This week’s episode is spurred by a blog post written earlier this week by our CEO Paul Roetzer.
The tables turn, and Mike Kaput, our Chief Content Officer, interviews Paul about this article. With each day that passes, and each advancement in artificial intelligence language and vision technology, it is becoming more apparent that there will be three types of businesses in every industry: AI Native, AI Emergent and Obsolete.
[00:03:58] What do you mean by AI native AI, emergent and obsolete?
[00:07:36] This isn’t just about marketing
[00:10:24] AI can help us communicate with customers on their time, regardless of time
[00:20:21] Dentists are using AI too
[00:29:26] Mike’s example of how AI could have helped him in contract work 15 years ago
[00:34:35] How does this affect me and my career? What should I be looking for?
[00:39:10] Investor advice
Links referenced in the show
- Marketing AI Conference (MAICON) 2022
- Marketing Artificial Intelligence: AI, Marketing, and the Future of Business by Paul Roetzer and Mike Kaput (Matt Holt Books, 2022)
- Blog post discussed in this week’s episode: The Future of Business is AI or Obsolete
Watch the Video
Read the Interview Transcription
Disclaimer: This transcription was written by AI, thanks to Descript and has not been edited for content.
[00:00:00] Paul Roetzer: Welcome to The Marketing AI Show. The podcast that helps your business grow smarter by making artificial intelligence, approachable and actionable. You'll hear from top authors, entrepreneurs, researchers, and executives, as they share case studies, strategies, and technologies that have the power to transform your business and your career.
[00:00:20] My name is Paul Roetzer. I'm the founder of Marketing AI Institute. And I'm your host.
[00:00:27] Thanks for joining us for episode 16 of The Marketing AI Show. Before we get started, I want to take a moment to tell you about our show sponsor MarketMuse. MarketMuse suite and AI powered content intelligence and strategy platform analyzes millions of articles on demand, uncovering gaps and opportunities for better content.
[00:00:49] Imagine an on-demand content audit that automatically identifies your best and worst pages. Content with high ROI potential quick wins at risk pages and more. MarketMuse uses AI to accelerate content planning, creation, and optimization. So you can build authority on your topics. Get started for free today at marketmuse.com.
[00:01:14] Now onto the show.
[00:01:17] Alright, here we go again. And I'm joined today by Mike Kaput, our chief content officer and my coauthor for the upcoming book, Marketing Artificial Intelligence: AI, Marketing, and the Future of Business available now for preorder. What's going on Mike?
[00:01:33] Mike Kaput: Uh, much. How's it going? Glad we're back at it.
[00:01:37] Paul Roetzer: Yes. Third
[00:01:38] go at a weak limb.
[00:01:39] So we were staying committed. She didn't believe us. We could do this weekly. I'm not throwing you under the bus, but we're on it, Cathy. Don't jinx it. And we got plans for the next few already, so that if you're new to this format again, uh, some episodes of marketing, I show, I interview experts, authors, entrepreneurs, and AI.
[00:01:58] Um, and then Mike and I do a weekly. Relatively new third time, third, go at it weekly. And we basically just talk about big ideas, news trends, like picks, pick a topic. That's interesting that Mike and I would usually sit around the coffee maker and talk about and thought, you know, might be really interesting format to, to share with people.
[00:02:17] So today's is a little different and kind of original. We're going to flip the script and Mike's going to interview me, um, because there's some things we've been working on and thinking about and writing about recently that we realize are probably worthy. Conversation if nothing else, conversation starters. So it's all yours, Mike,
[00:02:37] Mike Kaput: You're in the hot seat now. Um, great. Well, I want to kind of tee this up before diving into some questions, but Paul published a blog post this past. Um, that is titled the future of business is AI or obsolete. And when I first read this, it really struck me as kind of an overarching, uh, point of view.
[00:03:05] The future of business and how AI is going to fundamentally change that. And there is a lot to unpack in this post. So I want to kind of tee it up for people if they haven't read it yet. Um, the first sentence really kind of sums up the thesis here. So I want to read that out loud and then. Dive into some questions.
[00:03:26] So the post starts off by saying with each day that passes and each advancement in artificial intelligence, language and vision technology. It is becoming more apparent that there'll be three types of businesses in every industry, AI, native AI, emergent and obsolete. And you go on to say, I can't really see any example of a business in any industry.
[00:03:53] Or business model where this won't be true. So do you want to maybe start by unpacking? What do you mean by AI native AI, emergent and obsolete?
[00:04:03] Yeah. So this post kind of came together over a few nights of jotting notes on my phone. You know, while I was laying in bed or, you know, put my kids to sleep and just have ideas.
[00:04:15] And so I started off and it was originally just going to be a LinkedIn post and it was pretty much going to go through that, that lead and just say, here's where I'm at. And then I realized to make a claim like that there has to be more validation. That's more context to why we're saying that. And so what it started as was this idea that when we think out into the future, you're either going to.
[00:04:39] Create a company that is smarter than the existing. Players in the industry. So pick any industry and you can look at it and say, well, we could just build a smarter version of that. And from the ground up, the company is built with AI at its foundation in the product and services, and then all the operations of the company.
