00:00:02 Speaker 1: To understand why the cosmetics supergiant Lorel Group is teaming up with IBM, you must first take a closer look at its products. Take lipstick, for example, It's one of those things that seems straightforward, a waxy cylinder that you rub on your lips to turn them a different color. Easy, right, Well maybe not, as my colleague Lucy Sullivan found out when I sent her an assignment to Lorel's North America Research and Innovation Center. 00:00:29 Speaker 2: All right, I am reporting live from the Looreal visitor's parking lot. Malcolm told me that he would be sending me to Paris, France for this Looreal excursion, but instead I am in Clark, New Jersey. Pass a lot of strip malls on the way here. But to be fair to Clark, New Jersey and Lorel, this is a beautiful compound. It kind of looks like a spa. 00:00:59 Speaker 1: Lucy went into the center and was blown away. The facility houses about six hundred scientists and experts across skincare, makeup, fragrance, hair care, innovative packaging, and tech. It is one of the largest formulation lab spaces in the industry. It's the size of six basketball courts. The reason Loreel's facility is so big and has so many people is that everything Loriel does to bring a product to market happens here, from molecule discovery and product development to consumer testing. The center even has its own mini factory. My conception of lipstick, that it's just a waxy stick was plain wrong. Lipstick is a high performance product born from years of research, consumer insights, and precision science. Lipstick isn't simple. It's incredibly complex, and one of the main reasons it's so complex is just a nature of fashion trends. The kind of lipstick consumers want is constantly changing. 00:01:57 Speaker 3: A lot of our consumer insights with Floreal is like, where are consumers going in the future. 00:02:02 Speaker 1: This is Nadine Gomez. She's vice president for Loreel's research and innovation development team. 00:02:08 Speaker 3: Our chemists are working on five six years down the line. We predicted that consumers wanted more of a softer look on their lips as well. 00:02:15 Speaker 2: So how do you predict something like that. 00:02:17 Speaker 3: We see slow signals from fashion houses and social media and things like that. We kind of see that trend evolving a little bit, and then we know at five six years it's going to become. 00:02:28 Speaker 1: Big Lucy talked with her about the origins of one of their products, Mabelne matt Inc Liquid lipstick. 00:02:36 Speaker 3: Our competitors have two steps. The first step is a base go it's super opaque. You get the color and you get the maddy, but it's very, very drying ellips. You cannot wear that, honestly more than ten minutes. It feels like your lips are like aching at one point. So we had to develop a top cot and you'll see many of our competitors did the same thing. It's like a bomb. You put it on top, it's super comfortable, but we also noticed that consumers kind of get tired reapplying a bomb. So we're like, what can we do to create this two step into one step? 00:03:05 Speaker 1: So Loriel had a challenge, how do you make a comfortable liquid matt lipstick that doesn't require consumers to reapply a top layer of bomb. Solving this type of problem takes a lot of resources and a lot of expertise, and crucially, it takes time. Remember, Nadine said that working on a breakthrough product such as matt Inc can take years before it comes out. But can this process be accelerated taken further? Be even more sustainable. That's what IBM and Loreel are hoping to find out. My name is Malcolm Gladwell. You're listening to the latest episode of Smart Talks with IBM, where we offer our listeners a glimpse behind the curtain of the world of technology. In our last episode, we talked about how an AI assistant created with IBM Watson X helps future teachers practice responsive teaching by simulating class interactions with students. In this episode, we take you on an even more unexpected journey into the world of cosmetics, hair care, skincare, fragrance, makeup, and how a custom AI model could help Loriel's researchers shape the future of what we put on our faces every morning. I want to say on lipstick a moment longer to help illustrate what goes into Loriel's product development, and let's focus on matt inc lipstick. Loriel wanted to create something that was comfortable and could be applied in one step. 00:04:42 Speaker 4: So to go from two step to one step, we had to look cross functionally and try to figure out what can we bring into the product to make it more comfortable, and luckily we have many different types of products at Lorel. 00:04:53 Speaker 1: That's Alex Good, a senior chemist who leads the lip products team in North America. She says the trick to making matt incwork was finding an elastomer, a substance they were already using in foundation. 00:05:07 Speaker 4: We have this elastomer that can give you like more comfortable and make it feel like there's like something on your lips, like a cushion. 