Exploring the Power and Ethics of AI Technology in Marketing with Parry Malm

On this episode of Industrial Strength Marketing, host James Soto sits down with Parry Malm, CEO and co-founder of Phrasee, to talk about his journey as a marketer and entrepreneur. Malm discusses how he fell into entrepreneurship and the challenges of marketing in today's technology-focused landscape. Soto and Malm  delve into the world of AI,  its impact on marketing, and the ethics of it all. This episode also explores the intersection of marketing, technology, and business strategy.

Articles and resources reference in this episode:

• Connect with Parry on LinkedIn
• Follow Parry on Twitter
• Visit the Phrasee Website

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Transcript

Perry Malm: 0:00

I think what's happened to marketing in the last 20 years is really sad. Where marketers used to be, you know, Don Draper types, sip your whiskey in the corner office and come up with crazy big ideas and go hard with them. And now 90% of marketers time is spent in a whole bunch of different software platforms and Excel spreadsheets.

James Soto: 0:34

Welcome to the industrial strength Marketing Show, we challenges leaders across the supply chain to make their people marketing and technology, their businesses a strength of their business. Hi, I'm James Soto. I'm the founder of industrial the family and strategic marketing brands trusted by leading organizations around the world. I'm sitting down with innovators with industrial marketers, and technology leaders to talk about their careers, their insights, marketing, and technology that makes an impact on their businesses. I'm here today with Parry Malm, CEO of frasi. I've been following crazy for a long time, I've been speaking about it at events around the country in the manufacturing technology space. And we're gonna really get into some great things today. So Perry, welcome to the show.

Perry Malm: 1:21

Yeah, thanks.

James Soto: 1:22

We're really glad to have you here. And I just thought we just get right into it, like, tell, tell our audience a little bit about your journey as a marketer, as an entrepreneur, you know, and, you know, just as an expert, and your little slice of the AI world?

Perry Malm: 1:38

Well, I mean, like most people, I fell into it. I think people who aspire to be entrepreneurs are constantly chasing their, their tails. And the people who I think tend to have the most success are people like me who never intend on doing it. But discover a problem, experience a problem, and then figure out ways to solve that, that problem. The fact that I'm I know a bunch about AI is cool and stuff. Doesn't get me a lot of friends at parties and whatnot. But ultimately, you know, the most important thing is that we solve customers problems, the fact that we use AI is cool. But the most important thing is the problems get solved.

James Soto: 2:21

Yeah. And, you know, that is that is the fundamental reasons of starting a business. So when you looked at starting the organization, and as the right place at the right time, what was the quintessential problem in the market you're solving for?

Perry Malm: 2:35

Yeah, so this goes back a number of years, when I was a branch site marketer, I was living in Amsterdam at the time, and we would send out millions and millions of messages to millions, millions of people. And I used to always look at it and be like, what is the right message to send out via I mean, back then this was in 2007, or something. So it's mostly on email and brochures and direct mail and stuff. And he's always go like, we're spending millions of euros. Surely, we should spend a bit of time to figure out what language is good. So I started doing a bunch of tested and tried to build a bunch of models and one thing led to another and before long, I had the light bulb moment, going, if I'm experiencing this problem, then probably others are too. And it turns out, I was right.

James Soto: 3:25

Yeah. And tell us a little bit about Phrasee. Like one of the things we've heard from our audience, is they want to hear unapologetically what you do. How does it help them? Hey, James, you know, you know, I know, they're gonna say, have Perry, tell us more about what Freezy does? And, you know, what are these amazing things that you're unlocking with your technology?

Perry Malm: 3:45

All right, well, welcome to the shameless plugs zone.

James Soto: 3:50

No, one apologetic,

Perry Malm: 3:54

The concept with Phrasee is very simple. We use technology to not just create content, but to create good content. Now, you may wonder what good means. And it means a number of things. First of all, content that's generated needs to be on brand, and it needs to be trustworthy. It can't be factually inaccurate. It also can't invert and people with the need to continuously approve things because they don't trust the AI itself. And ultimately, that must perform strongly. So that's ultimately what phrase he does. We we generate and optimize and understand the language that makes people click. So the greatest and easiest way to think about it is that if you get emails, push messages, Facebook ads, whatever, from companies like Groupon or Walgreens are like loads and loads of companies, then you've experienced raising technology without knowing and you've probably bought something that's a direct result of the language we not only generated but optimized, and depending upon what you bought, either say, you're welcome, or I'm sorry, I can't be in charge of your own shopping habits.

