How to Use AI in Ecommerce: What Works, What Doesn’t (Top 10, Ranked)

Welcome to the Operators Podcast. This is the AI Throwdown Edition. We are going to rank the ways that you should be using AI in your business. We're gonna yell at each other, argue, disagree, and at the end, there's gonna be a harmonious package that you can take and turn into profit in your business. At least that's the hope. Today, I'm joined by the core 4, the originals. We got Sean, We got Matt, we got Jason. These guys are absolutely using AI every day. They're at the frontier and they're gonna share with you how they would rank the best use cases. How we doing, everybody?

Awesome. And guys, I heard that EY is gonna be delivering the final tally of the 4 of us at the end of this thing.

These results, the tabulated results of what we came up with are guarded under lock and key. We've had independent auditors go over 'em, so it's the best guess kept secret in e-com and, and we're gonna reveal it on this show.

I sound really old when I say Ernst Young, guys. It's EY. Sorry. All right, flip it over. EY. Yeah.

Don't date yourself, dude.

Yeah, I'm excited to be here. This is gonna go awesome. And I just wanna say, if you're still using blank AI tool, you're fucked. It's done. That's the worst one.

Sean's actually launching his own AI tool in this, Sean AI. So we'll talk about that during the sponsorship reads. Matt, what's going on with you, man?

I am AI red-pilled at the moment. I can't, I can't, I don't think I could actually spend more time building stuff right now. So this is the perfect time for the, to do this episode.

You're like fully jacked into the Matrix. You just, you plug a USB into your neck.

Yeah. If there was an IV, uh, drip, I would probably have it.

We are going to talk about the best ways you can use AI in your business. Perhaps there's been no bigger word in 2026 than AI. In many ways, this was probably the year that AI became truly useful in building your business and an enterprise. It was more of a toy chatbot before. And I think with the kind of explosion with Claude Code and then Codex, like now you really can build amazing things. We're really starting to see transformation in our businesses, but it's almost overwhelming. You know, you kind of said it, Matt. I think we've all felt the, gosh, there's like so many tools, there's so many different ways to use this. What we need is focus. Where do I use it? Where do I start? And that's what we're gonna try and give you in this episode is really practical from our point of view, how you use it and what areas of your business are the best to use it in. So here are the choices we came up with of areas of your business you could use it in. And what we're gonna do is we're gonna go around the world. Each of us is going to tell you how we rank those choices, what the best one was, what the worst one was, um, underrated, overrated. Uh, and then we're gonna give you like, hey, if we kind of tabulate and average all that together, our group consensus about where you should be spending your focus when it comes to AI. So the choices that we looked at were content creation, customer service, data and reporting, internal knowledge base, leadership and people, market research, operations, paid media, product development, website and CRO. All right, are we ready, guys?

Let's do it.

Here we go. Jason, we are gonna start with you. When you hear those lists of different ways you can use AI in your business, obviously you're in the weeds every day with HexClad, huge business. What is the most underrated and what is the most overrated thing on that list?

But before we go into it, I was really trying to figure out how I got chosen to go first on this one, honestly, and it's probably because You guys know I'm gonna be the fastest and I'm not gonna suck up all the airtime that you guys will. So I'm thinking that's it.

You've already used 30 of your 60 seconds, Jason, so you better speed this up.

I mean, data reporting has been a game changer for us at HexClad. Like, I was thirsty for information and this is how we got it, you know, combining our data warehouse with Saras Analytics, Fulfil, MCP connectors, building stuff in Claude Code. You know, all that stuff together. My ops team has amazing dashboards. I get like daily reporting on our company performance that I literally would have to pick, hunt and peck through spreadsheets for. With Note, I get it with a narrative. You know, for me, that's like, you know, this is all personal to me, right? Because I'm a, you know, I'm not in the weeds necessarily on a lot of things. We have great teams at HexClad, but like it's made me a much better manager and just, you know, better, it allows me to really focus on making strategic decisions. On the content side, in terms of worst for us, like, you know, hex-clad pans do not render very nicely, you know? And so I think content is probably great for like apparel and other things where you need multiple color, you know, I'm sure Simple Modern and Pela and even Ridge, I'd probably get a lot out of that, but we get like very little, we get very little out of that. So that's top and bottom for me.

Sean, what do you think? Is it like, is that a HexClad-specific thing on content, or do you think that's a more generalizable thing that it's still hard to make good content?

I think it's internal to HexClad because they have a team that doesn't wanna embrace it. I'm telling you.

Shots fired, Jason.

I love it. I love when Sean blows up what we do. It's the best. It's great for us.

Yeah, I'm telling, like, go into my ad account right now, look at all of our statics. They have AI as a huge piece of them, right? And I have a beautiful ad account right now. I can send over some great, great creative. I get not trusting it for video yet because it's hard to get relevant, good videos, right? It takes a lot of skill, but you take the render of your pan, you say, put it in this kitchen with this chef, you're getting great statics coming out of it. And I, And if you guys want to, I'll do it right now, live on the podcast. I could hook, I could jump into Higgsfield and I can, I can generate these in less than 30 seconds and you'll have great statics you'd want to run.

You should do it while we're talking, Sean, and then show Jason what's possible.

I must say, um, I might, like with my content team, I'm sure they're, they are using it, uh, for statics and stuff. I guess I haven't been like, I haven't seen it as like a super massive unlock, but I just may not be educated enough on the subject. So, hey, maybe I'm wrong about that being the worst.

I think when you're running something like HexClad, you were already producing great content, but the quantity of content that you can produce that's really good is dramatically increased by AI. And like the cost per piece of content can be driven way down, but it's not like what you guys were putting out there is going to be made better by AI because it was already excellent. But as a big part of the game, in '26 is like, what is your volume of creative content? How many different shots on goal? How many different looks can you get? And I think that's what I'm hearing you say, Sean, is like the ability to scale up to hundreds, thousands, tens of thousands of creatives with AI. Obviously, like that is just not doable with human effort.

World-class graphic designers are hard to find and expensive. AI brings everybody to maybe not world-class, but You know, top 5% designers. If you have good ideas, you can produce them. And for statics, it's an entirely solved issue. Like if you have a brand and you're doing email or landing pages or static ads, like AI can definitely do that once you have good resources, like just good base assets. Videos are still hard. And I'll give everyone a lot of, a lot of credit that like it's very hard to get a good brand video because of like the product fidelity, right? Like holding the product to be consistent in scene to scene to scene. They're working on it though.

