How Manufacturers Can Experiment with AI Marketing

Artificial Intelligence (AI) tools lead the forefront of current marketing conversations in 2023. Joey and Nels are diving into how they can be used by industrial marketers and what you need to know to be prepared for the coming revolution. 

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Joey Strawn: 0:00

Welcome back, everybody to another episode and another season of the industrial Marketer Podcast your place for the tips, tech trends and tactics for industrials who care about driving leads and revenue to their businesses. We are back, it is another season and another year, I am one of your hosts, Jo E. AI, and I am here with some artificial intelligence. And we are going to talk about some fun technology. Oh Nels. I'm so excited to be back. And we decided this quarter, we're going to talk about tools in the tech of marketing. And what better place to start than the buzziest buzz topic that is happening right now, which is AI Artificial Intelligence and the tools that surround it. I mean, are you excited to dive into this topic? Are you excited to be back one for season three Nels?

Nels Jensen: 0:55

Oh, yes. And just to let you know that the title for this episode should be aI marketing strategies for manufacturers. Perfect. That's what that's what AI tells us.

Joey Strawn: 1:07

We will listen, we are now answering to not the man but the machine. And so this is gonna be great. Now unfortunately, for all you listeners, Nelson, I are still humans. This is still a human led podcast, we haven't given it all over to AI just yet. But we are going to dive in. I mean, again, this is a topic that everyone from in the trenches, to hopefully CEOs and leaders and drivers of their businesses are thinking about these tools, not so much, you know, the Minority Report or iRobot of it all, that's not really the the artificial intelligence that we're going to be diving into. But you know, what tools are out there that are usable? What investments should be thought of, or structural infrastructure, ideas and concepts should be considered for your organization? Those are the types of things that we really want to dive into on? Where do these fit in? What do they realistically look like? And what does it mean for people in our industries, which is more like the industrial manufacturers of the world? The supply chains of the world? The E commerce businesses of the world? Like what, what is AI going to do for us?

Nels Jensen: 2:19

Yeah, and I think, you know, really, this falls into a larger b2b marketing category, like a lot of ours do. It's not, you know, hugely, it's not hugely different that we'll talk about nuances and subtleties. But let's also be transparent here, Joey, we are not experts on the AI applications here. This is very much new to us, as well as new to, to the people out there who are listening. We have been playing around a lot with these experimenting, we've used some of these tools. But we're learning it too. And we're learning the power and the the guardrails and the you know, limitations and all that. So we're right, jumping in there with everybody else. And I would say we are far from the authoritative sources on what exactly you should use. So I think, maybe around one corner ahead of a lot of us, right.

Joey Strawn: 3:10

I think that's a very important point, Nelson, I do want to back that up and say, Yeah, we're not experts, and we're not authorized resellers or re dealers have any of these tools, either anything we mentioned in this episode are things that we've either played with, read about, or have heard about, or have been told to explore. So these are tools that we know are in this universe, these are things that we know, are going to pop up in conversation this year, whether it is you know, at a meetup, or at an industrial group or online, or maybe your boss comes to you and says, Hey, what about this chat GPT thing? You should have some sort of answers and know what's on the horizon. And that's really what we want to focus on today. I mean, knows we're not even, we're gonna mention a lot of tools, I think throughout the conversation, but really, the heart of the conversation is like, what does it mean, for us, you know, manufacturers and marketers and industrials. You know, how does it realistically going to be implemented? I mean, like, let's be clear. We're not saying that by the end of this year, everyone's gonna be wearing virtual reality headsets, and, you know, the haptic hands and, you know, controlling things in an artificial realm. That's what realistically, can we think about this year, and then we just want to go through a lot of the types, there's a lot of pros and cons, and a lot of ways this technology is being used. So we just want to focus on some of that. I mean, I'm getting I'm excited. This is something that you and I've been reading a lot about Nels. So I know you, you and I have each played with different tools and systems. And so I'm excited to see what you've learned and figured out and I'm excited to bring to the table what I've got, I mean, but just kind of to start when you think of artificial intelligence and these AI tools and this big trend that's been happening, it's being talked about right now. What do you feel it actually means for for industrial marketing, and people doing what we do?