[00:04:55] So you just build a smarter business model than what already exists. So that's AI. Company wouldn't exist without AI, AI emergent our existing companies that realize the opportunity AI presents for efficiency and growth, and they start applying AI and it might be to the product or service to start and then carry it into HR and finance and marketing and sales and service.
[00:05:19] And, you know, but you really start to embrace AI and all the benefits at all. And you adapt your company so you can remain maybe not only competitive, but continue to be a leader in your space, the AI native companies, you can sort of hold at bay because you also evolve. And then there's the third category, which when, as I was running this.
[00:05:40] What else is there besides those two, it's just a relevant company, these obsolete companies. And what I was saying is it's not like overnight, like if you, the example I give given the post we might, which we might talk about is marketing agencies. Like if you don't use AI as a marketing agency, that's fine.
[00:05:54] Like you're not going out of business tomorrow or next year, or maybe half the year after that. But very gradually. And then all of a sudden you will be irrelevant. Like someone is going to build smarter versions of everything you do. So if you offer writing services at your content marketing agency, people are going to use AI tools to write content more efficiently and maybe better than you do.
[00:06:14] If you're an ad agency and you create ads or you, it. People are going to come along using AI to do that better and faster than you do. Like, and it just keeps going down link. Does that graphic designers, illustrators, whatever it is, someone's going to build a smarter version. And it might be your current competitor.
[00:06:30] That's starting to invest in AI now, or it might be some AI native company that comes up out of nowhere. And just as a smarter solution that you never even thought was possible. And so that was the foundation of the post. And again, it just sort of started as this. Probably 200 to 300 word LinkedIn posts.
[00:06:47] And I was like, wait, there's, there's a lot more to this. And I kept, it's kinda like when I wrote my first book, the first chapter of the agency blueprint is eliminate billable hours. And to write that chapter in an industry built on billable hours with tens of thousands of agencies that use billable hours to make that state.
[00:07:05] Required significant confidence on my part that I had thought through the arguments against that statement. And I felt like with this post, I needed to do something similar where if I was going to make a claim like this, I didn't want it to just be clickbait. That's not what this was all about. It was like something I truly believe, but I had to then validate for myself that this was actually a true statement before I turned it into something.
[00:07:28] And that's where the rest of the post sort of came in.
[00:07:32] Awesome. And we should definitely make clear to anyone listening or anyone reading it. This is not just about marketing. It's about fundamentally the future of every part of every business. So we're really, um, you know, definitely a bold claim, but I think you really lay out a very logical argument for this, and obviously your entire argument relies on.
[00:07:56] Artificial intelligence either. Yes. You're using it. You're adopting it or no, you have not. So I really want to kind of tee up for listeners. Why is that? Time different. Like why now? I mean, AI, we, you, and I kind of see behind the curtain a little bit and kind of understand the power of AI, but to kind of a more novice marketers salesperson business person.
[00:08:23] Why is AI this fundamental change in a business?
[00:08:29] Paul Roetzer: Yeah. And for this one, you know, when you really step back. Previously simplified it a little bit. So, you know, the most obvious things are data, so there's lots more data. So, you know, AI needs data to learn. And so the explosion of data from all these different tools and technologies we use as marketers and as business leaders, there's just more of it.
[00:08:49] So the first and foremost is there's just a bunch of data, almost too much for humans to even process and gain insights from. Um, the second thing we often talk about is like cloud infrastructure, the available. Models already trained to use. Like, if you go, like, let's say you want to predict customer churn or do you want to forecast sales or, um, recommend content on your site?
[00:09:11] Something like. Five years ago, you couldn't just go do that. Like you would have to build something or you'd have to maybe look at some of the early entrance in this, in the software space who tried to build some things that weren't very good. It just wasn't accessible. Well, now you have Microsoft Azure that has pre-trained models to do things like this.
[00:09:30] You have Google cloud and then you have the dominant one, which is Amazon, AWS. And you can go to AWS right now and there's like 30 pre-trial. Machine learning models to do all of these things and more speech recognition. We talked about recommendation engine based on their own, the one they use for products on Amazon.
[00:09:48] You can apply it to other areas where you can recommend content or products or services. So there's just like, those are really obvious things, but then you start to think about consumers and how we are so used to, uh, personalization and convenience in our consumer lives, all the apps we use, the shopping experiences we have.
[00:10:06] And when we go to our day job of being business professionals, You expect a similar level of personalization and convenience, no matter what you're shopping for. Even if it's business software, if you have to work around, if there isn't an intelligent chat bot helping you out, or, you know, cause I may go on at 10 o'clock at night, I'm an entrepreneur.
[00:10:24] Like I don't, I don't just shop just because it's nine to five, like I'm going to go on a business site. Like I might be on your B2B software site at 10 o'clock at night. I don't want to have to like, wait for support tomorrow morning. I've got 10 minutes now. And I want, I want an answer now. And so if I can't get that level of service that I'm used to, I get frustrated and just like, fine.
