From Spreadsheets to AI Agents: The Ecommerce Data Playbook

19:07Jason Panzer:The future of Saras Analytics is agentic, Sean. So I imagine a user coming to Slack and asking a question, saying, okay, I just launched this product last week what's my perform what's the performance of the car right and i expect them to get a summary of the product performance since launch on vital parameters that can be used to assess whether the launch went well did not go well or if the average uh all in a matter of seconds what does that mean from a practical standpoint right if you think about it when i launch a product the orders are getting logged in shortify and amazon so my sales data is in these two platforms at least uh when there is traffic advertising campaigns or money is being spent so that's data is sitting in facebook and google and all these other places somebody clicks on the ad lands on your website the conversions on the page the activity on the page is not in gf4 or edge mesh or one of these tools so for a product owner or for a marketer or even a ceo for that matter to understand how did my product launch go you need data from all of these systems to understand what is my spend what is that spend leading to from a conversion standpoint?

20:20Jason Panzer:And what does that mean to us from a revenue standpoint? And how does that face against the targets that we have set for ourselves? So getting that summary today is so hard. It takes multiple people going to multiple systems. All of that is going to get reduced to seconds actually going forward. And that's the direction that we are taking our customers.

20:43Sean Frank:every SaaS company says they are AI powered but very few can explain what it actually does for the revenue of my brand this is why PostScript's approach stood out to us they don't just build AI for demos or buzzwords they built it to drive real incremental revenue PostScript's AI called Shopper it shows up inside of SMS at moments with real buyer intent when shoppers are likely asking questions hesitating maybe even about to drop off Shopper can answer product questions instantly answer questions about fit, availability, recommendations, order issues, the kinds of stuff that people usually bounce for.

21:15Sean Frank:This means more conversions, higher AOV, less lost demand. So you are driving more revenue and doing it more efficiently. Check out Shopper from Postscript. We use it at PILA, which is why I am telling you to check it out.

21:28Krishna Poda:You need a data foundation underneath your AI, right? Like that's fundamentally, if you don't have a good data foundation under your AI, it's useless. And that's why like quantitative people have been less interested in really adopting AI, right? Until Claude's most recent release. And you could like really power Excel with Claude and do really good stuff in Excel with Claude. But even then, you know, there's so much time going into compiling and scrubbing data. And it's a very manual human task. And if you, you know, if you were running on more than just Shopify, even if you're only running on Shopify, you still, you're probably still using Northeem, right?

22:12Krishna Poda:Or using another MTA tool. And you're getting marketing metrics out of that. You have an ERP system. And so I've been a big believer in data warehouses for like 15 years. I mean, if you look up the company Snowflake, right, like these are huge, multi-billion dollar businesses that make a lot of money. Because this is like in the financial markets, data warehouses are huge. And basically all throughout like large scale businesses, data warehouses are huge. Because when you have all your data clean in one place, you can make better decisions. It's all about like making good decisions. So for us, we pull in Shopify, Amazon, Costco, Northbeam, and many other sources, and it's all in a great, clean state within the data warehouse.

23:04Krishna Poda:And then we pull it out into dashboards. We pull it out into daily email summaries, data summaries. And now with AI, you can get the real power, the real leverage is actually like querying the database through AI to just answer questions that you have. Like you just, you know, I'm constantly going to chat and asking questions now. But now I'm going to be doing the same thing, asking questions about my data. And it's just going to allow me to make way better decisions. it's it's like astonishing how good this is going to be yeah a simple workflow there sean uh

23:46Jason Panzer:jason for you like the way things are going to look very soon once we launch uh iq for you is you get up in the morning you open your slack you just say add the rate for a fact you how how are my sales yesterday you get an answer uh if you see that the sales number in a particular country uh you know are below what you think uh should be you can just ask a follow-up question saying the sales seem low what happened and the agent is actually going to get into your data understand how the marketing team marketing performed previously what could be some of the reasons why the numbers are lower than expected and it will come back to you with a better than usual like better than expected answer sometimes you'd get surprised at the quality of answers dci llms are able to produce with the right set of context and training you can actually ask a simple follow-up question saying okay why if the save slow it'll come back to you with the summary so you can start your day interacting with an agent getting to understand the current state in a matter of a couple of minutes and then get on with your day you know what is the traditional workflow there you wait for an analyst to put all of this report together usually manual they're on leave one day you're not going to get that report and even if they are working on that day you probably have to wait for them to compile everything and then ship that file to you.