We didn't say the specific But an example that we are seeing in another context is we've been working with Cody on something and he started this project and within 24 hours he had this unbelievable email system built where you give it personas and you give it the different email kind of emails you want in your flow and you give it different hooks and you give it different offers and you give it different images and you show it other people's imagery and offers, and then it's able to, its ability to generate emails is pretty remarkable. And I think that this is going to be kind of a theme of this episode is the ability to systematize, turn things into systems that were a lot of individual tasks in the past. Email might be an example of this. And what I'm seeing the very best people do is they're able to systematize their use of AI. So it's, it's not just like, hey, I can create a few static images with AI. It's that I can rethink my entire email flow where I, I can build this thing that's automated and is constantly testing new creative and is constantly learning and getting better. That's built off of all these subsystems.

What Jason is hitting on is, is still a little true, like very intricate product details, like design details. It still does screw those up sometimes. Like we have this problem at Pela still, right? Like some of our designs and our cases just are so detailed. that when you ask it to render that case, that design into something else, it'll mess up some part of it. So our rule for this, and Mike, I think you're hitting on it, is like, we, we looked at it like, what's the workflow for this? So we generate a lot of them, but then we have other AI, I guess, AI, uh, tools that will evaluate the quality of the image. The one place that we've had a hard time using them is actually on PDP. So like when somebody is making the purchase, their, our view is like their expectation is that the image of what I am seeing is what I'm going to get in the mail. And if AI screwed that image up in any way, that could create problems for us.

Right.

So we tend to still lead with like an actual render of the product because we can't have it like hallucinate some part of the design or some part of the color. So I think depending on like how detailed the fine, like the fine details of the images, they do still like produce weird results. But I think that you solve that, Jason, with just workflow. Like we'll just make 100 of an image and then have it pick the 3 best ones based on like fidelity. And then a human can check it. And that's been the unlock for us. Like we save thousands and thousands of hours in photography every year because of this.

Look, and I'll say that we're rendering more things than ever before. We're doing more product photography than ever before, but because it's such a good base to then go build assets off of, right? Like, you know, We have a studio that shoots every single day. We take that and now we can take that one photo that we took that we love and take it to 50 different directions. And to tie back to what Mike was saying, I think the future of email agencies is entirely cooked. Like, you know, Mike said, take, you can take hooks you like, you can take emails you like, you can feed it into Claude and you can, you know, get great emails coming out of it. What I did over the weekend was it's hooked up to my DAM, so like where a digital asset manager, it's hooked up to my go-to-market board and I just said, make me all of the emails for these campaigns. And I did not feed it hooks, I did not feed it promos, I did not feed it everything because it's stored in my internal company database and I immediately got 50+ emails that are good enough to run. So it's not even like, you have to labor over what you're feeding it anymore. You can just say, make me good emails, and it just shoots out emails. So the future of email is totally disrupted.

And this is probably going to be an indication of how this conversation is going to go, is that even some of these areas that we're not getting a lot of use out of right now, there are ways to use them. So Jason, this might be last on your list, and that doesn't mean it's not usable. It's just like, hey, you guys haven't found a way to unlock it yet. But Sean, you said something really, uh, I think important, which is there's a direct correlation between how much context you can feed AI and how useful it can be in your business.

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Jason, now give us your ranked order of all of those things.

So we did data and reporting, uh, customer service, huge, right? Just money. That's, that's just money there. Um, leadership and people. I think that kind of it does come together with a few others, like, well, there's market researchers, product development, internal knowledge base, operations, paid media, website and CRO, and then content creation. Like, there's some overlap in why I feel the way I feel about a lot of these, but I just think on leadership and people, it is, the AI is like having an advisor around all the time. And I just find that it gives me great ideas in getting stuff done. And a big part of this is, and I'm seeing this across my organization, not just Jason, right? But like, I'm seeing people do really good work and take leadership positions and roles on projects. And the ones that are doing it right, I mean, it's pretty amazing. Like, there's people who I like, I really thought were not like very productive and they're embracing it and they're actually saying, wow, I can actually do stuff now. It's like, it's on— I think it's unlocked people in a lot of ways. The people who are really like saying, I think the people who are embracing it, you know, it's been a big unlock for them, you know? And that's just kind of the way I look at it.

This is a great segue, Matt, because this is your least valuable was leadership and people. So it's a great transition to you that it's 3rd on Jason's list and you ranked it as the least valuable. I think I might've ranked it as the least valuable. Even listening to Jason is making me rethink my choice. Why did you rank it as last?

Yeah, Jason's making me rethink mine now. Thanks, dude. I ranked it as last and I put this comment in like, please, for the love of all things holy, please like don't allow AI to manage relationships. in your business. So like, as a leader, I just don't think you should outsource leadership to anybody else, not even like, let alone an AI, I guess is how I looked at that. But Jason, I, I think I didn't come at it from your perspective, and it's actually making me think, Mike, uh, like, do we all have a, uh, has the bar for mediocre gotten higher? So like, you know, AI is making like your best people like 10 or 100x better. But is it making people just good enough to keep them now?

I don't think so. My observation is that somebody's ability to contribute to your organization is a combination of what are the developed skills that they have and what is their drive aptitude intelligence. And that there was a time where it doesn't really matter how much drive aptitude intelligence you have. If you don't have certain skills, if you haven't gone through certain apprenticeship, you're just not able to contribute. And one way that I've seen what you're talking about, Jason, we have interns this summer that have produced things that we are leaning on pretty heavily in production on the website. And that is, I mean, they started in late May and that would've just been unthinkable a year ago. And like the analogy I would draw here is that in like Engineering, software engineering, for a long time, there was this idea of the 10x engineer or the 100x engineer, but there really wasn't the business equivalent of that idea. You know, nobody's like the 100x accountant, but I think when you add the ability to be like basically a 100x engineer on top of any person, then now you're starting to see that idea on the business side of like, wow, yeah, you really can have the 100x analyst, the 100x, you know, CFO, whatever else. Because now they're able to systematize their skills, and it really is more about drive and ambition than it is about how many individual skill sets do you have.

