← Skin Anarchy
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- [00:00]
A
Hey guys. Welcome back to Skin Anarchy. Today's episode is going to be about a topic that I think we just don't know much about at all. And I think a lot of us feel that way. I think whatever industry you're in, AI has kind of taken over in a lot of ways and it's here to stay. And it's better if we start understanding what it really means in our respective industries and our work and our day to day. And I can't wait to talk to our guest today. He's a dear friend of mine and somebody we've had on the show before, but now he's really, really leading the charge when it comes to really education and AI and understanding what this means, businesses, what this means for so many industries and wellness and longevity and beauty and, you know, you name it. So without further ado, please welcome back Chaz Giles. Welcome, Chaz. So excited to host.
- [00:42]
B
Thank you. It's obviously great to be back and great to talk to you again. So really looking forward to it. I mean, I think you hit it nail on the head. There's not, I think any conversation I have that people are like, wait, tell me a little bit more about what I should be thinking about with AI. So it definitely is dominating, you know, much of the conversation for good and bad reasons. So we'll, we'll unpack a little bit of that.
- [01:02]
A
Yeah, and I wanna, I wanna go into it, like super go into it. But I know, Chaz, you have such a phenomenal like background and your career has been so multifaceted. Like I remember when Revia, you were really knee deep into in Revia. And like I remember our conversations were so, like there's just so much you were doing so ahead of your time. And I feel like you've always been that way and I would love for you to kind of just refresh our listeners, you know, on your journey and like where it really started and talk to us about where all of this began for you. Because I feel like you've always been in tech, you know, like you've always been on that cutting edge of like technology. So.
- [01:32]
B
Thank you. Yeah. No, you know, sometimes I have the great fortune of being right place, right time and perfect and other times I've been too early in that process. So, you know, sometimes you get lucky, sometimes you don't. But yeah, I think most of my career has been around technology and innovation. I've been in that for 20 years. I started my career at Procter and Gamble. I then was doing some venture capital investing and incubation of early stage companies and brands out in Silicon Valley. I started my first venture backed company, which was in the education space. And then I was leading innovation and external innovation for Estee Lauder companies for about seven or eight years. And so that was obviously the you know, kind of jump into the beauty industry and all the things that were happening there. And that really kind of opened up a lot of the doors to kind of where and how technology could really start to accelerate the things that were happening in beauty. There were so many great things that were going on, but a lot of the technology breakthroughs that were happening in other industries hadn't quite made it there yet. And so that was really the springboard for a lot of the work that, you know, you and I have talked about. And so after I left Lauder, I started Rever, which was, you know, a precision medicine company, bringing precision medicine into skin care. So AI, dermatology, clinical measurements, you know, brought to the mobile phone. And that was really at that moment when, you know, science based skin care, science based beauty was really starting to get its footing. We were ahead of our time in bringing in some NASA, you know, imaging technology that could measure skin biology through a mobile phone. And then we actually were able to, you know, do like 3,000 formulations on demand. So a really, really different model. You know, in many ways we were probably about seven years too early. And some of that, as we see some of that coming out, that gave us, you know, that deep run into AI. We were using imaging, neural networks, a lot of data science to really make all of that possible. And that's what really brought us to, you know, what we're doing now with Elite Labs, which is really helping companies start to bridge that gap between like the hype of AI and where we were in that frontier technology and the really, really nerdy geeky stuff into what can I actually do for my brand, what can I do for my function, what can I do for my company and how do I make that real? And so because we've been in beauty, because we've been in large companies, because we've been startups, like, we kind of uniquely understand how to put all those puzzle pieces together to kind of help people start to take advantage of that. And then we still build lots of things that we want to see in the world and do that on our own outside of that as well. But yeah, it's, it's been all around technology and really just how do you turn technology into things that consumers love and are good for business?
- [04:01]
A
Yeah, no, I mean, I think that's what would reveal, like, I was so blown away the first time we spoke, because you're right, you were way ahead of your time. And I remember thinking about this. I'm like, I know the world is going to get a point where they're going to be like, well, where is the truly personalized, you know, version of skincare? And I just, I feel like now, I mean, now everyone's putting their. Their selfies into chat GPT, you know, and like, trying to figure this out, like on their own. And it's just like, it just makes you wonder because. And that's exactly why in the intro, I was like, we need to figure out, like, where does AI fit, you know, in all of our industries? Because it's like we keep. I at least keep seeing this where people are trying to figure out how to really optimize their use of AI, whether they're in business, whether they're working for another company. But it's like that connection point, you know what I mean? Sometimes it just doesn't happen. And so I think that's where I really want to start. And I would love for you to kind of give us like a general overview, like, from your perspective. Where do you see AI right now when it comes to the wellness industries, the beauty industries, like, that kind of space, you know, where do you think it's really positioned right now?
