Kevin Kerner: 0:00

Hello everyone, I'm Kevin Kerner and this is Tech Marketing Rewired. Today I am thrilled to be speaking with Jim Lecinski, former Google exec and professor of marketing from Northwestern Kellogg, who literally wrote the book on AI marketing. It's called the AI Marketing Canvas and is about to do it again. He's coming out with his second edition in the fall, because apparently the world has changed a bit in the last four years Go figure. Because apparently the world has changed a bit in the last four years, go figure.Kevin Kerner: 0:25

In our discussion, jim breaks down why now is a marketer's moment of truth before they get left behind. You know, you maybe feel like you're late to the game, but you're really not and now is the time to jump on board. What I love about this first edition of the AI Marketing Canvas is the practical advice and roadmap that Jim lays out. That makes implementing AI across an organization, even large ones, very accessible. So we spent a good deal of time on the foundational aspects of his framework, like the five-stage roadmap, but we really dug into how this approach is even more applicable in the age of agents and MCP and composability and all the other emerging trends that are out there. So if you're struggling to make AI work in your marketing organization or just getting started?

Kevin Kerner: 1:16

This episode is definitely for you, but before we start, I want to mention this podcast is sponsored by my company, mighty and True. Mighty and True helps CMOs accelerate strategies that may be lagging or you can't get to them due to resource or skill constraints. We help deliver on these strategies with our expert team tailored for your use case, plus a combination of our expert frameworks tailored for technology marketing, ai and the human context of our tech marketing experts. Learn more at wwwmightyandtruecom. Okay, that's it, let's get to it. This is Tech Marketing Rewired. All right, jim, how you doing. Thanks for being on.

Jim Lecinski: 1:56

Hi, kevin, great to be with you.

Kevin Kerner: 1:58

Yeah, I was super excited to talk to you. I know I reached out kind of cold and I was really happy that you decided to join me. And of course, I read your book and so I'm a little bit of fanboying over having you on the podcast this morning.

Jim Lecinski: 2:12

Wonderful. Well, I'm excited to have this conversation and been looking forward to it.

Kevin Kerner: 2:16

Yeah, yeah, I just wanted to kind of jump into it because there's so much to talk about From your perspective, like what's happened? What are the big things that happened between the first edition and this pending second edition that made you and your partner go okay, it's time to write this second edition.

Jim Lecinski: 2:34

Yeah, so we worked on the first edition of the book, the AI Marketing Canvas 2019, 2020, we met over Zoom during that COVID period and kind of wrote it virtually, which was kind of a cool experience. And at that time, pre-chat GPT, obviously, what we'll call right brain gen AI was really not commercially viable at that point. Commercially viable at that point. So most of what we had focused on in the book was, I guess, what we're now referring to collectively as sort of old school predictive AI, machine learning, sort of left brain AI, and so we focused on applications where marketers could, for example, you know, look at weekly sales data, market share data, spreadsheets, csv files, those kinds of things, use AI to help spot anomalies that kind of left brain, by the way, still very viable applications of AI today.

Jim Lecinski: 3:33

But what's changed obviously is yeah, now the generative pre-trained transformers have hit the scene over the last couple of years. I guess we're coming up to the third anniversary this November of the official ChatGPT launch and you know, google was slow out of the gate with what then was known initially as BARD, but has roared back with Gemini 2.5 Pro and I guess we'd probably throw Claude from Anthropic in there as the three what's commonly called frontier models, and so you know sort of the power of those and the commercial viability, the accessibility of those, I think has moved us from a left brain AI world to now a whole brain left and right, predictive and generative world, and that's what I mean, the agents and other things we can talk about, but that was the big prime mover for us to say, hey, time to do a revision, update the book.

Kevin Kerner: 4:26

Yeah, that's great, yeah, and I think the marketers, because of all those things, are still reeling from this pace of change. I mean it's just crazy how me as a marketer, I love it, I love testing all this stuff and being inside of it, but it's just really crazy. There's another goal of the book just to give them more of a sense of control, like even more control, because the first reading, the first book, really felt like, OK, this is a roadmap to do this stuff. But this is the next edition, I guess will just be a further insight into that, trying to give people a sense of control over things.