00:05:14 Speaker 1: She handed Lucy two jars. The first jar contained the former version of the product that was used in super State twenty four. By the way, this is exactly why I sent Lucy to the lab in my place the. 00:05:26 Speaker 4: Samples, and I actually have something for you to try here, so you can try this is what was the initial product. 00:05:34 Speaker 2: Okay, so this is like it sort of looks like okay, it is. It looks like vacline that has like more of a color. It's kind of a beige, looks like some skin. 00:05:44 Speaker 5: Okay. 00:05:45 Speaker 2: So this was from the two steps this would go on after Oh okay, right islet Okay, So. 00:05:55 Speaker 4: It feels like very wet. As you can see, it's kind of it's gonna absorb into your skin and leaves and then you're gonna feel the dryness. 00:06:03 Speaker 2: Of the product. Okay, so we're gonna move. 00:06:05 Speaker 4: From the clay product that you have on your hand now to the elastimmer or. 00:06:09 Speaker 6: You try half hour. 00:06:11 Speaker 1: This jar held the elastomer that Laurel had spent years developing in the lab. 00:06:16 Speaker 2: This one is it clear, looks like aqua for a much bligger and you. 00:06:23 Speaker 4: Can pay a physical layer that you're putting on your aid. 00:06:26 Speaker 2: Yeah, so that's much thicker. It kind of like clumps together. Yeah, it's more of a cloudy. It's less shimmery though that's intended. 00:06:35 Speaker 4: Yes, so this is a like a powder, this dispersed in dimetic code, and it creates like a comfort on your lips, a field like there's something there for a barrier to keep the film form on. And that's like the key ingredient that came from Foundation that we transferred into lipstick to give us this innovative product ahead of the market. Yeah, this is what gives it comfort. So the difference between super State twenty four and matt Inc is really the comfort. They both last a long time, but this matt Inc you don't have to apply the bomb over and over again, So you can apply matt ink once for the day and. 00:07:12 Speaker 1: You're good, right, Alex Good is under selling it here once for the day, and you're good. That's a liquid lipstick revolution. Literally millions of Loreal consumers around the world have worn matt ink. It's a blockbuster. It's also a marvel of science. The world's first liquid lipstick was developed in the nineteen thirties, and it was actually just a stain for your lips, barely counts as lipstick. Then came another wave of liquid lipstick when they were able to make it matt That was a two step version. It felt heavy on your lips. You had to keep reapplying the top coat. It was inconvenient. Lorel tackled that challenge in the lab, with chemists like Alex and Nadine leaving the charge their breakthrough matt Inc. But creating matt Inc took a long time, trial and error, the hard work of scientific experimentation. As Nadine told Lucy, the lipstick team had to put the new product to extensive tests. 00:08:13 Speaker 3: We do a very robustability system here. You know, we have color odor appearance. We monitor this in extreme conditions. We simulate a forty five degrees celsius and that can be something like a three year shelf life. I'm saying, we simulate your real life product. Like if you leave your lip gloss in the car in Arizona's one hundred and twelve degrees for three days, is it still going to perform? Is it gonna smell? Is it gonna look granted? Is it gonna change colors? 00:08:37 Speaker 7: We do all that. 00:08:38 Speaker 1: See what I mean. Lipstick is complex. Most people would never consider it a piece of technology, but one lip product has millions of data. 00:08:48 Speaker 3: Points, so much science behind. And you can see here how many scientists we have. You know, some of them have PhDs, some of them have masters degrees chemistry, biology, psychology. 00:08:56 Speaker 1: Also, when I first heard about this collaboration between LORI L and IBM, I was surprised. I thought, these are two very different companies. What do they really have in common? Meet you guys, Yeah. To find out, I went to the IBM Research Center outside New York City, which I have to say is one of the coolest buildings I've ever been in, A semi circular modernist masterpiece with a long curving wall of windows, looks like something out of a Stanley Kubrick movie. I was there to talk with two experts from research and innovation at Lorel, Methu Cassier and Gabriel Bertoli metthew is VP for Digital and Transformation. Gabriel is the Chief Digital Transformation Officer for Formulation. These are the people whose jobs are to oversee big changes within the company. And Methu told me to try on some lipstick. 00:09:48 Speaker 7: I'm gonna make you try this one. 00:09:50 Speaker 1: Okay, this is super stay viniting final inc. 00:09:54 Speaker 7: Yeah, so that's a glosse. 00:09:55 Speaker 1: Never in my life put on lipsey. You have no idea what I'm doing. 00:09:57 Speaker 7: You don't have to put it. You can try it virtu. 