[00:10:42] I'll just go find another vendor who has solved for the fact that I shop anytime of day because of what I do. So those are some ones. Uh, the other one that we really focused on in the book, you know, Mike is, I think it was chapter one. We told the story of Google and Microsoft and Amazon and their commitment AI, and not only them, but the Salesforce and Adobe and Meta and NVidia and all these other players.
[00:11:07] And the point is, Hey, if they care deeply about AI and they're putting billions of dollars into it, they're going to be racing forward. What's possible. So if those major players are creating innovations with AI, what's what you're going to be capable of doing as a company becomes much greater plus the cost to do.
[00:11:27] Keeps going down. So what used to cost a bunch to train a machine learning model or change a language model or whatever you would. It wasn't even accessible to people three years ago and it wasn't very good. Now it's really good. And it's as accessible as signing up for an account in the cloud and paying for some GPU's or whatever.
[00:11:44] Like it's just way more. And then the follow on to that is venture capital money is pouring into this space, creating more innovative companies. Um, And so like those combined, it just makes it to where it it's gonna move so fast that if you don't figure this out in the next one to three years and you don't start moving in that direction, you're going to be in trouble.
[00:12:05] Like you're just going to be left behind. And again, it's not an alarmist thing. Like this is a realist thing, like the software's going to get so smart. Um, and you, you, you just want, don't want to be left standing there with your other competitors who move first because you do get a first mover advantage with this stuff.
[00:12:22] For sure.
[00:12:24] Mike Kaput: Yeah, I love that. You've kind of really laid this out in a single sentence. When you said, when you consider all these factors together, it's the perfect storm for wide-scale disruption and once in a lifetime wealth creation and career advancement. So what I'd like to do next is dive a little deeper into your, um, definitions and how you see AI native AI.
[00:12:51] And obsolete companies and what they kind of look like, because if I'm listening to this, I'd love to probably start understanding. Do I work for an AI native company? Do I work for an AI emergent company? Am I in danger of working at an obsolete company? All of these are really critical questions to start answering.
[00:13:10] So maybe start with AI native and tell me a little bit more about. How you see that come, that type of company. If there are any examples you can think of, um, yeah, just unpack that a bit for us.
[00:13:22] Paul Roetzer: Yeah. So again, I think these companies they're going to, to be built to solve specific business problems. So they're going to look at a market, look at an industry and say, well, we can build a smarter version of that.
[00:13:34] Um, And so they're going to value data more. Cause they're going to realize the importance of data from the ground up to do what they're trying to do. They're going to build more efficient businesses that the types of teams they build may be very different. So again, if I was trying to scale an AI native company, I might not even be hiring marketers.
[00:13:54] I might be hiring astrophysicists like the, and then we talk about this in the book too. There's these organizations, stitch fix comes to mind as an example, um, where, where they actually go hire astrophysicists, who were working on modeling the universe. And instead are now trying to build personalized clothing collections.
[00:14:11] So it's seeing opportunities to build companies in a completely different way that. That almost a favorite term of Elon Musk and some other entrepreneurs like that from first principles, like just go down to the very base and say, what are we trying to do? And what would it take to get there? Throw all the historical legacy ways of doing things out?
[00:14:30] Like there's an example. I, I talk about, I think later on, on the obsolete one about eight marketing agencies and I can come back to that one. Cause I, I ran one for 16 years. I can talk from a pretty high level about what goes into running an agency. And if I was starting. From scratch. I could pretty quickly tell you how to build a graphic design from a web shop.
[00:14:51] Uh, uh, a marketing automation company, a content marketing company, an advertising agency. If we just started from the ground up and build something. You could absolutely build something that far surpasses the capabilities of some companies that are around for 50 years, like, and in part it's, because they've been around that long and how slow they're going to move to do things.
[00:15:14] And again, you could do this and just enter any industry I know in the. In the post to, to validate for myself, I wrote like retailers e-commerce shops, agencies, event businesses, media companies, law firms, medical practice, artists, writers, designers, the list just keeps going on and on software makers, game developers, every one of them, if we set with the right minds in the room, give me two, three people who have some expertise in that industry.
[00:15:36] And then a couple of AI people. I can't come up with a single business model. We couldn't take. Because it's just, if someone hasn't done it already, there's so much efficiency to be gained by just thinking about it from the ground up. And that's what AI native companies do. They're, they're going to look at opportunities and industries and specific businesses.
[00:15:56] They're going to find the inefficiencies in them. And they're going to take over those industries by just building better moms. Yeah. What
[00:16:04] Mike Kaput: comes to mind when you say that is a conversation? We had a couple pods ago about Dolly to the, um, AI system released by open AI that automatically generates, uh, photorealistic images and art.
[00:16:20] The fact that tool exists, even though it's not yet commercially available. You could sit down and you would build a fundamentally different design firm or, you know, art collective or whatever, just because it exists that old ways of doing things are completely irrelevant. Now that that tool exists.
[00:16:39] Paul Roetzer: Yeah.