Jim Lecinski: 5:01

Yeah, I mean, that's an insightful question, right? Because as authors, obviously you have content goals, but you have emotional goals for your readers. And so, beyond the content, yeah, we wanted to give them the sense, as you suggest, of, hey, you know what I can do, this, it's possible, it's doable. Maybe I didn't grow up as a computer scientist. That's not my background or my training. Most marketers don't come from that kind of background. So we wanted to give them that sense of confidence and control, but at the same time, also give them a little sense of urgency, light some fire. Right, like you know. We say, like you know, let's do this. Yeah, it's not too late, but the train's leaving the station.

Kevin Kerner: 5:41

Yeah, it's so true. It's like it really isn't too late yet, but you really need to be on board now, like the doors are closing and it's a really good time for that. So one of the foundational things of the book is the five step canvas that you lay out. Read the book. If you could just briefly walk us through those stages and then I'm sure I'm kind of geeking out over the there may be some new use cases or case studies that are within those five steps that you could elaborate on. I'm just wondering what you're now seeing that might be more context to those five steps.

Jim Lecinski: 6:18

Yeah, absolutely. So you know the question that has frequently come up for us and you know, in my teaching and my consulting work, my advising work is like, ok, great, Now that I have, as a marketer, an understanding of how I works. And you know we'll talk about the use cases in a minute, I know what those are like how do I actually get started? What do I do on Monday morning? How do I implement this Right? Do I do on Monday morning? How do I implement this Right? So we go from sort of conception to implementation. And you know there's a lot of evidence out there from the AI Marketing Association and others that say IBM has a recent survey out that says actually sort of lack of understanding of how to implement this thing, along with lack of understanding of use cases, are actually the two biggest barriers holding marketers back, which you know I don't know about you, kevin, but that frankly surprised me. I thought it would be, you know, lack of technology understanding, lack of budget, lack of clarity as to whether it should be the CIO or the marketing team who leads, privacy hallucination concerns, and those are all important, but lack of roadmap, right was the big one. So we said, well, maybe we can offer a roadmap. So what did we do here is we used a research technique that sometimes works and sometimes doesn't, but we went out and did first party interviews, discussions, surveys with dozens and dozens of marketers, big and small, us and international, b2b, b2c, successful with AI implementations, and some who are struggling or failing, doing such that we could lay it out as a repeatable pattern for those next folks going down this path.

Jim Lecinski: 8:12

And so, as you alluded to, we identified actually a very, very consistent set of five steps and, briefly, those are as follows the first step is you have to have some data to train the machines, right? Is you have to have some data to train the machines, right? I mean, these machines all operate based on, yes, public data as pre-trained technologies, but you need to bring some zero and first-party data. So that's the foundation layer. You can't really play this game successfully to the fullest unless you have some training data that you can bring. And again, by the way, I should just point out, kevin, of course we're talking about enterprise, corporate versions of these tools. We're not talking about like free consumer chat, gpt, but like for your business. So I'm assuming you have chat GPT, enterprise or teams.

Jim Lecinski: 8:58

Second step then, and this is where a lot of teams kind of we found surprisingly misstepped. A lot of teams at this point want to go hire their own computer scientists and build their own algorithms and buy companies and do all this kind of stuff. But you know, it's sort of a crawl walk run. So once you've got your training data, the second step in the best practice is to say I'm working with all kinds of great partners, right? Any marketing teams working with TikTok and Instagram, Meta, facebook, google and YouTube. Maybe you're using MailChimp right From Intuit, if you're an SMB, for your email.

Jim Lecinski: 9:35

Well, guess what? All of those companies, all of your partners, now have AI powered features and so bring your data to those partners. So just two examples, maybe three examples this is Advantage Plus on Facebook. This is Performance Max on Google. This is if you're using MailChimp. You use their subject line generator tool right and their delivery time generator tool to find you know what's the best subject line for the right person to land in my inbox at the right time, and so in most cases, that's not even an upcharge or extra cost working with those partners, right? So just get that experience under your belt.

Jim Lecinski: 10:23

The best teams at this point name what's called an AI marketing champion, and that's somebody in the marketing department, typically a 20% second hat role, where they're starting to catalog old way, new way, across all these experiments, memorializing the lift or the delta between old way and AI powered new way, doing lunch and learns, book of the month, clubs, trainings for the marketing team, et cetera.