I think you're right about it, but I think there's just certain people who they found the ability to unlock themselves better to add more value.

I just think that's a cool idea, Jason.

What's a specific example you've seen that, Jason?

Like, I don't want to call out specific individuals at the company who have like impressed me like with, that they've been able to do work and bring thoughtful positions on things, and they would never have gotten that without Claude, right? But it's like, the fact is that they went into Claude and did the work, right? And then they came back with thoughtful position and thoughtful information. And there are people that just, I feel like they just didn't know how to get from point A to point B. And this has been a huge unlocked for them. So I do think it's taking like the average person and making them better. And that's what's— if they embrace it, and that's what's supposed to happen with AI.

Yeah, it's a development tool.

Look, Jason, it was near the bottom of my list too. So I think you are changing all of our mind. The beauty of the technology is it can teach you when you get stuck, right? And I'm sure there's like, all of us have tried to do something very technical in our lives. And like, I would always get like 80% of the way there, then I'd hit a roadblock. And like, I don't know how to go forward. I don't even know how to Google the problem having right now. And at least Claude is there with you and can just tell you what to do immediately. So I do think it lets everyone become technical, right? Like, you know, Mike and his interns. And I just didn't really understand what leadership and people meant in this exercise. I'm like, what the hell am I using AI for leadership for? But I think the people piece, if we can unpack that and treat that separate, we're recruiting for some roles right now. And if you have a remote job in 2026, hold onto that thing because I had a remote job opening and I got like 2,000 applicants. Okay. It's been live for about 4 days. So like the job market for remote jobs is crazy. How do you go through 2,000 applicants? We have tons of people, you know, when you apply, you have to like answer a questionnaire, right? Then we have AI go through and rank that. questionnaire. So we have Claude do it first, then we have ChatGPT do it as well. And so we have double AI rankings. And then it took up to my Claude so that I can ask questions, be like, hey, bring me the top 10 people who have e-commerce experience and don't live in California, right? And now I have, bam, out of 2,000 people, triple ranked with AI people, uh, that I should actually talk to. And it way saves time. So the people side, I would move that up. Leadership, I, I still don't really understand, but— Awesome one. If you're a founder using AI for analytics, you know the trap. Claude plus BigQuery gets you answers that are fast and wrong or slow and right, never both. The problem isn't your analyst, your data, or your tools. It's your business context gap. Sarah's IQ fixes that. Connor, who runs my marketing, cut his reporting time from 10 days to 45 minutes. IQ answers in plain English built on your business logic. consistent, deterministic, and fully transparent with the assumptions in SQL behind every answer. Same answer whether you ask, your CFO asks, or your board asks. And if you're a Claude user, the IQ MCP is ready to plug in today. Claude plus BigQuery isn't enough. It needs your context. Get it at the link in show notes or go to sarahsanalytics.com to learn more.

Well, one of the ways this is the most obvious in my mind is that AI is definitely going to impact the size of organizations. And so it might be number one in that sense that just Size of team is going to be transformed by AI. It's probably my single highest conviction. Teams are going to be smaller. The data really bears this out. You're seeing more small new business formation than ever before. And just in general, like, it makes sense. Like, leadership is hard and managing people is hard. And the more people you have, the harder it is to manage. If you can, you know, everything else being equal, get the same output from 20 people that you could from 40 people, that 20-person organization's going to do better because it's just gonna be tighter, more coherent, less things are gonna be dropped. There's gonna be less overlap and responsibility. There's gonna be less politics, less issues you have to deal with. And I think all of this is gonna be pushing organizations to be much more compact. Not surprisingly, we're seeing this in a lot of the e-com companies that are ripping right now. These are people that are technology, AI first. They start with small teams and then as they scale, they're not particularly compelled to add a bunch of people. And I remember when And then, you know, like I would go to, I don't know, business leader gatherings and like they would all ask like, well, how many people do you, you know, employ? How many people work for you? And that was like a badge of honor. I feel like it's almost inverted where now it's like, how few people do you employ in order to run your business? And so we're certainly seeing that play out already.

Yeah, we'll add very close to $100 million in revenue this year. I actually have to pull the exact number how much I think we're going to add in revenue over last year. It used to be if you had $100 million in revenue, you'd add 100 people, right?

Then it'd be you add 50 people.

I might add 5 to 10, right? So we're talking about $10 million per new head added in revenue, and it's because of leverage in AI systems. So we're still hiring. Team size is going to grow here, but disproportionate to revenue going forward.

And here's the beauty of that, Sean. When you do less hiring, the more revenue per person at your company, the more opportunity you're affording that person, the more opportunity to earn more, the more opportunity for scope and advancement. And so So like, I think there's this negative stigma about AI of like, oh, it's going to take all the jobs because of what we're saying. I don't think that's the case at all. I think it's going to redistribute jobs. And I think because everybody at your company has more responsibility, they're going to be able to earn more. They're going to be able to grow more. And that's a good thing, not a bad thing.

Yeah. Look, this is an aside, different episode. We should talk about how fucking expensive things have gotten because I remember in 2020, if you offered somebody a $60,000 a year job, That was good. You know what I mean? And now, like, it looks like the minimum you have to pay somebody who knows how to use a computer is $100 grand. And it's like, maybe this is just me being a boomer now, me getting out of touch, but like, I remember making $100 grand for the first time being like, this is crazy. I'm so rich. And like, I think real inflation's probably been 10% a year for the past 6 years because now I got people—

Yeah, but 10% for 6 years compounds real fast.

Yeah. I got people asking for You know, $180,000 for jobs that I used to pay $80,000 for. It's crazy.

Matt, you know, our friend Shireen had a great tweet on this subject. She said, the biggest threat indicator that AI can take your job, someone needs to tell you what to do. If someone needs to tell you what to do, how to do it, and when to do it, you should be very concerned about your future and find a way to fix it. So that was, Matt, that was your last one. I think we've all kind of like maybe reconsidered our stance. Jason, you moved us around. That's good. Matt, what was your top one?