- [05:00]
B
If I maybe just to help level set the conversation for everyone out there. Because I think AI is a term that gets thrown around a lot. So let's put a little bit of definition around that and that'll probably help us run through everyone today because ChatGPT has kind of been in the vocabulary. Everyone's like, yeah, yeah, yeah, I know AI. But that's really just scratching the surface, right, of like, what AI is and how deep it can go. So when we say AI, let's just think about it in the broadest term of, you know, how do we use this technology that can help us automate, help us accelerate, help us, you know, think in ways that are, you know, not just possible with the human capital and human teams that we have. So if we keep that in, like, the broadest sense, you know, where I think the beauty industry and the wellness industry is today with their usage of AI is really, really variable. And I think you have a lot companies that started experimenting earlier with some of the. I put in Copilot or I have my custom GPTs, and there was a lot of excitement and a lot of hype. That was being sold around AI, but it wasn't the use case, wasn't right for what the companies were doing. And so those custom GPTs copilot, they basically became like a better intranet, right? And so it was like organizing all the internal information of a company and making it more accessible, but it wasn't fitting to what the product developer was doing or what the chemist was doing or what the, you know, so social media marketer was doing. So, so there wasn't real value, there wasn't return. And that soured some companies on it. And it was kind of like, well, we've done AI. And you're like, well, what did you do? And it's like, well, we had a custom GPT. And you're like, yeah, that's why it didn't really help because it wasn't the use case that that was right. So I think that's the first thing is like people need to understand AI is many, many different things. And the most important thing is understanding what you're trying to solve for what is the use case that you're after and then finding the right slice of AI or slice of technology that unlock that. And that's really where you know, the value comes and, and that'll save companies a lot of, you know, anguish and, and, and lost dollars in that process. I think the second thing that I would say is most of the experimentation that we've seen, you know, when you get beyond that ChatGPT side, right, the kind of AI for PR sense of hey, we're doing something with AI, it, it really breaks down into kind of like four areas. So there's work that's going on for data. And so it is companies that are understanding data is gold. Data is the fuel that is going to drive AI. It' my unique competitive advantage because that data is proprietary. So if I'm, you know, one of the big formulation houses or fragrance houses, I'm sitting in on all this rich proprietary data. My ability to translate that to mine that into AI Gold is really, really high to create advantage, right? And so you have companies that are doing a lot of work in how to unlock that historical advantage and make that even bigger going forward. You have then work that is kind of connected to that once that data is unlocked into areas of form. So those companies who are creating their own formulas, or even those companies who are creating, you know, contract manufacturers and creating formulations for other brands, their ability to start to augment their formulation process creates a lot of advantage. One, because you have again, historical proprietary Data that you can lean on, right, that no one else has, that you can figure out how you create advantage. And it's also the core of the product you're creating. So how do you bring that to life? Better, faster, cheaper, right? And getting things to market, especially in today's game where so much of beauty and wellness is turned, turned into kind of fast fashion, right? Like the time cycles are being compressed. My ability to get to market three months earlier is a competitive advantage. And if AI can help me do that, that's a great use case for it. That's kind of connected then to product development, which is the third area that I really see it being used. And that's everything from conceptual work and how do I think about translating social trends and social listening into areas of innovation and opportunity all the way through things like, you know, synthetic testing for concepts and packaging you visuals. So really interesting use cases that product development presents and that has a huge again value and ROI for those businesses because I'm now to market faster, better products, better efficacy, unique claims, things like that. And then the final one that then touches a lot of brands that may not have that same depth if they're, you know, newer brands or smaller brands that might not have all their own manufacturing and things in house is really around like the EE Comm and DTC. And if I break that down a little bit, one of the biggest challenges for brands that are DTC today is trying to manage across all the different data streams, all the different platforms to really understand what and how you're performing and how to make smart choices. Because every brand is going to tell you cost of acquisition is going up, it's getting harder to find their people, DTC is much more competitive. And then you make a problem like that that's already complex even harder because you know, Shopify, Amazon, GA4 Meta, Klaviyo, right? Like none of them really integrate, none of them really talk to one another. Attribution is always different. So what is the true ROAS that you're dealing with, right? Is that program that you're running on a promotion actually driving ltv? Like I don't know that if I can't assemble all of my data in a usable fashion and then go very deep. And that was really hard to do previously because it took lots of expensive data analysts and you know, financial analysts to go do that. Now there's really interesting ways to use AI to really start to understand the levers of the business and how you can unlock value and make really, really smart choices to still win in dtc. And so those are kind of the four areas that I'm seeing it used now. And each one of them has a ton of opportunity for it to, to grow.
- [10:35]
A
That's phenomenal. Thank you for breaking that down. Because I think that's where I, honestly for me, I get lost a lot because like you were saying, everyone says I'm using AI, we're incorporating into our business model, we're trying to, you know, really leverage. But it's like it just makes you wonder like being on the outside trying to understand what a brand is truly doing. And I think even for entrepreneurs, knowing where to start with it, you know, that's a big hurdle, right, where it's like everyone is screaming AI, but like, like if I'm a brand new entrepreneur, if I have a say, a skincare brand, right. I just want to know like, where would a starting place for a new entrepreneur that has a decent brand? You know, you're selling product, you're doing great, but then you want to kind of up your game. What do you think the best place is really to incorporate AI or even start understanding its use, you know, in
- [11:19]
B
a smaller business for sure. And I think it's, it's, it's not just entrepreneurs that are facing that. I mean, we talk to CEOs of $100,500,000,000 billion dollars companies and it's, they have the same question, like, what, where, how, right? It's all the same. And the answer is honestly the same, whether they're big or small. That I would give think of AI as superpower. And you know, like when you were a little kid and people were like, well, if you had one superpower, what would it be? You can't pick them all and think of AI as kind of the same way. You can't pick everything. So what do you want to dial up? Like where do you want to create a superpower? And for most companies, the best place to do that is what they already compete on today, that they're doing well. So if I am a brand that is a, you know, performance based science brand, I'm probably going to lean my AI executions into things around scientific discovery, R and D formulation, claims, right? Like I'm going to do it in the areas that I already have advantage and that I've developed capabilities on. It probably doesn't make as much sense for me to start with something that's around my creative strategy, right? If I'm a science and performance and friend, like lean into the superpowers that you have today and Amplify them. But the bigger, you know, message in all of that is you have to start again with that use case. So the answer to every brand is going to be different because the challenges that every brand faces are different. The organization structure, the teams that they have in place, right? They're all different from one another. So it's really looking at your own business and understanding, where do I have my friction points? What friction points, if I can unlock them, will give me the biggest advantage because I don't have unlimited resources, I don't have unlimited time. So I want to put the biggest bang for the buck and focus it on unlocking the biggest things that move my business. And almost every entrepreneur or every CEO is going to be able to name off. These are the four things that are holding me back right now, right? If I could solve these four things, I can double my business, I can double my profitability, I can, you know, build my new distribution, whatever it may be. That's where they want to start looking for AI. And so that's a very different conversation than what most brands have been having historically, because it's been more of a push of, well, we have to have AI and, well, what are we going to put in our strategy around AI? So they turn on some features, you know, in their salesforce or in their whatever, you know, SAP, and they flip on some AI features and like, well, we're doing AI. But again, that's a tool that everyone has. So you're only kind of staying at parity at best. You're not, not building anything unique that allows you to compete differently. And that's really what, you know, we impress upon companies is how do you drive into creating an advantage in this moment different than anyone else that you're competing against can?
