E130: How to get your brand AI ready

26:10We have a bunch of scripts we already really like. So copywriting for sure, right? And then it's like brand guidelines, getting people up to speed, like a Ridge training bot. We have that going right now. It's helping with onboarding and everything else. Then the next one is we already have all these assets. This is pretty next level stuff, but now we're training custom GPTs to produce similar high-performing static ads, right? And we have a custom thing set up where every day, a hundred static ads get auto-generated and just dumped into a Google drive, right? So now, then we're building more automations on top of it to automatically launch those in ad accounts.

26:45So it's like, we've just, we've taken, we know what we like as a brand. We're now able to replicate that. We know what copy we like. We know like images we like. And now we've built like a ads factory that just all day runs and just dumps ads into a Google Drive force that could automatically upload it. So that's next level you can do on the creative side of the business. Mike, any good use cases for AI on the creative side? Or maybe you want to go back to the data side or even the technology side. Yeah, I mean, I think we're doing similar things on the creative side. It just allows you to do more, like to just get more throughput.

27:21What might have taken, you know, whatever half an hour to create a good creative before, like now you can get five if you're leveraging AI. So I think that makes sense. To me, one of the more interesting use cases is using it in product development. And I mean, AI is really good for gathering lots of information and putting it in one place and then either automating processes off of that or kind of synthesizing that and consolidating that where you have a real clarity of thought. So when we were releasing or working on our newest product that we released, we did a lot of customer interviews. And there's actually a service that helped us to do some of this that AI is really deeply infused in it.

28:05But it's pretty clever. I mean, if you've ever done customer surveying, for example, it's a qualitative exercise. You're usually like, tell me what you describe, what you like about the product. Tell me what you don't like about the product. And you're like asking them kind of qualitative short form answers, open ended answers. And so if you do, let's say you interview a thousand customers, what it used to be is literally like you're having somebody just read through all a thousand and saying like, hey, what do you think are the themes? Now it's literally like you can do that thousand. You can drop all of that into, like you said, a custom model or something, Sean, and just say, hey, summarize.

28:40What are the three biggest features that customers like and would want to be sold on? What's something that I might not think about emphasizing about the product, but that really stood out to customers? Like, what are some, you know, dangers about like things that people don't like that we might want to refine about the product? And it's just fantastic for that kind of stuff. So, I mean, really, this goes back to the idea that Krishna was talking about, which is to really leverage AI is directly contingent on getting good data. whether you're getting good customer interviews or Sean, like for example, with the using it to generate creatives and kind of creating an ad factory, you've got to get good examples of here's what works, right?

29:22And here's performance data so that it can kind of draw some inferences about what you would like. But once it has a good high quality data set, it's always going to be really good at coming back with suggestions. And I think that just more generally, what it can become is it can become a central hub where almost everything you know about a subject is consolidated. So like, it's great to have a place that you can go to and say, hey, generally across everything we've done, what works for us with creative, with our particular demographics? Like, what are the principles that should always be front of mind with any ad campaign?

30:00You know, if I said we're running these five creatives on meta right now what are some holes like what are some angles that we're not taking that have historically worked for us or whatever and it's just it's great at that kind of stuff so yeah and this is this is where we got super lucky that we've just had structured data for a long time like krishna can make fun of the daily growth dashboard all he wants but it's five years of sales data and spend data by every single channel like it's just that's a great foundation to start off of, right? And like, if you're not doing that right now, if everything is just, you know, different spreadsheets that don't talk to each other, nothing gets consolidated.

30:38It's just, you have a way harder time moving to an AI first system because it's all just structured databases, right? Mike, I want to bring up, you brought up the customer interviews. So yeah, one big unlock this year was making custom chat GPTs, right? So custom GPTs inside the business account or so you can do it in cloud side of cloud. And all of us have customer surveys, right? All of us have, you know, when somebody returns something, they have to give you a reason, right? And we make people type a sentence or two. We, I mean, we've been in business 10 years. We have thousands of these things and putting in there and making the customer bot, right?

31:18Making a bot that you can query against. Just like how Jason has a brand bot, he has the HexCloud bot. So if you have a question about, you know, hey, are you okay with this copy, HexCloud bot? It's like, no, we wouldn't really say this. We were more of like going for this ethos. You do the same thing with your customers. Be like, hey, do you like this ad? Do you like this script? Do you like this product? And you can just start getting, you know, actionable insights off of that. Yeah, so we have like personas in our business. Like we've defined, okay, here are some personas that we go after. And we have a custom GPT for each of them.

31:52like one's named Megan and she's whatever 20, 35 and she has one kid and she's kind of like an avatar. And so we've got like a Megan bot where it's like, okay, you see this ad copy. What do you think? You know, like, how do you respond to this product? What, you know, and, and it's like you said, Sean, it's really helpful. I think that it's, it's a really good example of using any kind of customer feedback because when we as humans hear customer feedback, we're subject to all the biases, the cognitive biases. So like recency bias, that's a real thing. If I'm a customer support rep, for example, and the last three tickets I had are about the locking mechanism on a bottle breaking, then I'm like, the house is on fire.

