EP 368: The Power of AI in Retail - From Product Pages to Profits

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You probably know this by now, but AI actually has a big impact on a lot of how we work, but it probably also has a big impact on how you buy things, right? How you live your life out in the real world. And AI is changing things very, very quickly when it comes to retail, where we spend our money, how we spend our money. And although it has changed quickly in the last couple of years with the big chat GBT moment, I think that we're in for a lot more innovation than we've experienced so far. So we're going to be talking about that today and a lot more on everyday AI. What's going on, y'?

All?

My name is Jordan Wilson and I'm the host of Everyday AI and this thing is for you. It is your daily podcast live stream, free daily newsletter helping us all keep up and get ahead with everything that's happening in the world of generative AI. So if that sounds like you, you are in the right place. Thank you for tuning in. Before we get started, have to give a shout out to our sponsors at Microsoft. So do you know about Microsoft Worklab? Well, why should you listen to the Worklab podcast from Microsoft? Because it's made for leaders who know they must adapt to to stay ahead. Worklab is the place to find real world lessons and actionable insights to prepare you for the next phase of AI at work. That's W O R K L A B, no spaces available wherever you get your podcasts. All right, well this podcast, like I said, is Everyday AI. If you haven't already, please go to your everyday AI dot com. Sign up for that free daily newsletter. We're going to be recapping all of the AI news and today's podcasts in in to the newsletter. So make sure you go check that out. All right? Before we talk about AI's impact on retail and y', all, it is a big one. Let's first quickly do as we do every single day and start off with the AI news. So first, there's some meta privacy confusion online when it comes to AI. Who would have thought? So recently a viral post has emerged across meta's platforms claiming users can protect their personal data from being used by the company and its AI. This message has gained a lot of traction, but it lacks any real late and wait and it's not going to be effective. So the statement which begins With Goodbye Meta AI suggests that all users must post it to prevent the company from using their information and photos. So this copy pasta appears to have started in early September and follows a similar wave of misinformation from May where users share post about not permitting data access. So Meta's terms and conditions clearly state that users allow the company to use their publicly shared posts, including photos and text for for training AI when they create an account. So yeah, you can't block them now. Sorry. All right, next piece of AI news. Notebook LM from Google has enhanced its learning experience with new audio and video features. So users for Google's Notebook LM can now upload public YouTube URLs and audio files into Notebook LM, expanding the range of source materials available for analysis. So if you don't know, the tool provides inline citations LinkedIn to video transcripts, allowing for deeper exploration of key concepts presented in lectures and videos. So the new feature in addition to this also we talked about this on the show is sharing audio overviews that can essentially generate a personalized podcast. So yeah, I'm personally huge fan of Notebook lm. Out of the thousand plus tools I've used over the last year that are AI, this is definitely one of the one of my favorites. All right, our last piece of AI news. UK regulators have cleared Amazon's 4 billion billion with a B4 billion AI partnership with Anthropic. So the UK's Competition and Markets Authority, or CMA, has decided not to investigate Amazon's partnership with the AI startup Anthropic, which includes a significant $4 billion investment. So this decision is pretty noteworthy as it reflects the regulator's stance on competition in the rapidly evolving AI landscape. So the CMA concluded that Amazon's collaboration with Anthropic does not raise any competition concerns, so therefore it's avoiding a deeper probe under Britain's merger regulations. An Amazon spokesperson welcomed the CMA's decision, emphasizing its acknowledgment of jurisdiction limitations regarding the partnership. So Anthropic obviously is co founded by former OpenAI executives and it has attracted substantial investments from various tech giants, including Amazon. All right, so there's a lot more AI news. If you haven't already, please make sure you go sign up for the daily newsletter@your everydayai.com hey John and Marie and Jackie and everyone else. Thanks for joining us to our live stream audience Podcast audience. You can always come back and watch this video, but I'm very excited for today's conversation. We have a leader in the retail in the Commerce industry. I'm extremely excited to have today's guest on the show, so please help me welcome y'.

