality role. So initially there was something that is not easy to explain to the board. And what we are trying to, to think about also is not just an ROI for, for this year, how many hours we save. It's about how many, how many new ideas we have generated. Since last year October, we, we, we launched our strategy, our human AI strategy last October. And since last October, Not even a year. We have generated a lot more new ideas that we can put out in the market for the customers and for the way we, we serve. This is the kind of value we are trying, we are trying to, um, to reinvent. So this is, I, I, we have a lot of leaders in the room. And I do hope that you can focus more on the human side. And for me, my, my vision for AS Watson is that I would love to be able To create a sustainable company that's powered by technology. But with a human soul and going forward, I think retail is going to be more human. It's not going to be less. That's a perfect note to finish on. Thank you so much, Melina, for talking to us about AS Watson CEO-led enterprise level AI transformation, about customer love as a metric for success, and finally the difficult leadership decisions required to get there. Please join me in thanking Doctor Melina Nay. Thank you. Thank you so much.

Nearly half of all Americans wear a fitness tracker, up from just 13% a decade ago. But while early wearables just counted steps, connected fitness giants like Oura and Tonal are using artificial intelligence to predict your recovery before you even feel it. 

Fortune senior writer Allie Garfinkle goes inside the $2 trillion wellness economy to test the tech, analyze the multi-billion-dollar data moats, and ask a fundamental question: Is hyper-optimization making us peak performers, or just more anxious?

Inside the "Lifemaxxing" Economy: Has Fitness AI Optimization Gone Too Far?