- [13:59]
A
Hell, yeah. No, that's amazing. I'm so glad you said that because this is the whole thing, because I've been wondering this myself in the sense of, like, we are now in a market, like, let's just take skincare, because I know a lot of our listeners, you guys, you know, they're in the beauty industry. They understand the beauty industry. So skin care, for example, very saturated space, has become incredibly saturated, in my opinion, in the last, like, two, three years, just blown up. And I wonder, it's like, when you are a new brand, no matter how much funding you have, no matter what, right. How do you truly differentiate yourself in this new ecosystem? Because now everybody seems to have everything figured out, you know, or so it seems through their marketing. And so that's Why I asked you that? Because it's like, when I look at AI, I think of it, like, similarly, you know, obviously, like, you're the expert. I don't think of it obviously the same way as you do, but in some way I do think of it. It could a tool to help me find that differentiator for myself. You know, how am I going to stand out and how am I going to amplify my efforts in a way that obviously my investors are happy, but then my consumers actually are happier because they're now discovering me, they're figuring out what I'm all about, that kind of thing. And so I think the big question I have is from that differentiation kind of stance, where do you think the leverage point really is for using, like, starting to use AI? Like, do you think we have to first figure out, like, this is what my differentiator is and then give it to AI, or do we use AI to help us figure out what can our differentiator be from, like, a long list of things that we might think are great?
- [15:24]
B
Yeah. And again, so this is where I will say there are multiple levels to AI and there are multiple types of AI technology. And so if everyone will humor me for a minute to be kind of geeky, the AI that you were kind of talking about in the end, which is how do I use it to help me figure out where some of my differentiation may be, that's going to be, you know, a more LLM conversational ChatGPT Claude type. And that's great, right? And that is kind of the first level of value, Right. When it comes to using AI, I'm using AI to help me with a task or a job. Right? So if I'm a solo founder or I'm, you know, a team of 10 or something like that, or I'm even a team, you know, of 2,000 being able to put in AI to help answer questions like that or help my chemist answer something on the bench, or my creative director creates artwork and copy factors faster. Those are all great. But that's going to give me kind of limited return on investment of what I can do there, because I'm only making one task, one job better. Right. That's kind of the first level of value. That second level of value then is how do I do that for multiple jobs and workflows? So instead of me just answering that question or just for my chemist, how do I now think about that for, like, product development? Right. End to end and how I start to connect those and streamline and so that's where companies are now jumping from that ChatGPT kind of world into, well, how do I automate and augment workflows that are happening today so that my product development can be 50% faster or my operations get, you know, X percent more efficient? Those kind of discussions, and that's really interesting and that's kind of the next level, which is a lot of the where people are talking about agents and agentic AI. That's kind of that next level and that's really the business value unlock that's been, everyone's been waiting for, for. Because LLMs are great, but I have to keep pulling information out of the LLMs. Whereas my agentic flows, I now can set up effectively AI employees and AI teams. And now I have hybrid human and AI teams working together and those get really interesting and really efficient and some very cool ways that add value. So that's kind of the next level. And then you start to think about doing that for the company. So now how do I connect that across the company, across multiple workflows? And that's where companies have to take a step back. Instead of just thinking about how do I use AI to bolt on to what I do today to make it, you know, 20% more efficient. I now am looking at this moment of I have this technology that is game changing. How do I need to redesign how my company operates, how work gets done inside my company to fully leverage this technology. And that's where big and small companies can take advantage of it. And I've had both of those conversations just in the last like three days where a company of four people was basically saying, look, we've raised capital, but we have to make that capital go as far as possible. So if I can restructure how I'm doing work, how do I use AI to take my Runway from 12 months to 24 months, right? Because now I can have AI employees and I can do all this other stuff with it, right? That's a very real value and use case right in there. And then they break down the workflows and all the places that they think it can be, you know, useful. And then the same thing that larger companies can do, right? How do I rethink that work? And that's kind of the level. So a long way around to be geeky. To answer your question, which is where do I start again? The where do I start? It is about finding your competitive advantage. But you don't have to go on this big, you know, kind of like 12 month endeavor to figure out My competitive advantage. Right. It's not the soul searching in that way. It's really around we create value through what things today. And for some companies that's, you know, great social media and they are a social media mobile native brand and that's like their big value pillar or one of the big ways they create value. Great. That's where you should be leaning into. It shouldn't be necessarily in your operations and packaging. Right. As an example. And then back to my performance marketing.
- [19:23]
A
Right.
- [19:23]
B
Or my performance science based brand. If that's where your focus is and that's a big part of what you're creating value, how do you start to put that in, in a way that can really, really start to drive distinction in the market. And if I build into that, think about formulation today, everyone will talk about the claims that are being made. And honestly, as you very well know, a lot of claims are tenuous at best, maybe bullshit at worst. Right. And a lot of that is because they're kind of getting watered down into what can be said from a regulatory perspective as well as, you know, ingredients used and keeping on that cosmetic versus, you know, RX side of the world. But imagine now you can start to use AI to help you formulate four claims objectives in mind. Right? So now I'm going after hyperpigmentation. So how do I look at the world of products that are out there working against hyperpigmentation? How do I start to break those down into where those products are clustering, find the white space from formulation and ingredients, find the white space from claims, and now start to design a formula specifically to tackle this white space that I can see in the market today? And how do I make those claims from just hyperpigmentation into maybe the four different biological processes and mechanism of action that ingredients are using to tackle those processes in a way that no one else can claim today? Right. So it's getting more mileage out of the formulas and the ingredients that I have by using a technology to give robustness to not only my formula design, but the claims that I can make and how I can actually exploit that in the marketplace. Does that make sense as kind of the.