All.

There we have him, Brian Gildenberg, the founder and the founder CEO of Confluencer Commerce. Brian, thank you so much for joining the show.

Thanks for having me on, Jordan. It's a great pleasure to be here.

All right, I'm excited for this one. And hey everyone, joining us live, please. If you have questions for Brian about the future of AI and retail, get them in now. But let's start at the top. Can you tell us a little bit more, Brian, about what you do at Confluencer Commerce?

Yeah, sure. So first of all, for those of you I've never met, which is from the comments, most of you, it's a pleasure to be here. Brian Gilderberg, so founder of Confluencer Commerce and also the managing director for Retail Cities, a retail research firm. I've been around the retail industry for probably close to 30 years now. And most of my career was in a business called Kantor where we studied the evolution of global retail going back to the pre Internet world. So I started studying retail the day Jeff Bezos wrote us for Letter to shareholders in July 15, 1997. So. So yes, I've been been around the space for a while, worked at Omnicom for a couple of years helping an agency kind of build its commerce strategy. For the last couple of years been on my own, mostly helping large brands and large retailers understand the transformation of the commerce, media and content landscape and the confluence of those three things and how that's going to change the way they go to market. And obviously over the last 18 months or so, this AI things become kind of a big deal. So we're paying pay more attention to this than I would have say two years ago, so.

Yeah, exactly right. And artificial intelligence is not new, so. But this whole generative AI movement is. But Brian, maybe can you catch us up for those of us that aren't super familiar with retail industries, commerce industries, how has AI traditionally been used? And then where are we at now with this generative AI boom? I know that's a lot to ask, but give us, give us a high level there.

Oh, sure. Well, that's simple. Well, I would say that, I would say, look, I think there's layers to AI, right? And you know this better than I do. AI as a concept has been used in retail for decades as an extension of machine learning to help scenario plan and model a operationally complex and granular business. Retailers been using AI to model things like forecasting and supply chain and pricing and all the things that you would think that from, from a mathematical point of view, quantitative AI has been a big part of the retail landscape forever. I think when you look at Gen AI now, the more text and image, text content and image based AI that sort of become all the rage since ChatGPT entered our collective consciousness. You've got, I think, use cases today which are, I think conceptually quite strong, particularly around the topic of personalization. Right. And personalization has been a parallel theme in retail for years as well. Well, so if you look, if you look at a large grocery retailer like KRoger in the US or Tesco in the UK, they've been personalizing promotions for people for years, right? Like tech. You know, Tesco was sending out 8 or 10 million different circulars every week when you were mailing circulars to people's homes based on your purchase behavior, based off of the data that they would collect off that relatively innocent piece of plastic you have called a loyalty card. So, so I could then take that, figure out what you were most likely to buy, and then tailor a target promotion based on that. So the, so quantitative personalization of retail has been a big deal for a long time. The, I think the application and potential use cases today are to take all of that data that I have on somebody, be able to layer gen on top of that and deliver them a genuinely personalized experience. And to go back to Jeff Bezos, I think we are very much in day one of that rather than day two or day three. So I think right now a lot of the use cases for Gen AI in retail, they get a lot of buzz and people like to talk about them in industry conferences. But right now what you're looking at is sort of basically an enhanced chatbot for the most part. So how do I use AI to be able to provide a technical service experience for somebody that feels more like a human being? Gen AI allows you to do that by targeting that messaging, targeting the content, in many cases making it culturally appropriate so that, you know, the AI sounds like a teenager or sounds like a Latino consumer or whatever it is. You can do a bunch of different things in the context of that. But right now I still think we're in pretty early days, so.

No, Brian, you bring up an interesting point, right? Like, is AI right now in retail and commerce essentially just a personalized chatbot? But, you know, maybe even asking. So when this kind of chat GPT moment happened, I think everyone in every industry was like, you know, looking at the long Run and saying, oh, this is going to shake up all of these trees, I guess. Have all of those trees been shaken yet? Is there still a lot that generative AI can do or do you think for the most part it has just been like, okay, well, we have more personalized chatbots that are, you know, driving sales?