[00:16:40] And I'm a parallel, I guess, going back to, I started this thing. We were HubSpot's first partner agency. Now HubSpot wasn't using AI at the time in 2007, 2008. I'm not saying as a parallel as an AI example, but what HubSpot did is they came to market with fundamentally better software. And in their case, like inbound marketing software evolved in like CRM and CMS for websites and stuff like that.
[00:17:02] But there are agencies that spun up and built services around HubSpot software. Like we did it, we just didn't scale. To the, you know, to the size that we could have, if that's what I wanted to do. But I know people who built 20, 30, $50 million companies on the backbone of HubSpot software. And I've, I've said in a tweet before, like, what we're talking about here is 10 times the inbound ecosystem, like at least the size of what you could build.
[00:17:29] So you could from the ground up, just build and you can specialize. If you want to like find an AI advertising platform that you believe heavily in or a social media, AI platform. And you can just scale surfaces up, or if you're those firms build service arms to what you do, and you can just redefine the way things are done in different industries.
[00:17:49] So, yeah, I mean, it's, it's such a huge opportunity. And I think I alluded to in the post. Yeah. If I, if I was running like a venture studio or a fund is all I would do, I would just prioritize industries, build an advisory board of experts in different industries. We want to target. Go and find all the inefficiencies within those industries and just build smarter software.
[00:18:12] Take over how they're run and the other one, the big guy or for bunch.
[00:18:16] Mike Kaput: Yeah. But that that's really compelling. And you know, w now that you said the venture, I think people would be interested maybe in slightly unpacking that more. Like, do you have any kind of, even just wild ideas of like, Things you would look at, say in marketing sales, operations, um, as an example there.
[00:18:34] Paul Roetzer: Yeah. I mean, for us, obviously language is a huge one. We've talked about this before. So much of what we do in marketing is language, understanding and generation. Now it's a really crowded space and that's the one thing I would have some hesitancy as an NCN, as there's dozens, you just did a post on 36 AI writing tools.
[00:18:50] So mean there's no lack of writing tools. Um, but I think when you get into industry specific, so let's say. I dunno, just pick manufacturing. Um, if you could go into manufacturing companies. Train language AI in that specific domain, like a specific vertical within, so let's say like energy or whatever it is.
[00:19:10] And the AI is trained on all the trade publication data and all the blog posts from experts in, in that space. And so you control the learning of the model. So the larger language model isn't learning from the web and Reddit and Wikipedia, and like all this other stuff. It's actually learning from expert knowledge within a specific domain.
[00:19:29] And then you tailor that language model to that domain, and then you train it to write ad copy and emails and sales emails, whatever it is. So that's what I'm saying. Like I would take language prediction, vision. And then I would go deep into like verticals within industries. It's kind of like landing AI as the example use there, Andrew Moon.
[00:19:47] Um, and his team at landing AI are doing where they started in manufacturing and they've like really drilled into automotive. And I think like pharmaceuticals and medical. So my guess is they're, they're actually taking a similar approach, not with language models specifically, but they're actually doing consulting into these very narrow areas.
[00:20:05] That they know vision is their big one that you can apply it to save time and money and increase output. So that's, I think the real opportunity and where I would focus if I was doing invention, you know, investing or building a venture fund, um, I would just pick those. Like I, I was golfing with a buddy of mine.
[00:20:21] Who's a dentist the other day. And we were he's using an AI tool, was awesome for what he's doing and realizing massive value. And we were like, what, what other inefficiencies exist in dental practices that you can build AI for? I think the more drilled in you get, then the more specific of either problem statements or opportunities you can identify and then you just look and say, okay, well where, where can we improve a repetitive process or where can we bring a creative solution that doesn't exist?
[00:20:49] Mike Kaput: Yeah, that made me think in the post that we just did with 36 AI writing tools. One that I included intentionally, even though this is not in our space is a tool from a company called Mike legal and it's called . And again, no affiliation or it's for lawyers and what it is is lawyer grade automated contract proofreading.
[00:21:12] So I'm going to read like, just a sentence on this website. Mike DocuSign is an AI. Microsoft word ad in built by the collaboration of lawyers and engineers. It proofread contracts and agreements as he draft and reduces the time spent on them by more than 70%. So it's not just, you know, checking your grammar.
[00:21:31] It's literally making sure all the precision and accuracy everything's accurate in the current. This is a job that, uh, you know, a paralegal or someone would be doing typically. So that's really interesting as really kind of clear
[00:21:48] Paul Roetzer: use case. Yeah. I think that's this domain specific ideas where the real opportunities are going to lie and where you're going to see massive innovation.
[00:21:55] Uh, because if you think about like legal as an example, you can't like if you're going to copy.ai or Jasper Hyperwrite or any of these tools that we like we've even test them, we use them, um, I can write a legal brief, like they're not, they're not trained to write legal briefs. They're trained on some datasets or examples.
[00:22:13] That's like, here's a, here's what a good ad does, which one can, you know, ads that convert. But if you think about, you know, these other disciplines, They need to be trained on domain specific content and inputs. And so I think the people who take these language models or take this predictive capabilities and figure out how to apply them into very specific areas can unlock a ton of value for companies.