James Soto: 5:08

Yeah, I have used an example from phrasing that was like driving like a 30% improvement rate. And I think it was subject lines for Facebook ads, it was something about, like, I used one of the examples I think he showed, which was, you know, one, you know, one level of copy that was around, like, oh, take this vacation, and, you know, summer savings. And then it's like sun sea sand, fun, you know, like, get in on the action, bunch of emojis. And what was amazing about it, and this one felt magical to me, which was that the AI was outperforming, you know, something a human row. And also as was unlocking, like the use of emojis that like, didn't have bias they didn't, there was actually some thinking and advantages to how it how it actually performed the job. And, you know, we've I've done this, I've spoken with that example, at a major technology event, and people actually kept picking the AI is the better copy. So. So that's like, one of the things that inspired me to reach out. Because, you know, I think when I kind of look at things, and what inspired me, in this episode was really a quote from Arthur C. Clarke. And it about, you know, it really kind of dives a little bit into this world. And it was in his book profiles of the future that he wrote any sufficiently advanced technology is indistinguishable from magic. And I think that is, I think, a lot of what we're seeing out here. So when, in the context of that, like, what does that make you think of when you think about what we can now do with the technology? You know, in terms of AI for marketing for content, stuff that people can see, they don't have to question it, you know, they can actually use it. You know, what, when you think about like it being distinguishable from magic, like, what does that, you know, what do you think of in the context of your world and AI marketing?

Perry Malm: 7:09

Yeah, I think people who view it as magic probably don't understand how it actually works. So it might be worth spending a moment to explain what a lot of this stuff does. Effectively, what AI is, is it's very fast moving statistical models. So there's sort of two main schools of it. One is prediction, or decisioning. AI. And the second is generate of AI. So the former prediction and decisioning is effectively you know, when you go into Excel, and you got a scatterplot, and you go like insert trendline. That's effectively what the vast majority of machine learning systems are doing, except with many more variables, many, many more data points at a much higher speed. From a generative standpoint, obviously, everybody is, has been exposed to chat GPT, which is like pretty impressive technology. Use Cases notwithstanding. But what it effectively is, is it's a huge statistical model that can predict the next best word at scale. So it can be given a prompt, and based upon that prompt, it can then go, well, here's how I should structured that answer using this huge sort of, like, multibillion parameter vector space of, of language tokens. So while it appears magical to the layperson, what it effectively is, is a huge amount of really fast moving statistics, which presents many opportunities, but also many risks.

James Soto: 8:38

Yeah, yeah, for sure. And I think we're now getting in the world where, you know, this has been opened up to pretty much everybody. And, you know, we're at the point where we're looking at like phrase engineering, because you can even take that to the next level and leveraging the technology, and get even better and more succinct answers. And so, you know, as you really look at the core technology, and this is kind of where our conversation started. Going. LinkedIn was, you know, with that magic, there are real issues, when we look at that predictive models, the patterns, the patterns, a pattern apply, you know, apply the insights and generative AI. You know, it comes to me like those key impacts, we see impacts on a number of areas, and I think there's like three different areas. So, you know, we have, you know, personalization, you know, and privacy, we have the bias and algorithms as an issue, which really can impact, you know, the human element to potentially negatively impact outcomes. And then there's the impact. I think a lot of folks are feeling today, whether you're a brand side marketer, or you're even in an agency so to speak, or you're an industrial business, it's like we're seeing in manufacturing, you know, big displacement due to automation and AI is also going to have an impact. So there's that third thing which Does that impact on employment? And so without going, you know, mainstream, it's just pretty amazing. So, you know, what, what do you see, you know, like this, you know, chat GPT the gender of AI, or even, you know, what's crazy doing that is so truly remarkable, you know, that, you know, you're gonna have to, you know, really kind of come out with, you know, real answers to those questions. So, you know, so since it's been thrust on us all, you know, what I'm, you know, what are your concerns regarding the ethical use of AI? So, you know, just set, let's say, just started with personalization and privacy, you know, how does that apply to it, you know, crazy is doing?