25:07Jason Panzer:You get an Excel file and you have to open it, read it, and comprehend what's in there. Imagine swapping that with a simple question and a nice summary that you can read in a couple of minutes and get up to speed. So that changes the decision velocity at a business. And having that power to any team that may not have budget to hire their own analyst is, in my opinion, going to unlock serious productivity for brands.

25:34Sean Frank:Yeah, and I just want to keep highlighting how people need to experience agentic work to understand what's capable, right? So right now, you can use Manus. Manus got bought by Meta. If you spend money on Meta, they'll give you Manus credits for free. Ask for it. And then just ask it to do something. It will go on the web. I mean, I probably did this on the podcast a year ago. I had it order me Chinese food. But like now I can do, like I told you guys, I had a task that would have taken someone an hour to do. And I'm like, hey, Manus, just go in there and take photos and screenshots of all these celebrities and compile me and then make me a little web app for it.

26:13Sean Frank:Like there is so much that can actually get done with the actual agents doing work right now. And I just want to make sure people experience that because it sounds like, oh, I'm just going to talk to the AI bot. Here's an example at Ridge. we have saracen analytics setup and we put all of our marketing data in there right and it's very easy when things automatically port right so meta spend amazon spend google spend like there's apis you can pull the stuff in but what do you do about podcast spend what do you do about tv spend through tatari right so i give tatari a budget and they spend the budget and then sometimes they don't spend all of it or they only spend so much of it and you have to know what they spend per day So they email that to us.

26:54Sean Frank:Our agent's just going to ask them that. Our agent will bug them every day. Hey, where did we spend yesterday on Tatari? And then that agent will get that information and it will put it in Saracen Analytics for me. That is a human removed from that loop. There doesn't need to be a human in that loop. It is getting data that is harder to find, but they can give it to an agent who can actually understand that. And then if Tatari was smart, they wouldn't have a human email my agent. They would build their own agent. So my agent's talking to their agent and just getting the spend data. And you just start seeing how more and more results and actions in a company reduce down to, oh, an agent can probably do that, right?

27:30Sean Frank:So I just want to highlight agents, they're way different than normal LLMs or AI. And I'm getting used to that from right now. Krishna, why don't you go and show us Sarah's IQ?

27:42Jason Panzer:Yeah, so let me give you a quick sneak peek here. So here's an example of life at a typical brand. So if you are a founder and executive, you're getting spreadsheets. This is usually what your team is struggling with. They might not be coming to you complaining about it, but they're definitely unhappy that they're doing some of this work, right? So you have data sitting across all of these different systems, advertising, marketing, marketplaces, analytics, 3PLs, etc. You have either your leadership team or your analyst pulling data manually from these systems and then sending that to folks like Jason and Sean who are then asking you questions or asking questions about data quality or asking you questions about how something was measured.

28:27Jason Panzer:And this is a tedious process, right? So someone goes on leave, you're stuck. What we've been doing all these years is we are giving these analysts and the leaders, operators in companies their time back by building what we call as the Data Foundation, right? So what we are doing is we are pulling data from, we have Connectors 200 plus platform. We pull data automatically in real time from these different systems, load that data into a data warehouse, clean it, transform it, and certify it so that your data is ready for reporting, analysis, or whatever stream that you might have. Once you have the data foundation, what are the kind of questions that you can answer?