My top one is actually, so I don't know if you guys, I had a really hard time ranking the top 5 because they're all so close in their value to me in the org, like in a company. But I went with my number one was internal knowledge base because I think the more context and more memory that these things have, the more powerful they get. And I actually think that competitive advantage right now, I mean, we talked about this years ago with Ridge, that because Sean has sort of enforced this like write everything down mentality because they're a remote company, that that has actually made them much, it actually enables them to go much faster in adopting AI because like they have an incredible amount of like internal intellectual capital that is documented in a way that is AI friendly, right? Whereas like most things are in people's heads, it's not very useful to AI. So for me, I chose this one because I think it sets up the rest for more success. Personally, like having a Hermes agent with a G-brain and a chief of staff that like literally has a, it's like a twin of my brain. Everything I do digitally is stored in this G-brain thing is incredible leverage.

Right.

And then now doing that at a, at a company level, like standing up a new company from day one and focusing first on like getting context into the place that AI can use is making the content creation piece better, market research better. Like even how, how do we want to set up data architecture in this company? Like we need to know how the company is going to run. Who are we serving? Like all that stuff seems to be getting better with more Context. Uh, and then I think shared memory and shared skills and all these things like AI is a single player mode. No question people are getting a lot of value out of that, but I still think AI multiplayer is a big unlock for companies. Uh, so that was my number one. That's why.

Jason, what do you think about that?

Dude, I just went for 20 minutes. How about we ask Sean?

Jason says 20 words. He's like, I'm done, guys.

I'm done.

I'll see y'all next week.

I'm just trying to let everybody get their chance here.

Yeah, unfortunately, I think I have talked the most, but I'm happy to share that, Matt, we got so lucky that we embraced being fully remote. And I do not think in-person teams have any advantage in the age of AI simply because by being remote, everything is recorded, everything is written down, everything is documented. And we got like, this is like, it's better to be lucky than good. We fell so backwards into that, but now I have everything for the past 6 years recorded that I can feed into an AI model. And that's why I said I hooked it up to Notion and I just said, make me emails. I don't need to go hunt those down. I don't need to go pull them from my favorite website or whatever, because I have 6 years of them documented in a folder somewhere. And every conversation, every Slack is just It is a huge advantage to have everything be documented right now. And I fully agree that it's kicking ass.

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When we first started embracing AI, like the first thing I wanted to do, and it was sort of, it was this internal knowledge base. And like, you just have it, Sean, because you documented all that stuff like really early on. I actually don't even know what the status of that project is. I gotta go check on it. I just asked some people to do it. But like, seriously, there's— but we're not gonna get into remote versus in-person here. I'm gonna not do that.

Well, no, we don't need to litigate that. No.

But I do agree that it's a benefit that we were not thinking about 2 years ago. Like when we, we've had a lot of discussion about this and I would say for sure it's something that has changed the calculus. Even, you know, somebody who's like kind of been like, Sean, you're just wrong on this. You know, like I, it really has changed the calculus. You know, one of the things that makes me think about, uh, there was a point in Amazon's history where Bezos sent out a memo and basically the memo was like, everything is gonna be externalizable. Uh, really what he was saying is everything's gonna be API based. It's just going to be, and if you don't like that, that's fine. You're fired. And he just, he just said, that's the way it's gonna be with everything inside of Amazon. It has to be API-based. And that single decision led to AWS and probably the core of the modern internet in the cloud. And I think in a same, in a similar way in your company, you have to put your foot down that we are going to document our context. You have to, because what is really depressing is when you look at your organization and you kind of say, what am I paying? I'm buying all these hours from people. What am I actually getting? And you realize like 40 or 50% of those hours are just people trading around pieces of information. And for the first time you can really be like, like for example, Jason, you're like, I don't know where that project is. We're hurtling towards a future where it's like you just ask Claude where, or, or, you know, ChatGPT or whatever, Codex, hey, where, where are we on this project?

Yeah.

And if you are in person, the one thing I would do, 'cause I had an in-person meeting yesterday, you have to record it.

Guys, these things, like that was why I said same thing as Sean. What an unlock to just do that. Just carry one around.

Yeah. And like, I would, I would make them mandatory across every office, every meeting, granola notes. 'Cause like we had a big meeting maybe a week ago. We're talking about like, uh, some very nuanced product decisions, like what type of product this is or whatever. And we recorded the whole thing. And then I just said, hey, Claude, turn this into a one-page visual chart. And it did. And then I can, now I have like a little poster I can send everybody, hey, here's how we made these decisions. It's a, it's a visual graphic you can easily understand.

Yeah. I mean, look, that, that is why I have one of these. This is a Plod. It's a digital recorder. It's, it does like automatic AI summaries. It gives me my transcripts. My chief of staff can just grab everything from this every single day and store it in my digital brain. Uh, I highly recommend that if you are in person, you get one of these.

All right, Matt. So we've talked about an internal knowledge base. Give us your entire ranked list. Yeah.

So, uh, and again, I think all of us are a little colored by like our experiences in our own companies. Uh, so my list is internal knowledge base, knowledge base number 1, content creation number 2. Market research, number 3. Uh, I'm a copy guy. I, I spend a lot of time thinking about personas and angles. So like, this has been wildly valuable just to be able to research what customers are saying. Um, data and reporting was number 4. Operations was number 5. God, I had a hard time with this, Mike. Like, operations is so valuable in supply chain. Uh, customer service, 6. Website and CRO, 7. Paid media, 8. 9 was product development, 10 leadership and people. Yeah, I had a really hard time. I could rearrange like 6 of these and argue all of them.

Yeah, maybe that's an indication of where we're at, that it wasn't, I mean, I think a year ago, if I would've looked at this list, I would be like, I feel really confident in 1 or 2 of these use cases. And even in this conversation, the ones I ranked near the end, I'm kind of being, I'm having my perspective changed on it. It's just much more broadly helpful. So Sean, let's go to you. I want to hear your list. What was your number one way to use AI in your business today?