- [21:00]
A
Absolutely. No, absolutely. It's, it's really, really very great to hear this, like deep dive into this because you know what's interesting and I know Rubia, you had started to do this where like you were, you guys were showing like there's more than just four skin types, you know what I mean? There's so much, there's so much to understand and Again, like, way ahead of your time, you know, like. But I feel like now we are now coming into that space in skin care where people are realizing hyperpigmentation isn't the same thing for, you know, Fitzpatrick's one through, you know, like, it's the same. It's not same. And we have to now build systems that we account for that It's a bigger conversation. It's more than just inclusivity now. You know, it's more than just, like I've said this before on the podcast, like I said many years ago, where we dermatology and skincare products are going to eventually bridge and it's going to come to a place where it becomes an adjunct therapy kind of space. It will no longer be where it's just like, I'm just buying a cute product at Sephora. It's going to be, I'm finding a solution that I need, that I've been needing. And I. And I really want you to speak to this, Chaz, because right now I'm looking at so many spaces, right? Skincare, longevity, wellness. Like, there are so many places right now where everybody is looking for personalized care in some way. And I think this AI conversation, that's where I really think it fits in so well for companies. Because it's like, now you have to figure out there's so much data available, even if you go on to PubMed, even if you go onto any open, you know, literature platform, you're going to find thousands of papers that you probably never read before you know that you ignored. A great example of that, in my opinion, is wound care, wound healing. I've been in that space, faced my entire career, and now when I see things like, oh, ghk, copper peptides, you know, I'm like, guys, we've known about this for 30 years, you know, right? Yeah, like, this has been around. So it's like now it's like, I feel like companies, they need to start understanding, like, there is information, there's knowledge, Scientists have been collecting it for. For decades. Like, and now how can we bring AI in and now accelerate your efforts into this?
- [22:58]
B
And you said, yeah, you said it very well because there's multiple things that exist in there. And up to this point, a lot of the conversation about AI has just been making information more accessible. So it was kind of, you know, like the Internet on exponential steroids, right? It's like, now the information exploded some more, but then you have a bigger bottleneck, which is your people can't process information that fast. And so that's the next step in using AI is how do you then apply the information. So a lot of the conversations we have with companies through Alita Labs is around that it's an applied AI. AI is great. It's a technology, it's a tool. Right? The companies that win, the leaders that win, the employ, that get ahead are the ones that figure out how to apply that within their sphere of expertise. And that application is what we were talking about before. It's not just I throw in a chatgpt or I throw in a copilot. I need to figure out to your point, I'm a science based brand. We're trying to go after longevity in skin. How can we learn from all the things that might be out there in other industries, in traditional medicine, in Eastern medicine, in all these different things. I'm sure you saw the kind of article or study that was out recently that was showing there's you know, a new pathway that was discovered in terms of how cells in the body is communicating. That's not the circulatory and nervous system. And it aligns much more with like TCM and traditional Chinese medicine around the, the kind of like energy flows. Super interesting in terms of how the body and cell is communicating. Well, how do you take that and everything that's now being discovered around that and start to think about how that applies to skin care? That's where this gets really interesting when now you think about, you know, the peptides and the copper peptides for organ regeneration and rejuvenation. What does that look like when you're talking about senescence and cellular turnover in skin and how might I think about that application? What does that do in formulations? How do I need to think about efficacy levels, stability, all those things? That's where modeling and AI becomes super powerful tool as an example and this is what pharmaceutical companies have known for long time, right? Like they're leaders in silico approaches. And all of this modeling that work has historically just been really expensive. So skincare did not invest in it, but now we're kind getting that inflection point that companies have to figure out how to do that or else you're going to be stuck with very generic formulas and just playing a marketing claims game where companies that are leaning into the technology are able to now get these huge performance gains because they're able to think about that differently in a way that doesn't require billions of dollars in R and D. It might just be a couple chemists augmented by some really sophisticated AI. To be able to get, get you there.
- [25:40]
A
That's. Yeah. No, I mean, I think that's the really interesting part is that this is really. I mean, science is now so accessible, like you said, you know, we're able to understand. And, you know, this is, this is big. One of my big questions, because I know historically, like the Estee Lauders of the world, the l' Oreals of the world, they've had these enormous teams in R and D, you know, the derms, the chemists, the, you know, all sorts of scientists. And that's a wonderful thing. But now it's like the question becomes, how do you create truly multidisciplinary teams using AI and really optimizing multidisciplinary disciplinary teams? Like, that's my question right at the end of the day. Because, like, I felt like for a long time we were all working in silos. Like, you know, I, I recently, I interviewed somebody like, I think a year ago, and she, she was very, very up there, you know, in the bigger companies, and she was explaining like, yeah, you know, we work with so many people, you know, like toxicologists, immunologists, like, there's so many people that are involved, but there's still stuff that gets lost in translation.
- [26:31]
B
Yeah.
- [26:32]
A
So I think one of my big questions is, honestly, I mean, it's great to be the l', Oreal, right? And you have unlimited funds and you can do this, but if you're not the l', Oreal but you're still a big company in the sense of, like, you've built a great base for yourself, how can you now take what you have, use AI and create teams that are going to be functioning up here now, you know, compared to like before, where we were still trying to figure everything out.