00:10:00 Speaker 1: Oh this may not be news to people who buy makeup, but it was news to me. You can try on Loreal products virtually. They call it augmented beauty. Oh my goodness. That is the strangest thing I've ever said. I look quite fetching, that's the way. 00:10:16 Speaker 2: It amazing. 00:10:18 Speaker 7: And I can just hit you can choose your color absolutely. 00:10:21 Speaker 1: So I'm on a little app it's looking at me and it's just showing me exactly how I would look with different shades of lipstick. So the odd idea of going into a store and trying on each one, you cannot do that from home, if you're not even at the store. 00:10:33 Speaker 7: Yeah, absolutely, that's all purpose. If you want to manage a trend, I would go for something more like pitch. 00:10:40 Speaker 1: You think I'm a peach person. 00:10:42 Speaker 4: I don't know. 00:10:43 Speaker 1: That looks I have to say that looks kind of natural. It just is enhanced. It's given me a boys share I would not otherwise have. This is why Loreel says it creates beauty products and beauty experiences. Loriel is a beauty tech company. Over the last decade, Laurel has seized the power of AI and more recently, generative AI technology has become a driving force alongside science and creativity. And while some of this digital technology is relatively new, Matthew helped me see that IBM and Lorel have always had a lot in common. 00:11:20 Speaker 7: I saw the original creator of Loyal, Jentulier, was a chemist in nineteen or nine, so one hundred and sixteen years ago, and he created this new air color type for the market in France, and then little by little, it has been always a very scientific company. So if you look a little bit at key facts, we invented sun filters in the nineteen thirties. There was a very very big milestone where we also invented not only product, but a reconstructed skin. So if you look at nineteen seventeen nine, we've been the created this reconstructed kin that helped us to go out of animal testing very fast and by the way, before the law even asked it to cosmetic companies. And then more recently, because it's a story of innovation, we launch on new molecules like one that you can find in laroche pose Milabi three, which is really helping people to find against some you know, spots that they could have on their skin. It's all about like big mountation, how to regulate it. 00:12:16 Speaker 1: Loreel and IBM were both started in the early twentieth century, Lorel in nineteen oh nine and IBM in nineteen eleven. Both companies have long standing histories of innovation, of using trial and error to improve everything they do. The two companies have been doing that in parallel for more than a century until recently. When does it start, When do Lorel and IBM start working together? 00:12:41 Speaker 8: So we started in twenty twenty three at the end of the year. But you know, really the discussion is really recent, absolutely, absolutely, it's really recent in reality, you know, I would say the first really interaction happened at the beginning of twenty twenty four. 00:12:56 Speaker 1: This is Gabriel Bertoli who I spoke to alongside too. 00:13:01 Speaker 8: What really played a key role here is we wanted to bring from a logic perspective to R and D together, which normally you know companies like us, you just go to a provider. You know it's a customer and the supplier and your work they delivered to you. Here the concept was totally different. 00:13:20 Speaker 1: Mid two said that the collaboration began with simple conversations. 00:13:24 Speaker 7: So if you look at the way IBM entered into loyal Labs, it's started by interviewing people what would help you to do your job? What is your business need? So it was, by the way, two months ago, a long series of interviews and from all the people around the world we have in research in Brazil, in India, in China, Japan, US, France of course, so we really want to make sure that at the end of the day, this new model, this new tool that we will give to people is really people centrick in the way that it selves their daily need. 00:13:58 Speaker 1: More. The point has leveraged technology for decades and accumulated amounted of scientific knowledge, everything from consumer aspirations and market trends, to the results of all the experiments conducted during product development, to which formulations melt in a hot car. It's hard to get your head around. Looreal isn't just a cosmetics company. It's a beauty data powerhouse. 00:14:24 Speaker 8: If we have sixteen thousand terabat of data coming from consumer insights, coming from market research coming from sales, well with the new technology, maybe by aligning those two and using best in class technology you can solve that problem. 00:14:45 Speaker 1: So you say you have sixteen terabytes of data, put that in perspective. How much data is that? 00:14:50 Speaker 2: Give me? 00:14:52 Speaker 8: This is one hundred year of Floreal data based on the last forty years of data the systems. So this is really I mean, we're talking about one hundred years of data that only Loreal have. Let's take the example of the ellipsis. I mean, you know, if ellipsex can be between twenty and thirty year ow material, each raw material will have I would say ten or fifteen way of doing things. 