Yeah, it's the same one as Jason— data and reporting. Both of us are on the Sarris Analytics stack, and I've talked about that and what that means. Sarris is the data warehouse we use. All that means is they take all your data from all your sources and then clean them up. What data? Product type, like product SKU. You know, it's very common to be selling one SKU on Amazon and one SKU on .com and one SKU in wholesale, and then they're all functioning the same thing with different names and different SKUs. So Seris comes in and cleans all that up. And then once they clean all that up, they build you a Tableau dashboard. But I don't care about that at all. They have an MCP that hooks up to Claude. So you have all of your company data that's incredibly clean, and then you hook it up to Claude. And when I say company data, you have all of your sales, all of marketing spend by channel, you have your, all of your expenses every single day coming in there. And so you get perfectly accurate, perfectly malleable data bytes every single day that you can sort and cut however you want with words in Claude. And it has cut reporting timelines at Ridge down to literally minutes, right? I used to spend, I used to by hand do every presentation at the end of every month being like, hey, here's what we did, here's what we closed out. And it would take me 1 to 2 days. Right? Every quarter I would do the quarter summary and it would take me 3 to 4 days and now it's 17 minutes. I just go in there and say, hey, rerun this for this new month with this data, right? So it, when I say it's like the number one use case, it has saved me personally the most time. Everyone in my company can now pull whatever data they want. I think data's a big bottleneck when you're trying to make a decision around product upsells, product pricing, MSRP, sell-through. Literally seconds, literally one ask of Claude because it's hooked up via MCP. It's totally changed the entire department. That is my best use case.

I am the most passionate probably about this. I didn't rank this number 1, I ranked it number 2, but one of the observations I would make is that all of us tend to use AI in our wheelhouse first. And my wheelhouse is like analytics. And what I would say is that I am using it now to produce outputs that are better than what I could do regardless of how much time I had. And that was pretty shocking to me. I thought that I would probably be able to automate and produce similarly, similar quality outputs to what I could do individually. I could just do it a lot quicker. Let me give a really simple example of this. So with Trevi, one of the biggest things we're trying to do is we're trying to predict how many returning users are you going to have every day? Because like, These consumable businesses are all about recurring users and understanding your LTVs and what you can pay. So it's a pretty big part of the business. And before, you know, I'm trying to do it on cohorts and I have kind of these retention curves and it's, it's taken up all the space in Excel and it's frankly not very good. And over time, what I've done first, my model has moved from Excel where I update things to it's Google Sheets. And it updates every single hour and everything's automated. It's constantly pulling in the most recent data. And what I did is I stood up all of these individual parts of the model. And so returning usage behavior on Amazon would be one of those parts of the model. And I'd stand it up and be like, hey, we want to build a model for this, build me a model for this. I was working with Codex and so we built a model. And then I'm like, okay, great. Now I need a 60% improvement. And I, you should consider these 3 or 4 factors you might not have considered. And what they'll do, the models will do, is they'll build a kind of like a theory of like, hey, here's how this could work. And then they backtest it and they have very mathematical ways of backtesting it and saying, if this were the model and I backtested and I went through all the data, how much better of a prediction system is there? And then I just beat the thing. I'm just like, okay, great. Now make it 50% better. Okay, great. Have you considered this? Make it another 30% better. Okay. It's not good enough. Do it again. Do it again. Do it again. And I've basically built this thing to where at this point what it does is it looks at every single user. It looks at what they've bought, how many sticks they bought, how much the total cart size was. Didn't they buy on deal? Did they buy on a weekend versus a weekday? What flavor did they buy? All of this stuff. And it projects out every single user and what they're going to do in the future. And then it rolls it up into one forecast and it does it in 30 seconds. And mathematically, it just blows out of the water what I had. And it's just like a small, like micro example of how, like our ability to understand our numbers for all of our businesses is just going to go to an unprecedented level because the amount of compute, the amount of intelligence that you can throw at any individual problem is just so much greater than before. If you had told me that there would be a tool that would get me where I didn't use Excel, I would've said, I don't believe that'll happen in my lifetime. And my Excel usage has dropped off a cliff because I'm doing so much more of it through the AI now. If you're scaling an e-commerce brand today, ads alone aren't enough. AfterSell focuses on the one moment that every brand already owns after checkout and turns the post-purchase moment into more profit. Monetize every order with post-purchase offers and thank you page experiences. Without disrupting checkout or hurting conversion. Enterprise-grade tech used by Gap, Ticketmaster, Macy's, and Target, now driving results for brands like True Classic, HexClad, Ridge, and Jones Road. I would know. This is the reason I ended up buying 3 pans from HexClad instead of 2. AfterSell has already generated over $1 billion in additional revenue for e-commerce brands, revenue that doesn't require more traffic or higher CAC. So check out AfterSell and tell them that the operator sent you.

Sean and I have very similar use cases here because we both were early adopters of data warehouse because we were smart enough to see the future. You know, I think Sean has had like major, you know, I have a major unlock with like, I get a daily report. I get it basically because, you know, I just get it. I get a daily email, I get a weekly email, I get a monthly email, like all the performance of Hexadad just pulling straight out of you know, out of the AI, out of Sarris and with MCP, with Claude. We also, you know, my finance and accounting department is now really starting to leverage it as well to get better reporting. So that, I mean, that's just huge for us.

Connor, my CMO, said the Sarris MCP will go down as the biggest thing we've done this year just because it has saved, you know, the executive team, across the board, easily 40 hours a month of work. And then everyone downstream saving 100+ hours because a lot of their job is reporting, pulling in graphs, checking data, seeing sales, making those tweaks, and just having it instant. Like, I really can't express the magic. And it's so hard because I want to show people, but it has my sales data. So like, it's like, it's really hard to like go out there and like do a Twitter post being like, look how cool this is. But anyway, here's my real sales data, which I'm not going to do. So Anyway, it's pretty awesome.

You have to have a data warehouse and there are so many downstream benefits of that. I think that's one of the reasons why this is obviously at the top of the list. And the gap between me having an answer to a question, even a very complicated question, is now seconds or minutes and not days. So, Sean, what was the worst thing on your list, the least helpful in your experience?

I chose product development. It kind of sounds like you guys are treating market research also as product development.

Like, I was trying to get more from marketing.

When I look at my list, I'll go top to bottom. Data reporting, I think, is just like a must-have. I think customer service is a solved issue. Half the tickets this year would originally be answered by AI or more. Content creation, we've battled that out. I think statics are entirely solved. Internal knowledge base, I think it's incredibly important. And with HQ, which is like, not to get into specific tools, but Notion is a great way to build AI context, but how do you share that across users? I think HQ could do that. And then I have market research, and it's amazing for finding out what to sell, who to sell it to, who your customers are. But then at the bottom of my list, I have product development as my number 10. It's because AI is not good at CAD files yet. It is not good at actually making tweaks inside of product files. Like, it could probably do it, but that is— those are very fine details you need to actually do, right? It can help you maybe think about a category to sell into, but it's not going to help you think the, you know, the way Ridge needs to approach that angle. And every time it tries to do it, it's very ham-fisted. So it's my least favorite one.