29:10Jason Panzer:right you you'd be able to understand your contribution margin by skew by campaign by product by product category etc so that will help you understand which are your top performing products which are the products that are leaking you contribution margin which campaigns are performing well which ones are not so that you can take decisions right you get some trackers that you can use so that you can understand pacing projection and your actuals you get a consolidated view of your inventory you get customer cohorts so you can understand lifetime value metrics etc so once you have all of these so this is your data foundation right this is your 101 right every brand in my opinion should or every company for that matter should invest in a data foundation where you have the single source of truth once you have the single source of truth that's where the agentic layer starts to kick in so so we have a product called saras iq and in service IQ, it's actually, what is the goal, right?

30:11Jason Panzer:Any stakeholder, whether you're a data engineer or a data analyst or a CEO or a CMO, we want you to get to the right answer from the right agent in the right language without you having to know how the data works underneath, right? All in a matter of seconds. How do we do it? So IQ actually comprises of six agents, each doing a different function, right? So there's an IQ analyst who is answering questions like, what happened in my data? When you ask this question, IQ analyst agent is dipping into your AI foundation that we built for you and comes back to you with a response. IQ engineer is troubleshooting a data pipeline break, right?

30:54Jason Panzer:These APIs change sometimes due to latency. You don't get the data. IQ engineer kicks in when you say, my data looks suspicious. Can you go check what happened? uh iq engineers right scientist is predicting what might happen in the future uh data quality uh again we want you to trust your data right only if you trust your data is when you're going to use it and only if you use it regularly is when uh your decisions will uh you know would be influenced by so so these are some of the agents that we're working on they're all under the IQ agent work for workforce and the way this works if I were to give you a quick demo so somebody who is signing up for IQ would get an interface like this where you can ask a simple question right so for example give me sales for the last 60 days broken down by category essentially what have what is happening behind the scenes is iq is taking your query connecting to the data foundation uh it's picking up the business logic that we have trained uh iq on specifically for your business and your business context and generates the query and it generates a summary that you can quickly read and understand or get an answer to here right so typically you would be either trying to find an answer to this question on Excel, or you could just come here, ask, and get a simple summary out like this.

32:35Sean Frank:Okay, Krishna, but the pushback I'm going to have for you is, why are you building IQ from scratch? Why don't I just take the data and just put it into Claude 4.6 and have Claude do reasoning models on top of it? what's the advantage of building the agent yourself?

32:55Jason Panzer:Yeah, that's a fantastic question, Sean. In fact, IQ is built on top of Cloud, right? So we use Cloud internally along with a couple of LLMs. What Cloud does, Cloud is a general purpose tool. IQ is a vertically specialized tool for e-commerce, right? IQ understands your business context and those are the agents that we're building. So we are building agents that understand your business context, what the metrics in your business are how they are defined what is the data saying about your business so we build all that context into IQ we then pass that context over to Claude and then let Claude do its magic right so Claude is generating the sequel for you Claude is helping us generate this visualization Claude is helping us generate this summary but what IQ is doing is stitching all of those workflows together passing the business context so that we get at least 9 out of 10 questions right when we answer a question that's one the second thing is if you ever see any of the announcement that come out from let's say HIGPT or plot where they are launching a new model the way the launch process works is they basically write a series of tests and they test a new model and the old model against those same set of tests which are called benchmarks and only after the benchmark surpassed is when the model gets promoted to production or general purpose use for by users in IQ every time we get a question right we actually ask the user to just hit a thumbs up the moment you hit a thumbs up this question gets locked into our evaluation framework saying okay we got this question right so the next time let's say today there is Opus 4.6 Claude comes up with Opus 5 and we want to make sure that Claude is actually giving you the same answer.

34:49Jason Panzer:There is no guarantee that Claude will give you the same answer. But by hitting the thumbs up here, we create our own testing suite where we know what is the question that Claude asked, what is the right answer, and when we are trying to upgrade our models, we run, you know, parallel test against the old models, the new model, and only upgrade to the new models when we are getting the answers consistently right and beyond a certain threshold. You cannot do that if you are on a Claude. right uh that would be a secondary the third reason is user management and commissioning you know uh if everybody is using clorb on their own desktop it's very difficult to build and maintain context across everybody so a simple example of that could be let's say i'm downloading sales from amazon my colleague at ridge is also downloading sales from amazon and both of us are coming up with net revenue my formulas are different uh my spreadsheet numbers are going to be different the same thing can happen in Claude as well right because people might be feeding in different cortex and they might be getting a different answer and the confusion you know increases by going through IQ you avoid that confusion because now you have a standard definition of what electric means for a business or for a department and that is governed that is governed by IQ quality agents just to ensure that you get the right answer so So these are just a couple of examples that I can give you why IQ right out of the bat gives you a little bit more than what you could get with Cloud.