Well, I think there are. The short answer is no, I don't think we're anywhere near where this is going to go. Right. So, you know, as I think Bill Gates once famously said, the most common mistake people make is to overestimate the pace of tech change in the short term and dramatically underestimate it in the long term. I think that's exactly where we are with, with Gen AI at the moment. I think right now the most interesting use cases for a on the retail side, and both Walmart talked about this a lot in their earnings call last quarter, aren't on the consumer facing side but on the business facing side. Because as is particularly Walmart and Amazon, which have large third party reseller marketplaces that they're bringing to market that involves hundreds of thousands or in some cases millions of sellers. The ability to put AI as a layer so that those sellers can access the platform and vary content and buy advertising more quickly and effectively. That's been the single biggest commercial use case for AI and commerce today isn't on the consumer facing side, but on the business side. I think on the consumer facing side there's so many interesting applications that could come out. I'll just give you one simple one, right? So there's a company called Fetch in our space today that basically takes all of the receipts that you have, collects them all, you know, aggregates that data and resells it, but then also provides you basically discounts based on how much you spend. Right? So basically they're paying you to give them data so they can resell it. So it's a good business model, works for everybody. Cool company. Imagine if somebody decided to do that with all your receipts, but instead of that fed all of your receipts through an AI engine and now all of a sudden you've got an AI engine that's basically your auto replenishment for your groceries for a year so that you've got this whole thing now where basically I've got what I've bought in the grocery sector for the last 52 weeks or something from everywhere. And that you've got a predictive AI engine that basically tells you what your replenishment cycles look like for various and sundry things so that you take 80 to 90% of the thinking and decision making out of your everyday grocery purchase. I think those are the types of applications that are going to get really interesting over time and that those are going to fundamentally shape and transform how people decide where and when they're going to shop.

Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on gen AI. Hey, this is Jordan Wilson, host of this episode very podcast. Companies like Adobe, Microsoft and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use gen AI. So whether you're looking for chat GPT training for thousands or just need help building your front end AI strategy, you can partner with us too. Just like some of the biggest companies in the world do. Go to your everydayai.com partner to get in contact with our team or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on gen. So you know Brian, you brought up a very interesting point there at the beginning of your response. You know, drawing on this, you know, Bill Gates quote about overestimating in the short term and underestimating the long term. So how do you think maybe retailers are underestimating the long term of generative AI? Maybe. Where are they missing that big opportunity?

Well, I think right now, I don't know, it's an underestimation, but I think it's just a lack of clarity on where the single biggest digital asset today that retailers have to create demand and sell advertising is search. So if you think about what gen AI at a simple level can and will over time do to search and how ad supported that may be versus how organic that may be, that's going to be a foundational question as this unfolds. So you've already got perplexity today who's looking to try to figure out how to monetize their gen AI platform from an advertising perspective. Now they're not going to introduce as of now ads into the search results, they're putting up ads around the search results. So that's a, that's a thing. But over time, every, every content engine in the history of the world has sowed the seeds of its own destruction by, by then embedding advertising within that. And I do think that over time that balancing act is going to be interesting. But the whole, the whole notion of how search evolves and then how gen influences that and how people use gen AI instead of search or in addition to search or unpower all the search is a really interesting question. I also think and that as you look today, so much of product discovery today in the world is being driven by AI, but it's an AI that isn't a retailer one or even necessarily a tech platform like Amazon. It's TikTok's algorithm, right? Like so much new product discovery takes place on TikTok today. And all of that is algorithmically served up to individuals based on their content consumption behavior. And today TikTok for people under 35, TikTok is the primary way that people find out about anything. So, so, so that right now is the single biggest use case that we see for AI in the world is just trying to understand TikTok's content algorithm to the degree that you can then, that you can then put new product in front of people.