Nels Jensen: 5:01

Well, I guess I have a hard time with the big, grand umbrella of it all. I think it's if you if you are in the industrial sector, you understand predictive maintenance, right? You understand how equipment has patterns and telltale signs. And there are definitely things that it tells you are going to happen. And the key thing there is closer to a timeline for when they might happen. And I think artificial intelligence is largely prescriptive in that, yes, it's, it's telling you it's roadmap, it's telling you where to go. There's also you get into some of the different applications about actually doing the work for you. But to me, that's the more cautious area to go to me, I view it as more powerful for a roadmap and guideposts than I do for the actual execution.

Joey Strawn: 6:07

Yeah, and I think that's an interesting point to make, too. And, you know, this is a fancy term, and a lot of people are talking about this now. But even before we started recording now, so you and I were talking, saying, well, these are stuff that we've been using for a while, like artificial intelligence is the way that we're sort of putting it all under the same umbrella. But machine learning has been a topic that we've been talking about for a while it's in a lot of the tools that we use, like the SEM rushes and the male chimps of the world, you know, that's looking for machine learning. automations, you we talked about big data, you remember, like a handful of years ago, when big data was the buzz buzzword? Well, the natural next step in the gathering of big data is that, well, we have to have computers to compute the massive big data, and then tell us what that means. And that's what intelligence is, that's artificial, right.

Nels Jensen: 6:58

And that's what history of that industry. 4.0 is basically putting data to work for insights. And, you know, and that, and I think the reason why this is such a big topic right now is just we've reached a tipping point where it's yes, it's not just for the data nerds out there, we've now reached the point where the usability is such that almost anybody can do it. And, you know, we've come to the point where we have to figure out more about training and more about, you know, applications. And we'll talk we'll talk more about that, you know, coming up, but, but we have definitely reached a tipping point, in terms of AI where it's, it's just not, if you're just thinking, well, that's for the data scientists and the, you know, analytics people, you know, no, it's we've reached that point where it's going to be for all of us. And, you know, if you're, if you've been lagging, and you know, yes, now, it'd be a good time to get up to speed and begin to stick your toe in the water and figure this out.

Joey Strawn: 8:08

Well, and, and that's a great segue to because one of the things that, you know, we want to definitely talk about today is the ways that we can realistically see this happening within the near future. So I mean, again, you don't have to dive in and become the biggest data scientist in the world. But now it's to your point, we've reached the tipping point where it's going to become not only commonplace, but sort of expected, especially with the proliferation of, you know, tools, like I mentioned, jet chat GPT earlier. You know, last year, there was a big social media meme explosion around AI image generators, like mid journey, and Dali, and some of those, and, you know, it was an entertainment and meme driver a lot of last year, but to your point, we're getting in now where it's going to be a lot more economical, it's going to be a lot more systematic and a lot more scalable. So a lot of people will have access to the tools. So sure, sure, chat bots are a normal thing that I see. I would bet by the end of this year, a lot of chat bots are run by some AI mechanism.

Nels Jensen: 9:15

Oh, and we've seen that we've all seen that on on websites, too, as the, you know, the volume and importance on online research takes hold and in the industrial sector chatbots are I now see them more often than I don't exactly that people people might not think of that as artificial intelligence. But it is a very simple even if it's just sorting you into one of four or five different categories for you know, some type of connection somewhere that is still, you know, artificial intelligence at us right now,

Joey Strawn: 9:53

And especially with tools, you know, we're getting better about having access to systems you know, Bambara is a Big tool out there, but intent data, you know, the more access to intent data and historical relevance of activities, the more what do we mentioned earlier, those big data patterns can be recognized by machines that are trained to do it. So all of these pieces have been in play for a handful of years. And I mean, in real in an honesty and in all real NIS advertising has been doing this for years. Programmatic advertising is based around the art, like artificial intelligence and machine learning of large amounts of advertising and big data. You know, this is something that has been in use for a lot of marketers for a while whether or not we've actually thought of it as artificial intelligence.

Nels Jensen: 10:45

Yes, and, you know, if you are putting your even lead scoring into a CRM, it is feeding into an engine that, you know, HubSpot AI, or you know, some of these other ones as well. And I think the one the one thing that to me, we're kind of jumping all over the place, but that's okay, the analytics side of it. So GA four, we did an episode recently on GA four. And that's one of the cool promises of the latest platform for Google is that they're basically going to help you determine what campaigns have a higher level of success predicting audiences, basically helping you, you know, just give you a much clearer course of action to take, you know, for a campaign, you know.