36:22Jason Panzer:But the beauty of the AI foundation that we built for you is if you want to use Cloud, you love it. You can absolutely connect Cloud to the data foundation and help.

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37:19Yeah.

37:20Sean Frank:You know, it kind of sounds like you've built, um, the skill that is, you know, e-commerce analyst for Ridge into, uh, the agent. Right. But it's not really doing a gentic work right now. It is, it is, it is, I guess it's going to your database and inquiry, but it's not going to go out there and do work on the open web for you?

37:43Jason Panzer:It's not yet. So it is in our roadmap. So I'll give you a simple example. Let's say your campaign performance is dropping and we have one of the IQ agents detect that your campaign performance has dropped. What do you do next? Do you pause the campaign or do you reduce the spend threshold? There is some decision you might want to take, right? From an IQ standpoint, by the end of this quarter, we will have a few agents that will give you this signal saying, hey, this performance seems to be off. But we don't necessarily have the setup today to go take an action on your behalf. But that could be an extension of IQ down the line.

38:20Sean Frank:Yeah. I had this debate with Connor, right, about where the world's going. Because, you know, Meta bought Manus to turn it into an ad-buying agent, which will do agentic work, right? And House is building an agent. and I'm sure Northbeam is going to build an agent. And what does this mean is that all of the work and data they have will have someone full-time, someone, it's going to have an agent full-time prompting you to do what it thinks it should do, right? And right now, those prompts are going to go to a person. It's going to go to Connor or Jimmy, my VP of marketing. It's going to go to one of these people and there's going to be a market to build the Ridge agent who will take in all of these inputs and then actually summarize them for you.

39:03Sean Frank:Because House might be saying I should spend more money on YouTube. And Manus from Meta is going to say I should spend more money on Meta. And Northbeam is going to say, eh, you know, they're both kind of right. You should probably do this. Who's actually going to be processing and grokking all that information and then giving you the unbiased thing to do, right? And, you know, it's going to be the Ridge agent. It's going to be the HexCloud agent. Someone's going to have to build an agent to receive all these inputs. Think about the inputs as APIs. guys, someone's going to need the central repository.

39:32Sean Frank:And it sounds like that could be the Sarasai Q agent. All of these different agents have to talk and bring data into you. And then someone has to synthesize it, clean it, and then give the Ridge version to a Ridge executive to make a decision. And it's just a very exciting future. What I just described might sound like nonsense to so many people on this podcast, but I'm telling you it's where the world will be in three years.

39:53Krishna Poda:It is 100 % the future. What you're talking about is 100 % the future, right? Like, what you need to do is figure out what agents should be doing for you, right? And how to implement that over time, you know, until it turns out that that kind of workflow system has changed. I mean, once it's sort of set up that way, just like, you know, in www.in a browser you know the internet is done through a browser ai is going to be done through an agent and and your workflows are going to happen through an agent that's just like the way it started and that's that's just where it's going to go and what we're what we're should be all figuring out is like what agents do we need in our in our business that's what's

40:42Jason Panzer:happening yeah absolutely jason that's one of the things that i've been doing internally actually for the last 18 months is doing these AI audits of workflows and processes internally, documenting every step of the process, and then looking at each step of the process to see whether that step is AI replaceable fully, or whether that step is AI assisted, right? Or that is something only a human can do. What we have identified is that for many of our workflows, the simple example I gave you around customer success is the first step a human does is actually go and tell the customer who reached out saying give me a few minutes let me check and get back to you right that is a step that requires a human involvement that could be fully replaced by ai right so we are doing these ai opportunity audits constantly across teams documenting these workflows and then going after these workflows which have high ROI if we introduce AI and AI agents and going about doing that.