So yeah, I'm glad to be that in that over 35 group because I don't understand TikTok and dancing and pointing at things. But yeah, apparently that works and that's the future of buying products. But you know Brian, I want to go back to something that you talked about that I think is huge here, right? So with commerce, retailers like searches is huge, right? It can't be understated and you know, just so everyone knows background, right? I'm 15 years, I've been in and out of SEO and I've never once said, you know, traditional search is dead, right? Even though they've been saying that for 15, 15 years. But with AI, with these answers engines like perplexity and AI overviews from Google, I mean you have to start thinking about is tradition, traditional search going to work the same way. So maybe for people out there, whether they're, they're retailers or consumers even, right? Using these perplexity or you know, now you see Google is just, you know, kind of auto suggesting these things with these AI overviews. How does that impact big retailers and consumers and what should they be aware of, right when they're oh, now all of a sudden just being served these things by AI.

Yeah, well, fortunately having the blessing of being markedly over 35 feel like I've been to, feel like I've been to something like this movie a Couple of times before. And the closest parallel I can think of is, you know, let's call it 10, 12 years ago when people started to figure out that, that Instagram was replacing Facebook. Right? And at the time, no one could really figure out how to, you know, no one could really figure out how to buy Instagram search for the most part, right? So, so that what people were, so that what people were doing was that they were, they were trying to figure out how to get the, how to get. How to take the organic environment and figure out how to adapt. How to adapt to search. So I think today that what you've got is so that for a long time on the, on the TikTok side, you've got a, You've got a bunch of people now that are trying to use. Trying to figure out how to crack the code on that organically without being able to, without being able to buy that. Being able to buy search that way. So I think there's an enormous amount of work being done right now in the commerce ecosystem around figuring out how do you get content that gets picked up by that algorithm, how do you then embed that content? And then most importantly for bigger companies, which are where I spend most of my time, how do you then scale that?

Yeah. So one other thing I want to talk about is we said, hey, is AI right now, for the most part, retailers and commerce, is it just kind of a glorified chatbot, a better chatbot? How do you think this has played out so far? Far. Right. Because at least for me, even I think a big part of, of, you know, retail and commerce for me is I'm asking questions about products either before I buy them or after, you know, with, you know, following up on support and those things. Has generative AI actually been a quote unquote, game changer for at least that one specific part of this process? Or would it still just be something you'd say is incremental?

I think right now it's still in. I think a lot of it's still early days on the PI. On, on from a piloting point of view, I think people are still learning how to har. How to harness the technology and then figuring out that rather than trying to replace a person with AI, which often doesn't work quite as well as you would hope it would, is providing access points that a person wouldn't be able to do or get to and then layering AI in to create a better experience than nothing. Right? So, and that's sort of, you know, as I, as I got fond of teaching my kids when they were doing math at an early. It's, you know, when you do it, when you divide something by zero, literally anything is infinitely better than nothing. So, so, so and that, that I think is where the most interesting start cases are. Where I can reach more people with more use cases more easily, create more touch points to create a, the semblance of a personal relationship where there wasn't one before rather than trying to replace a personal relationship with AI which almost never goes well like if I, you know, and like an example that a friend of mine uses all the time is Erica, the Bank of America sort of AI. You know, Erica is incredibly clear that it is not a person. Right. So it's like, hi, I'm an AI engine, I can help you do things that otherwise would be a pain in the neck. So, so here's how we're going to do that. If you need to talk to a person, click here. Like I think that's a very real way to set the problem up as opposed to calling into a, or logging into a customer service experience thinking you're going to get a person and then getting AI back. I think that creates disappointment. So I think Gen AI allows you to get to a broader set of use cases from a back and forth perspective and to create the sense that somebody's getting their question answered, which I think is great. I also think too that the other interesting application for AI as you look at the consumer facing world is not from a text point of view, but from a content point of view. So Amazon talks a lot about this with its third party sellers that they're creating an ecosystem where it's much easier for sellers to create, to create images in AI, to vary their images in AI. Like if you look at anybody that knows anything about selling things on Amazon, the frequency with which you refresh your content is an important part of winning consumer attention and of winning organic search on Amazon. So the ability to do that quickly and cheaply through AI then creates a better experience overall. Not just for the user because they get something new and refreshed, but for the brand because they, it allows them to vary their content without having to have an individual deployed against that, that content variation.