32:35You know, this locking mechanism is an absolute dumpster fire. It might be that those are the only three tickets we've gotten on it out of like a million bottles. This sure feels like it's a major issue. And we deal with this all the time when we try to go to our customer support reps and say, hey, what are the issues we should be aware of? Obviously, they're going to filter everything through what they've seen the most of or their kind of recency bias. Another example of bias is that I have ways that I tend to think about our customers and I tend to think about the business. I'm really curious what Jason's doing right now.

33:08But I have all these like ways that I tend to want to think about the business. And when I get feedback that confirms that, it's confirmation bias. I latch on to that feedback. When there's feedback that doesn't fit in that, I can tend to discount that because I'm a human and this is how my brain works. But if instead I just feed a bunch of data into a model and I just say, hey, objectively, what are the trends here? What are the hotspots? What are the inferences that you would take from this? You're able to get away from a lot of these biases that really were bound by as humans. and customer feedback is like at the very top of my list, whether you're doing product dev or you're talking about customer support or anything else.

33:50Jason, what do you like about Saris? You were gone for the past week in Italy. Were you checking Saris dashboards? I wasn't checking anything in Italy, but I will say this, like whenever anyone on my team's like growth team or anywhere else like sends me data anymore, it's likely coming out of Saris. And every once in a while, I'll get something from them and it looks really slick. And I'm like, where'd you get? Is that Saris? And I'm like, yeah. And I always feel really good about our investment. I do get a daily email of some metrics out of Saris that comes through. But one of the things that I've been thinking about a lot lately is AI.

34:23I mean, duh, right? Everyone is. Duh. But like, I finally woke up a few weeks ago. I finally woke up a few weeks ago. I was like, I got to get serious about this. And I was actually talking to the Saris guys and they launched a next generation AI powered stuff in there in a platform, which I'm excited about. It's Sarah's IQ. It's invite only right now. If you mentioned operators podcast, you can probably get access, hopefully. But you know, the next level of all of this is how you first with AI, you need to have all your data somewhere. I mean, you can, it's going to be really messy to just drop stuff into different AI platforms.

35:03If you have your data in one place, and then you can leverage, you can really leverage AI. So the way I've thought about our investment in SaaS analytics was getting all of our data in one place, understanding our customers better, understanding our marketing metrics better, but then being able to lay your AI on top of that is, I think, the really exciting next level here. You brought up bringing in Costco, Amazon, and Shopify. The ability to have all sales channels with the same data is so important. If you're listening to this and you sell on Amazon, you know how painful that platform is. You have to wait a couple extra days to pull your reports.

35:39The SKUs aren't the same as the rest of your SKUs. The titles are different. So to get actually clean data to compare, it's very manual. Using Xeris Analytics, they matched all that up for us. So they cleaned all that data. So I can actually look at what is the true margin profile of an Amazon sale? What's Amazon return rate? What's Amazon customer frequency? All the type of data, compare it to our.com and have a comprehensive overview. And now with their new AI tool, I don't have to look at it. I could just ask it. I could just tell me cool questions. So doing that across our five Shopify stores as well.

36:10We have an EU Shopify store. So now I can compare EU ring customer cohorts versus Canadian ring customer cohorts versus Amazon ring customer cohorts. So now we're getting to different functions of my business across different channels, different categories, and actually looking at the margin, the repeat rate, and like, is it worth investing in these different things? So if your business starts to feel like a spider web and just like keeps going and going and August, just like this nonstop proliferation of different channels. Maybe it's time to check something out like Sarah's Analytics. I remember Sean at the very beginning, this is a very typical Sean thing.

36:46Sean was like, what is this? Why do I need this? Right? And I'm actually really happy that you've come to understand like how important having a data warehouse is and what it does for managing your business. Because you are not like a true believer from the beginning. No, I was angry when you told me I needed to pay for something new. But Jason was right. He convinced me. He got me on board. I'm using it. So a rare Jason W. So thank you, Jason. Very rare. I'm on Saris Analytics. He's on Saris Analytics. Proud sponsor of the Operators Podcast. So if you want a data warehouse and you want an AI-powered data warehouse, tell Saris about the Saris IQ feature and then you crave it.

37:32You're demanding you get access to it. So, all right, guys, talk to you later. Yeah, Mike, I have a lived experience here. So in the beginning years of service, we used to do a lot of survey analysis where our data scientists would go in and build these clustering models and stuff like that, create these word clouds saying which word is getting used where, what combinations of words are showing up. And that would inform Prada. If there are issues in the product, like you said, It could inform marketing because they could now take a different angle. All of that is now reduced to a few set of prompts now.

38:09It's just incredible, right? So generative AI, by definition, is more text-heavy, video-heavy, image-heavy. So any use case which has AI, any AI use case which is relying on any of these three input streams, AI is fantastic. What we are doing with IQ is trying to make AI work with data, right? Which is, while it is in a structured format, we also have to build a lot of context, just like you're building a persona context of, you know, you mentioned the example of Megan, who has a certain buying behavior, so on and so forth. We are also building context with respect to e-commerce so that we can take the structured data, put a layer of language on top of that, train the AI so that it can then answer these questions so that, you know, you can get what you're looking for.