41:46Jason Panzer:And we are seeing some incredible success. This is something I'm happy to share with any of your listeners if you're interested in talking to me about it.

41:55Sean Frank:Okay, so let's summarize Serious Analytics so far. It is a data warehouse solution. So you guys, it's going to come in here and it's going to clean your data. And that's been the business the past 10 years is it's going to take all your data from all your e-commerce sources. It's going to make sure they all are accurate. They speak to each other. It's going to bring in your marketing information. It's going to bring in your cogs. It's going to give you the truest sense of daily profit, daily contribution margin. And it's all set up on great infrastructure like Tableau and everything else, right?

42:27Sean Frank:They're going to come in here. It's something like$10 ,000 a month, maybe a little bit more for our wonderful listeners if you're too big. And they're going to set all that stuff up for you. that's the service of the past 10 years right now with the building is saris iq which will take all of that data and just make it easier to access you'll just be able to type in hey summarize this for me just like you do with claude it's built on top of claude just like you do with chat gpt it makes an llm out of your data in your database just for you just for your team so it's proprietary it's safe and you can get your information back quicker easier with maybe more detailed prompts The future is the Sarah's IQ agents coming out, right?

43:08Sean Frank:Where they will start doing work on your behalf, answering questions on your behalf. And I'm predicting it's going to go fight the meta agent for you. It's going to go fight the house agent for you. And it will end up getting, you know, the cleanest truth across all these different data sources. Marketing is not a solved problem. And that's probably why it's such a big industry is that, you know, it's a trillion dollars a year in marketing spend, give or take, right? Between fees and everything else. It's a big, big part of the economy. and half it's wasted. It's like, it's the only part of the economy that is totally inefficient in that way, right?

43:39Sean Frank:Like, if you talk about oil and gas, their loss rate is 0.01%. Talk about meta ads, half of them are just lit money on fire. And in the next five or 10 years, we will solve that inefficiency. We will remove that, but it's going to be through agents fighting each other and really eliminating all the waste, fraud, and abuse. Jason, what's your response?

43:58Krishna Poda:I mean, you look at the nail on the head, right? It's exactly right. Right. And it's funny, you've seen tech stocks like getting hammered, right? SaaS company stocks getting hammered because the people don't understand like or are concerned about the impact. And that that AI has what is it going to do to these SaaS businesses? And the right SaaS businesses, just like the right humans, are going to learn how to leverage this. So you look at the disruption in the workforce that people are afraid of. The people that are good at their job and leverage this to be even better are the ones that are going to be the winners.

44:44right and the companies that are really embracing the SaaS software companies or tech companies that are really embracing like how do we how do we integrate and and just make it a part of our

44:57Krishna Poda:dna are going to be the winners and i think that's the the the real you so you have to make that decision and then it's like how do you go do it right like that's the hard part how do you go do it you know you've been talking about this for a while um like you know can see it you can see that you know i i got a demo and i saw how i saw agents you know working but that sean's point about that agent looking out versus looking in is it is a really good one it's it's just like the speed of the rate of change here is like so geometric it's it's crazy it's just it's it's it's wild

45:38Sean Frank:Long-time sponsor Northbeam is launching incrementality later this quarter. This means that you can now have the trifecta of marketing measurement all in one platform. That is multi-touch attribution, media mix modeling, and incrementality holdouts all inside of Northbeam. You can automate that lift testing end-to-end, unify results with your MTA and your MMM. This is a lot of letters, but if you know, you know. And you can start to cut what doesn't work, and you can scale what works. And you can do this all with confidence. This is why this is such an incredible ad to Northbeam. Northbeam's incrementality measures what results marketing is actually generating, not just what they're claiming credit for.