My experience with that, Sean, is that coming up with a concept is much quicker now with AI. But the problem probably here is that there's so much context that AI doesn't have about the actual physical world and making things for us. So like, for example, uh, there's all these like rules about tooling and all of our lids come through tooling. And so we'll come up with ideas all the time that we'll send it to China and they'll just laugh. They'll be like, yeah, that's cute that you would think that would work. And like, I think this is a good example of AI. AI can dream up stuff that doesn't really make sense in the physical world because it just doesn't have the context of the constraints that you have around the tools. Like it might make sense in a CAD file even, or conceptually, but it just like doesn't practically work for whatever reason. And you know, like you can design the coolest lid ever in AI, but if it leaks, like who cares? If it's not durable, who cares?

Totally. And we've all had, we've all had that experience working with, uh, you know, there's product designers and then there's design for manufacturing and it's different things. 'Cause the product designer will be like, like, it's one perfectly molded piece and it's all made out of aluminum and it's, and it shines, right? And then the person who actually does design for engineer or design for manufacturing, they're like, yeah, so it's gonna be $80,000, but it's 8 cents if we make it in 4 pieces. And it's like, we've, we've all had that.

So, uh, Matt, Jason, either of you using this in product development?

Actually, no, I'm with Sean in, in, in our world, it's borderline useless outside of, I think, ideation. Uh, so like our, I mean, our product team is sort of broken up into like, there's the weekly design drops. So like, what else are we putting on the cases? So I think for that, it's been actually really helpful, like to explore the fringes and edges of like interests online. But I think that's less like hardcore PD, which Sean is hitting on. Uh, but no, like mechatronics, uh, electrical engineering, like all that stuff. No, nobody is genuinely using it in their, I would say like their actual work, right? Um, it's just not good there yet.

Yeah. And Jeff Bezos just raised like a bunch of money to go out there and build like the AI model for CAD. So like maybe this is all coming, but like, yeah, we use it to make a lot of color choices or whatever. Um, you know, the, my bottom of the list, I, in number 8, I have operations. Number 9, we have leadership and people. Number 10, I have product development. I wanna talk about operations because it's, it's Mike's number one, and I'm like, how are you getting value out of this? Teach the audience.

Well, I, here's my thought process in putting it number one. In operations, so we're talking about things like fulfilling POs and moving things around with logistics and placing your purchase orders. I mean, I would put inventory and, uh, like planning kind of in here, Sean, which probably would change your perspective if you grouped it in there, but it's like, All of the blocking and tackling to keep items on shelves, to keep things in stock, basically is the way I think about it and keep and getting product to the customer. And the thing about that is, number one, there's really robust digital context basically on all of that stuff at this point. There's APIs you can get, you can get all the information so you don't have a context deficit. And there's not super intricate judgment going on usually. Like it's, there's not a lot of qualitative pieces that they, you can't teach the AI to have. And so for example, like inventory and planning, which I know is one you've talked about, like you can just kind of solve that. Like, hey, here are all my constraints and here are all, here's all the pieces of context and I want you to come up with a plan. And it's just not hard to move your organization towards the way that we're planning out our inventory, the way that we're placing our purchase orders, the way that we're putting it on ships, the way that we're moving it to 3PLs, the way that we're sending it out. to POs, to our vendors is just like, it's just like clockwork and it's much more automated. And I, the reason why I think that's number one is because it has to happen. Like your business basically dies if it doesn't happen, that there's real money to be saved and that it removes all of this complexity from running your organization. Like the more that you can just focus on product and marketing with your organization, those are the growth factors. the better. And it just totally takes operations to much more of like a solved game. So I've been amazed at our team internally, the tools they built, the automation just in the last few months. We have an internal app builder that we basically have like a context layer, and then on top of that context layer, you can very easily build apps. A lot of the most useful apps have been built by our operations groups. So I'm just very bullish on this, and I think You're just gonna be able to run world-class operations with less people. You're gonna be able to run 'em much more inexpensively. You're going to be able to have much more clarity on what's going on in your supply chain at all times. And those are kind of the lifeblood of a business.

I agree with you, Mike. You know, my team in ops, look, fulfillment, you know, is a big thing, right? And we are, we're actually trying to con— we're constantly trying to take down our percentage Uh, you know, uh, of COGS, uh, or percentage of revenue fulfillment costs. And we, we're global. We have many distribution centers. Um, we are having to get stuff in and out of Amazon. You know, we've got many, many, many containers on the water 'cause our products are large and there's a lot of stuff moving around. And I know that my operations team has found like major, major savings and major, you know, just efficiencies through this, through Claude, through MCP, using fulfill to do that. It's been like a huge, these guys are thrilled. Like seriously, these guys are so happy. I love to see it.

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I didn't think about the inventory planning or buying, 'cause once again, I think that is a solved issue, like inside of your organization. I was even thinking about it as like a data problem, but you're right, if that's bucketed under operations, I was thinking, I'm like, pick, pack, and ship, I'm like, how's AI helping you with that, right?

And one of the themes I think that's come up in this conversation is AI has not punctured the physical world yet, and that that is coming. You know, like the next phase is going to be when it really gets into the physical world and it really can help in product development, and you do have robotics And there is this extension, but we're not really there yet. So I would agree with that, Sean, but I, yeah, I think there's a lot of, I guess one way of thinking about it is if you say data and reporting is really valuable, it's, it's conjoined twin as operations, right? Because what you're doing with the data and reporting is usually you're turning that into operational decisions. And I kind of see them as 2 sides of the same coin. And I think that that's why I put them 1 and 2 is that it's like the information about how we need to, like what we need to actually do. And then the doing of those things, if you can use AI to be better in both those camps, like, man, you're going to be in great shape.