Jim Lecinski: 10:54

But you can't rely as great as they are, you can't rely on outsourced partners forever. You have to at some point start to build that competency, in-house and in-source. And that's the third stage of competency, in-house and in-source. And that's the third stage, doing some in-house experiments. And then at that point you say, well, now we really know what the ROI of this is, and now it's time to go to the CFO and say we need some more staff, maybe we need some technology that we need to invest in, maybe we need to even buy a company. But now you can put a solid business case in place at that fourth step and that's where most companies will and should top out. But there are a few companies we talk about John Deere and others in the book who actually have taken what they've built for themselves in those four steps and they're able to then license it, sell it to partners or competitors as a totally new additional revenue stream, and so that's sort of the zero to hero one, two, three, four, five stages up in the canvas, kevin.

Kevin Kerner: 11:51

Yeah and it doesn't. It doesn't. I mean, it's such a really logical, good way to think about this stuff. It doesn't surprise me that adoption is an issue more than the technology it's like. It's like you get access to the technology, everyone wants to be in it and doing it. Sometimes you just don't know where to start. Sometimes, and certainly if you're a large enterprise, you just don't know how to scale. Like, how do you scale this stuff?

Jim Lecinski: 12:19

It's a people problem. It is right, it is. And you know, of course. You know we've all learned this term over the last couple of years pilotitis, right, Like it's easy to spin up. Easy to spin up. Lots of pilots have something cool to talk about on stage at a conference, but it's sort of a side dish, right, it's not center of the plate. So you know, to use your word, how do we scale it right? So you know, I've done some, been fortunate enough to have done some work with great folks over at Coca-Cola who are leaders in this AI space, certainly within the packaged goods, cpg, consumables, category, and they talk about plan for scale from the start. Don't just spin up little things that sound cool and then at some point you go can we scale this? Does it even scale? Should we scale it Like plan for scale from zero?

Kevin Kerner: 13:01

Yeah, you know what confuses that some of that to my perspective, some is how composable this stuff is, how easy it is to buy a new tool, hook it up. It's just supposed to work. There's so many smaller tools now the stack is sort of breaking apart into all of these pieces and then at the same time in your core platforms HubSpot, Salesforce, everything else they have AI built into them. So it's really as a marketer, there's a lot of shiny object syndrome going on, totally.

Jim Lecinski: 13:50

Now I have a hot take. Chase for small tools and centering on a few big platforms is generally my advice. I talked to a marketing team who shall remain nameless last week who said to me oh you know, we found two guys in Bulgaria who no one's ever heard of before, right who, who've written this cool thing, and I'm like, well, great, except it doesn't integrate with any of your other tools. And they said oh yeah, well, we're getting Deloitte in here now and they're going to write some custom code for it. I said guys, you know what? Pick a frontier model. Now let's give them all our data.

Kevin Kerner: 14:25

Let's just hand them all our data too. I mean right, this is great.

Jim Lecinski: 14:29

Pick a frontier model, set some use cases, train the heck out of your teams within the frontier model, along with some of your core tools like Salesforce or HubSpot, etc. But you know, this proliferation of tiny tools is not really a good plan.

Kevin Kerner: 14:48

I'm dying to ask you of the companies that you see, because you have the canvas out there now when do most companies, where are you seeing most companies being at at this stage of the five steps, can you even guess?

Jim Lecinski: 14:55

So that's going to vary by category. So, given that the first step is proprietary, zero first-party data, obviously the business models, the categories, the companies that have that inherently have a leg up. So, if you're in financial services, insurance, every one of your customers is completely identifiable, right? You know their name, their email address, their mobile phone number, all that kind of stuff. You have a direct one-to-one relationship. However, if you're selling toothpaste right through drugstores and Walmart, now your ability to have zero and first-party data is not quite at that level, though, that said, there's lots of CPG marketers who've been doing this for a long time, gathering data. So again, it depends on category, but I would say most marketers sort of find themselves in stage two to stage three. Right, we've been working with our partners, trying a lot of their tools and their platform. Maybe we've spun up one or two bigger swings on our own, but that's where about most folks are again, with a few exceptions like John Deere and Starbucks and Coke that are, you know, really best in class.

Kevin Kerner: 16:03

Yeah, I want to get into your two box thing here in a sec because there's a big lead to it, but the but, the um, I would guess that it's mostly internally facing stuff. It's not externally facing but on the monetization side that you mentioned, that particular stage is super interesting to me, both from a potential new product perspective but also from a brand perspective. I think you posted something about was it Ralph Lauren did something about? Is it Ralph Lauren did something? Yeah, that's I mean so to extend your expertise of AI to either a new product or a new brand, like a new brand experience is really interesting. I'd guess just mentioned that as a comment, but I would guess a lot of companies aren't there yet. They're more in the internally facing stuff automation and synthesis and those types of things.