39:01But in general, yes, any use case where one of these three things are there are ripe for innovation and automation and driving efficiency. Christian, you mentioned this, but I think it's actually worth saying that pretty much everybody who runs a business at any kind of scale had to have some structure to their quantitative data. Now, like you said, Sean, you guys have been meticulous about it and you probably had probably a more structured and more extensive data set than a lot of brands keep. But everybody has to have some of that stuff. One of the things that is interesting about AI and this next generation of AI is that a lot of your structured data is visual.

39:42And that doesn't really fit into a spreadsheet. But AI makes it possible to now have a lot of your structured data that's kind of lived off the spreadsheet, you can now incorporate that. And we've kind of said it, but we haven't said it in those words. And I think that that's a really interesting thought that all of your data that's been non-numeric has been basically qualitative and off spreadsheet up until now. And now we can start to marry our quantitative data with all this other data, the visual data and everything else. And that's where some of the breakthroughs are as well. Dude. Okay. Totally.

40:19And now I circling back, the CX thing is so real. I mean, we had a CX person who at channel and Slack emergency, we have a huge issue breaking out. And I'm like, what's the issue? They're like, we sell a key case. It's like a thing, you put your keys in. They're like, they're breaking. And I'm like, okay, well, if they're breaking, that costs the company, whatever,$10 million. What's going on? We dive into it. There was eight tickets that day. I'm like, guys, you know, I sell about a thousand a day. I'm like, okay, we're fine. Right. And it is, it's the reason to buy us. If you get eight tickets in a row, you're like, oh my God, the world's built in that.

40:56I'm like, Hey, look, there's a lot of customers. There's a lot of tickets that come in randomly. Eight of them can happen in a row. Right. And, and, and nothing changes. AI does help with this. So. I was just going to say that AI helps reduce informational asymmetry. And that's really the problem is that that customer support rep doesn't really have the context of how many that you've sold or how many people got one and left a five-star review. All it sees is the, you know, the eight tickets and eight unhappy customers. And so they're doing exactly what you would expect them to do. And this is where we all deal with informational asymmetry in our businesses.

41:33One of my executives knows something that I don't know because we have to kind of divide and conquer. AI gives us a really easy way to consolidate all that, where there's less informational asymmetry across the business, where to the extent that I feel comfortable having transparency, a customer support rep can know as much about the business as I can in a bunch of different areas. And that's never really been possible before. Yeah. Imagine a scenario, Sean, where one of those eight customers is a really high-value customer for you who is having a bad experience. how does the customer support rep understand that this particular user is a high value customer, right?

42:10You hook it up back to your data warehouse, push the data back into your CX software, and boom, now you have a ticket, you have the customer LTV, and that is telling the support rep saying, hey, this is a really high value customer. We need to do something about this, something maybe a little bit more seriously or treat with a little bit more of a precedence, right so all of those things are possible oh yeah dude and you could just imagine a world where all these systems work together so much more you know playfully right where you know not not only are we seeing the customer's name the customer's issue the customer's ltv the last review he left the last ad he saw right like this actual context being passed across that's that's going to be a beautiful future as soon as we get there not too far so we can pick a few use cases and start It's all in there for you.

42:59Yeah, man. I love to hear that. Custom operators, you know, Serious Analytics features coming out soon. Mike, you brought up information across your organization. So we're trying to go through all the ways you can use AI today in your business. We started with data. We talked about, you know, using AI to launch better, faster, cheaper software solutions, right? We talked about AI ad factories. We can go in deeper in that if you want to. The customer personas is, I think, is a great one. Hexclad brought up the chat brain. I just want to bring up getting information across your organization. So, I mean, all of us are probably, and actually we'll go back further.

43:36This is where I think being remote has advantages, right? I always say it's better to be lucky than good. Because we were remote for the past five years, everything had to be written down, right? Or recorded. So we have good looms. We have a whole Notion training board set up. And the person in charge of Adam at my team is a psychopath. So it's all incredibly structured and he's done it forever. And then, you know, we have processes documented. We have, I think Slack's just like a master case of having everything we've ever done in one spot. And then we've been recording all of our meetings forever, right?

44:14We have all these standup meetings that are recorded and they get put into, you know, transcripts or whatever. And with Granola, which is like an AI note-taking tool or even the built-in one in Gemini, They're awesome, dude. Like we have everything we've ever talked about and we can get those summaries across the whole business way easier. So are you guys seeing any value in that? Yeah, I mean, I think absolutely. And one of the reasons I do think it's a good point about remote organization, Sean, I think that remote versus in-person does force you to build different muscles. My take has always been that in-person, the advantage is that there's room for conversations and ideas to spontaneously kind of come to the surface.

44:58And it's very difficult for spontaneous idea generation in a remote context, but that also the structure that's required to do remote well makes communication a lot better. I think that on a basic level, what goes on in companies is 90 % of running a company is communication. It's moving information around inside the company. and one of the ways that AI is going to be more effective, make organizations more effective, is that you will need less nodes to move the same amount of information around. Or another way of saying is you need less people to move the same amount of information around. And the more people that you're trying to keep on the same page, the complexity basically just goes exponential, right?

