How AI Will Reshape Beauty, Science, and Leadership with Chaz Giles

22:29A great example of that, in my opinion, is wound care, wound healing. I've been in that space 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. 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 decades. Like, and now how can we bring AI in and now accelerate your efforts into this? And you said, yeah, you said it very well because there's multiple things that exist in there.

23:02And 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 employees that get ahead are the ones that figure out how to apply that within their sphere of expertise.

23:44And that application is what we were talking about before. It's not just I throw in a chat GPT or I throw in a co-pilot. 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.

24:18And it aligns much more with like TCM and traditional Chinese medicine around 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 skincare? 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?

24:49What 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 a super powerful tool as an example. And this is what pharmaceutical companies have known for a 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 of hitting 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 &D.

25:33It might just be a couple chemists augmented by some really sophisticated AI to be able to get you there. 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, 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 'Oreal's of the world, they've had these enormous teams in R &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 teams?

26:10Like, 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 recently I interviewed somebody like I think a year ago, and she she's 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. 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.

26:38But 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? 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 kind of glue that held functions together and processes together. So you relied on people to move data and do analyses between that.

27:15And 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, 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.

28:04That'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's interesting in this moment is the Lauders, the L 'Oreal's, the P &G's 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 competitors. Like of their competitors. They have more resources. They have capital, right?

28:36They have technology teams that are in place. They should be winning. They are not, 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.

29:15They 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 and 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'll leave the company's names out of it.

29:49But 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 and in some of these areas, but then they pull 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 the, 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?

30:19Or they are growing their capital and they're growing 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 my weight class?

30:53And 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 that's a real advantage. That's so interesting to hear that. That's so interesting because I always wondered, I'm like, wonder what these larger, like the Estee Lauder's L 'Oreal's, 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.

31:23And so it's interesting to hear you 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.

31:52I 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.

32:23And 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 have to be, you know, 18 months and millions of dollars to go do.

32:58So 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 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 retail activities. but starting to take all of that qualitative historical data that they know 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?

33:36Just 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, know, six weeks and$50 ,000 to do consumer studies.

34:24No, that's interesting because I almost wonder also, when is it too much? You know, because for example, I want to reference the longevity space for this because I know in the longevity space, biomarker discovery is huge right now. 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.

35:04They'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? 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.

35:37That 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, 100 % there can be too much AI used.

36:15And 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.

36:51And 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 new products to new consumer segments that were unserved in new ways. Or I can make them available in channels 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 organ and functional pieces that I have today? And if it doesn't, there's no reason that I should be putting it in.

37:26Yeah, 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? And I'm curious because you have like the Reddits, 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?

37:58It'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, 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 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 and understanding how do I do social listening and identify early trends?

38:43How 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.

39:22It 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 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.

39:56Again, 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?

40:28Same 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? 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 like where does AI really fit into your goal at the end of the day?

41:05That's the question that is 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 midsize 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. I'm 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. Yeah, it definitely changed the game. It changed the landscape in a lot of ways.

41:33So things that used to be protectable and I'll make the analog into beauty and have 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 now you have fundamentally changed what can companies compete on. And you have the value of companies that were hundreds of millions of billions of dollars that were erased overnight because AI basically said, OK, look, now AI can do this.

42:06So 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 more 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 in the cost of actually creating a brand and selling to customers.

42:42Now, AI has dropped that even further because what used to take 10 people and maybe$3 million now might take two people and$500 ,000 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.

43:26And you could see that in how they went to market and the way that they competed and the way that they grew. You're gonna 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.

44:00So 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 and all the things that go with that.

44:49That'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? Yeah, and there's going to be a lot of pressure on returns and other things. It's definitely going to be a dynamic and kind of tumultuous time for a little bit. No, it's very, very, it's cool though. I think it's pushing a lot of things forward.

45:16You know, for me, I personally care about science above all else. And I 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. For 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.

45:44And 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.

46:14And I think there is definitely possibility here. The one thing that I do want to touch on before we wrap, and I know I love our conversations because we can cover so much ground. I think the 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.

46:55And 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 perspective of how do we deploy AI in a way that makes sure we keep the best things about our human capital.

47:29And 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, right? 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.

48:02So 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. 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?

48:44And no one knows how to answer that question. And it's interesting. So, you know, outside of this, there's a lot of executive coaching that I do. 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 redefine myself and how I can navigate this space.

49:22And 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. 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. 20, 30 years of experience, you know? So that's good, 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.

49:51And, you know, we kind of joke and say, okay, you've watched the video, you've read the book, you've listened to the podcast, right? 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 apply this in my board meeting next week? How do I apply this in my executive leadership team meeting?

50:25How do I help my teams think about it? 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?

51:00How 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. Yeah, that's that's I mean, that's a huge, huge point that you brought up. I mean, honestly, 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.

51:27And 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've worked for the biggest names in the industry. What now do you do that's going to be like, yeah, this is now my differentiation 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 a huge question. Yeah, yeah. It's one where we're definitely working on solving and there's a lot to it. But yeah, it's a big one. And so it's where a lot of people are.

51:59And 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 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.

52:35And if you just focus on this, that is an AI problem and an AI solution. You're missing 80 percent 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?

53:06Yeah, 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've got all this knowledge like stored with years. How do you still protect yourself? Say you're saying, OK, I'm going to figure out how to do this. You know, I'm going to utilize AI for myself. off, 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?

53:31You 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? No, 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 talked to a lot of executives about in that is 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?

54:06This 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 nonverbal 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 biomarkers.

54:45Right. 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. 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. 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?

55:18AI 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?

55:53And that's a lot of the conversation. It's not information. 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 large 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. 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.

56:26And 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. Well, we'll have to do it again and go into the next level because there will definitely be more to talk about. I can do a whole masterclass with you. You just tell me, you know, but thank you so much. This has been amazing. Thank you for having me. It's great to talk.