Joey Strawn: 11:37

That's one of the major tenants of GA for is one of the reasons the platform is evolving, the way that it is, is stepping away from individual properties and managing those but looking at the entire matrix of everything going on, and then get gathering learnings from those activities in those trends across the spectrum. And so machine learning has been something that they're very heavily pushing, and I'm happy that you mentioned analytics, because nails this is really, really where the rubber meets the road. And I think where we should spend kind of up a good meat of our conversation today is what what is this going to look like in 2023? Like, there are a lot of different aspects of our jobs that we you mentioned analytics. I've mentioned content creation, to some degree with chat GPT, or chat bots and things like that. But there are tons of uses for these, like, you know, just to go through some of the types that you and I mentioned, when we were planning this is content generators, image generators, advertising, analytics, CRM, and automations, devel development and code generation, a lot of these things already exist in already are out there. I mean, just we can start at the top on this one, like content generation, what do you Nelson imagine the future looking like with these AI tools? And content generation? Or like, are you going to lose your job? Are we going to have to like, replace you with a robot like a Nelson bot point?

Nels Jensen: 13:05

Oh, yeah. The not yet. And I think there's a general, there's, there's the basic, mostly right. View of content generating AI? And, you know, it depends, it depends, obviously, what is the pool of information that this AI is working from? So you have the the new, the crazy tipping point tool is the chat GPT that is...

Joey Strawn: 13:35

Schools are banning, and then everyone's getting up, and I was like, Oh, it's right, in the presidential speeches or whatever.

Nels Jensen: 13:41

Right and it's, uh, but it is, if you've never played with it, do so I realized, if you jump on it a lot of times right now and you don't already have an account, you're not going to get one right away. It's really cool. It's, but it's scraping the web to write these things for you. You give it a prompt, and we can talk a little bit more about, you know, the importance and understanding what exactly you're asking it to do. Because that becomes, you know, if nothing else, I'm not going to lose my job because, you know, how do you interpret exactly what you want? Exactly, putting that into an ask? So but that's different than like, Jasper, which is actually made by the same people open AI, the same company. You know, we're, we're Jasper will take for instance, I can provide, here are three documents that I want you to use to draft and then you ask for whatever 1000 word blog post, and you give it obviously instructions, but Jasper is basically allowing you to provide the inputs, you know, chat GPT is not it's basically and I'm sure at some point it will. I mean, this is the people running this open AI the company, you know, are very smart. They basically put chat GP He out there. And they've probably advanced their development work because of everybody playing with it by yours in a matter of two weeks. Oh, yeah. Or I don't know how long it's been out there. But it's. But yes. So there's there's fundamental differences in terms of what are what are these tools using to generate content? And that's super important to understand. And then, yeah, and then

Joey Strawn: 15:25

I think when we compare pros and cons later, that'll be Yeah, that'll be something to dive into, especially with the imagery stuff, because that's got to come from somewhere. But like, even things like content bot, you know, I think is a fun one. One of the things that you and I have talked about I, this is the joke that I like to say right now. But I really view especially the content AI generators as like a really great virtual assistant. You know, it's like, give them a prompt, and they're smart, and they can go anywhere on the internet and get everything and put you together something. Yeah, but it's going to have to be edited. It's going to have to be reviewed for accuracy and make sure that the sources are verified. There's work to be done. It's not a final product, but it's a great assistant.

Nels Jensen: 16:08

A shout out to our peer Virginia Roberson who she calls it her, you know, her top notch research assistant. Yes, exactly. And we'll talk a little bit more to as well research is certainly a huge use for it. There are others as well. But yes, she's, you're both correct, right there, virtual assistant research assistant. You know, it is very, these are very, very powerful tools. And I'm, I've gone from the oh, well, we'll just wait and see how this plays out, too. All right, it's played out enough now that I need to get a better handle on it.

Joey Strawn: 16:45

One of the first things that I remember seeing in the market, or at least conversations about this, where the image stuff like mid journey was one of the first ones I remember our developer, we've had him on the show before Brian shared a bunch of Dali stuff early on, like last year. And I was like, Oh, wow, this is pretty crazy. And then you dive into like, where's this? Like? How is this getting? You know, photo, I think is another one. So there's a lot of the image stuff that I don't really understand. But that's where I kind of first saw it. And that's gonna be really interesting in my mind to watch on, like, what can those images be used in AdWords? What's the copyright on these like, because images are different than the semantics of words and how words are put together? Because those images have to be sourced, the pixels have to be sourced. So it's, that's an interesting, what's kind of one of the first ones I ever saw. But getting deeper into it, like you mentioned, analytics. And I've mentioned advertising, like those are two very in the weeds element of something that we do as a marketing team, that now AI is kind of dipping their toe into, I mean, outside of you know, like the ad roles and the programmatic advertising. You have sem rushes and GA fours and machine learning analytics platforms that are giving pretty good advice on how to update pages, how to look at optimizations, and where to target advertising. So it's