Jim Lecinski: 16:50

Yeah, that's right. And let's chat for a minute about sort of use cases. Since you teed that up, you know like lots of really strong, good consultancies and ad agencies and advisors out there cataloging. You know I saw an article the other day, the 347 use cases for AI in marketing and like great, I'm glad somebody's comprehensively logging that.

Jim Lecinski: 17:12

Yeah, I know, but like most marketers can't go meeting to meeting saying, Kevin, is this, use case 341 or 173, right, Like so? You know, being a business school professor, of course we like to think in two by twos.

Kevin Kerner: 17:26

Yeah, I love it. I love a good two by two.

Jim Lecinski: 17:28

Right, don't we all? So here's how I think about use cases. So the first is what is the goal you're trying to achieve by employing AI into your marketing function? So, two broad buckets. One is productivity efficiency doing the same as you always did, but faster and cheaper. Efficiency doing the same as you always did, but faster and cheaper. And then the second possible goal is value creation, transformation, growth, new to the world, never before possible things. And then the other axis, the vertical axis, is and who would benefit from either of those two kinds of outcomes?

Jim Lecinski: 18:08

You, internally, your marketing team behind your firewall, behind your four walls, or, externally, your customers. So now we have a sort of a very nice two by two. Just two quick examples internal and productivity. Well, we all write project briefs, creative briefs. We've all done this for a hundred years. Well, you know, now, with a little bit of training, you can get a first draft spit out pretty quickly of a creative brief out of most any AI tool, even basic chat, gpt. Well, it's no different than the creative brief looked like when you or I started our careers, kevin. But instead of taking six weeks to get to a first draft, now we have a draft in six minutes. So that's clearly internal. Your customers, I hope don't see your creative briefs so right, that's internal and efficiency.

Jim Lecinski: 18:56

And then you mentioned Ralph Lauren. Ralph Lauren, IKEA and others are kind of mining that fourth quadrant where it's external and value creation. So you know, ikea is a great example. We know them for cheap bookcases, but of course their mission, vision and life is to help all of us, you know, economically affordably transform our home into that sort of Scandinavian concept of Hygge, right, that coziness and comfortableness.

Jim Lecinski: 19:22

But I don't have an interior designer eye or expertise. I'm not particularly good at it or expertise, I'm not particularly good at it. So IKEA would love to give a human interior designer, a person with that expertise, to every one of its customers. But as a worldwide company, there aren't enough interior designers in the world and even if there were, ikea couldn't afford to do that. So what do they do? They built a chatbot experience where I can take video of my living room, my bedroom, answer a bunch of questions back and forth, and it now custom designs a personalized makeover room experience for me. So that's not sort of just doing the same thing I always did before, but cheaper or faster. That's a new to the world value creation, transformational experience for their customers, and obviously it's external, so you know. So the first question is which of those four quadrants are we looking to drive value in? You know, and I ask marketers, what is it in your business? If you could either predict it better, or generate it better or faster would unlock incremental value in those quadrants.

Kevin Kerner: 20:28

Wow, yeah, that's such a great way to look at it and I guess, depending on your business model, you could start on one end or the other. Like we work with a lot of SaaS brands I'm thinking of, like Qualified I talked to their CMO on one of the podcasts but they have an external product that is called Piper and it's an SDR or BDR AI product and they monetize it and sell it and so they're very much heavily on the external side and it's a growth thing. But then you have retail or even agencies. Really, if you think about agencies, we're probably more productivity and internal than we are growth and external right now. I think that will change over time.

Kevin Kerner: 21:11

On the agency front super interesting thought that I had as you were talking through things. Of course I'm thinking selfishly, but I was. We built a platform called Flow, which is a it's a like customer dashboard S thing. We built it in Glide, which is a no-code app, and Glide has some AI capability. But the thing we're finding that agencies can activate are all the frameworks that we've built over all the years. You know, I've been doing this for a long time and so I've built so many, just like you know these type of things. I wonder if you see agencies starting to monetize their knowledge set into an externally facing framework product in some way. That seems like a great use case for the four box here.

Jim Lecinski: 21:52

Yeah, totally. And look, I mean, you know, I don't need to tell you agency world I spent 18 years of my career in the agency world. Right has sort of generally struggled through sort of digital transformation. And what do we do about social? What do we do about mobile? What do we do about search, et cetera? And now here comes another existential moment for the agencies. Well, search, et cetera, and now here comes another existential moment for the agencies.