- [26:54]
B
Yeah, yeah, you, you hit on a few really, really interesting points there. One is, how much is getting lost. If you think about being in a large organization, how much is lost in the handoff? Because people were the, the kind of glue that held functions together and processes together. So you relied on people to move data and do analys analyses between that and that wasn't necessarily what made those people super valuable or special, but that turned into 60% of their job. Now you have agents that can do that and allow that PhD biologist or chemist to now add her specific value on top of all of that, or your CMO to do incredible things because all these other processes are happening. So there's a ton of value in that. But that again, takes a redesign to work. Because if you simply just say Well, I want to plug AI in and give intelligence to my company. Company like, like that's not functional. It's not getting me a value versus how do I rethink my insight to kind of on shelf process and flow using human capital and AI capital in a way that is unique to the data I have, the value I can create. That's where some really interesting things happen. Then that ties into the second point that you make which is big company, smaller company. I'll say mid cap for a moment and I'll take out the really really small ones. Not because there's not opportunity but just for comparison point. What interesting in this moment is the Lauders, the L', Oreals, the PNGs of the world, right? Like your large companies have the advantage in AI. They have the on paper advantage because they have more data than any other competent like of their competitors. They have more resources, they have capital, right. They have technology teams that are in place. They should be winning. They are, right? Because they cannot get out of their own way in terms of how to do that redesign. Like they are so large that they are not able to break down work to operate differently in the way they need to take advantage of this technology. So they are doing the bolt on, they are doing the. Let me put in one big corporate solution for AI which effectively looks like SAP or Salesforce and enterprise solutions, which is not how AI operates and not how you get great value out of AI. The mid caps are winning disproportionately. They see that this is their moment and they are still small enough to be nimble. The CEOs are willing to take a little bit more risk and they are able to rethink how they operate, how they function, how they design their work in their teams and they have enough data to unlock and they are leapfrogging the capabilities of their larger competitors in ways that like will build sustainable advantage for many, many years. It's a really interesting moment. And so it's kind of a wake up call to a lot of big companies like I kid you not and we will name the company, we will leave the company's name names out of it. But you know, we had work in projects that some of these bigger companies were leaning into 12 months ago and they were kind of on the cutting edge in some of these areas. But then they pulled back and said oh well, we need to figure out the governance or we're trying to get our data in order or we're trying to figure out, you know, these big bureaucratic processes. We are now 12 months later, and they have made basically no progress. Right. They're still in. We're figuring out our internal processes. Meanwhile, you had mid caps at the same stage that have deployed and cut their time to market by 50%. Right. Or they are growing, growing their capital and they're growing their. Their revenue, where they can now double revenue and not have to add any headcount because they have that capacity and capability augmented with AI, like, they are building real capability. So it's a really, really interesting moment. I mean, time will still tell in terms of how it plays out, but it's not playing out how it would on paper. Right. And I think this is also what the very small entrepreneurial companies understand. Most of the conversations we have, they come to us and it's like, hey, I know I can't compete dollar for dollar, so how do I punch above class? And now you're into a point where you're like, all right, cool. This is like what we can do and how you can look as a team of four, how you can operate as if you are a team of 40. And. And that's real advantage.
- [31:04]
A
That's. That's so interesting to hear that. That's so interesting because I. I always wondered, I'm like, wonder what these larger, like the Estee Lauder l', Oreals, like, what are they doing, you know, in terms of, like, really utilizing AI? And it's like, I think, you know, when you're on the outside, you almost feel like there's a version of it that exists that we don't have access to and nobody has access to, and only they're using. And so it's interesting say that, because the natural question I have as scientists is how can we now optimize the data we're collecting to now, like, accelerate our efforts with the AI? I would love for you to speak on this, you know, because data is not all the same, and especially when it comes to consumer data, you know, like, collecting that as a brand. Like, what is your opinion on that? Like, where do you think companies can optimize the data they're really getting from their consumer base to now, like, accelerate, you know, this process of whatever that is, whether it's innovation, whether it's marketing, whatever it might be.
- [31:53]
B
I think the first thing is that they just need to get their data in order. And I say that at the very fundamental level. It is, again, the fuel, it is the gold. So if you don't have a place, if you don't know where you're storing It. And you don't have a bank, right? You don't have a vault that you're putting that in to keep the metaphor going. It gets really hard. And, you know, I've said this a few times, that there never was an award for the world's sexiest data. So companies never really paid that much attention to it. No one got promoted because they had the most beautiful data in the world. And now we're at this moment when everyone's like, I need data. And now they're trying to figure it out. So I think there's a really interesting starting point for most companies, which is just how do we get our data out of its silos? Because today I might have my marketing data over here, and I have my Shopify data somewhere else, and I have my production data and my research data, and it's all disconnected. Getting it into one place, having it somewhat standardized now allows you to start to play with it in an AI world. And there's some simple tools that make that much more cost effective. This doesn't help. Have to be, you know, 18 months and millions of dollars to go do. So that's the first part, is just starting with that, then as you think, consumer data. I think one of the really interesting places that companies are starting to explore, but there's, there's a lot of really rich room in this is how do I start to think about my consumer insights, my consumer testing, my claims, my clinicals, like all these things that are the upstream part to my content, my social media, my, you know, DTC or my, my retail activities, but starting to take all of that qualitative historical data that because most companies have run their panels, they've done the consumer testing, they've done the quant and qual studies, but it sits in a spreadsheet somewhere or a Word document, right? Just bringing that all together and starting to create these rich profiles of consumers tied to feedback, tied to product, tied to review, tied to purchase data. Right now you are in a place that you can start to do some interesting analysis with that and start to do some interesting predictions vis a vis new products I'm launching. And you get into your synthetic testing and your digital twins and some really interesting places that AI is starting to get used that can really streamline the consumer process, streamline the marketing, you know, kind of creative process as well as, you know, just make my product better land with consumer, tightening up the impact without having to spend, you know, six weeks and $50,000 to do consumer studies.
- [34:24]
A
No, that's interesting because I, I almost wonder also when is it too much, you know, because I, for example, I want to ref to the longevity space for this because I know in the longevity space biomarker discovery is huge right now. And I, and I rightfully so because it should be, you know, for example, like exosomes. Everyone talks about exosomes. No one talks about using exosomes as biomarker, you know, like reservoirs. And so my question really becomes like, where do you have that line as a company? No matter how like, you know, biotech heavy you are, no matter how, you know, science heavy you are, where it's like it's too much, you know, too much AI usage where it will no longer resonate with your consumer base, you know, because I know like with AI, a lot of times a lot of consumers are asking for transparency about it. They're asking like what are you utilizing? What are your backend, you know, processes that you use to really make your product or your platform. And then if you're a company that's providing that transparency, where do you think people kind of like fall off, you know, and they start thinking, well, you're just running my information through like some sort of a system that doesn't understand me. You know, it's not really personalized. Like what are your thoughts around that in terms of the balance act?