Nels Jensen: 18:12

Yeah, one of the beauties, one of the beauties is how they link together too. So programmatic tells us, here's, for instance, you know, avenues to track users, and where, show them this message where they go. Right, right, and then you know, you're using some of these different channels to do that. And what you might might find out is that you see analytics that oh, yeah, there's a huge number of people who are seeing this. And there's very little stickiness, right, so you're learning two things that yes, this is it does deliver audience, it does not deliver value, you know, I mean, it's the the, that's the power of the analytics is isn't just telling you, Hey, do this and saying, Hey, do this. And oh, by the way, that wasn't the greatest thing. You know, it'll ultimately help us, as I like to say, play offense and defense at the same time, right, double down on what works and cut back on what doesn't, right.

Joey Strawn: 19:12

It is, it gives us an additional reach as marketers to get our hands around the big data. You know, it's the data source that we talked about earlier. And that's really what a lot of this comes back to is having the history of metrics, having the access to Internet Trends and culture and the ways that we use words and images is pulling all of that together, where we wouldn't have the time and energy to do so.

Nels Jensen: 19:37

Right. So. So with automations, right. So, you know, when I first heard about this, you know, I'm much newer to marketing than you are, but when it was like sort of this personalization is going to be automated. I'm just like, Well, that'll be interesting. And it's, you know, and many in many cases, it wasn't per internalized it was targeted. Right? But it has been it has evolved to be personal where you actually can have the attribution sources so that they can, you know, yeah, deliver something 100% unique to me versus you even though we have many of the same behaviors, right?

Joey Strawn: 20:18

Well, and you mentioned within automations, I'll expand that to include like CRM platforms and marketing automation tools, too, because that's one area that we know have been playing around with artificial intelligence for a while. I mean, HubSpot in general has been talking about their machine learning, they have a lot of big architecture that they're working on, around machine learning to provide insights on everything that's happening within their tools. But the big one right now, I think that a lot of people hear about and hear talked about is Salesforce, Einstein, Salesforce launched their Einstein platform a little while ago, but any, a lot of people probably heard it and know what the name is, they may even use it. But it's based on the machine learning, it's based on machine learning and insights and trying to help make a lot of those things that used to be very manual within automation tools, a lot more machine focused, or a machine driven. So whereas, you know, a handful of years ago, we as a marketer, you would have to go into HubSpot, or go into Salesforce and make filters and rule for every situation that someone could fall into. Well, now we can use tools like Einstein or the machine learning from HubSpot to identify intent factors from their users history that we wouldn't have access to mixed with location data mixed with previous activity within the CRM to provide them a very uniquely designed experience on a page. And all of that can be done with human and machine hands, you know, collaborating along the way. You know, that's one of the things that it's an, that's an interesting area for me, as I see it coming together almost in full fruition in some of those technology platforms. Because eventually, you know, HubSpot has a chatbot feature, so does Salesforce and Salesforce marketing hub. So the chats will be in there, the email marketing will be in there, you can connect advertising platforms and programs to those types of hubs. So I see those as being a central crux of how in the future we're gonna use this, this universe of AI, if you will.

Nels Jensen: 22:29

So let's, let's talk about the pros and the cons.

Joey Strawn: 22:33

Yes, this is, there's a lot on both sides. This is one of those where I feel like if I had been in a debate team in high school, and this was the conversation, you would have to be able to argue both sides, because there's validity, I think everywhere.

Nels Jensen: 22:50

Yeah, so to me the most obvious from the benefits, and I'm looking at it much more narrowly than you are right, my role is content, long form content. But it's definitely the the research, and definitely, you know, time and speed and efficiency. So research can be what I've found is the research to me, tends to be the more micro or macro you go, the better, you know, it's not it's not and that's not meant to be general, it's meant to be get away from as vague. At all costs. Yeah. And then, but the time and speed is, is it really gets you started. And in some cases, it delivers great efficiency, there are some things, you know, if I'm like, you know, what's the value proposition for having XYZ for a small manufacturer? And it's like, okay, I know, I could sit there, oh, yeah, you know, whatever. And it might take me three minutes to write the two sentences. But this, you know, might deliver something that's like, Okay, I could use that. And, but then that gets to the cons that we'll get to in a minute. And about, should I use that exactly there?