46:15Sean Frank:As a CEO, that's like music to my ears. Sign up now and you can lock in 50 % off unlimited tests for the year. Yeah. And an earlier point you had, Jason, was that if you're lazy to learn this, like, you know, it could be a detriment. If you're lazy, you should be at the cutting. you should be at the front line for agents they do the work for you like i cannot stress that enough like i'm like i'm bad at a lot of stuff the agents are good at stuff i tell them what to do and they do it it's like it's like every ceo's dream right and now everyone can be their own little ceo so if you're lazy i really hope you embrace agents and if you want to taste look right now i have this tweet it is it is kind of like geo cities building agents and and what i mean by that is like it's the early internet and it's not going to be very useful and it's going to be replaced very very soon there's going to be commercial great agents coming out from the saracen analytics of the world from the metas of the world whatever but you should still do it and play with it because it's a good hobby to have right just start using manis right now like you should you should get credits from manis you should jump in there and start seeing what is capable what the work you can do do a fun little hobby project and it is just cool what it can get done it's a great point

47:24Krishna Poda:It's a great point, the hobby project. Mike was tweeting about, Mike told me, I reached out to Mike about Open Cloth because he was tweeting about it. He was all about it, right? And people were talking about security issues, et cetera. And I just wanted to know from Mike what his thoughts were because he was really all about it. And he's like, I treat it like a pet. It's like his little pet project. And what you're saying, Sean, is like, you're having like, it's a little hobby, you know? Like it's something that is gonna, it's like a self-improvement. By doing this stuff, you're actually focused, everyone's focused on their health, right?

48:04Krishna Poda:Well, this is like, this is actually gonna really help you. You know, this is like a health thing that you need to do is like make this a hobby. Nine months ago, I was like, I was really not concerned about AI at all. And then like, we talked to Craig. He came in, Craig Fold came in and we just started talking and he just showed us me like that this is crazy like i got i have this totally wrong like i was totally wrong this is changing my life and and you know and and it has and you just have to put the time in and i think it's the the one thing that i'm getting out of this conversation sean even though it was it was apparent to me but not as not as apparent as as it is now it's like everybody needs to have the word agent in their head and be thinking about how to contextualize and integrate everything into that term.

48:52Jason Panzer:Yeah. I'd say think agent first and then human next. At least that's the mantra that I'm following here at SARS. And that's been helping. Yeah.

49:03Sean Frank:John, you were saying something? I just want people to try an agent today, right? So go to ChatGPT, go to Claude, go to Manus. All these have agentic flows. Have it order you lunch. Just see that it can take action in the real world. I think that is a very eye-opening thing. And then once you start doing that, then you can start understanding what happens if you set up an open claw. And I want to be super clear. The tools right now are not really production grade, right? You don't want to be rolling them out across your business. But in three months, they will be. In six months, they will be. It's going that fast, right?

49:35Sean Frank:The stuff that they're actually capable of doing. So for right now, the takeaway from this episode is get your data foundation layer set up. So, Serious Analytics is a partner of the podcast. I pay for them. Jason pays for them. It is a reasonable fee for them to set up and manage your data warehouse. Now, you're not locked in. It is the same infrastructure that every major Fortune 500 company has, right? But they're going to set up. They're going to maintain it. They're going to work really hard to keep your business. The other thing, I mean, it is blue chip data infrastructure. Then they're going to lock you in because you're going to fall in love with the Serious IQ features, the ability to actually query and get answers back about what's happening, your data, your sales every single day, set up a gented workflows inside of there, right?

50:20Sean Frank:Every single day, tell me how many phone cases I sold yesterday, right? You could do all that right now in Sarah's IQ. And then eventually they're going to have agents out there battling the rest of the agents. Krishna, what else do you want to say on the pod?