[00:22:36] Yeah, that's
[00:22:37] Mike Kaput: really cool. So. Obviously, not every company and most of them out there are not AI native, but you do have this category of AI emergent. And I think that a lot of potential companies and listeners would probably fall into a category where they are AI emergent or could be. Can you unpack that
[00:22:57] Paul Roetzer: for us?
[00:22:58] Yeah. So there's what I mean. If we just see us as 23 million businesses in the U S give or take, probably it goes up or down a half a million every year. Uh, They are going to those businesses are going to be either emergent or obsolete. Like that's what we're saying here is like, it's all other, so looking forward, AI native could be built from the ground up.
[00:23:18] And maybe there's a collection of those that exist. There's going to be examples of that. But the vast majority of companies, vast majority of people listening to this are working for companies that will either be obsolete or they will figure out AI and they will evolve. So I think. Most companies have the ability to, to be AI emergent.
[00:23:37] There's no real obstacles to doing this other than a vision to make it happen. And then the support of the leadership team. So I think what you need to do is you look at again, like we talked with the previous one, these were inefficiencies in your business. Where's time wasted. Where are these repetitive processes and how can you use smarter technology?
[00:23:58] To solve for that, to get rid of that waste and then to free up that time and redistribute it to other areas, other growth initiatives, other community initiatives. So the, you know, the example we give is, again, it's just like such an obvious example, but Netflix, for people who maybe don't know started as DVD delivery services in like the late nineties was 97 at launched.
[00:24:19] So their, their business had nothing to do with online streaming I'm on streaming wasn't even possible in 1997. Really. So. You know, the internet connections to do it. So now, you know, it's a streaming company with AI everywhere in the organization. Um, so that, that's an example of like an AI emergent. Now that again, as an extreme example, you don't have to be doing all that.
[00:24:39] Like I would consider my, my agency, so para 2020, again, which I sold last year. They could be considered a, uh, a, a low on the AI emergence spectrum. Like they've, we've started you and I were using probably a dozen different tools with that agency. So we were piloting it now again, there's going to be these levels of AI emergent, you know, that would form over time.
[00:25:01] So you're going to have the people who kind of dip their toe in the water and do a few pilot projects and don't really ever fully commit. And then you're going to have the people who start stacking success stories. Oh, we used it for copywriting. We used it for ad spend. Uh, we used it to do some evaluations of our.
[00:25:16] Um, professional development work, like, you know, to use NLP, to assess the sentiment and tone of people's feedback of their peers, whatever, whatever it is, like you start finding these uses until you eventually achieve like true transformation with AI. So there's going to be a lot that kind of fall in that realm, but the people who do nothing, they'll just be irrelevant.
[00:25:36] Like again, This isn't going to happen tomorrow. It's not going to happen in a year, but it will happen. And by the time you realize it, it's going to be too late, because there's going to be AI native and AI, emerging companies in your space that have already taken the market share, taken the, you know, um, the opportunities that you had for growth and kind of eaten in.
[00:25:59] Mike Kaput: Yeah, and that, I think that's a really good summary of kind of that third category of the obsolete companies. And what I loved in the post is that you kind of gave a hypothetical example using, like you mentioned a business, you know, extremely well, which is marketing agencies. So I was wondering if, maybe, do you want to kind of broadly paint that example for us to.
[00:26:20] Connect all the dots here.
[00:26:23] Paul Roetzer: Yeah. So if you're not feeling how are agencies run? I mean, in essence agencies that charge by the hour have to achieve certain utilization rates. It's not like law firms and probably account. I don't know. I've never worked in an accounting firm. I don't know if they do it by the hour, but people who are reliant on human outputs to generate revenue.
[00:26:41] You have to hit certain utilization rates. So the whole financial model is dependent upon people doing X amount of hours per month, out of however many hours they work. And then you manage to hit those rates. And there's nothing fundamentally wrong with that. Um, but if that is the core of your focus, if you're so stuck in the traditional financial model and needing to just hit this and you're cutting costs everywhere else.
[00:27:04] And you're just trying to maximize for profits within an additional, a traditional model. And that's where you're at. And then over here on beknownst to you is a smaller agency that is testing AI for ad services and maybe adding some new services to their mix based on some efficiencies they found there.
[00:27:25] Maybe they're using some AI writing tools and they're freeing up their writers rather than producing. Three posts a week. They're producing seven at the same number of human hours and the contents better. And it's better targeted to search intent. And now all of a sudden they can start generating more leads than you can.
[00:27:42] And again, you start doing this across paid media, email marketing, is that wherever they, they play in video is another huge one. And you, they keeps finding smarter ways to do things. And then all of a sudden. A year passes and that, that other agency is found like 20% more time in the year. Now they can redistribute that, do more client work with it, or they can redistribute it to do more with sales and marketing of their own and creating a better growth engine.
[00:28:08] And so the whole point is like, as you start taking these very, what seemed like very small steps you can read. Rather significant gains in time and reductions in costs and become more nimble as a business. And that's what you're going to be faced is you're going to have competitors regardless of your industry coming in.