Joey Strawn: 24:09

Well, and that that, to me, is the immediate benefit of all of this as the speed and the amount that can be analyzed at once. I mean, it's just there is no comparison to AI and artificial intelligence can do the work faster than we could ever do it. So that to me is I think, is to us, the big pro, and even in my world, like the the social strategies or the development code that can be written from Ai saves a lot of time, you know, on an agency side or you know, a time spent on a salaried worker, the less time you spend doing doing things you shouldn't have to the more time you can spend doing things that are really profitable to the company. If you're, you know, an owner of a company and you're looking at your workforce. There are a lot of ways that are probably appealing to say, well, I could really have this core team focus on these profitable areas and have ai do some of these menial tasks and keep the gears spinning, and I can use my human capital, if you will, a lot more effectively, you know, I think that is a huge Pro. But on the flip side of that is, to your point nails on the con is? Well, should you use that, you know, the is? Is it trustworthy? Where was it generated from the, you know, images, output images, and it's a little bucket because the that, like, there are artists that make art, and they put them on the internet, and then it pulled together in a new thing. And it's like, Well, where did the all that come from? So that's its own little world, but like, with the information and the accuracy, is it trustworthy? Now, it's like, if you ask it a prompt, can you be sure that if then you put your name on it and post it, what it puts out there isn't going to be ripped apart by the online? You know, truth patrol, you know?

Nels Jensen: 25:57

Sure, sure,

Joey Strawn: 25:58

How much time should you have to spend to validate what's in it, as opposed to the speed in which the prompt was given? Yeah, you know, that's a big question mark of I don't know that answer. But that is kind of one of the cons of, it's almost like when Wikipedia was the thing, you know, was started to be a thing. Colleges like, wow, that thing is super wrong. And Wikipedia is way incorrect. And it's like, they're actually it's like, way more correct. And so I don't know, it's out there.

Nels Jensen: 26:26

But there will be there will be a time when a major brand sends out an automated email that basically misinterprets a term. Yep. That that is vague. And there will be the, you know, mini crisis PR, and all that it will happen. So um, you know, I can't tell you when or where, but it will, so I wouldn't, I would,

Joey Strawn: 26:49

I'm going to put my money I'm going to do like a Babe Ruth point now is by this time next year, you and I will have a very soft, solid case study of a company who did that within 2020. Yes. Or they sent out an email and mislike misconstrued a social term that has a slim meaning.

Nels Jensen: 27:07

Oh, 100 100%. So the trustworthiness to so you when you talked about the the image generators? So obviously, you know, when we when the internet really took off and reached its tipping point, you know, a lot of people talked about the, oh, there's no more barriers to publishing isn't this great? And it's like, as somebody who basically grew up in the editor, gatekeeper world, it's like, yeah, be careful what you wish for. And maybe, maybe agree, sure enough, we've seen lots of abuses of whether it's social media, blogs, news, you know, whole news channels, you know, ever so I'm, I'm really concerned about deep fakes, I've seen a video that basically, you know, is a deep fake of a politician delivering a message that they never delivered on things that they would never have said. And so it's pristine, it looks perfect. And you know, so there's a whole, and that's, you know, that's less of a concern in manufacturing and industrial marketing, that it is societal. But this at some point does become related. If, if you can't trust sources of information, or platforms, then that detracts from the efforts that we're doing, you know, to market for the manufacturers.

Joey Strawn: 28:38

And I'll put it in a really, you know, trust is something that especially since the blooming of the internet, that's become a very valuable commodity. If people can trust you, if they can feel like they can believe what your company puts forward, that gains that gets you a lot of capital. So with this kind of blow of artificial intelligence, if there is a layer of untrust, and just the media in general, I'll put it in a very real example. Now, some of our, some of our clients or some of the people that are industries, work with aerospace, or work with defense or work with airplanes, and have to have very specific, very pinpoint measurements and specifications and realities to their work. And if it's an, you know, an automatically generated or AI generated message that's not true and tells an a layer of falsity then those could have real effects and real life effects for companies that don't get bids companies whose jobs get refused on delivery of QA. There's a lot that can go into backing up the claims that the speed in which you know the AI generated it sure correct was given to you. Yeah, I you know, we've already talked about them not being fine already. It's Virtual Assistant world like that, to me is a con is I don't ever want it to become a crutch where people are just like three words in a prompt, copy paste pasted on a website. Like it's not, it's never fine already. He's got to have eyes on it.