So yeah, and what, you know, I'm glad we can get into this because I even personally think that's one of the more exciting and you know, low hanging fruit use cases of generative AI for commerce and retail. Yeah, it's like we don't need that same, you know, one or two product photos that look like they're from 1990. But maybe what dangers are there in that? Because, you know, I remember reading a lot of stories early on in the generative AI phase when the, you know, AI image generators weren't that great and people really didn't know how to use the large language models. But, you know, are there some maybe dangers in, you know, retailers just blanket, you know, putting Gen. Gen AI out in the wild without enough human in the loop? What, what dangers are there and what should retailers be looking at to avoid some of those common pitfalls?

Well, yeah, I mean, I think there are, there are clearly, there are clearly dangers that, that come from, you know, poorly trained and hallucinating AI. And you know, every, everybody on this knows more about this than I do. But, but yeah, you want to try to avoid the, you know, what, Some, some of the challenges that things like Gemini have obviously run into over the, you know, in the recent past that are well publicized. Yeah, we're all learning. Right. So, yes, I think there's certainly a risk for hallucinatory content or I think the actual bigger risk is. Another friend of mine describes this as not so much that the content's wrong, it's that it's homogenized. So the idea that the biggest risk from AI is not black popes everywhere, but that we're drowning in a sea of sameness and, and that that AI is going to optimize for content that's just going to be remarkably similar to itself. Right. And the metaphor that always sticks in my head, again being old, is that, you know, I'm old enough to remember when everybody discovered clipart in PowerPoint and everybody had the same nine pieces of artwork in their PowerPoint slides because they thought they're being creative, but it was always the same nine things with the same, the same stock photos of people with their arms in the air, excited about stuff. And I do worry that we are, from a content point of view, going to be headed towards a homogenized and AI produced content ecosystem to the degree that the phrase artisanal content popped into my head one day. So I think that you're going to see agencies in the future that are going to talk about artisanal content created by humans, for humans. So, so much so that I bought the URL artisanalcontent.com I love it.

I love like, like grabbing those little, like, like little URLs. I've been doing this for a long time.

Yeah, me too.

That's, that's a good one. For the Future. You bring up a good point. I was actually talking with a good friend about this, you know, maybe six months ago, this concept of. Yeah. Will things created by humans in the future almost be. Yeah. Artisanal. Right.

So, yeah, it'll be, or it'll be organic content, but not the way we talk about organic content today, but organic, like handcrafted content. Like, here is your curated heirloom image. Right. So, you know, like an heirloom tomato at a farmer's market.

So, yeah. Yeah. A wild world to think about. But it's, it's not actually too hard to see that becoming the truth. Yeah. And if so you, you have the URL there, Brian. So, you know, one thing that you, you talked about a couple of minutes ago is kind of retailers layering in AI. And I'm wondering if maybe especially after you just gave this clip art example, if they're just layering it in too quickly, too haphazardly, you know, maybe where they're just trying to sprinkle some AI on everything and not necessarily, you know, training it or, you know, making sure it's, it's, you know, using RAG to bring in their own data to kind of tune different models. Are there problems with that? Are companies maybe not training AI like that? They should be, and just using it at scale?