Jim Lecinski: 22:14

Well, you know, like a big holding, companies right are sort of low, low, low margin business. So CapEx, opex, investment acquisitions, you know, acquiring technology, paying for talent, just is difficult in the general agency economic model, so they're kind of a little challenged in that sense. But you know, if you think about this, even internally there's so much knowledge, process flow, tools, techniques, you know, even using I'll use the consumer facing version of it. But for agencies to build internal or enterprise custom GPTs right that just do this like agencies are really good at even competitive analysis, well, every agency has a process for doing this. Well, instead of starting from scratch, with your assistant account executives doing that, opening a blank Word document, for gosh sakes, build an internal I'll call it custom GPT right, that jumpstarts that.

Kevin Kerner: 23:06

Yeah, and that every company has those too, not just agencies, but every company has their institutionalized way of doing things. Okay, so I wanted to talk about another trend that I'm seeing quite a bit, and maybe it's one of the dangers. You point out some dangers in your book, but the thing that really seems to be front of mind for many larger enterprises certainly is privacy and security and the sort of danger of large language model versus the now creation of these small personal models that might be much more secure. Maybe generally talk about what dangers you see in the five dangers, because I wanted to dig into that you to at least bring, at least bring that up a bit. And then how do you? What's the? What's the change in the privacy concerns for enterprises right now?

Jim Lecinski: 23:53

Yeah, well, look, I mean, I think it's surprising these sticky stories Most marketing teams that I talk about still remember. I guess what was Kevin two summers ago, maybe longer, when those Samsung engineers dumped a bunch of their code base from remember, from the Galaxy phone into public chat GPT and then it leaked and all this. So, like this, this is a really surprisingly sticky story. When I talk to marketers, like we don't want to be like Samsung, you know, like OK, well, first of all, that was three years ago and second of all, they were just throwing stuff into consumer public free chat GPT.

Jim Lecinski: 24:28

But you know, I'm seeing sort of almost a weird rebound privacy thing happening. As, rightly, cios and CTOs don't want that to happen to them, so they're being very cautious about vetting, choosing the right tools for their enterprise to be using and to be the authorized or approved tools. A lot of organizations or CTOs are taking a lot of time to do this and that means the staff, the workers, the marketing team or otherwise don't have sanctioned tools at their disposal this afternoon. And so now we see what we'll call gray market use right, meaning when I'm not on the VPN, when I'm not on the corporate network, right and I saw a study the other day that said almost two thirds of employees privately admit to using gray market tools with confidential proprietary corporate data. So it's this weird like as as much as CTOs are trying to be cautious in order to avoid privacy problems, that caution is causing privacy problems.

Kevin Kerner: 25:34

Yeah yeah, isn't that interesting. And and if you do implement an enterprise ai, it better be a good. It better have the same functionality as the tools outside, because if it's not, they're not going to use it, even if you have it. I have one customer we work with. They do have an enterprise tool in-house I won't mention who it is, but but or what tool they have, but they talk about how it doesn't work and they have to go outside and do other things. So it's really you're exactly right.

Jim Lecinski: 26:00

Yeah, and and, and. By the way, again, we don't want to, you know, throw shade here where it's unwarranted. But I hear a lot of teams say that. You know, my CIO has decided we're a Microsoft shop, we use PowerPoint, we use Outlook, we use Teams and therefore you get Copilot, and not even the best version of Copilot. And the teams all know, right, because they're helping their kids with their homework in the evening, they know that what they have at work is not anywhere near as good as the consumer AI they're using. So that causes all kinds of privacy problems, problems, All right.

Jim Lecinski: 26:34

So the first is you have to have the right set of tools to avoid a privacy problem. The second is you have to certainly have some policies in place. Every company should have a really robust AI policy, and that's not just sort of blocking and tackling what you can and can't do, but it's also your belief system around AI, right? And you know what will you use it for? What won't you use it for? And we've seen Unilever, for example, come out and say you know what we're in the skincare, beauty category. We're not going to use it to have, you know, unrealistic imagery of humans for our beauty brands, right? So that's not a technical thing, right, that's a philosophy thing.