45:43So if you think about how hard is it, Sean, for you to keep you and Connor on the same page? And it's like, well, it takes communication, but it's a heck of a lot easier than keeping you and Connor and the rest of your organization on the same page, you know, 50 other people or 100 other people. And at a thousand other people, there's a point where it's like it's this is almost impossible to keep everybody on the same page. And I think AI makes it much easier to do that because information can be asynchronously accessed. Like right now, if I want, you know, if you want to keep people on the same page as you, you're learning tools through writing and stuff.

46:22But also, like I would guess that part of the problem you have, Sean, is that there's just a ton of that data. Like if you take all the communication that happens in Ridge, there's just tons and tons of information being passed around. Right. What I think that the future of AI is, is that you go from prompting, which is like, OK, we've got all this data and I can go and I can ask the GP. questions to where it can kind of proactively help you. So for example, what would be awesome in Simple Modern, and I think what we're building towards is instead of me going to my custom model and saying, you know, like, Hey, how are sales on this issue?

47:01Like what, what issues have come up that I need to be aware of that I need to be managing that, you know, I get on there every morning. I'm like, Hey, what's up? What's up chat? What, like, what do I need to be knowing about? Like, fill me in. And it's like, well, hey, here's 15 different interesting things that are going on in the organization, information that's being passed around, decisions that are being made that you just probably want to be aware of. Here's a couple in particular you might need to insert yourself into. It'd be like, oh, man, that's that's super helpful. So I think that the more effectively you move information around, the less people you need and the less people you have, the easier it is by definition to keep a team high performing.

47:37And then I think we're moving towards a world where instead of me having to go and prompt the AI to tell me what to do or to give me something, it's going to be able to start prompting me and saying, hey, because I know you and I know the organization, here's five things you need to look at. Here's a couple of ideas I've come up with. It's kind of like, you know, when you have a really good employee, they don't wait to be told what to do. They proactively bring things to you. And I can totally see the AI getting to that point over the next few generations. As we've said, all of this is contingent on you have to have the information for the AI to have context to be able to get to that point.

48:13Sean was saying about being really digitally native, right, as being a remote workforce, I think is really valuable in the, like, ramp up phase for being AI native. Like, I think it's really valuable. He's got, because just everything is documented. Everything is there. Everything is digitized. And the AI could hopefully read it properly and do something with it. However, if a less remote, more in-person organization does the work to get there, I think ultimately it's the same. I do think that the drive-by discussion in an office is incredibly valuable. And that's what remote organizations miss.

49:05but I also think that an organization like Ridge which is I would say average age is pretty young is maybe less dependent on the drive by but in my world I just had a bunch of bankers in the office yesterday and they were talking about the great new HexCloud office and we were all talking about the ability to just be together and do like a quick 10 minute drive by and that's literally what I do all day is people do drive-bys me and I do drive-bys somewhere else. And I feel like we get a lot done. But certainly being like really digitally native and remote with everything in living in Slack and Looms and everywhere else is a huge advantage in the beginning.

49:49Well, yeah. So in the future, you have to wear those pendants that record everything. Because I'm going to tell you about something. This isn't built yet, but I'm going to build it by the next time we talk. Okay. So all meetings are recorded at Ridge. Okay. All Slack is essentially public except for DMs at Ridge. Everything that happens in Notion is public and I have, and I have serious analytics. So I'm going to have my head of AI build a Slack channel just called what happened yesterday. Okay. And I mean, I can, I can get this done in a week because it'll just, it'll build an NAN solution. So that's like an automation tool that will just go into, I'll have all transcripts and all Slack communication.

50:32be automatically scraped into a Google Drive, labeled by day. Then I will make an NAN thing, pull it into a custom GPT that summarizes it. And then I'll make the same NAN pull from Sales Analytics. What do we sell across everything everywhere? And it will just be a Slack channel that just says, hey, here's what happened yesterday. Here's what you talked about in all the meetings. Here's what everyone's working on. And here's your sales. I think I can get that done in like a week. That's our vision for IQ, actually, so that you don't have to get NAN, get a license for it, move that data to Google Drive.

51:05Instead, that same data can be fed into your house infrastructure and then IQ can build the same thing on top. So that way, your transaction data, your conversation data, your review data, all of that sits in a single place, right? And then you build an agent on top of that which gives you the answer that you're looking for. So behind the scenes, we are also calling a different set of AI models. We use Gemini, we use Cloud, we use OpenAI, so on and so forth. And we sort of orchestrate it. But yes, so that's a use case. Internally, we are building our data foundation. Myself, I'll give you some interesting use cases that I have.

51:44So Mike, you touched upon user persona. From a marketing standpoint, we are figuring out how to get 5X with a lean team where we can use AI and amplify our marketing messaging. So that's one use case. the second use case is we have different types of customers with different engagement models i want to understand which are these engagements going really well which of these engagements are you know going a little slower than i would like right now that would involve getting on calls with a bunch of different people track their tickets so on and so forth i don't need to really do any of that right i need to sit down build a context pull in my slack messages pulling my Jira tickets, so on and so forth, put it into my data lake and then connect AI to that data lake and start asking questions and get my answers back.