Nels Jensen: 30:14

That's why my job is not in jeopardy in the near Exactly. You know, I could certainly see where a small content team might not have quite as many editors gatekeepers AI strategists, and is relying on AI, especially as it gets better, and it's getting get a lot better faster, too. That's the that's the other thing. But yes, it's, um, you know, so at a certain point, if everybody is using largely the same sources, then isn't all this going to be a certain sameness? I mean, you know, that's, that's another downside, that you, how do you differentiate your product or service or your message? So, you know, there's, there's still always going to be value and creativity, there's still always going to be value and quality? So, yes. Is AI going to eventually get better at that, too? Yes, I mean, you can use even Chet GPT. Now, write me an 800 word blog post on XYZ, and in two minutes, it's done. And then you can say, make it funnier, and it does. So I mean, it's like, and I'm actually laughing. So it's, you know, I realized this all isn't final ready. Now, it's going to continue to get better. But, and we'll talk a little bit more about this coming on, but it's really super important to understand how to use it. And, and where it needs to go into that. It's not just, oh, let's, you know, let's get the interface and go. It's like, what is it you really, really want to ask it to do? Right?

Joey Strawn: 31:58

Yeah. And I think that's, that's, that's a good point, too. And I think another pro that is good about this is it's it's scalable, you know, it does become a layer of assistance and a layer of help to a team that, you know, nowadays is mobile is spread out can be small. So, you know, this could be a good layer and help smaller teams become a lot more scalable, and provide scalable pot, good, positive solutions to their audiences and to their, their constituents. I mean, this could be really, really good for small manufacturing teams, who have just been struggling to compete with the big boys for a lot of years, because now you can have some help, you can have a layer of support and scalability that you didn't get before. So there are ways for small industrials, small supply chain manufacturers to really take advantage of the tools that are out there. But you know, it really is focusing, there's a lot that's going to be popping up. There's a lot of shiny buzzword effects, fun things that are going to be out there, and really kind of focusing is going to be that I mean, that's what we're gonna focus on our second section of the episode Nelson, kind of on the shop floor is really how we're prioritizing.

Nels Jensen: 33:14

Yeah, well, I'm eager to hear how you're going to use this. Yeah, I

Joey Strawn: 33:19

have asked you to put the how you're going to use it together, which I'm eager to hear as well. The last thing I want to touch on before we move on this onto the shop floor is just if, and I come in, I'm very curious, on your opinion on this, given your history in journalism, and as an editor and having seen some of these machinations in the world, is, what is what does plagiarism look like in this sort of world? Like if machines are just pulling from everything, and it's all the same? Pool eventually, like what you said, there's sameness that's inevitable. There's that zoo for something like this?

Nels Jensen: 33:55

Or maybe it is maybe it isn't? I don't know, I don't know, even if I were to ask, you know, a tool to do the exact same thing. One day later, will I get the exact same response? You know, that's an interesting test showed up, right? Should I is it or, you know, yes, you can learn more. So it should evolve. But yeah, and you know, plagiarism is, you don't hear as much about it, or I don't anyway, in journalism circles, because I think to some extent, there's an awful lot of sort of link attribution anyway, so there's less incentive to actually plagiarize. But we talked about this offline in terms of even academia where it's like, okay, how do you how do you make sure your student actually wrote something as opposed to just pull it off the internet to begin with? So that exists now? Will it be harder? You know, I think it's there's just going to be a whole lot less of authentic IQ, individual work. I mean, it's just now it's like keyword. There's so many tools you can use, what is the keyword strategy for something, and there are tools already that you can just go and type a couple of things in. And it helps you do it. So, you know, I think we're as long as we're viewing this tool sets, I don't know that plagiarism will become more of an issue, maybe, maybe it will. But I just find it hard to believe that a lot of people are going to fully automate blogs, you know, right, or, I mean, it's, I think social posts, you maybe will see a lot more similarity in social posts, maybe No, I don't know. What do you think