Jim Lecinski: 27:14

So you need the right set of tools, then you need the right set of technical and philosophical policies and then, of course, you need to train the people right, like you need to really sit and train your staff. I've seen lots of companies get through those first two hurdles but then they just sort of send an email and say it's live, have at it right. And so you know, like you got to, you have to have a series of trainings and I will say you know, I and many of us here at Northwestern have been, you know, spent the past year and a half training our staff right At the university, in the business school, like how can you make best use of these? And, by the way, what are some of those washouts and cautions so that you don't run afoul and get yourself into some big trouble with some of the five dangers?

Kevin Kerner: 27:59

Yeah, that's great. Okay, I want to be cognizant of time here, so I have a bunch to cover with you, but I wanted to get into a bit on. I have selfishly. I'm the dad of some young kids and I have some kids in school. I saw something this morning on the local news about AI schools and there's some AI schools where kids in high school are now going to these AI schools Maybe it's even grade school, I don't know and they work for two hours, trained by AI, educated by AI. I'm sure they have some people helping them there, but then the rest of the class day is all hands-on work in the stuff that they just learned. That's just one example of the disruption that's happening inside the education system with AI, not to mention you as a professor having students and needing to guide students on how to use this stuff. You as a professor having students and needing to sort of guide students on how to use this stuff Just what's your perspective on how education is changing or may change with all this technology.

Jim Lecinski: 29:07

So my feeling on this is that there's two types of knowledge. Broadly, there's durable or enduring knowledge, and then there's temporal or perishable knowledge. So you know how to structure an argument right, like present your case. I guess that goes at least back to Plato and Socrates. It mattered for the last two millennium and, by the way, that's durable knowledge. If we're on this podcast, right, in another thousand years we'll be talking like you need to know how to structure an argument right, fine, but then there's also like perishable knowledge. And so if we were doing this as a marketer in the 1950s, you'd want to know how to put a successful radio drama together sponsored by your brand, right, like not too many radio dramas sponsored by brand marketers today, but in the 50s, like that was a real deal, right, like the Lux radio soap hour, those kinds of things. So that's perishable knowledge.

Jim Lecinski: 29:57

So now you know we need to make sure that we don't fall off the cliff either way, right, we don't want to ignore knowledge like how to use these AI tools of the moment and say, oh, that's not our job as educators. We only need to teach you the durable. No, we need to teach you both. But of course, we can't only teach you the temporal or the perishable stuff, like when you set up a new account on TikTok, click the third button when you you know. No, we need to teach you how to think too.

Jim Lecinski: 30:30

And so you know my philosophy and in my classes, and you know in our business school here at Kellogg, we try to balance those two things. So I love you know the example that you give. In fact, I'll be teaching a new AI for marketers class starting in January for the first time, and, yeah, we're going to sort of teach some of the enduring fundamentals about AI that go back to Alan Turing from 1950. But, same thing, we're also going to have them build. So they will do everything from create ideal customer personas that they can chat with for their prime target audience. They'll be vibe coding, landing pages and apps all the same kind of stuff that you talked about right.

Kevin Kerner: 31:25

So hitting that right balance is difficult, but sort of durable and temporal is, I think is a somewhat of a balance between the two. You can get to both, but I agree you could get really out of whack. That's why I worry about for younger generation is it's instant and temporal which I'll start to use and it's not as durable and that's going to really hurt because you need to make these tools work. You need context that I mean you just can't. I mean we had to build spreadsheets. You know, you and I we were in the spreadsheets, right?

Kevin Kerner: 31:58

Uh manually building them. So our staring at a blank page, that type of stuff that I just thought about for our kids. Yeah, yeah, there's so much to get through in the book. I wonder if you could, uh, as we close the the this portion up, if you could just um talk about where. If you're a marketing leader who's listening to this, like where to start, like what's the place to start? Is it the two box? I'm sure it's getting the book, but what would you recommend that they do today after listening to this?

Jim Lecinski: 32:21

Yeah, actually, I would say the first thing to do is that you personally, no matter how senior or junior you are, you personally need to invest at minimum one hour a week reading, learning, playing with these tools.

Jim Lecinski: 32:35

And so you know, I did a seminar for a Fortune 500 corporate marketing team last week and I said OK, here's what we're going to do. Everybody is going to order a pizza tonight using ChatGPT Agent and watch it, take over the browser and click the buttons and order your cheese and pepperoni pizza, right? And so I use this analogy it's like if you wanted to learn how to surf, you wouldn't watch a PowerPoint about surfing. I mean, I guess you know you'd have the safety briefing from the instructor and maybe a little slideshow, but at a certain point you got to get on the surfboard. So sure, yes, the two by two of use cases, yes, the five watch outs, yes, the five stages of the canvas. But even before that, you must book a sacrosanct hour on your calendar to read, learn, play, try, put the stuff on your phone that's uncancellable, immovable. Every week, at minimum one hour. I mean, you know, you and I do this kind of stuff for a living and we can't even keep up with it.