52:31Same thing with sales calls, right? I have to, so I have to chase my sales team to help me understand what are customers asking you about? What are the things that we are not doing today that customers are asking because of which, let's say we are either losing deals or, you know, deals are getting delayed, so on and so forth. All of that is in chat transcripts. So we have a connector in the platform for a service called as Clary, where all of these chat transcripts are created. We are moving that into a data lake and then using AI on top and building agents on top. So yes, while today what you have with Pulse is structured data, there's a lot of unstructured data that we could be moving for you and building agents on top of that.

53:15Okay, it's no secret that margins are getting squeezed and profitability is a challenge for brands in 2025. Not a secret. That is where Northbeam's profitability benchmarks come. It's a new feature. You could check it out. This is a tool from Northbeam that tells you the exact targets that you need to hit to get to profit inside of Northbeam. So no more guessing, no more month-end financial surprises. Hitting these targets helps you ensure that your ad campaigns are actually driving the right behaviors to make your business profitable. That is wonderful. The brands that track this stuff probably already do in Google Sheets.

53:45I know I do. I'm going to be checking out this Northbeam new feature. And Northbeam is kind of is a big level up from this. And here's what sets kind of what I have seen. Here's what sets profitability benchmarks apart. So instantly you get to quantify your goals into clear benchmarks. Every ad gets measured against those targets right inside of Northbeam. That's kind of cool, actually. And you see and you can kind of at a glance see which ads are doing well, which needs to go against profitability benchmarks, not just ROAS or whatever else you're using right now. You can scale up the winners, you cut the losers, you know the drill.

54:16It is hard to argue that this is not great for brands. Profitability benchmarks from Northbeam is like having a speedometer in all your channels. Campaigns, ads, you can kind of see everything at a glance against profitability, not something else. So stop leaving money on the table, fuel your creative performance, make every ad dollar count in every channel, hold them all to the same standard. If you want to reach out to Northbeam, just let them know the operator sent you and they will take good care of you. Not to make this, you know, remote company versus non-remote company, but I think we got very lucky at Ridge a million times.

54:50And here's a couple of examples recently, right? Our North Beam data is incredibly structured because Connor has OCD, right? So because our data, every ad we ever launch is like the name of the ad, the type of ad unit, where we're running, like all that type of stuff makes it very easy now to put AI on top of it and be like, give me the best ads, right? So that's been super beneficial. And it's the same thing with this daily growth dashboard, right? And having everything recorded and now I'm working with Sarah's analytics that like I just get a daily update. So across all channels, all businesses, and also not just the sales update, what people are working on, right?

55:27Like, hey, we, you know, what's something we talked about yesterday on standup? We are changing our luggage line, right? So, you know, tariffs are raising prices everywhere. where we want to make a more competitive luggage line. So I have to go through and I have to make those changes. We have to physically redesign the thing. That would be, hey, we resolved this project yesterday. We know what we're doing. We're moving forward. That can be recorded, put into my Slack, and I just get an automatic executive update. So communication is going to be a big one. But guys, we talked data, technology, creative, branding.

56:00I mean, how else are you guys using AI? What are some big wins that you're like, okay, I was going to hire somebody, but I don't have to now? I mean, I'm going to tell you what, my biggest win, my biggest win personally, my personal biggest win, I've said this a couple times, is that I'm all about being efficient in communication. And there's so much coming at me. It's out of control. It's a fire hose drinking 24-7. And, you know, the days of people sending me an email with a bunch of attachments that I need to go through are done. Like, I'm just no longer looking at them. Like, stop it. Because you can just send me a summary using chat GPT.

56:38We created JSON GPT, but it's very easy to do it any way you want. And it's almost disrespectful now to just send people voluminous information and a block of text and an email and expect them to figure it out. And I'm literally just dealing with this this morning. And this is someone from outside the company who's asking for my help. And they sent me an email with five different attachments. And like, it just doesn't, you're not giving me the answer to the question. So I think this is going to make communication for me, communication, like Mike said it, Mike was a hundred percent right. Like running a business, it's all about managing the communication and being good at a business.

57:19Fundamentally, once, once you've established the baseline of a decent business with product market fit, right. That's actually selling and collecting money and is financed well. the next thing is always about communication. We're seeing it in product development right now. We have so much great stuff going on in product development and we're talking about this one product that we're going to launch. It's like there are so many things that in the process could have been so much better with better communication. And so it all comes down to communication. I think for me, that's the people that are going to be the most successful are going to really leverage the AI for communication.

58:02I know I'm very biased because this is a specific issue to me, but I really believe that deep, deep down in my soul. So I'll throw two out. One is, and I want to kind of riff on what Jason was saying, the problem in our world is not that we don't have enough information, it's that we have so much. And obviously the amount of information being generated in our businesses and in the world, you know, every hour is just almost completely overwhelming. So the issue is the curation of information. And using AI right is that you have something that can take all the information, but then it can curate what you look at.

58:43So Sean, to take a next step in your example, it's great to get to a point where there's a thing that can say, here's everything that happened in Ridge yesterday and can put it in a Slack channel. The problem with myself, Jason, you just kind of said it, the average employee is that they'll look at that channel for a week or two and then they'll be like, you know, 60 % of this, 70 % of this isn't relevant to me. And then they'll kind of stop reading it. What we want to get to is, yes, we have somewhere where everything that happened is known, but then there's this curation to each of the people in your organization of here's what you need to know of everything that happened yesterday.