- [35:29]
B
I probably have two answers and they will be contradictory, right? The first answer with my AI hat on is there's never too much data that simply just doesn't exist. And the beauty of what's happening right now is the speed at which AI is advancing and models are advancing is unreal. Right? And most companies, even in the more biotech heavy, you know, pharma field are not keeping up anywhere near with how fast models are advancing. So there is never going to be too much data that you can throw into your models and throw at AI to help you start to understand where you might be able to build some unique advantage relative to competition, right? So that's the first thing in terms of where you use it as a company, a hundred percent there can be too much AI used. And this is the balancing point that I think every company has to figure out, which is I have an org chart and that org chart is connected to functions and those functions are how I create value from my company. Right? I should be using again back to where we started. AI should be used in places that it's giving me more value than I can create with my org chart today. So how do I augment, how do I unlock problems how do I create more value for my customers? And if we take that lens, we are not using AI for AI's sake. That's pointless. We are using AI to deliver real value to my consumers. And that might mean I can keep the same products and the same performance that I'm making today, but I can make that cost less or that might mean I can improve my performance and I can give, you know, new products to new consumer segments that were unserved.
- [37:06]
A
Right.
- [37:07]
B
In new ways, or I can make them available in channels that. That I couldn't reach previously. So it's all about the value that I can create with that. And I think that's the rate limiter for where and how I use AI. It's. Does it create new value relative to the kind of Oregon and functional pieces that I have today? And if it doesn't, there's no reason that I should be putting it in.
- [37:27]
A
Yeah, no, that makes sense. And, you know, it also, like, I wonder, because when it comes to, like, market research, for example, you know, like, I feel like market research has been a big bottleneck for a lot of companies in terms of figuring out, like, where is the real consumer need and where can I really evolve into? I'm curious, because you have, like, the Reddit, right? Like, tons of forums out there where consumers are really giving real feedback all the time, every day. And I just wonder, like, you know, now that we are in this new technology age, you know, where do you think this is going to lead us when it comes to really serving the market in a way that's meaningful? It's outside of our silo as a company, as an organization, and it's going to really push somebody towards truly figuring out what is the modern consumer really looking for versus what I think they're looking for.
- [38:09]
B
You know, you're spot on. And I think for better or worse, and regardless of what you think about fast fashion, right, as you know, what it's done and how, what it's created, it is a great example of how a couple companies understood how to leverage data automation and early stages of AI, now later stages of AI into creating real advantage and changing how an industry competes. So they were incredibly smart in understanding how do I do social listening and identify early trends. How do I then take those trends and very quickly get them into some type of prototype product that I can test in a very small scale? If that test is successful, I then can ramp up production. And so now I go from trend, which I will use synonymously here for consumer need. Right? I Go from some type of need, some type of signal that there is a consumer unmet need into a potential solution for that, multiple solutions for that in a way that I can see which one is going to work or not work at levels. And then I can quickly ramp that up to meet that need in a way that doesn't, you know, crush my supply chain. It doesn't, you know, waste and create all this excess inventory. And then I can scale that, right. And, and I. They did that in a way that created orchestration and automation, not just within the company, but within the ecosystem. So it was, how does the company operate even faster from sign signal, through creative, through kind of prototype design. And then how does that coordinate with their vendors, their suppliers, their distribution networks to be able to go from trend to product in seven days? Right. Like that's something that is just almost incomprehensible, right? Of how fast that's actually working. Again, it has some other very negative impacts and lots of ways that we can unpack later. But that idea of how can I use this technology to surface real need? Because there is no lack of noise out there. TikTok, Instagram, you know, Twitter, where everyday consumers are just like flowing, flowing, flowing from all these things, how do you make sense of that? How do you translate that into potential opportunities and white spaces? How do you connect those white spaces with ingredients and formulations and biology? And then how do you translate that into products? If we keep that in a skincare world, same thing as we think about hair care, same thing as we think about, you know, makeup in different ways. So the process is the same, but it's all about understanding, getting data, translating that data into the ways that I monetize that. Right? A product, an ingredient, a formulation, like whatever. And then how do I now communicate that, test that with my audience and my consumers?
- [40:49]
A
Yeah, no, I mean, it's really, really fascinating. This is like such an interesting time, I feel like, to be in any of these spaces, you know, because it's like you have to really kind of, I don't know, find your own thing, you know, figure out what works for you and where does the like, where does AI really fit into your goal at the end of the day? That's the question that I think. It's the million dollar question, I think for a lot of people, you know, in terms of like, how to really make a small brand into a mid sized brand and then scale that, you know, I guess also one of my questions, Chaz, I mean, you've been in the investor world For a long time. Investment world, you know, for a long time, like, what do you think this all means for, like, investors and like, really the funding side of everything, you know, cutting through and figuring out which brand is worth investing in, that kind of thing.
- [41:28]
B
Yeah, it definitely changed the game. It changed the landscape in a lot of ways. So things that used to be protectable, and I'll make the analog into beauty in half a second. But, you know, if you go back 10, 15 years, software as a service, like, that's where every investor was pushing, right? This software is going to eat the world, was the famous Andreessen quote. And now you're living in a world that AI is eating software and software as a service, and AI is writing software. So, so now you have fundamentally changed. What can companies compete on? And you have the value of companies that were hundreds of millions or billions of dollars that were erased overnight because AI basically said, okay, look, now AI can do this. So sustainable advantages is definitely changing. I think it leans into some aspects that are more traditional, like brand building, protecting distribution, moats, things like that now become even important. Because what it costs to run and create a company now dropped by another order of magnitude. Just like when, you know, Shopify, Stripe, all those things made it so much easier to launch a brand. So you had a whole new wave of indie brands that could now could get into dtc, could start to reach their consumers. Because you had social media, you had Shopify, you had Stripe, and you simplified these ways. And the cost of actually creating a brand and selling to customers, now AI has dropped that even further because what used to take 10 people and maybe $3 million now might take two people and $500, completely making up those numbers. So that's changing. I think the other thing that it is doing is giving more emphasis to how companies are organizing their structure and their operations and their data to win in an AI world. So there is going to be a new crop of brands that grow up, that are created, that are AI native in the same way brands were social and digitally native, and a digitally native DTC company and a social native DTC company, they fundamentally move different than their counterparts. And you could see that in how they went to market, in the way that they competed, in the way that they grew. You're going to have the same thing happening in AI. And what I mean by that is a company that might be one or two people that has AI running everything from its regulatory to its legal to its distribution, its procurement, its operation, its inventory, Right? You can think about a company that is two people that have very deliberately and intelligently built the AI employees and the AI workflows around that company that can now compete with a company of a hundred people. Different economics, but same return in some of that. So I think that's what it's really going to change. And I think what's hard in the game today, especially for many investors, is Beauty has had a weird mix of investors and not the investors are being weird, but you had brand and kind of traditional business investors and then you had companies that were maybe more pharma or biotech and you had science based investors and technology based investors and not too many that understood how to cross that chasm. And now AI is kind of pushing that to say, well, to be successful you're going to have to figure out both. And I think it's going to reshape the type of investors that also come in and are comfortable deploying capital into some of this new cohort of companies because they have to understand what it means to build an AI native company as well as what it means to build a brand and a beauty company. All the things that go with that.