Kevin Kerner: 33:40

Yeah, that's really good advice. It just surprises me that there and I know there is probably a good portion of executives don't do this. They just don't have the time or their mind. Maybe they think I'm not technical, I can't do it. Gosh, we do this program here. We call it Sparks, and we do it once a month. I wish we could do it more often, but my team would kill me and so we do exactly that. So we do like an exercise where everyone does it. There's a brief. They do it for a few days a week. It should take them only 30 minutes to an hour to finish the Spark and then we present the spark at the end. This this week is uh. This particular spark is, um, I think people a lot of our of our employees go into Gemini but they don't go into AI studio, and so I wanted them to see what was in AI studio, and so we created this silly little exercise they're going to do and I and I I think it's kind of like a, you're kind of a uh, okay, we're just so busy, but when you do it, when they see it, the light goes, a spark happens, it just like turns on and they think oh wow, I might use this for these two things, so you, can't really

Kevin Kerner: 34:41

you can't really understand it unless you do it. You know you got to be in it, that's it so okay. So I warned you about this. I do an AI roulette question. That's going to be the best question you've ever you've got. You've heard, at least today. Let's try it on this podcast. So I'm going to hit perplexity here. It's going to give me a. I've loaded in your profile a little bit about what we're going to talk about, so let me do this here fast. Okay, your book provides a logical map for AI, but people are all often illogical. What's the most surprising human reaction you've seen to your framework and what did it teach you?

Jim Lecinski: 35:15

Well, perplexity, good question. I think a surprising reaction that I get is we don't have time for that, we need to just jump right to stage five or we're a big company, we don't waste time with all those basics, we just sort of jump to the end. And you know, as you see in our conversation, Kevin, I like to often explain things through parallels or analogies or whatnot. But this sort of like you know, okay, if really you would want to sort of jump in a Formula One team Mercedes, Patronus race car without having taken driver's ed, without having driven a minivan around the block, Like really, you know, you get yourself into some pretty bad trouble, into the wall right On the fourth turn at the Speedway there in Austin, right Crawl, walk, run right, Like no, there's a reason. You can't just jump to stage five, even if you have the will or the budget or the mental capacity. You've got to work yourself through the stages.

Kevin Kerner: 36:18

Oh, man, you mean, I can't just go to monetization. I got a number, I got to meet.

Jim Lecinski: 36:26

Yeah, but it sounds funny when we say it, we have a little sense of humor here. But even really really great marketers and I and I do mean this like McDonald's, right, like, acquired a couple of companies early on in the early days, right, where they were trying to do some things with facial recognition through the drive-through, but yet they hadn't collected the training data or I hadn't worked our way up the canvas. That's remember dynamic yield, right, they end up selling that off to MasterCard and now they're McDonald's, do some really great things there. But you know, I sort of say that as a hey, if McDonald's can't just jump to stage five, probably you can't either.

Kevin Kerner: 37:01

And they do systems well, right. Yeah, everything's a system. They should be able to do it, right. Yeah, that's really really good advice. Well done perplexity. Well Jim, this has been fantastic. I really good advice. Well done perplexity. Well Jim, this has been fantastic. I got more out of this than I think I got out of my four years of undergrad I didn't get much back in the day.

Jim Lecinski: 37:23

I don't know if some of it applies.

Kevin Kerner: 37:24

Maybe, maybe there's some durable stuff, but I follow you on LinkedIn. I definitely want people to pick up the book, and definitely the second edition. I can't wait to get. This has been fantastic. I'm so thankful that we met and that you joined me. Are there other ways that people can get ahold of you other than the LinkedIn?

Jim Lecinski: 37:43

Yeah, best way is to follow me on LinkedIn. Send me a DM. I'm pretty active there, as you mentioned, and that's the best way to follow and find me Cool.

Kevin Kerner: 37:53

Well, you're welcome anytime to Austin and you got a free dinner at Guero's. If you ever make it down here, you're on. Great Thanks, kevin, cool. Thanks so much, jim.

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