59:21And I think that that's when it's going to get super powerful. Another use case that I think you're going to hear a lot of in the years to come, and I've started to play around with, is just using AI like a sounding board business coach. where I give it, like when you're an executive, your job is judgment and decision-making. So often you're making decisions about, do I hire or fire this person? How much of a raise do I give this person? Do I take the company in a direction that's going to be really frustrating to these parts of my team or could even lead to a layoff or could lead to really moving somebody's cheese?

1:00:05and it's difficult to have a context where you can really discuss everything you're processing as a CEO within the organization. But, and a lot of people that's their spouse, some people are part of things like YPO or Vistage. Like I had an entrepreneur this week that I work with asked me like, do you have any good suggestions for business coaching? I feel like I need some, you know, like some business coaching. And I can really see the models being good at this. Like I've actually been surprised when I've uploaded, you know, like I probably had about an hour conversation with one and I gave it context.

1:00:43And then I said, hey, I want to talk through a couple issues and how I'm thinking about it. And it was pretty good. It was pretty helpful. There is a risk right now that the models are pretty sycophantic. And so they will like confirm what they tend to confirm what you're thinking. And I think that that's a way where they need to get better. But everybody could use a sounding board where it's like, I'm making this key decision. Here's all the information. I'm thinking about going this direction, but I'm not totally sure. Help me walk through this. And it's like, if it helps you make 5 % or 10 % better decisions, that's incredibly helpful to the organization.

1:01:22Because when I make a poor decision about a key strategic decision, it can cost us millions of dollars. So that's another example that I think you're gonna hear more and more people using it like a business coach. Dude, totally. I just wanna, to one point, 60 % of people won't end up reading it. 70 % of people won't end up reading this daily summary. Every week we have a weekly update where people go through and they summarize what they worked on. And I know that maybe five people read all of them. It's a public channel available to everybody. You can know everything your coworkers worked on. Very few people end up reading it at Ridge.

1:01:59But then when people talk to me, they're always blown away that I know exactly what they're working on and what's happening in an organization. People at Ridge think I have an amazing memory that I'm just so deep in the weeds. I'm like, guys, I just read the weekly updates. I'm like, you summarize them for me. So I know what to follow up on. Well, to take that a step further, Sean, I'll give you an example of something I did in the very early days of Simple Modern that I went away from, but that I'm going to go back to eventually. Like one of the things you're doing as a leader is you're trying to keep a pulse on how's your team doing, right?

1:02:28Not just how are they doing performance wise, like how are they doing personally? Like, are they at risk of churning? Are they getting burned out? You know, like whatever else. And so like one of the things I did in the very beginning of Simple Modern is at the end of every week, I sent people a survey and it was like, you know, what'd you work on this week? What were big wins? What were big, you know, L's? How are you doing on a scale of one to 10 emotionally, you know, physically, whatever, It was just kind of like a, you know, like how you doing and what did you do? So I did that up until we got to like 10 employees or something like that.

1:03:01And then I stopped doing it. And part of the reason I stopped doing it is like it takes up their time. But a bigger thing is it's like I just didn't have the time to look through all the information anymore. But imagine a world where like at the end of every day, I'm like, hey, everybody on Simple Modern has a 60 second survey they take at the end of the day. That's just kind of like how you doing. and then I've got a model that's letting me know like, hey, here's the three or four people that are not doing well, that are really having conflict with their boss, that are really stuck, you know, whatever.

1:03:27It's like, well, I can be a much better manager. But again, it's because I can feed way more information into something and I can curate. Like if I'm constantly being bombarded with how everybody in my organization is doing every day, like I'm going to just tune it out. I can't process through all that. But it would be really helpful if I knew like, okay, here are the two or three hotspots or whatever. Dude, I think that's great. Now, quick aside, Mike, Jason, do you guys have Slack at your company? Yeah, we started on Slack. And Sean, this is actually an interesting thing about Simple Modern.

1:04:00We're kind of hybrid in that we're in office the middle three days of the week, and then it's work wherever you want, Monday and Friday. And we were this way before COVID. In fact, when we really first got started, because we bootstrapped, we were basically still working previous jobs. So everything was through Slack. It was like we were a remote company. And so we have this kind of heritage, even though we're an in-person company, starting basically as a remote company and then being a hybrid model. And then obviously during COVID, we were fully remote for a while. So similar deal. Everything's run through Slack since basically day one.

1:04:35And we actually like, it's funny, we were trying to figure out how to handle Slack archives because we had everything in there. And for a while, what I would do when I would hire people is I'd be like, you have to go through and read every public channel on Slack. That's all you're doing your first two weeks. You just have to read through the entire company history. And then it got too big. And then we started to realize like, oh, you know, there's kind of legal liability of just holding on to all that data. And so now we only keep two years worth of Slack data and then you dump the rest. Yeah.

1:05:06Jason, do you have Slack too? Yeah. We use Slack. We've been on. I actually was like a super early adopter of Slack back when I was a banker, when it is like the investment banks weren't even letting you use Slack. But we were small so we could do it. Like most bankers, they won't allow you to use Slack. I don't really use it anymore, to be honest, because there's just way too much communication coming my way. But my teams, everyone uses it. People know they want to get to me. Slack is not the optimal way. Yeah. The reason I just ask is, I mean, like, look, it's great to have, because like it is perfect for this AI future.