00:15:21 Speaker 1: Gabrielle is talking about how things used to be done. Researchers at Lorel needed roughly twenty five ingredients for a new lipstick formulation, but they have to choose from a pool of hundreds, if not thousands, of raw materials, And even after they settle on the ones they want, they have to figure out how much of each ingredient they need and in what form, what molecular weight, what combination. It's not just a math problem. It's a problem that requires balancing multiple perspectives safety, performance, quality, compliance standards, sustainability, and more. It can take years. But what if you could simulate hundreds of cars parked in a sweltering heat. What if you could do all those trials and errors virtually over and over and over again. What if instead of mixing materials together by hand, you could ask AI to predict what combinations might work best and then try those out first. 00:16:20 Speaker 8: This is ten on the power of twenty five. This is one hundred billion of years for a human to do a change in the formula or the possibility they have. You can only do this by using technology, power of technology and data that you have. 00:16:41 Speaker 1: This, Matthew says, is where IBM can come in to help take things further. Using artificial intelligence, IBM can help Loriel create a custom AI model that helps to crunch those numbers to be a companion to the researchers to give them superpowers. 00:16:58 Speaker 7: We don't want to replace the intuition of sent this. We just want to make sure that this intuition is really augmented by some calculation poor that, as Gabrielle said, then does all ten and the poor of twenty five solution and se probably try this one, this one, this one, it looks like a better solution, and then ultimately that's really the decision of the chemist to make it happen. 00:17:21 Speaker 1: Well. To make a predictive AI model that can give Loreal researchers those superpowers, you'd need that mountain of data, years worth of laboratory testing and all Loreal's data digitized and AI ready. You need to train artificial intelligence on everything the company has already done in order for it to predict what it could do. 00:17:43 Speaker 6: Loriial has one hundred years a course of data, fifty years of digitized EGGDA. 00:17:49 Speaker 1: This is Miriam Ashuri, Senior director of Product Management for IBM Watson X. Lorel has the data, and part of IBM's job is to help put that data to work, which involves ensuring data quality. Mariam talked about the concept of AI ready data. 00:18:07 Speaker 6: The sole purpose of this data engineering pipeline is to clean the data and we call them AI ready data makes them ready to be consumed by AI. So basically looking into biases and the data to fix the distribution, looking into guard brains that we are putting into place in terms of removing personal information. 00:18:29 Speaker 1: Marriam that explained that a custom model like the one IBM is creating with Loril can be more efficient and targeted than the larger general purpose AI models. 00:18:39 Speaker 6: You've heard about large language models. The reason that they call them large language model is they are exposed into really large amount of data. So the larger the model, the more take of all the models are, but also the larger computed requires that translates and increase carbon footprint and entergy consumption, that translatestan increase latency that's your response time that translatestand increased costs. So you started seeing that enterprises started grabbing a much smaller model customize it on their proprietary data that's the data, their DOMAINO specific data, or the data about their users to create something differentiated that is applicable to a real world use case but also delivers the performance that they needed for a fraction of the costs, and that's why there's been a lot of push around using custom models versus very large general purpose models. 00:19:39 Speaker 1: So how is a custom model created? Miriam says, you start with the base model. Imagine you're buying a car. You could get a minivan, or a sedan or a sports car, and then you get to customize it. You could add a sunroof, leather seats, or a rearview camera. Turns out you could do the same thing with your AI model. You pick a base and then you cut customize it. You tune it on the data unique to your organization. 00:20:04 Speaker 6: We do believe that one model doesn't fit all use cases. You want to truly have access to any model anywhere, and by any model anywhere, I really mean any model anywhere, open source, proprietary, low call out your machine wherever the model is. You want to host it yourself, because then you would be able to take advantage of the best of the technology at any point and pick the right model for the target use case. 00:20:33 Speaker 1: So a custom model tuned on Lorel's data would be more targeted and efficient than a general purpose model. It would understand the researchers world and provide transparency into its workings. That's part of the magic. And what could a custom AI foundation model do for a company like lorel. 