Joey Strawn: 35:41

It's social as well as social kind of is all the same already. It sort of sounds a lot the same. So that probably is already happening. I I'm interested to see to see what happens. I think I agree with you. I don't I don't have much more to add, than then what you said, I think it will become kind of a not so much a stickier issue. But it'll be more on the duplicate content side, like what is what is the search engines? How did the search engines differentiate between content that's, you know, generated by the same types of tools, you know, to generate the same types of content? That'll be that'll be an interesting,

Nels Jensen: 36:18

That's a that's a good point. Because that's, that's already been in play. Right? You know, exactly how differentiation and creativity, those are always going to be tenants of, of messaging, right? Yeah. Well, okay. Let's,

Joey Strawn: 36:34

I'm excited to hear what you're prioritizing. We've talked about a lot, there's too many tools for one person to do everything. So let's head on down to the shop floor and else and tell our listeners how we're prioritizing things this year. And maybe it can help them and get some direction on how they want to prioritize. So let's head on down to the shop floor. Sounds good. All right. I don't want to repeat a lot of what we've said, this has been a very in depth and good conversation, I think. But you and I were both like, okay, there's so much going on? How are we even going to focus this. And so we each came up with three ways that we're going to be focusing this year. So I'll go through my three, and then you can go through your three, I'm excited to hear what you've got. So I mean, for me, we talked about it, my big one is just what can it do in the areas of research, I am very interested to see how good and helpful it can be in the areas of like keyword research and idea generation, especially for content calendars, or keyword, you know, ranking opportunities. That is, that's kind of the first thing that I really want to dive into. I'm a big fan of sem rush already. And you know, there's a lot of the AI tools and machine learning that it's incorporated. So just the research angle to me is super exciting. The next one, the big area for me this year is going to be analytics and feedback loops. You know, with GA four and us having to transition and a lot of people transitioning to GA for that system and platform this year, I'm really expected to see a lot of good optimizations and machine learning analysis to come back from that. And that's kind of I'm excited to see what it can do, whether it's, you know, a big query integration with GA four, or whether there are some different sort of AI software's that need to be incorporated into it? Or if it's just a lot of combined apps that work together to output you know, machine analytics. I'm just curious what that what those feedback loops could look like. Because that conversation, if it if speed is involved, and accuracy is involved on the data, that could really make things interesting from a marketing perspective. And then last for me is customer interaction like engagement metrics, like how is AI going to be used in conversational marketing and chatbots? Like, will all chatbots be mostly AI by the end of the year? Or will still there be people, you know, typing in chats at a chat center? You know, the personalization? How personal? Can content on a website or in an email be and how relevant to an audience and how perfectly timed? Can it get those kind of those three areas like how to research and generate the directions, how to actually engage the customers and keep them engaged? And then how to get feedback on all that engagement like that. That is my kind of world of AI this year, there's going to be a lot of fun things that happen in code development and image generation and some others but I'm going to let better qualified people focus on

Nels Jensen: 39:47

You know, I, I love your list, and I especially love feedback loops. And let me just go on a short little tangent here. We'll get to my list soon but okay, the world of hiring It is so broken, right? You hear about these companies that do 10 rounds of interviews and, you know, you hear about in manufacturing, you know, we have runners, the the usually brand new people to the workforce who don't even make it through their first, you know, full day on the job, the closing the feedback loops on the recruiting, hiring, onboarding, retention, you just, it's like you can, you can improve your communication so much, if you can close feedback loops, you don't need to have the third round of interviews, if you can adequately capture what happened in the first two rounds of interviews, you know, it's so to me, and I'm just sort of riffing off this, I hadn't really thought about it. But AI should help the recruitment world, and the onboarding world so so much, it's Oh, please, please,

Joey Strawn: 40:53

I will add a layer to that, I will add a layer to that, as well, as someone who has been in the seat of hiring and looking through resumes, I have seen how the quote unquote, automated systems have worked in the past. And it's a matter of like, look for these six keywords in a resume. And if those keywords aren't in that resume, then put them in the Do not contact folder and yeah, me as a hot that does those to me eliminate so many good candidates, because it's not learning. It's not. It's not intelligent. It's all just based around arbitrary placeholders and milestones. And that, to me, is an area of just like you said, if there's a machine or if there's an AI that can legitimately scour through the resumes and highlight the most relevant and helpful ones that would one get people the right jobs and help hiring directors and decrease the amount of interviews people have to do it with just that's a good, amazingly apt area for 2023 exploration for somebody. All right, app developers get on their AI. All right. All right. Now, I want to know, what are you focused on this year?