- [44:49]
A
That's brilliant. Thank you for sharing that. Yeah, I mean, I'm very curious to see like what companies, the big investors in Beauty especially, you know, I follow quite a few of them. Just curious, I'm always curious to see who they invest in and why and all that. And I just, I'm very curious to see how this evolves in the next like five years, you know.
- [45:04]
B
Yeah. And there's going to be a lot of pressure on returns and, and other things. It's definitely going to be a dynamic and kind of tumultuous time for a little bit.
- [45:12]
A
No, it's very, very, it's cool though. I, I, I think it's pushing a lot of things forward, you know, for me, I personally care about science above would love to see. There are so many amazing brands, I think in all of these industries that I feel like you can never go wrong if you just push your science a little further, you know. So I'd love to see the investment world kind of echo that and say, yeah, okay, you know what I mean? In your deck, I want to see a lot more.
- [45:34]
B
Sure. And I think the reason it hasn't, the reason it hasn't been to the forefront is there weren't great ways to communicate that to consumers. Right. The science wasn't being necessarily valued by all consumers. And so because things would get so watered down, like a hyperpigmentation claim I could have amazing science behind it, but when it hits the shelf and I have, you know, four words and three and a half seconds to communicate that in social media, they all kind of sound the same. I think this is though, a moment where people can now start to dive deeper. And I think some of these technologies will allow consumers to. It will make it more understandable to consumers about what that science is actually doing and how that science can benefit them. So hopefully it does start to create some of that distinction for those who care. And I think there is definitely possibility here. The one thing that I do want to touch on before we we wrap and I know I love our conversations because we can cover so much ground. I thing that's interesting or one of the big things that is not being talked about in AI right now. A lot of our conversations are like what you and I are having now, it's an AI application to the company, to the brand. But what's interesting when we take a big step back and we look at the industry is we know a lot of these companies are going to be reshaped and redesigned. And a lot of the conversation that's happening in the industry is not. It is from the point of the executive, the employee, the team member, and they're trying to figure out what AI means for them. And so you have like these two different worlds and different narratives. From a company perspective, everyone's excited about AI from the outside of like what it can do for the company and how it's going to take the company forward. But from the individual's perspective, you have executives that are like, I have two decades of experience and investment and sacrifice and blood and sweat and tears that I put into building this expertise in this career. And everyone's telling me that AI is going to come make this obsolete. And I think there's a really interesting dynamic that a lot of companies are going to have to deal with from a company company perspective of how do we deploy AI in a way that makes sure we keep the best things about our human capital. And when everyone's scared about AI, that's really hard to do. And it's really hard to get adoption, to get the value out of AI when your teams are really afraid of it. And so companies have to address that head on. But then at the individual level, a lot of executives have to have that sober and objective conversation looking themselves in the mirror that says, am I ready for this next chapter?
- [47:52]
A
Right?
- [47:52]
B
Am I ready for this next generation of leadership? And what's going to be required because AI is not going back in the bag. Right? It is not slowing down, it is not stopping. And companies are not going away. So what's happening is they are changing and they are being redefined. And so what leadership means and what it means to go from, you know, a director to VP to SVP to C level, that's being redefined. Find. And there's not great blueprints out there and there's not great support and other things for those employees and executives trying to make that transition. And I think that's something that's kind of getting lost right now in the overall conversation is how do I as a leader navigate my function, my team, my company through this transformation that we're in? And I think that's going to start to get a lot louder in the conversation because that fear is going to go up and companies are going to be looking to the leaders and saying, well, what are you doing? And no one knows how to answer that question. And it's interesting. So, you know, outside of this, there's, there's a lot of executive coaching that I do. And, and so people are sitting down and they're interviewing for Chro or they're interviewing for CMO and cfo and they're all being asked, what are your plans for AI? How would you use AI in this function? And the reality is the interviewer asking them that question has no idea. And they, as the person answering that question, really have no idea. And so there is this moment in this vacuum that I think the next generation of great leaders are going to take advantage of to say, okay, now's my chance to really redef myself and how I can navigate this space. And I think there's going to be a lot of movement inside of companies and it's going to come from sometimes non traditional and unexpected places.
- [49:29]
A
That is really fascinating. I'm really glad you brought that up, if you don't mind. I have one follow up question though for you on that. You know, where do you think a lot of these, like a lot of the really high level executives, where do you think they can start then? Because, I mean, yeah, you're right, they have 20, 30 years of experience, you know, so.