1:05:41It's all written messages. It's all, there's a lot of context because there's messages, replies before it goes back forever. But also it's just, it's so expensive. Like if I was in person, I'd be on Teams or whatever. I'd have a hard time justifying the 20 grand a year for Slack if I was in person. Operators, Black Friday, Cyber Monday is coming up. Is your SMS list ready? If not, get on PostScript. They are helping us drive 14 % more email signups through their better opt-ins. They're helping us get 6 % more SMS subs every single day because they have perfected the art of pop-ups. You need to build your list.

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1:07:25We covered five good pillars. I'm going to summarize those in the intro and the outro, but what do we leave out that you're like, this actually blew my mind and it's making us more efficient? Yeah, for me, from my own personal experience, I've been playing around with AI everybody has been playing around with AI but how do we have an AI strategy is a leadership problem statement right? So having a leadership strategy for AI and how the company could move forward with AI has to come from the top and then you have an execution plan against it where people are contributing ideas and then have a structured place where you can capture these ideas and start executing one by one and measuring the outcome of those experiments is something I would highly recommend.

1:08:10It's a structure that we are following internally. We follow a three-lens framework experiments that could fail, and I'm okay with them failing. And the successful experiments are either driving efficiency. In your case, Sean, you're creating more ad copies per unit hour, right? So per dollar spent, you're creating more ad copies, or it could be new capability where you're launching your products based on what you're synthesizing from your reviews or other things, right? So we are capturing everything internally. I'm forcing everybody to write a lot. I'm literally chasing 10, 20 people every single day, asking them to write, asking them to document, asking them to even document their thinking, right?

1:08:58For example, let's say you're launching an ad campaign. Generally, what ends up happening is you're getting into a room, you're brainstorming, coming up with an idea and then leaving. And I can maybe get that information from a chat transcript, but I'm actually asking people to write everything down so that that can then become context to AI on and so that AI can build on top, learn certain patterns, so on and so forth, and try to get better on my company's context. So yeah, strategy comes from the top. Experimentation is critical. Sean, you've said, you've highlighted multiple experiments that you're running with different tools.

1:09:38All of them are great. Some of them will end up scaling and become standard operating procedure for your teams. And some of them might actually become your capabilities. And having a dollar value against all of those things will basically help you see what is the ROI you're getting on these AI initiatives. Totally, man. And look, and for the past six months, everyone's been saying AI, AI, AI. Like it's just, people have been repeating it. We're starting to see actual business use cases across all these different verticals. So I think we're definitely in the, we're outside of the experimental phase and it's like, yeah, people are using this in workflows and actually making productivity improvements off of it.

1:10:17Right now we are almost to the automation phase, But it's like, look, I mean, my team's smaller and revenue's up. So it's like, so they have to be the marginal increase in productivity value, I think, can be contributed to AI. The other thing we're doing at Ridge is, you know, I had like a chief of staff. He was head of remote. Naturally, he's transitioning to like VP of internal projects. And AI is the biggest one of those, right? Like he's setting up an NAN. He's a savage when it comes to it. So this is what he wants to spend his time doing. One of my favorite AI story before we wrap up, Sean, is we work with a large retailer.

1:10:52Their CEO originally did not have investments for AI, but then he decided not to open one of the stores, retail fronts, and then redirected that entire spend for AI initiatives. So that's what we've been helping them with in 2025. Let's see what unlock they get next year. But yes, it's a good time to start taking AI seriously and start figuring out how to incorporate it into workflows. Okay, beautiful, dude. Okay, and this is going to be a long outro. It could even be an intro. We're going to see what we end up doing with it. But we talked about five core pillars of AI. First is going to be data.

1:11:28Look, the first thing you can do if you're just a simple business just on Shopify is you should start tracking some sort of daily growth dashboard, right? Ours was five years and it was every sales channel as we added them, every spend channel. This is mission critical just to start documenting all this different stuff. Additionally, when you're pulling reports, when you need to validate data, I've just been uploading spreadsheets into ChatGPT and it's so much better than I am. It saves hours and hours and hours. The data side is just start documenting your data. eventually if you have really complex data you want to move to something like a like a service analytics like some sort of data warehouse because then you can put an ai interface on top of it they have they have pulse they have iq so pulse is your data lake data warehouse feature everyone once you get to probably 50 million dollars right like if you have multiple shopify stores an amazon store in a wholesale account like you have to hook all that stuff up you put iq on top of it you put a chat interface on top of it.

1:12:31And then it's just, you're automatically getting this data that you would pay. We were paying four different analysts to pull it for us. So data is one of these core pillars, right? The next one is asset creation, right? Marketing in general. We built an ad factory. So using ChatGPT, plugging directly into the API, we were just auto-generating tons of static ads, right? We've automated that full flow. And dude, we're launching probably five times as many ads with the same number of people. And that's how we've been able to get advertising scale right now. So we're crushing that. We talked about brand.

