Everyday AI Podcast – An AI and ChatGPT Podcast - EP 368: The Power of AI in Retail - From Product Pages to Profits

Episode Date: September 27, 2024

Your retail strategy isn’t complete until AI is in the mix. Retail giants are harnessing the untapped power of AI—taking product pages from basic listings to profit machines. But are you missing out? We break down the real-world ways AI is changing the game for retailers everywhere with Bryan Gildenberg, Founder and CEO of Confluencer Commerce.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan and Bryan questions on AI and retailUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:1. Current Commerce Ecosystem2. AI in Retail3. Potential Dangers of AI in Retail4. Role of AI in Content CreationTimestamps:02:00 Daily AI news05:10 About Bryan and Confluencer Commerce08:04 Retail personalization backed by data has long existed.12:51 Future of search and advertising with Gen AI.15:53 Cracking TikTok's organic reach for big companies.19:07 AI alleviates tedious tasks, improving consumer experiences.22:11 AI might homogenize content; "artisanal content" emerging.25:19 Data scientists are chefs; data engineers, food.28:47 Identify user experience gap, assess commercial benefit.32:32 AI: Solve specific problems, personalized, not tech problem.Keywords:AI in retail, Bryan Gildenberg, Jordan Wilson, AI R&D, future applications of AI, AI use cases, AI's impact on retail, AI's role in personalization, AI's role in content creation, training for AI, AI governance, AI responsibilities, AI's role in search, GenAI, AI in content creation, machine learning, AI in supply chain, AI in pricing, data engineering in AI, AI implementation, AI strategy for retail leaders, generative AI movement, traditional AI usage, AI competition, data requirements for AI, AI's role in customer experience, future of human-created content, dangers of AI in retail, impact of AI, AI and TikTok.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the Everyday Podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live and Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. 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 GPT 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's 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 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.com. Sign up for that free daily newsletter. We're going to be recapping all of the AI news and today's podcasts in 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 weight 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 posts 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 training AI when they create an account. So yeah, you can't block them now. Sorry. All right, next piece of AI news. Notebook L.M. from Google has enhanced its learning experience with new audio and video features. So users for Google, 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 linked 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 L.M out of the thousand plus tools I've used over the last year that are AI. This is definitely one of my favorites. All right. Our last piece of AI news. UK regulators have cleared Amazon's $4 billion, billion with a B, $4 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. And Amazon spokesperson welcome the CMA's decision, emphasizing its acknowledgement of jurisdiction limitations regarding the partnership. So Anthropic obviously is co-founded by former Open AI 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 at your everyday AI.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. 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 guests 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 retail, get them in now. But let's start at the top. Can you tell us a little bit more, Brian, 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 Geldenberg, so a 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 Cantar, where we studied the evolution of global retail going back to the pre-internet world. So I started studying retail the day Jeff Bezos were just first letter to shareholders in July 15, 1997. So I've been around the space for a while. I've worked on the compound for a couple of years, helping an agency kind of build its commerce strategy. And for the last couple of years, been on my own, mostly helping large brands and large retailers, 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 thing has become kind of a big deal. So we're paying more attention

to this than it would have said two years ago. 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 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? 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 have been using AI to model things like forecasting and supply chain and pricing and all the things that you would think that 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 content and image based AI, that's sort of become all the rage since chat GPT 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. So if you look at a large grocery retailer like Kroger in the U.S. or Tesco in the UK, they've been personalizing promotions for people for years, right? like Tesco was sending out eight 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 I could then take that, figure out what you were most likely to buy, and then Taylor and Target promotions based on that. So quantitative personalization of retail, but a big deal for a long time. 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.A.I. 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 AIA 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 chat box. for the most part. So, you know, 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 than the context of that. But right now, I still think

we're in pretty early days. So, you know, Brian, you bring up an interesting point, right? Like, is AI right now in retail and commerce essentially just a personalized chat bot. 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, 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 Gen A.I. at the moment, I think right now, the most interesting use cases for AIA on the retail side, and both Walmart talked about this a lot in the earnings call last quarter, aren't on the consumer. consumer-facing side, but on the business-facing side, because as 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 expect, 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, a cool company. Imagine if somebody, someone's a business model. It works for everybody, cool company. imagine if somebody decided to do that with all your receipts, but instead of that, you've had 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 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 show. Adobe just introduced an entirely new way to create,

bringing the power and precision of its creative suite into one conversational experience. Meet Firefly AI Assistant, now live in the Adobe Fire. Airfly app, the all-in-one creative AI studio. Powered by Adobe's creative agent, Firefly AI assistant lets you start with your vision, just describe what you want, and shape the outcome as it takes form with the assistant. The assistant orchestrates multi-step workflows, drawing on 60-plus pro-grade tools across Adobe Creative Cloud apps, including Photoshop, Illustrator, Premier, Lightroom Express, and more to help bring your ideas to life.