[00:28:27] Who have figured out how to do things that you don't even know exist. And all they maybe did was got a subscription to some, you know, AI writing license, or they went on Amazon web services and they found somebody who could build some machine learning models for them and spent 10, $20,000. And they saved a hundred, like things like this happen.
[00:28:46] And it, it doesn't take a hundred, 200 to half a million, you know, thousand or half a million dollar project to realize gains. It might be a $19. SAS product that saves you 3, 4, 5 hours. And so again, it's like, our time is so valuable and if me as a CEO or an entrepreneur, if I can like find something that saves me five hours, I can do a lot with five hours.
[00:29:11] Like I can create a lot of value for our customers, our sponsors, our team, if I have five extra hours in a month. So for me, little things like that can go a really long way to just building smarter businesses.
[00:29:25] Mike Kaput: Yeah, that's awesome. And kind of one example I was thinking of as you are talking to build on that is, so I used to be, um, you know, freelance writer, I'm working with a lot of like businesses, um, on, you know, web copy content, things like that.
[00:29:40] And I remember, um, an agency hired me on a contract basis to help. With a massive project they are doing, this is probably almost 15 years ago at this point. And the project was to write product descriptions for a jewelry company. And we have, there were thousands of products, uh, thousands of descriptions to write.
[00:30:03] I was probably one of a team of at least five writers working full time. For months on this project, probably two months. And honestly, I think the project, I don't know the exact number that the agency Quaid bay was at least mid five figures, probably for all this work. One person armed with the right tool, one person, agent.
[00:30:25] Could probably secure that project today with AI, you know, it's just crazy. It's fundamentally different. Whereas we needed tons of people and tons of time and resources to do that project
[00:30:39] Paul Roetzer: 10, 15 years ago. And again, like you, you have to understand AI to look at that problem differently. So if that CEO of that agency or whoever's doing the BizDev had no idea.
[00:30:49] And then the company that hired them had no idea. Then they're just going to waste six months of their life, like writing product descriptions that you and I could probably knock out in six hours with the right training and data set. Like it's just, and again, that's where I, you know, kind of circle back to like the whole origin of this post and, and why I wrote it when I wrote it is I keep trying to figure out how to make people care, to like, feel the sense of urgency because.
[00:31:19] It, once you realize what AI is capable of, you look at everything differently. You'll get every business differently and you just, you see the inefficiencies that. Are all over the place within those companies and you just want to fix it, but like, I don't want to, I'm not going to go do 10 consulting gigs a month and start trying to like, fix this for everybody.
[00:31:38] So our play is like, if we can just teach people and it doesn't take much like intro to AI for marketers free online class every couple of weeks, or read the book and like, you've got enough knowledge now to start looking at everything differently. And I think that that's what we're missing in the business world.
[00:31:56] And specifically within marketing a new world, we play in most often that there's just an unawareness, that solutions are sitting in front of you because you, you don't even know how to look at the problem.
[00:32:10] Mike Kaput: Yeah. It's so fundamentally different. Yeah. You don't know what you don't
[00:32:13] Paul Roetzer: know, right? Yeah. Like I was, I was talking to somebody about video the other day, like thousands of hours of video.
[00:32:22] And I was like, oh yeah, I could do all kinds of stuff with that. And I'm like, what? And like, well, AI can see now. Like it, it, it can, it can auto tag everything in there and it can automatically splice it and create, you know, shorter versions for things. And you just got. Thing. So again, like if you're looking at video, you may be a media company that has thousands of hours of video or whatever it may be, or, or you may be a, uh, a brand that has thousands of hours of video.
[00:32:48] And to you, you just put it on YouTube and forget about it. There's a transcript. And maybe some people find it there's so much more you can do now. Like you have assets just sitting there, it can be cut up and analyzed and searchable and, you know, you can find the exact moment of the exact thing that was said.
[00:33:04] And so I, yeah, I mean, we just. It's really hard. Now, once you have the knowledge to, to turn your mind off, when you're talking to people and looking at businesses,
[00:33:14] Mike Kaput: that's kind of like being unplugged from the matrix or something like you're just seeing things very differently. Yeah. Another interesting example along those lines is, you know, there's several very sophisticated.
[00:33:26] AI tools for logging sales calls. So they'll listen to all of your sales calls and analyze what's working. What's not, um, what phrases work. I mean, that's just a fundamentally different approach that wasn't possible probably five ish, years ago, 10 years ago, that is, has massive financial implications for any sales team.
[00:33:49] Paul Roetzer: Yeah. And those are examples that are all over the place of just the people found a solution and most marketers don't know to even look for the answers.
[00:33:58] Mike Kaput: Yeah. So kind of on that note, I did want to, as we, you know, I've got a couple of questions left here as we're kind of, um, you know, getting to the end of this post, but when I'm reading this post, listening to this podcast, hearing about AI native.
[00:34:13] AI emergent or obsolete businesses. It begs the question, like how does all of this affect me? My individual career? What should I be thinking about? What should I be looking for?