Tech

fortuneaifeatures

3 We're going all out. Welcome to the life maxing economy. We're capturing an additional 1000 points of data every second from our camera that's tracking 36 different points on your body. The global consumer wellness market is now worth $2 trillion and nearly half of American adults own at least one wearable of 33% points from a decade ago. Sherman and Allie here today. We are going to show you the new Order Ring 5 and our thoughts on it. When it says you're getting sick in 3 days' time, you will be sick, and it's the AI that's tuned to you. Now AI is. Transforming passive step counters into active coaches with hyper personalized insights designed to optimize every hour of your day, awake and asleep. So it's your AI spotter. Exactly. But some people are getting burnt out. Do you really need to be wearing 56 devices? No. Will all this information really make us healthier, or are we just becoming data obsessed? There's no reason to overoptimize. That 1% improvement is not going to change your life. Amazing. Later March. Hey Ali. Hi, Coach Woody, it's so nice to meet you. Super excited to have you here. I'm so excited to be here. Yeah, this is where the magic happens. The global smart fitness market is projected to grow to over $60 billion over the next decade, fueled by an increase in wearable devices and connected gym systems. Oh, it's helping me. Maybe you start to reach failure or fatigue. You cannot complete a rep. Burnout will kick in to lessen the load so that you can finish the wrap, so that you can finish the set. And fitness tracking isn't new. Basic pedometers go back decades, and more modern devices tracking calories, heart rate, and sleep surged in the late 2000s and early 2010s. AI is transforming the capabilities of these tools. Connected devices are no longer just collecting data. Now they're telling users how to use this data. AI really kind of underpins almost everything that we do, and we're becoming predictive in that. A world with generative AI allowing members to talk to the product in kind of human language. Tonal makes smart home gym equipment with AI-powered resistance weights that learn and adjust based on the user. They also offer AI coaching software and human-guided workout classes. Founded in 2015, Tonal has accumulated trillions of data points from everyday lifters to brand ambassadors like LeBron James and Serena. Williams Toal says its members have lifted more than 235 billion pounds, building the largest strength training data set in the world. What our members are telling us is that I want to run a marathon or I want to get my mobility back, you know, we're now offering through Tia, which is our total intelligent assistant, an ability to ask those questions, predict outcomes for individuals, and prescribe them workouts along the way. Pal is one of several connected fitness devices introducing conversational AI coaches into their products, but established large language models pose a significant threat. If chatbots can analyze the same fitness data for free, they risk reducing connected devices to what they once were pricey data collectors. Do you feel like you're competing with the large LLMs on that level? I think what they built is a phenomenal system where those systems fall short is in areas of specialization. What we bring to the table is a wealth of data, 8 years plus worth of workout experience for hundreds of thousands of people that allows us to augment what those companies do and specifically address. Strength training outcomes in a way that these companies just don't have systems trained to accomplish. What are we looking at here? We've talked about all this data. This is a system that we use that measures the force input for the motor. Every time you pull on a cable, go ahead and grab a cable. We're collecting data 50 times a second, thousands of data points every second that aggregate to give us a really, really, really highly specific force curve on what it is. That you're doing, how that complements your given move and how we build all of that into a system that personalizes your lifts. Companies like Tonal are positioning their proprietary data as the defense by matching trillions of data points with its own closed loop large language model. Tonal says they provide personalized analysis. This also adds a lucrative subscription revenue stream to their existing hardware sales. We didn't want to just be a wrapper on top of one of these big foundational models, which no harm or no foul to any company that wants to do that. What we wanted to do was take the time to really train our system on the data that we have and augment it with the expertise of our staff. To what extent do you feel you're competing with chat GBT or cloud? I mean we certainly have observed people taking medical information and uploading it to. Chat GPT or cloud and asking questions and we thought oh we can do this but we can do this with with the data not just as you prompting the LLM. Why don't we have that machine intelligence, the doctor in your pocket monitoring what's going on with you and then giving you an insight. Aura, the Smart ringing and health tracking platform that monitors sleep, physical activity, and other biometric signals, originally only sold physical devices. But in 2021, Aura introduced a subscription-based revenue model built on AI-powered personalized health insights. Under CEO Tom Hale, that software model became a revenue driver. I think AI and our particular take on it is going to be a pretty big mode. The second thing that is a competitive mode is the data set itself. The data is actually the scarce resource here. Our data set, 42 billion hours of data across a huge population, that data asset is what fuels the intelligence. The intelligence is the thing that you can't. Create unless you have the data. The company says it generated over $1 billion in revenue in 2025, doubling sales for the second consecutive year. About 20% of that comes from subscriptions. A rumored IPO candidate, or it could be valued at more than $16 billion if it goes public in 2026. Where we end is everyone's already got a supercomputer of some sort, and there'll be a device that you wear on your body. And that device will monitor your physiology, and those things are looking out for you and making you healthy. And by the way, that will bend the curve not just of your personal health outcome, but bend the curve on a societal health outcome. We spend 1 in $5 in this country of our GDP on healthcare. That's ridiculous compared to European countries where it's like 1 in 10 or less. We think that should change and we think this is part of that change. So this is my favorite drawer here. This is my wearables drawer. OK. We go ahead and open it up. Oh my gosh, there's so many. This is where people get the most shocks. It's probably like 30 different watches, smart rings, bands just in this drawer right here. Shervin Shaikh, known as Shervin Shares on YouTube, is a fitness influencer known for reviewing the latest products in the booming health and fitness technology industry. I've With every fitness tech equipment out there, and today I'm reviewing the tonal 2. He has done paid branded content for Tonal and Aura and earns commission on Aura rings sold through his affiliate link. And then what have we got in this corner over here? So this is the filming setup. What did we say? Sherman and Ali here. Sherman and Allie here today. We are going to show you the new Aura Ring 5 and our thoughts on it. So we're going to show you our little colors. What do you have on? What do you have on 2 Aura rings right now, OK. And then I have a Fitbit, the new Fitbit Air, and then the Garmin Circa. And then the whoop on this arm. So there's 3. There's an invisible one as well, exactly. And then just the Apple Watch on this side. When every choice becomes a data point from sleep to heart rate variability, some question whether all that information ultimately does more harm than good. There are also questions about the reliability of this data. Aura is currently facing a lawsuit accusing the company of providing misleading sleep tracking information. In a written response to Fortune, Aura said that they stand behind their claims of accuracy and are committed to communicating how members should use the data that Aura is measuring. I think a lot of people are kind of hitting a wall after years of over optimizing perhaps um I certainly have a complicated relationship with the data myself. How do you think about that, especially in the context of this massive data set you have? Our goal is to improve people from a strength outcome perspective and improve their health. What we're attempting to do is build a system that allows you to basically get that same value out of your training that you would get with a personal trainer. But importantly, without the cognitive load that would require you to even think about data. Has there ever been a time where you sat there and were like, oh, I have too much data? Always for me specifically, like, do you really need to be wearing 56 devices? No. And I think there was one point where I was wearing 2, like I was wearing an Apple Watch and a Whoop, and people were like, why are you wearing 2 watches? And now it seems like it's become more common where I see other people wearing 2 devices, and I guess I've become the guy who wears 6 devices. So many people think if I buy X, Y, Z, you know, this gadget, this fitness tech is going to make me fit. Buying anything is not going to change your behavior. If we use the data to influence how we feel, then we forget how we feel, and I think. How we feel is is the biggest metric for how that day is gonna go.

Relying purely on lower-funnel performance marketing is a quick path to stagnation when your category dries up. Marketing leaders from Sephora, Mars Pet Nutrition, Instacart, and Unilever break down what happens when you scale from digital performance sheets and explore why earning a C-suite mandate requires incremental data proof points, and how to go big without going home.

Sephora, Instacart and Mars Pet’s Marketing Leaders on Building a Cultural Identity Engine

Everyone says “AI fluency” is the new must-have, yet most employees are left guessing what that means in their day-to-day work. This panel zeroes in on the core tension leaders face: how to responsibly introduce AI tools while reskilling employees, when the tools themselves are changing week by week—or even day by day. We’ll examine how true fluency can be built amid constant change, and what kinds of learning strategies actually endure beyond any single model or platform. 

Anu Madgavkar, Partner, McKinsey Global Institute
Yelena Naginsky, Talent and Performance Lead, Google DeepMind
Moderator: Ruth Umoh, C-Suite and Leadership Editor, Fortune

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