50:32Krishna Poda:Let me jump in for a second because, you know, when you guys sent out the invite for this one, the reason why I was excited is it says in it leadership and decision making right and then you know we're talking about leadership and decision making this is going to relate back to everything that we're talking about here it's like where does good decision making come from you know comes from i mean the first thing is good data you know the the second thing is is good judgment right and then it's you know taking decisive of action like these are these are like so like you got to have the data foundation to do it and you know sure you can go to shopify and just like download stuff or look at stuff but like you just you have to have this data foundation and then you have to apply you know you have to apply judgment like data doesn't make the decisions for you but you just have to have it and and it's all like relates back to leadership because leadership is knowing what matters right in the data and and particularly like understanding that the data is is is probably not never going to be totally complete right um it's like all models are wrong i say this all the time and but you have to have like you get the data as clean as you can like you get it to like saris is going to get it to like 95 percent um and then you're making this if you're not making decisions on data you're just like you're just using your gut and and honestly that's just like overconfidence period like the level of overconfidence that's out there is it's there's so many people that are just like they're like just making decisions on their gut you know what they're doing there.

52:21Krishna Poda:They're like, they're literally flipping a coin.

52:25Sean Frank:Sean here to tell you about Saris Analytics and Saris Pulse. Ridge is profitable every single day. And we've taken that super seriously since we built this business. We track contribution margin by day. We look at the SKUs we sell every single day. And we have to do this manually up until Saris Analytics came out. We take all of our SKU level data. We build it into the data warehouse. Everything that goes into making a true P &L, I get on a day-to-day basis. Saris Pulse gives you clarity so your CLO and your CFO and your CMO start speaking the same language. Contribution margin shifts teams away from hoping profits survive the season to manning them in real time.

53:00Sean Frank:Book a walkthrough with the Saris Pulse team today. Click the link in the description and thank you Saris for bringing you this show.

53:07Jason Panzer:So I'm curious actually from a leadership standpoint how are you getting your company to think AI, leverage AI? What are some strategies that you

53:17Sean Frank:are adopting i'd be interested to know yeah how do you get buy-in across an organization for ai it's a great question um you know jason brought up uh craig foals from chat walrus so craig friend of the pod he probably has ai operators coming out he's going to be uh you know in the sphere and ecosystem um he was at crocs setting up ai for them like in the 2020s right so like before AI was where it is today. So he's seen it from the ground floor. So he has like a training course thing called Chat Waller. So that's the first thing is get it for everybody because we have people who from 18 years old to 75 years old who work at Ridge, they're going to have different levels of proficiency and you have to teach them what is an LLM, what is Claude, how do you log in, the basics, right?

54:06Sean Frank:So make everybody take that course just to start off the rip. And then the second most powerful thing is showing what can be done. so we we've did this in the past we did game building days where you know you put teams of five people and you're like hey we're gonna use cloud we're gonna build the game in an hour right and the game is actually fun and engaging and pretty pretty exciting what you can do in an hour in cloud right and then then you show them a demo of what can be done um like you know that's not that's not a game today after this i'm gonna do stand up i'm gonna show everybody the cool manis agentic workflow thing i did and i'm like hey check this out i post create the creative i make in AI all the time.

54:43Sean Frank:I make amazing ads in AI and I'm posting. I'm like, Hey guys, this is what can be done. And then once people are, they have to be, you know, interested, they have to opt in to wanting to learn this stuff. And then we have a bunch of resources. You know, we have an amazing VP of internal projects, Adam, who's like the best with AI. We have a guy, Jules, who's amazing with AI and they're setting up NAA and workflows. And they're very much like, they are super nerds on the cutting edge of what's happening. So they're there as a resource, but like you have to get people excited to actually try this stuff.

55:11Sean Frank:So that's how we're getting people to opt in.

55:14Jason Panzer:So are you going to force people's hand by setting up, let's say, something like the AI demo day and bring your best ideas and demos? Or are you going to let people opt in? I'm asking more like a founder CEO or the head of the company. How are you thinking about it?