1:13:09I'm going to put brand and customer in the same one, right? So we have, Hexcat has the brand brain. You should probably make one of those. Jason has a Jason GPT. He's sick of people communicating at him. So he built a way to custom and summarize this and then mike's using ai to do product reviews customer reviews make his products better make his customers happier and better reach his core customer which is megan in this instance right then we talked about um tech and shipping it's just really really good for code and then the last pillar that we talked about was communication across an organization right the more remote you are the better you document stuff the easier it is to put chat on top of it.

1:13:49And then just start getting summaries, making sure information's shared across. So anything I missed, guys, on this AI super episode? Sean, that was awesome. That was an awesome recap. Yeah, I think this is great because everybody knows it's the future, but we're really trying to figure out how do you actually do stuff with it. And I think everything we talked about today, this is like actionable. You can do it today and make your organization better today. There are clearly going to be use cases that are going to arise as time goes on. There's I think this is very actionable, Sean. And even for me, it's inspiring to be like, okay, what are some of these areas where I'm not using it that I've heard you guys mention?

1:14:26And one final takeaway that I want to add is that you may not see today for yourself like an incredible value, but there are a lot of tasks that you're doing now that are going to ultimately be in the AI. and now is the time, even if you're like a senior exec, right? Now is the time to start learning it and leveraging it. Like everyone needs to be learning it and leveraging it because all of a sudden, these things are only getting better and it's amazing how much better they're getting. So even if there's, we're talking about certain use cases now, there are just gonna be so many more coming down the pipe.

1:15:06So you just gotta get ahead of it. Yeah, dude. And I tried to make this episode actual use cases right now? Because everyone knows AI is the future. Like we've been hearing that from every big tech lead and every panel you've heard, right? But it's like, how are people using it today to actually make more money? I think we nailed some good ones, right? I mean, if you, all of the ad copy, most of the ad scripts, all of our static ads are being generated with AI, humans off. This is like where we've reached full automation scale. We've had a couple good AI videos from Icon, But like, that's still harder.

1:15:41That's six months from now. We're using VO3 for some hooks. But like, anyway, it's all it's all coming down the pipeline that more and more there's going to be less, you know, big head stuff and actual use cases in the moment. Krishna, any parting words on this awesome pod? is thinking out aloud at this station. So for me, I'm actually curious, is it a push from your side, pushing AI to the team or is it more grounds up in terms of people coming up with use cases and then you approving it? Or is it just Sean just pounding on them saying, guys, go figure stuff out? Dude, it's such a good question.

1:16:17I make the team use AI a lot. So like every month, we have a Friday AI competition where people play games And like, it just, it's asset creation. It's like, make a landing page, make a game, make something in 30 minutes, show it off the last 30 minutes. And the winner gets like 500 bucks. So we're doing a lot of that. And I've been like, I'll take over standup sometimes and show the entire company some cool AI thing, right? I'll be like, hey, look, I made this image. I made this product. I made these ads, right? And that's getting 80 % of people to adopt, but probably 10 to 20%, maybe 10 or less are AI super, super freaks inside of Ridge.

1:16:56It's just like people where if I never brought it up, they're like, they are on TikToks, they are on Twitter, they are looking for like the most cutting edge stuff. We probably have like, you know, five to seven of them inside the organization that like, they're the people where people are going like, oh, I need to level up my game. Because we have a guy, Antonio on our team, who he's like, he's in charge of freight shipments, like making sure shipments go where they need to go or whatever, has fully automated his job. He went on a standup and he's like, yeah, I automated my job. Here's how I did it.

1:17:30And he's like, I'm going to go do something else. And he's just all of the data. He's like, he was doing triple match. He's doing all this type of stuff. Completely automated his job. He's like, I wake up, I click this button. Now I'm doing other stuff. And I'm like, good on you, man. So yeah, there's a lot of cool stuff like that happening in an organization. So one of the most amazing things that I've felt with AI is when I was working at these large companies, it was always difficult for me to understand the perspective of, let's say, not my manager, but my skip level and so on and so forth.

1:17:59Today, I can ask chat GPT and or, you know, upload a bunch of context and say, how would my manager have reacted to the situation? What am I missing? Right. So Mike touched upon it, having an executive coach in your pocket all the time. So anybody can level up very, very quickly. So that's one superpower, obviously. So for anyone who is curious, willing to learn, and has the ability to problem solve, AI is just going to amplify their careers, right? So it's amazing. I think with a small team, what you could do with AI is just significantly more, right? So that's the superpower, super unlock for us.

1:18:40And that's what we are trying to do here. And I think it's the same for you guys as well. Dude, great parting words. That's the pod. Thank you for making it operators. We're not AI yet, but it could happen in the future. Jason, Mike, Krista, all you guys are fantastic. Leaders in your spaces. Talk to you soon. All right, thanks for making it all the way to the end of this episode. Wherever you are in the world, it is awesome to have you here. If you do not already subscribe to this show, that is my one ask, is please go to whatever platform you are watching or listening to this on. It could be YouTube, it could be Spotify, Apple.

1:19:14I don't care. Just go hit the subscribe button. Please pump our egos up. it helps and before we go one more thank you to the sponsors fulfill postscript north beam saris and rich panel awesome guys running these companies we all use them these are our vendors that's the only reason they're sponsors of the show so thanks again to those people