[00:34:25] Paul Roetzer: So the, you know, we talked about, if you're at the obsolete company might be time to like, look elsewhere, if you understand it and you take that initiative.
[00:34:36] And this goes back to a couple episodes ago where we talked about what's lost what what's gained. And when that's some, a little more personal, like once you understand AI and you realize what it's going to be capable of doing, and if you're a writer or designer advertiser, social media pro, or whatever, And you can start to see around the corner and realize, okay, so AI is going to be able to do X, Y, and Z.
[00:34:56] I spend 40 hours a month doing that right now, and I'm not going to tell anybody there's an AI tool that can do my job for me yet. But like, if they're going to find out real soon, so maybe I should be the one that tells them, and then I can redistribute those 40 hours. So you start to individualize. As you understand AI, you start to understand how careers are going to evolve, where the machine will fit in, where the is going to augment, what you're capable of doing.
[00:35:20] Maybe in some cases replace what you're doing, but that opens up all new opportunities for you as a professional and as a human. To do more interesting things. And so understanding AI's impact on you and your career, and then the organization you choose to work with. Or even if you're an entrepreneur, entrepreneur, the organization you choose to create and run.
[00:35:43] To be honest with yourself and say, wow, I, the company I work for might be in trouble. Like nobody here understands this stuff. And nobody here cares to understand this stuff. Uh, you know, you know, you've, if you're a change agent, if you're someone who brings ideas to the table, if you're in an organization that doesn't embrace those ideas and doesn't encourage people at every level, from the intern on up to bring new ideas to the table and find better ways to do business, then you're probably.
[00:36:12] That's the right company. So you want to be in an organization that's going to embrace innovation, um, except that the leadership team. Isn't always going to have the right answer because they may not be as tapped into this stuff. As you may be, you may take a personal interest. Watch a couple of Ted talks, go to a couple of courses, come to a conference.
[00:36:33] Like you may take the initiative to gain this knowledge, but if the people who make decisions at your organization, don't want to hear it. And aren't going to take action on it then. Yeah. I mean you for your own fulfillment and for your own career advantage. You might need to move somewhere that encourages more of this kind of thinking and enables you to make a greater impact on the company.
[00:36:55] Mike Kaput: Yeah. That's a really good point. And I'm sitting here thinking, oh wow. If, if I was at one of those companies, how would I go? Starting to do that. And one idea that comes to mind is, you know, it's not like every, there's a list of every company using AI, but go to any AI vendor website that we mentioned on the site, in our newsletter, whatever.
[00:37:15] And go look at case studies, look at who, which companies are already adopting. Some of these tools. That's probably a decent leading indicator of where to start looking who is start talking to.
[00:37:28] Paul Roetzer: Yeah. And I think you, you know, you've got to train yourself to make business cases for stuff. So, I mean, we do this internally, like Michael would come to me and say, Hey, you know, he knows what the goal is.
[00:37:36] Like, we, we have a mutual goal of like how much content we could create, what we want the growth to be of our audience and, and, um, you know, our contact database and things like that. So we have shared goals and a clear priority to those. Mike figures out how to do his job. Like I'm not telling like what the right one to write it.
[00:37:51] So if Mike comes to me and says, I think I have a way to create more content, it's going to be better. It's going to be like $49 a month on a try this tool for three months. Like, dude, go for it. Cool. Like that, that's the end of the conversation because he frames it under a business case. I have a goal. We, we know what that goal is.
[00:38:06] I think I have a smarter way to get to that goal. Okay. As the CEO, like, I don't really need to know exactly what that tool does or how it works. And I don't even in this case, what do you mean care was AI just so happens to be, that's what Mike's looking at. So you need to know how to talk to leadership.
[00:38:19] And usually it's going to be framed around goals of some sort and mutually agreed upon goals, which hopefully you have. And then you can save this. And I think I have a smarter way to do this. I just need approval on a $99 a month license or whatever it is, and I'm going to, here's how I'm going to benchmark it.
[00:38:33] And here's, I'm going to make sure it works. So again, you're not selling AI internally. You're not trying to convince people to do AI. You're trying to convince them that you have smarter ways to get to. And to do your job and that eventually it might be cost savings. You might reduce costs. It might reduce the need instead of hiring five people that have the right product descriptions.
[00:38:52] Maybe you only need a half of a FTE. Um, so that's, you know, that's an important thing in your careers, know where you are, if you're the right fit for you, where you want to be longterm and then know how to make business cases that drive change and open up the opportunities that you're looking for in your.
[00:39:09] Yeah, that's awesome.
[00:39:10] Mike Kaput: I think that's fantastic advice. Um, so my last question that I wanted to ask you, and we can, you know, if you have any closing thoughts here, uh, on this topic, we can also, um, cover those, but I wanted to talk about an area that you also know very well, obviously your marketer, former agency owner, but you're also an entrepreneur and very clued into the world of startups.
[00:39:35] So. How does all of what you're talking about, effect that next generation of startups, what should entrepreneurs and or investors be thinking about when forming and funding new companies based on everything we've talked about?