55:30Sean Frank:Yeah. So we don't start with the AI demo day. What we start with is we're going to build a game. So everyone has to build a game. You're forced to do that. But that should be fun. it's like and you know we do we do a couple hours of this every every month and the winner gets a thousand dollars or whatever if you build the funnest game and then people are like there's money on the line and like in just get people's hands in the tool to build a solitaire competitor or whatever right with like i built minesweeper but like it's random or something like you just build these little games or whatever you get people excited and then it's like hey you know this is something we did like bring us your least favorite thing to do and then on stand-up in real time we're going to build this an ai solution to solve it for you and we had a guy uh antonio he is in charge of like through a matching inventory shipments so like what showed up at the warehouse what the vendors say they left what did the doc say they had like does it all make sense is all the costs that job sucks right we built ai to do all of that and we did that live in stand-up and it just shows like oh cool i don't i can replace parts of my job that are bad so that's how that's how we get people excited about trying that is awesome yeah i'm just gonna say that is awesome

56:40Krishna Poda:i mean that's that's totally the way to do it you're literally gamifying it right both ways

56:48Jason Panzer:right one is incentivizing people to uh build stuff but you're also uh gamifying it because you're asking them to build games and get excited and start using tools so great advice there show

56:59Sean Frank:Yeah, I think that's what high school and middle school and elementary school is going to look like in the future is people building stuff in AI. Jason, you said something earlier about the data project's never done, right? And it's because if the data project was done, your business was over, right? Like the data is ongoing. It's a box that keeps building and building and building, right? It's never ending skyscraper because as you grow, every day you have more data coming in. Every month you have new products launching. And we should just talk about that, that like, this is not, there's going to be ongoing maintenance, right?

57:40Sean Frank:This is your entire livelihood, everything that's ever happened. Your brain is a very powerful agent and you've worked on this business exclusively for five or 10 years or whatever. You just have a lot of decisions and data you have to unpack. And teaching that to somebody else so they can write it down and put it into Tableau and code and everything else is just – it is a journey. So this will take a couple months to get started to build your data warehouse dream. Then every day after that, there is a little bit of maintenance going on to it, right? But the power when you finish it is you end up getting something that tells you contribution margin every day, right?

58:17Sean Frank:Jason, if your marketing team comes to you and they're like, hey, we want to run a 30 % off sale, right? Hey, we're going to do 30 % off sale. We think revenue is going to go up like this. They never think about contribution margin. But now you can be like, well, I'm just going to ask Sarah's IQ, what happens to my contribution margin if we run that 30 % off sale? So it is, you unlock a next level of thinking, but work does have to go into it. So Jason, you want to unpack any of that for us?

58:41Krishna Poda:When we're kind of defining like what is the next good offer, right? that we're going to run for some offer period, we've got lots of different options, right? We've got gift for purchase. We've got buy more, save more. Or do we just go with our best, discount our best bundles? Do we just do across the board discount? We can actually look at the data from previous sales periods and do that. How are you going to do that properly without a data warehouse? And you've got to load all that data in there. That's actually, that's dirty. work. That's like dirty, hard work. That's why you need someone like Zaris to do it, right?

59:21Krishna Poda:It's just like, that's really not an AI-able problem. It's the foundational data layer that every real business, you're not a real business unless you have a foundational data layer. And it does take time to set it up. So this is, there's a little bit of a process here of just having a little patience at the beginning to get it all going. It's not like, oh, I flip on Shopify and I start seeing numbers come through.

59:52Sean Frank:Well, dude, you don't get clean data unless you label it. So what you're talking about is labeling the data to actually make sense and ingest it. Krishna, final words for the podcast, my man.

1:00:07Jason Panzer:Sorry, Sean, I wasn't ready for that, but final words for the podcast. I love that. Yeah. Final words for the podcast. Get your AI foundation ready today because agents are coming. You will see a serious, serious unlock in productivity. So don't let your team struggle with spreadsheets. Get them on a foundation. Give them their time back. Unleash their creativity to help grow your business.

1:00:34Sean Frank:You heard it here first, guys. The agents are coming. The agents are coming. Krishna, I appreciate you coming on the pod. Thank you for all you do. Thank you for being a proud operator supporter. Jason, great seeing you on the pod, brother. Anything you need from me, you hit me up. We're here. Keep rocking. Talk to you guys later.

1:00:51Jason Panzer:See ya. Nice talking to you both of you.