00:20:54 Speaker 9: Accord with more contain the complexity of the formulation. 00:21:00 Speaker 1: That's Gillomme lais Moline, an IBM distinguished engineer and one of the people working on the AI model. 00:21:07 Speaker 9: And to hype ower the formulator to go not only past but also I would say, be able to include more complexity or so in the formulation, more personalization, more certain ability, better selected ingredient. So it's really a tool to help them and to also help them to unniche the creativity. 00:21:34 Speaker 1: Kayomi is saying that with its custom AI model, Loreel can improve every step of its product development pipeline, make the process faster and more sustainable. But he's also saying that the model could help Loriel create something that's never been done before. What could that product be? So I'm mourning you with that. All my questions are going to be really dumb. 00:21:59 Speaker 5: Okay, no, please by all me. 00:22:03 Speaker 1: To find out what people at Loril are dreaming of. I spoke with Trisha Iyagari, global general manager at Loriel's Mabeline brand and they asked her about her own dreams and how technology and science could help bring those dreams into the world. Do you have a secret wish list of things you think that this partnership could produce, Like, is there a product out there that's been technically too difficult that you think could be a worthy target? 00:22:29 Speaker 5: There is one that I think could be really amazing. 00:22:31 Speaker 1: What's that? 00:22:32 Speaker 5: So? Shine products in general are harder to create, and we're unable to create a shiny, long wearing eyeshadow. So basically like a shadow that could stay on your eyelids, that won't settle into creases, that won't move all over your face, that has a glossy effect. It's like the holy grail. 00:22:51 Speaker 1: That's the holy grail. Yeah, yeah, you may have seen that look in fashion shows, but that look isn't real, not for people be and lucy. 00:23:00 Speaker 5: Anyway, if you're walking down a runway, you see a lot of makeup artists doing techniques where they put some shadow on, they layer vasoline over it, and like slather vassaline on somebody's eyes to create this very like glossy look. But you know, within five minutes after they walk down the runway, I'm sure it's all over their face or being washed off. So the look is kind of more of like a fashion look that we've been unable to create, and real, real consumers can't wear it because it would get it everywhere. 00:23:30 Speaker 1: Trisha had another thing on her wish list too. 00:23:32 Speaker 5: The other that we would really like is semi permanence makeup. So we've talked a lot about really really comfortable thin film makeup that you could wear all over your face and that you can sleep in and then it will last a couple of days basically, so whether it be on your face, on your lashes, on your brows. So anything that's like more of a semi permanent meaning lasting for three days or more, would be amazing. 00:23:59 Speaker 1: Yeah. Yeah, And you say those two things, they have been the whole How long have they been on the wish list of lorel Oh. 00:24:05 Speaker 5: My gosh. I have been trying to develop this shiny eyeshadow since I started. What year did I start? Like twenty ten? And I'm sure many people had asked before me, and we tried so many iterations of it and nobody's been able to achieve it. 00:24:23 Speaker 1: It's clear that Loreel's experts like Tricia have a lot of ideas. I once said what I called a magic Wand project, where I called up scientists and technologists in as many different fields as possible and asked them what they could create if they could just wave a magic wand and make it real, and everyone had something they'd want to create everyone, that's not the issue. The issue is that there are a million different impediments to make the ideas on the wish list reel. Lack of resources, lack of time, some crucial bit of know how is lacking. There's a ga between what we want and what we can actually have, and one of the simplest ways to think of the promise of AI is that it can narrow that gap, not close it, of course, but do enough that people with dreams realize there are more things within their grasp than they could ever have imagined. Smart Talks with IBM is produced by Matt Romano, Amy Gaines, McQuaid, Lucy Sullivan, and Jake Harper. Were edited by Lacy Roberts. Engineering by Nina Bird Lawrence, mastering by Sarah Brugaier, Music by Gramoscope. Special thanks to Tatiana Lieberman and Cassidy Meyer. Smart Talks with IBM is a production of Pushkin Industries and Ruby's studio at iHeartMedia. To find more Pushkin podcast listen on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts. I'm Malcolm Laba. This is a paid advertisement from IBM. The conversations on this podcast don't necessarily represent IBM's positions, strategies, or opinions.