- [49:45]
B
Yeah, right. Yeah, it's a great question. And you know, this is what we've been helping a lot of them do is start to put that together. And you know, we kind of joke and say, okay, you've, you've watched the video, you've read the book, you've listened to the podcast, right? And, and now you kind of Go back and you're still sitting there and going, but now what? Like, how do I actually apply that? And I think that's the biggest gap that's happening in AI right now is so much education is around, well, what is AI and what is an agent and what should governance be? And it's not taking into account what is my role, how do I need to strategically think about this, how do I, I apply this in my board meeting next week, how do I apply this in my executive leadership team meeting? How do I help my teams think about. Because that's what leaders are being asked to do, and they can't answer that question yet. And so I think it comes down to the real application of AI. You have to have some AI fluency to know where to ask the smart questions. But then it really is about having a coach, a guide, a community that you can pressure test this with, that you have that support. Because AI is changing so fast. Even if you get up to fluency today, by next week, there's going to be new developments that you have to figure out, and your company and your teams are going to be looking at you to say, what do we do? How do we navigate this? Right. And that's what I don't think executives have enough of right now. They don't have that support, that, that kind of mentorship and community to help them navigate through that. And so, you know, it's part of what we're trying to solve. But it definitely is a big problem that a lot of executives are facing.
- [51:15]
A
Yeah, that's, that's, I mean, that's a huge, huge point that you brought up. I mean, honestly, there, there's so much shifting. I mean, I've seen it myself in the beauty industry especially. There's so much shifting going on right now with, like, CEOs changing and, you know, everyone's moving around. And that's very valid. And I mean, I haven't thought about it at all, to be honest, but I do wonder, like, if you are somebody, like you said, that has 25, 30 years of experience, you worked for the biggest names in the industry, what now do you do? That's going to be like, yeah, this is now my differentiat point. This is why I am the best person for the job, to push you forward using AI, you know, like, utilizing it in your current structure. That's, that's a huge question.
- [51:51]
B
Yeah, yeah, yeah, it's, it's one where we're definitely working on solving and there's a lot to it. But, yeah, it's a big one. So it's where a lot of people are. And I think the hard part in that is just answering some of these other questions at the company level are hard for individuals to do when they're really concerned about where they're going to end up. Right. And that's what I talk to a lot of companies about when they're trying to and we're helping them implement, you know, AI solutions. We focus a lot on organizational behavior and org design and change management. Because two biggest problems in AI have nothing to do with technology. Right. It is data, which is a solvable problem, and humans, which is a much harder problem to solve. And it's about adoption and how does this fit in my workflow. And like all of those things that make change inside a company difficult. And if you just focus on this, that is an AI problem and an AI solution, you're missing 80% of what your organization is, which is human capital. And that human capital is in the aggregate and that's at the individual. And so those have to be solved. And it's a place that, you know, companies have to take very deliberate time and it's a place that team leaders and executives and CEOs have to spend a lot of time both individually. Like, how do I lead this as well as how do I make the right environment and ecosystem possible that my company can actually take advantage of this technology?
- [53:06]
A
Yeah, that's really fascinating. And real quick, I just want to ask one thing because this just came to my mind was if I am like a C suite level executive, right? And I, I've got all this knowledge like stored with years, how do you still protect yourself? Say you're saying, okay, I'm going to figure out how to do this, you know, I'm going to utilize AI for myself. I'm going to get better, you know, and I'm going to become a better asset for this company. But how do you still protect yourself at the same time where it's like, I don't want to give everything I've got to this one company because what if they get rid of me, you know what I mean? And then they've got all my knowledge, they've got all my experience like stored somewhere. Like, how does that work? You know?
- [53:37]
B
No, it's, it's great. And this is, this is, that is the very real question that people face. And what we, you know, kind of, and I talk to a lot of executives about in that is the, the value of information is going to continue to go towards zero Right. So it's not about the information, it's about how to apply, how to integrate, how to lead the teams and the transformation. And if I take that in an analog for a moment to help. There was a lot of conversation when AI first was kind of peaking that this was going to make doctors irrelevant. Right. This is going to replace physicians, replace medical professionals. And you've seen it augment, but it didn't replace. Right. You've seen it even in places like radiology where it was like, we're going to be done with radiologists because now we just need to read screens faster. It's now a tool that they use to get through even more and to go deeper into each one. But there are things that AI and machines do really well, and there are things that humans do really well and the understanding of a patient who's sitting in front of you and all of the non verbal cues and the emotional state and how they're communicating, how they look like things that aren't just a diagnostic measure of temperature and biology. Biomarkers, Right. Those are all things that factor into how a physician understands what they're seeing back in a radiology report or diagnostic to put together what this could be. Right. So AI helps accelerate that, but it cannot do that singularly by itself because it doesn't have those other things that humans do really well. Business leadership is even more like that.
- [55:06]
A
Right.
- [55:06]
B
I can give you information as the CEO, but how is your team structured? What are the organizational dynamics? What are the internal politics that are happening between your CMO and your CFO and your COO and their teams, Right. Of how this is going to play out. AI is not going to solve that for you. You as a leader are still uniquely positioned to take that information and translate it into valuable action and execution within your company. So it's not something you should be super afraid of, of like I'm training my replacement. What you should be focused on is how do I lean into those things that are very hard to replace, the intangibles about what make me special, that makes me indispensable. Right? And that's what we really focus on when we're kind of coaching executives into how do I make this transformation, how do I become indispensable in this AI future? And that's a lot of the conversation. It's not information. Like you can know everything about AI, but if you don't know how to translate that into action and strategy and execution and how to get your organization to move and to incent people to adopt because a lot of the things that we do in larger organizations, like we don't have direct power. We have to influence. We have to, you know, finesse. Right. Like things to get accomplished. And that's a very human capability.
- [56:20]
A
That makes sense. Thank you so much. This has been amazing. I love talking to you. I honestly, I could talk to you for hours. You just have so much knowledge and I. I really, really love that. Because honestly, this is the best conversation I've ever had about AI. Like, I've been asking these questions. I feel like randomly I never understand. I never get an answer. So thank you. I can't thank you enough.
- [56:36]
B
Well, we'll have to do it again and go into the next level because there will definitely be more to talk about.
- [56:42]
A
I can do a whole master class with you. You just tell me, you know, for sure. But thank you so much. This has been amazing.
- [56:48]
B
Thank you for having me. It's great to talk.