It has, um, in the operations thing, like, so we run our own factories and we do demand planning for us is almost daily, right? And Claude is now doing that just because we have data warehouse. We have all this stuff. Like we used to, our process was sort of like our factory managers, let's just, whatever that title is. Every Monday they would sort of have their own demand plan for the week. It's like, these are the tools we need to run and these quantities and these colors. And that was how we kept the whole Pela, like, on-demand inventory thing going is we were all kind of always trying to be like a day or 2 or 3 ahead on what we thought would be needed, right? By our various channels. AI is doing that now and it's far more accurate. Our efficiency is actually quite a bit up at a factory level. Uh, so I would, I would bucket that as operations, Sean. Um, but it, it is like, it is like, Mike, you're right. That is more data. It's just that it's capable of processing a lot more information than our team could do it. Like humans could do it before. Like it can look at every single order, whereas before we would look at like aggregate data, 'cause that's how a human would process it, right? It's like, show me the averages, show me the summaries. This thing is looking at like every single order and every single item. in real time and making better choices for our factories.

So I have a take on this, and that is that everything in your business will eventually be like a prediction market. The prediction markets are these amazing things that suck in all this information and can say, at this given point in time, this is the most probable outcome. And we've seen that they like are really accurate. And like a lot of what we're trying to do with our business, like with our you know, demand forecasting, for example, is basically that. It's just a really inefficient market that is not doing a very good job of sucking in information and bringing it up to the second. And with these systems, you're just gonna be able to be like, yeah, plug into all these data sources and then just give me like, as of right at this second, what is the best prediction of the future and work around that. And our businesses have not been anywhere near that agile, but that's what's coming. So as I said earlier, my last one was leadership and people. I ranked that as my worst. I think that talking to Jason, if I think about it from a different angle, I can see that differently. So obviously using AI to manage people is probably terrible. Don't do that. But obviously it's pretty profoundly gonna impact the way that we think about leadership and people. Here was my ranked list, 'cause there's one of these I wanted to call out. I know we've discussed as a group a lot. Number 1, operations. 2, data and reporting. 3, customer service. 4, content creation. 5, internal knowledge base. 6, paid media. 7, market research. 8, website and CRO. 9, product development. And 10, leadership and people. But guys, I know that for each of you, I think customer service has been the most transformed to date by AI, and it's just really remarkable what we've been able to build internally. We have a bot and the, just the volume of our tickets it does, how quickly it does it. You know, one of the sponsors of the pod, Rich Panel, has unbelievable offers in this area. Has that been as true for each of you in your organizations that customer service has been dramatically improved by AI?

Oh my God, yes. It's not even, it's freaking insane. It just like, even just think of it this way, even if you didn't have AI that was capable of answering all of the basic questions, which it is, it can handle like 98% of our tickets. It's still gonna make all your agents way smarter. Like, you, you know, Mike, you've already said like, you can ask it a question or Jason, you can ask it a question and in seconds, it's giving you an answer. That applies up and down the org. So why wouldn't it impact customer service the same way?

Me and Richpanel have a bet that by the end of the year, they'll be doing over 50% of my tickets with AI, and they're getting— I think they're there right now, right? So totally AI, top to bottom, resolving tickets for us, and customers seem to like it more, right? The, the AI chatbot has like a 98% CSAT score. So like everyone's having a good time talking to the AI chatbot.

It's 'cause it's just giving them money, Sean. That's what's happening. It's like sending out $100 bills without you really I should, I should dive into that.

Maybe, maybe I do have a free money bot.

Well, I said this before, but when we first implemented this, the biggest concern is, are we gonna be able to nail voice with this AI chatbot? And very quickly we were like, wow, this thing's way better than we thought. It's like you said, Sean, it's getting better CSAT scores than we would've ever expected. Uh, sometimes like somebody wrote in about, uh, you know, like an Oklahoma City Thunder item that we had that was sold out. And the AI chatbot noticed that they were writing from California and was like, yeah, we know it's tough to be a Thunder fan, you know, with all those Warriors fans around, but we're going to get this back in stock later, blah, blah, blah. I mean, it was just like riffing on things in ways we wouldn't have expected. And we actually learned that like the worst part about this transition to AI for us was like this thing had no chill. So it would be like somebody would send in a support inquiry and like, 2 seconds later they would get a response. And so it was like, hey, you know what, we call it Hallie. We're like, Hallie, you gotta chill out. You know, like we gotta give 'em like a few minutes before you respond. And so if that's your biggest problem is it's being too prompt in responding to customers, that's a great problem to have.

I wanna talk about AI job displacement for a second. You know, so let's say American hiring was a curve like this, right? And like it was a steady line on the graph and With the rollout of AI, maybe the curve has inflected down a little bit. So like we're hiring less people than we would in a world without AI, but job openings are still happening. I just told you that we're going to hire 10 people here, right? So like there are still new people being hired. Maybe it's at a less frantic pace than if we didn't have this AI boom or whatever, but where that's definitely jobs are going to be lost, it is international CX agents. Those jobs have already left America, right? They've, they've moved over to the Philippines or whatever else, and now those jobs will be totally eliminated by AI. So like, do not hold Manila real estate is what I'm saying right now. It's like there's—

Well, let me divest as we're speaking.

Not investment advice.

Or, you know, we just talked about how powerful it is with Excel, and Mike, a top 1% power user hasn't logged in this year, right? And, you know, that's going to be very, very bad for the knowledge economy of places like India, right? Where big banks have outsourced lots of their, you know, computing work, right? Their manual data work to India, Pakistan, wherever. And now they're just going to do it locally with Claude for probably the same price, right? It won't be cheaper to use Claude, probably the same price, but it'll happen instantly. So.

And it'll be better quality, you know, like, and you have all your contacts, like, It's, it's an overwhelming value proposition, Sean.

Is it an onshoring of productivity? Is that how you guys look at that? Like so much productivity was outsourced. Is that re-onshoring?