Nels Jensen: 42:06

So I what I will be exploring with AI in 2023. And we've talked a little bit about it. So I my list of three, the the first one is what I call framing and experimentation. Right? So I'm basically looking at it from a content strategy perspective. So I'll be experimenting with, you know, because the better set of instructions you give, the better result you get. So I'll be trying to learn how to frame the ask to get a better outcome. And then the the depth part of that will be the applications in the execution, right? Can you really ask it for this sight type of task? Or how good is it, you know, when you, you know, are doing completely different

Joey Strawn: 42:50

Or, or maybe it's just sort of for someone who tasks. So to me, that's the, that's sort of the big picture. We've talked about research to me, I'm much more interested in the micro research, I, I see a lot of the macro, to me is not nearly as helpful, at least in the things that I've tried and the things I do. So again, it's it's really more in depth research. But I think there's huge potential from a content perspective on that. And the third one is saving time. And that, for example, where it helps at a macro is like writing a summary of something. So it could be as something as simple as, you know, okay, I've come to the point where I need to, you know, talk about what are what are the benefits of, you know, adding 3d printing or additive manufacturing, you know, just I need a quick little summary here, whatever, that's been written a million times, right. So I could go pull something directly from the web, but it's actually easier to put it in the tool and frame it a little bit and get something that I want. Similar for a list of benefits or a list of hurdles or challenges. It's like, the little the little list of goals, if you will, it can be very good at generating those. The big mystery for me is the idea of of not using it as a research assistant, but using it for a rough draft. So a first draft, and that's where I think I'll be spending considerable time including next week, you know, looking at okay, so how effective you know, can this be isn't, you know, if you're a really slow writer, it might save more time. People, a lot of people find it easier to edit what is in front of them instead of generate something that isn't there. Right. needs a kickstart maybe giving seven prompts around a single idea is what they need. It saves 30 minutes of staring at a blank screen. And didn't come up with those seven prompts on their own. One of the things that you said that I also want to loop back to because I think it's going to be interesting to see where it goes in the future is the framing. That to me is going to be very interesting. I remember when colleges started to teach, like the different Boolean search codes and the different ways to search Google, I was like, Well remember, if you put quotations around it, it will search the exact phrase. And if you put site colon quotations around a thing, it will search just that web, there's all these specific ways you can search. In Google, or in search engines. We're gonna have a whole new lexicon and, and dictionary of ways to frame questions to AI, that pump out the best answers, and we're gonna have to as a society of humans, be trained in how to do that. It's almost gonna be like learning how to communicate with the robots, but like, ya know, to your point analysis, like in academia, I, it would not surprise me in, you know, three or four years that a part of a marketing class would be, hey, how do you frame a search within an AI platform to get the correct content generated or to get the correct sources referenced or whatever it may be? That to me will be an interesting new area of exploration in the future.

Nels Jensen: 46:26

Yep. So yeah, it's it's a, it is a new world, even though it's not totally new world. But right. There's always like one, yep, yep.

Joey Strawn: 46:38

Feels Well, it's a new year, a new you a new Nell's, a new AI. It's a new universe of all of this artificial intelligence. I mean, like, like you said at the beginning, and I do want to reiterate, we we're not developers on any of these, we're not experts on these tools. A lot of this, we're exploring at the same rate that everybody else is. And, you know, this is something that we know is going to make a difference in all of our realms. And so if you have tools that you've been using, or if you have things that we didn't talk about on this episode that you think are worth exploring, let us know. You know, email us at podcast and industrial marketer.com. Follow us on social media or comments on the blog posts on our website, industrial marketer.com. For this episode, let us know what you're exploring how you're finding useful industrial marketing uses for artificial intelligence, or even some of the problems that you've run into. A lot of this year, we're going to be diving into the technology and the tools that surround us as marketers and so we want to know those hurdles that you're running into. Follow the show, share with your friends, let's pull together and and ask us those questions. Because those are the things that we want to be answering and talking about this season on the show. So again, if you haven't subscribed to the podcast, what are you doing? Come on, you know you want to and email us your questions and your and your comments at podcasts and industrial market. or.com Nelson, are you excited? Are you pumped about what the machines are going to do for us?

Nels Jensen: 48:07

I am putting the machines to work hopefully I will have let it outcome.

Joey Strawn: 48:12

Lets put them to work and hope that it's not like a terminator output. But for right now. We're good. They're going to be our great virtual assistants. And we're going to put them to work and let's see what great marketing we can make in 2023