Oh, God, Yeah. So, yeah, there's hundreds of them. Yeah. I think there's, I think there's a series of interesting issues. I think that, you know, to your good point, I think most of the challenges that people have with AI is they just don't really understand the value creation process in AI. They don't understand the enormous amount of work that needs to go into training AI, both from a workflow point of view, but also from a data point of view. So where and where and how am I going to decide the degree to which I need the AI that's powering my business to be trained in a specific way to me. Right. And then if I'm going to do that, where's the data going to come from to do that? Like, you know, what's what, you know, what's what fuels this content engine? Like, you know, so many of the clients that I work with today saying, oh, AI, we need data scientists, we got to go figure out the same thing. It's like, no, you don't like data scientists. Do one thing. What you actually need are data engineers. Right? Like, you need people. Like the way I was, the way I put this to a client once, they were like, we hired a bunch of Data scientists not doing anything. It's like, it's like opening a restaurant, having 12 chefs, but no food. So, like, you know, data scientists are chefs, data engineers make food. And what you've got to do is you've got to figure out where and how you're going to use the publicly available or, you know, licensable LLMs, the degree to which you want to custom train them, the resources that it takes to custom train them to get to the outcomes that you need for your business, the data requirements that that's got, and then of course, the management of that process. So, you know, one of the things that's always occurred to me with AI, it's like, well, you need to train it, you need to make sure it's got the right information. You need to govern it, you know, early on, you've really got to make sure that it's doing the right things. It does well with a well scripted, well defined job description. In the end, for all the talk about how AI is a techno technological transformation, it sounds more like an employee than anything else, right? Like the way people talk about AI is exactly the way I would talk about a new hire, right? Like, you know, 90 days in, you got to make sure this person knows what they're doing. You got to train them, right? You got to make sure they've got governance so they don't do something really weird and stupid like they'll learn more over time, and they're going to do really well with a fairly well curated inspect job description. You know, the, the metaphor, my, the metaphor a friend of mine uses all the time talking about this. With training AI, it's like, you know, training AI is a bit like training a dog, right? Like, you don't want to train the dog to sit and roll over at the same time. Like, you know, you get sit nailed, you know, sit, treat, sit, treat, sit, treat, sit, pet, and then eventually sit, then you move on to rollover or fetch, right? So just training one thing at a time is a skill set. And I think that the really differentiated skill for companies going forward won't necessarily be how fast their machines learn. It's really going to be how well their people can teach. So, yeah, machine teaching seems like a competency rather than machine learning.

Yeah, and that's a great point. And I do have one or two more questions for you on that, Brian. But real quick, I do have to shout out Microsoft here in the Work Lab podcast. So if you don't know, the Worklab podcast from Microsoft is made for leaders who want to understand how work is changing. So effective leaders, they adapt, they stay ahead of trends, and they embrace any edge they can get. They also know that AI powered organizations will be better at spotting opportunities, creating new products and business models, and maximizing value. So for real world lessons and actionable insights to help you stay ahead, check out the Work Lab podcast. That's W O R K L A B, no spaces available, wherever you get your podcasts. All right, yeah, you like, y' all gotta go check it out. They just dropped a new episode, so definitely worth, you know, listening if you haven't already. So Brian, getting back to that, I love that analogy, right? You can't, you can't teach a dog to sit, stay and roll over and shake all at the same time. You kind of have to take it piece by piece, step by step. So maybe with that, where should right now, like, you know, you've given a lot of great advice already here, but if someone, you know out there, they're a retail leader and they're hearing like, oh yeah, yeah, we've been guilty of that, how should organizations maybe get their AI strategy right? Or maybe for those that haven't even built one out yet, where should they be looking?