AI is not going to lead to job loss. It's just going to shuffle the deck. And I think if you want to get hired at a bigger company, I think your job prospects in the next 10 years are worse. But I think it, like, you're just going to see so much more, so many more people start ventures of their own. You're going to see so many new smaller firms and And part of the reason for that is that like, if you think 50 years ago, it was difficult to compete with the incumbents because they could just do things that you couldn't do, right? And there's actually a great post, Aaron Levy, great, that's his name, right? Box Aaron. Like, so there's this Javits paradox thing and he talks about the evolution of marketing agencies and that basically for a while, like you had to go with one of the big boys if you really wanted the best stuff because they had these huge creative departments. And you couldn't get the level of creative output if you didn't go with one of the big boys because the scale like created a competitive advantage. And that you would've thought that the invent of Photoshop would've lead— led to, you know, less designers, but it had the opposite impact. It just exploded the number of designers and it made it now where smaller design firms could compete with the really big boys. And this is exactly the same case, like to have a really great customer support department 15 years ago, You know, you had to be at whatever size, X size. Now you can be 1/100th of that size and have that same level of customer support because of the tools. And all of this favors small businesses basically, and entrepreneurs and new venture creation that I think we have great customer support, but you know what? You could start a new company tomorrow and probably have a similar level of customer support because of the tools that are available. And that there's just so much opportunity in that.

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Matt asked if it's going to be an onshoring of productivity. Well, it depends where they build the data centers because that productivity might, might go to space. But no, I do think the job loss narrative is a lie because those jobs have already been lost. They've already been shipped out of America. And I definitely agree with Mike that Facebook will have less employees in 10 years than it has today, and the company will be way bigger. They're going to get hyper— I'm talking about $10 million per person at Ridge. They're probably going to talk about a billion dollars per person or something crazy. I mean, they probably won't just because of the real estate investment, but I do think they'll have less people. So The economy will be bigger, it'll be more dynamic, and it's gonna be way more smaller firms. I believe in that future.

And where do you guys think that we're gonna be competing as brands? So where is competition going 6 months from now, 12 months from now, 24 months from now? Like, and I'll give you a bit of a frame on this. I had somebody send me a message on X around creative, you know, and his question was something like, If 6 months from now or 12 months from now, AI is capable of making video, right? Which it's probably going to be in such a way that everybody has the ability to make hundreds or thousands of pieces of creative all at the same time and upload those into ad platforms. Is that where is the competitive advantage in getting attention and getting distribution? Like that's kind of the game we're all in, right? It's like, how do we acquire a customer profitably? How do we make our unit economics work? I'm curious if you guys have any takes on this because that is, I didn't have a good answer. So, you know, help me. What is your take on this, Sean?

Well, my take is if we're in a world where anybody, so Chinese factories or black hat dropshippers can make any beautiful creative they want, they can impersonate any celebrity they want, and they can put their product in those ads, how do you continue to get sales and transactions, it comes down to, do you have more budget? Right? Like, do you have— because what we're forgetting is it's— there's still a delivery mechanism. The delivery mechanism is Facebook ads, and Facebook wants to charge a higher and higher CPA and CPM every single year. And the problem with, you know, this competition coming in is, is will they have everything in place to support a $100 CAC? Right. And that like the, the maturity, the reason why you have to lower OpEx to put as much money into ads as possible. Right. And like Jason's talking about lowering cost of shipment to make sure he gets as much money into ads as possible is I think we're going into a future where capital is, is the thing that lets you win. Right. Which sucks if you're a new— if you're, if you don't have a business and you're listening to this right now, but at least in the consumer space, You know, we're going to a more nationalized system so that there will be tariff borders going up. It'll be harder to get direct-to-China shipments coming in. So you have some sort of protected moat there. And then just naturally those people are going to try to win on price. But if you win on price, you can't win on CAC. And I just think we get to a world where there's like a, there's like a, there's a floor price of everything sold on the internet and it's just going to be higher than, than you think.

Yeah. Scale economies and some of the traditional moats I think are going to come back to mattering. And like one example in our business right now, there's parts of our business where I'm like a little bit concerned about, you know, how competitive forces impact it. And there's other parts where I'm like, I feel great. Perfect example, we're doing, we're embroidering like 1,000 units a day to 2,000 units, somewhere in there right now. And we just bought 4 more embroidery machines. I think we have something like 12 to 14 embroidery machines. That's just a competitive advantage. Right? Like, that's literally like, you gotta have the machines. There's very few people that embroider at scale in the United States. The ones that do often have like 1-month ship times. And so like, that's just an asset that is not easily disrupted by AI because it lives in the physical world. And actually what I'm thinking about is how do I do more embroidery? Like, I've got this competitive advantage. How do I spin up more businesses around it? But you're, you're gonna need to be thinking that way, I think, Matt. Is that like some of the things that were really helpful in the last 10 years won't be in the next 10 years. And then the one other thing I'd say is like, I think brand's gonna be harder than ever to build, and I think it's gonna be more valuable than ever. I mean, brand is the ultimate equalizer that somebody else is selling. Yeah, it's like, I mean, everybody can go and build a water bottle. Hell, Sean did it, you know? I'm just kidding. I wouldn't cut that out. But the, like, anybody can go and order a water bottle from one of any really good manufacturers. And so, like I said this to my team before, if this means something, if this SM at the bottom means something, really bright future. And if it doesn't, we're screwed. We'll eventually go outta business, the end, you know? And so, like, brand building is going to, like, we've, e-commerce, it kind of bifurcates into, you've got kind of dropshipping, kind of like, what is the opportunity of the moment? hit it and get as much money as you can really quick. That'll always be there. But if you're like talking about like enterprise value creation, it's gotta be brand, right?

And I think we should do a whole episode about me launching water bottles because I would recommend absolutely nobody do it right now. It's like, how are you gonna stand out? It's the craziest market on earth. Like, you know, my micro says it's a knife fight, but Ridge can do it because it has People showing up every day on its website. It has some sort of attention already.

We've been talking about use cases with AI. We ranked a list and you're probably wondering, hey, where did you get that list from? Well, actually what we did is we took the operator's knowledge base, which you can go to. It is now online. We will leave the link in the show notes, and it literally has cataloged everything we've had on this podcast, everything you've heard on Mops and any of the other operator's properties. It is incredible knowledge base where you can go and say, hey, I'm wanting to know like, what have the operators said about this or that? And you can immediately pull all the quotes, look at everything we've said over the years. It's insanely helpful. I'd advise you take a look at it. But we went there and we just said, what are the top 10 things we've talked about when it comes to AI? That's how we got the list. That's what we ranked today. Find ways to apply AI. You're going to drive more value. Keep coming back to Operators Podcast. We love having you with us and we will see you next time.