[00:39:51] Paul Roetzer: Well, first, if you're an investor and you invest in software companies, the thing I will tell my friends is if they don't have an AI roadmap, then I would not invest in them.
[00:39:59] So if you're doing a startup today, that is a software company. And you're trying to build solutions that people would find valuable and help them their business. And you're not at least exploring how to build AI into your software. It it's, it's already obsoleted, like, honestly, like it's just, it's just not going to be relevant because it's just going to be table stakes in five years.
[00:40:20] Like it's going to have to have it in there. So if you're building software three to five years from now that it's not fully infused into what you're doing. Then you're going to have a problem because someone else is going to build it in there. So I would start with this idea that like software companies moving forward, it's just essential.
[00:40:40] It should, it's just software. Like we shouldn't have to differentiate AI from normal software in the future. Um, but we're nowhere close. Right now. So that would be my, my main thing is like, if I was an entrepreneur, if I was starting a business, I would be, you're always looking for efficient ways to do what you're setting out to do.
[00:40:57] So for us as a, you know, relative startup, um, the Institute, you know, I think of us as a media company and event company and an online education company. And so I'm constantly saying, well, how do we build a smarter version of each of those? How do we do media different. Who can we look to for inspiration, like Washington post and Buzzfeed and like, oh, I don't care.
[00:41:18] What other marketing? Platforms have done like marketing media companies. I want to say, like, what are the most advanced media companies doing that we can bring to ours? And the same with the event business, we did that episode last week on AI for event market and event businesses. And then online education is huge.
[00:41:35] Like there's all kinds of applications because it's basically an e-commerce like you're, you're selling. Products online. So you want to think about what are the best e-commerce businesses doing? How are they personalizing content and offers and driving conversion rates? So I think, again, as an entrepreneur or an investor, you just have to say, how do we build smarter versions of whatever it is we're building?
[00:41:54] And that means you're, you're going to need to understand AI and apply it and have advisors or staff. That can lead that charge because just because someone's a developer doesn't mean they have the first clue about machine learning. Like I've found that out time and time again like that. Just even data scientists, like they're not, you can't assume just because someone has a techie title that they understand AI, it's not the case.
[00:42:20] Mike Kaput: Yes. I couldn't agree more with that. Um, now, Paul, this is awesome. I would encourage everyone listening to go check out the post. We'll obviously have a link in the show notes. Um, it's really well worth a read and really well worth taking some time to really think about the implications here. They're going to be critical and it's not just.
[00:42:42] Worrying about, you know, becoming obsolete though. That's certainly a concern. There's huge opportunities here for anyone who has sort of the will, um, to go after them. So that's all I kind of had to ask you about this. Paul, do you have any kind of closing thoughts here on this? As we
[00:42:58] Paul Roetzer: wrap up mean. Just want to encourage people like this.
[00:43:03] Isn't hard. Like this may seem like, oh man. Yeah, emergency sounds like a lot of work. And I don't know the first thing you don't need to, like, you can start small, you can find a single tool that starts you down the path and then just stack successes. And that's how we tell people, even in big enterprises to do it, you just, you don't need to go get separate budget approval.
[00:43:22] You don't need to go like make a big business case. Just try a tool that does it. That's why we focus a lot on this AI and action idea. And we do these webinars where we show you out works. We're just trying to break down the walls here and make you realize like any company of any size. Can I can adopt AI and then gradually make these cases over time to con you know, to continue to make those investments.
[00:43:44] And that's what I would tell people. It was just, we always tell them, just take the first step, just be curious about AI, take the first next step, and you will get there. Don't try and go from no AI to all AI overnight. It's not going to happen for anybody. It's not happened for us. And we spend our lives looking at this stuff.
[00:44:01] Like there's still plenty of parts of our brains. Where we could apply that we haven't even thought about it yet. We spend too much time talking about it. We start doing more of it on our own. So, no, I think it's just an important topic that people realize, again, not to be alarmist, but be realist. Like this is it's happening, like one way or the other.
[00:44:21] And I think we, you know, w what is it? And there's like, don't be agency B. So we have like, agency. Uh, evolves and the integrates AI agents to be doesn't and, you know, becomes obsolete and it's like, don't be agency B would be my message. I can be the one that goes and seeks out the knowledge and starts finding ways to apply.
[00:44:39] You'll be more inspired. Your team will be more inspired. And, you know, I think it generally would just make work more enjoyable moving forward, which we're all looking at.
[00:44:49] Mike Kaput: Absolutely. Amen. Um, Paul, this was awesome. Thank you for both writing the post and allowing me to quiz you on it. Um, thanks to everyone who is listening.
[00:44:59] Um, we will see you next week.
[00:45:01] Paul Roetzer: Thanks everybody.
[00:45:03] Thanks for listening to The Marketing AI Show. If you like what you heard, you can subscribe on your favorite podcast app. And if you're ready to continue your learning head over to Marketing AI institute.com. Be sure to subscribe to our weekly newsletter. Check out our free monthly webinars and explore dozens of online courses and professional certifications until next time, stay curious and explore AI.
Cathy McPhillips is the Chief Growth Officer at Marketing AI Institute.