Perry Malm: 10:45

Yeah, well, we're, we're fortunate in some ways, we're, we're a little bit detached from many of the challenges that that hyper personalization and big data warehouses companies face, but just sort of diving into the whole school of generative AI and some of the the ethical conundrums which one must face, particularly with large language models, they're trained upon effectively the entire Internet. So what they did is they scraped the entire internet, more or less, so all of Wikipedia and all of your old Tumblr, blogs, and all this kind of stuff. And then they basically turned it into a to a series of individual tokens, which are like words and stuff, and then produced this huge statistical model. So like, from a technical standpoint, it's absolutely fascinating. But the challenge is, it's trained upon the entire Internet. And I don't know if you've ever been on the internet, but it's filled with idiots, like 99% of the content on the internet has been generated by an idiot. People with bias, people with wormhole world views, all this sort of crazy stuff, all this sort of embedded bias that we have, in US only at a huge scale. So what that means is that this model is inexorably biased. If you ask it to produce content, targeting women, or targeting men, or if you ask you to write job descriptions for specific job roles, or if you ask it to, this is a trick I pulled to generate the pseudocode algorithm to predict somebody's credit score based upon their gender and ethnicity, then you can really start to uncover the biases. Some of them are overt, you know about race and gender and stuff like that. But some of them are implicit biases invisible to the human eye. And I think that's really quite dangerous. You know, the way that I phrase chat GPT is it's sort of like an economist. They're always confidence, and sometimes, right. So now we have this authoritative, authoritative AI system that can produce compelling answers that only sometimes are right. I don't know, that strikes me as being quite existentially challenging for truth for the internet. And moreover, for marketers, where if you're in trusting systems to generate content for you, how do you know if it's right? How do you know if it's biased or not? And ultimately, how do you know if it's good?

James Soto: 13:35

Yeah, it's almost like freedom, you know, the we created here in the States, we created a country that's based on, you know, with that freedom is the assumption that we'll use it responsibly. So, so if we're looking at an internet of idiots, so to speak, is it you know, that we've really gone out there, and, you know, the assumption is, when we're scraping with, you know, getting all that data with AI, that that content was created, you know, with good with good thinking and responsibility. And I think we can argue there's a lot of bad stuff out there, but there's a lot of good too. But the question is, what's, you know, how do we how do we scrutinize that, you know, and I think in this world, you know, that, that, you know, we do have this opportunity to personalize our advertisements, you know, campaigns. So, it's not just what we write, but it's actually how we're doing that targeting and personalization. And so the AI can tailor marketing messages to be more relevant. And then now we're talking about influence and, and those things, you know, how to be effective by looking at behavior, preferences, demographics, but, you know, that relies to your point on collecting a ton of data and analyzing a vast amount of data. So in that context, you know, we look at those privacy concerns. So it's really essential that we, we we really be transparent about Howard collecting and using customer data. And obviously we have to give, you know, the user empowerment to opt out. But in that context like how do you see, let's solve for that in the sense that, you know, how should businesses that are engaged in AI share how they collect customer data? How do you see that?

Perry Malm: 15:21

Yeah, I mean, it depends on what data is being collected. And how sensitive the data is. A good example is that if somebody is collecting personal health data, then presumably you need to be very upfront about it. But if somebody is collecting, like, when you bought dog food last, I mean, you could certainly make arguments about how his personal data, but also like, who really cares, right? I think just being honest and upfront with people is the key. I mean, it's when companies, you know, pull out Cambridge Analytica and collect data and use it for spurious motives, which I think is is morally wrong, but also leads to social ills. But like, if you're, if your like, corner store, knows how many Red Bulls you bought in the last week, who cares?

James Soto: 16:21

It's an inventory question. What do we need for Joe? Or for Perry? You know, if you look, you know, and to the extent that, you know, you have your business here, but if you were to paint a picture of what like privacy looks like now, you know, versus like, what, like, what paint a picture of it like now versus let's say, five years from now, you know, what do you see privacy looking like, you know, given the rate of evolution and innovation, and what's really going to be put in market?