You can also get started with creative skills. a growing library of pre-built workflows for common creative tasks like batch editing photos, creating mood boards, portrait retouching, and creating social variations. Every step the assistant takes is visible so you can refine, redirect, or take over at any time. You stay in the driver's seat as the creative director. Adobe Firefly AI assistant now in public beta. See it today at firefly.adop.com. So, you know, Brian, you brought up a very interesting point there at the beginning.

of your response, 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 this is 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 and search. So if you think about what Gen. AI at a simple level can and will over time you to search and how ads supported that may be versus how organic that may be, that's going to be a foundational

question as this unfold. So, you know, 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 thing. But over time, every content engine in the history of the world has sowed the seeds of its own destruction by then embedding advertising within that. And I do think that over time, that balancing act is going to be interesting.

But the whole notion of how search evolves and then how Gen A.I. influences that and how people use Gen. AI instead of search, or in addition to search, it's a really interesting question. I also think 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? 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's the primary way that people find out about anything.

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. Yeah, I'm glad to be 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, you know, 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 or 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 traditional search going to work the same way? So maybe for people out there, whether 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. I 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, that's called 10, 12 years ago,

when people started to figure out that Instagram was replaced in 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 that what people were doing was that 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 to the 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 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 about it, how do you then scale that? Yeah. So, you know, one other thing I want to talk about is we, you know, said, hey, is AI right now

for the most part retailers in commerce? Is it just kind of a glorified chatbot? know, a better chat bot, right? How do you think this has played out so far, right? Because at least for me, even, I think a big part 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 the, I think a lot of it's still early days from a piloting point of view. I think people are still learning 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, and that, that I think is where the

most interesting start cases are, where I can reach more people and 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 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 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 answers, which I think is great. So 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. 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 it allows them to vary their content without having to have an individual deployed against that content variation. 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 I remember reading a lot of stories early on in the generative AI phase when the AI image generators weren't that great and people really didn't know how to use the large language models, but are there some maybe dangers in, you know, retailers just blanket, you know,

putting Gen AI out in the wild without enough human in the loop? 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 clearly, there are clearly dangers that come from, you know, poorly trained in hallucinating AI.

And, you know, everybody on this knows more about this than I do. But, yeah, you want to try to avoid the, you know, some of the challenges of things like Gem and I have obviously run into over the, you know, in the recent past that are well publicized. Yeah, we're all learning, right? So, so, yes, I think there's a, I think there's certainly a risk for, you know, hallucinatory content or I think the bigger, 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, you know, black popes everywhere, but that we're drowning in a sea of sameness. 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 clip art and PowerPoint and everybody had the same nine pieces of artwork in their PowerPoint slides because they thought they were being creative, but it was always the same nine things with 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, artisanal content.com. I love it. I love like grabbing those little URLs. I've been doing this for a long time. Yeah, me too. 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 organic content, but not organic like 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 in a farmer's market. Yeah, yeah, a wild world to think about. But it's not actually too hard to see that becoming the truth. Yeah. And if so, you have the URL there, Brian. So, you know, one thing that 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, 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 they should be and just using it at scale? Oh, God, yeah. So, there's hundreds of them. Yeah, 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,

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 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 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've got to go figure out to say, I think. It's like, no, you know. Like, data scientists do one thing. What you actually need are data engineers, right? Like, you need people, like, the way I put this to a client once they were like, we hired a bunch of data scientists and not doing anything. It's like, oh, yeah, it's like opening a restaurant, having 12 chefs, but no food.

So, like, you know, data scientists or 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 LOMs, 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 to talk about how AI is a 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 home, right? Like, you know, 90 days in, you got to make sure this person knows what they're doing. You've 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-cureated and inspect job description. You know, 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, pet, and then eventually it's sit, then you move on to roll over a 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 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 Work Lab 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 got to go check it out they just dropped a new episode so definitely worth um 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 organizing 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 biggest gap between where your user slash shopper experiences 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.

You know, 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 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. 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 biggest applications for AI. 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 made the consumer experience better, but that don't have an obvious commercial outcome or an obvious short-term commercial outcome. That's where the vision thing comes in, right? And it's like you've just got to trust that you have an idea of what your shopper experience needs to be

and that I'm going to spend a little bit of money trying to understand how to make that better with the idea that I know that this better experience will manifest itself in better commerce over time. I can't spend all my money. 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. IR and 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 it's important as well. All right. So, Brian, we've covered a ton in today's conversation, right? Yeah, yeah, how generative AI has, you know, impacted us from, you know, it's evolution and impact and 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, then 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 good friend Deb Weinsweger runs a course I researcher probably knows way more about it than I do. Always just says to the She goes, 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 help solve them, rather than here's the tech, what business problem am I going to have? You know, so, you know, 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 so much for taking 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. I much appreciate it. 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 everyday AI.com, sign up for the free daily newsletter. Hey, speaking of that artisanal content, me, the human, I'm going to go relisten to all of this great content that Brian just gave us and write a newsletter for it. you recapping it all. So make sure you go check that out at your everyday AI.com. Thank you for tuning in. We hope to see you back for more Everyday AI. Thanks y'all.

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