Well, I think that as with many things, I think you have to start with the business opportunity first, right? Like what's the, what's the biggest gap between where you're, where your user shopper experience is today and where you want it to be? And then what's the biggest gap in the experience? And then clearly, secondarily, what's the commercial benefit of solving that problem, Right? So if you take those two things together, you then get to the simplest two by two matrix in the world. If I've got high commercial opportunity and a massive gap in user experience, that's an easy one, right? So I'm going to deploy resources against that. And then what you need to do is just need to do a good old fashioned value chain exercise of what are the steps that it takes to get here? And then rather than saying, how do I get AI to solve that problem? How do I use AI to solve the steps along the way and the point solutions that go along the way and then figure out, you know, and this is one of the things that Amazon's brilliant at, right? What Amazon's really good at, even in the pre AI world is not trying to solve tech problems en masse. They solve them by what Andy Jassy refers to as primitives, right? So I'm going to build little tiny building blocks that work and then I'll figure out how to connect the things that work together. So but I'm going to do really small specific things that fix a specific problem. Then the connectivity is my, is. It will be my secret sauce later. But if I try to do something that's too connected early on, that tends to be geometrically harder. You know, solving three problems at once is eight times harder than solving one, not three times harder. So solve specific problems along the way, fix those things, test, monitor, and get them working. And then the secondary use case is how you tie those things together. So I think that's one. And then, yeah, I think you're going to find that there are opportunities that have very high commercial outcomes but that are kind of invisible to the user. One of your users jumped in earlier and said that supply chain is one of the, the biggest applications for AI. I would agree with, I would agree with Jackie on that front. There's a ton you can do behind the scenes to create tremendous commercial outcomes for your business that may not be entirely visible to the consumer. And then there's going to be things that make the consumer experience better, but that don't have an obvious commercial outcome or an obvious short term commercial outcome. That's where, that's, that's where the vision thing comes in. Right. And it's like you just got to trust that you have an idea of what your shopper experience needs to, to be and that I'm going to spend a little bit of money trying to understand how to make that better with an, with the idea that I know that this better experience will manifest itself in better commerce over time. I can't spend all my aim, I can't spend all of my money on that. No company can. But I can and should be spending money right now on what I might call almost sort of pure AI R&D work. Right. Because no one knows where this is going to end up. Right. So you're the most interesting use case most businesses will discover for AI is one they haven't envisioned yet. So that keeping an open mind and really understanding where the technology is going, immersing yourself in it to the degree that you can follow the trends, I think is important as well.

All right, so Brian, we've covered a ton in today's conversation, right? Yeah, yeah. How generative AI has impacted us from its evolution and impact in, in retail over the decades, you know, recent personalization, you know, search and content creation. We've tackled it from all over the place. But maybe as we wrap. What is your One biggest takeaway for whether it's it's for, you know, retail leaders out there in the space tuning in, the average consumer wondering how AI is going to impact, you know, their retail journey. What is that one biggest takeaway that you have for our audience?

I really do think it's that if you think about AI as a technology that's going to transform your organization, you're going to be disappointed. I think if you think about AI as a specific way to solve specific problems in a way that you could not solve them before in a way that's faster, cheaper, more personalized and better, I think that's going to be a super powerful pathway to figuring out how to harness this technology. In the end, I really do think that a lot of the best principles people are going to use to manage AI are going to be the principles that you've used to manage and grow people in teams. So good training, good governance, clear accountabilities, clear responsibilities, and a really well thought out plan for what it is it's supposed to do. That's probably more important to me than anything else. My, my good friend Deb Weinswig, who runs course I Researcher, probably knows, who knows way more about that than I do, always just says to the anxious, look, AI isn't a tech problem, it's a business problem. And the more we can start to think about the business problems and then how the tech helps solve them rather than here's the tech, what business problem am I going to have? You know, so you know that that'll get us out of the whole phase that you go through with any emerging technology where it's a solution, searching for a problem.

Yeah, love, love that so much. So much great content and insights from you today, Brian. So thank you you so much for taking the time out of your day to join the Everyday AI show. We really appreciate it.

Oh thank you very, thank you very much for having me on. Much appreciated.

All right, and hey everyone, if this was helpful, please let us know. Share this with someone who needs to know. We love having industry veterans coming on and sharing their expertise. So if this was helpful, please go to your everydayai.com Sign up for the free daily newsletter. Hey, speaking of that artisanal content, me, the human, I'm going to go re listen to all of this great content that Brian just gave us and write a newsletter for you recapping it all. So make sure you go check that out@youreverydayai.com. thank you for tuning in. We hope to see you back for more Everyday AI. Thanks, y'.

All. Bye.

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