Perry Malm: 16:52

Yeah, I think it's gonna vary jurisdiction to jurisdiction, and it's ultimately going to be regulatory frameworks, which are put in. So I know that the the EU already has some rather strong privacy laws with GDPR, and all this kind of stuff. And I think those are going to strengthen. And we're probably going to see some level of regulatory frameworks put in by the EU to cover off generalized AI systems like chat, GPT, and whatnot. In the US, I think the path will be quite different. There will be regulations put in, but it's going to be driven through case law, rather than legislators. So there's already two interesting lawsuits, which are either happening or have been threatened in this space. I know that there's a class action suit, which is either in the process of being filed or has been filed versus GitHub co pilot, claiming that there's been copyright infringement of, of their source data that's collected through open source code and proprietary code, which is held on GitHub, I don't know what's going to happen with that. The second one is, I want to say stability, AI has been threatened with a letter of intent from Getty Images, who claimed that their image generation AI, illegally scrapes Gettys imagery. So they're claiming copyright infringement. Also, the EU tend to be more top down, they have you know, it's a it's a code of laws type thing, where they'll come in, and put in a framework, which will probably be clunky. But it'll keep a bunch of bureaucrats in work in Brussels, so good for them in the US, is going to keep a bunch of lawyers, kids, in private schools. So that's something which we should feel thankful about. And then in the UK, I don't know, we'll just do something, it'll probably be wrong, and nobody cares.

James Soto: 18:54

Yeah, so it's the Wild Wild West, as we say, here. And there's a lot happening. And, you know, when we were when we were kind of going back and forth on LinkedIn a little bit, you know, the kind of second point here is that there's real ethical concerns around the, you know, the use of AI and marketing. You know, the potential for bias in manufacturing was a huge, huge skilled labor gap. So we do a lot of recruitment marketing for manufacturers and folks throughout the supply chain. So, you know, there's a lot of potential for bias in the algorithms that power AI, and the marketing of their, of it with it. And these AI systems are only as unbiased as the data and the people who, you know, who've influenced the training. And so if that data is, you know, biased or skewed in some way, you know, like, yeah, the thinking is, it'll be reflected. So, like, suppose like, AI, like a system is trained on data, like disproportionately representing certain demographic groups. You know, like we, you know, we were obvious They will say, you know, they'll make some inappropriate or wrong assumptions. And so, ultimately, you know, we'll be unfairly, you know, targeting people or discriminating, you know, either intentionally or not intentionally. So, ultimately, to that point, when you look at the, you know, ethical use of AI, looking at the biases, you know, how are you looking at mitigating that risk? And because you're your front facing with content and copy? So how do we, you know, with marketers having to really look at diversity, equity, inclusion and belonging? How are you ensuring, like, how do you see us ensuring that we're being diverse and representative, you know, in actually what you are building?

Perry Malm: 20:44

Yeah, so first of all, what many people think is that the source data is the problem, and it sort of is, but without that huge volume of source data, the large language models wouldn't be possible in the first place. So you're kind of stuck with a huge amount of data that probably is biased and over represented with middle class plus white males, right? But the thing is, you need that huge volume, you need the billions of parameters to get the level of, of complexity that large language model has. So then the question has to be, how can you post process the output, and set rules and frameworks to make sure that when it generates something that is either hallucinating facts or is overtly bias that you can overcome it. And that's really where we've focused over the years, I mean, we came up before largely with models existed, we built our own language generation system, which, which is still in use, we've integrated a bunch of the large language model stuff. But what separates us is that we've already built all of these tools that can guide it at source, and then also manicure it after the fact. Many systems do not have this, and I would imagine, at open AI, they're very actively injecting directionality. So when it first came out in December, you could have a generate all sorts of reprehensible stuff, all the obvious stuff, it could, it could, you could tell it to say that, that Hitler was a super nice guy or something completely wild like that. And they put in, you know, a whole bunch of a whole bunch of these rules. The challenge with that is that somebody is always going to figure out a new way to be an asshole. So how do you keep up with it? Fortunately, for Freezy, we're in a much work contained environment, because we're working with with marketers, the challenge becomes more about not the ability to generate content, that's just the catch the challenges, then the yards after the catch to get to the end zone. And that's really where our focus is.

James Soto: 23:01

At. And so that's an iterative improvement of, of what you're saying, as you're getting insights, you know, in real time, is that, is that how you see? Exactly,

Perry Malm: 23:11

exactly, it's like, no, the ability to generate language is effectively a commodity. Um, you got shut GPT, which is effectively like a million monkeys on a million typewriters, and maybe eventually, they will write the complete works of Charles Dickens. But you need to be able to tell what's good for bed, before you put it out in the wild. So that's really where our focus is, is to tell good from bad in terms of brand, in terms of accuracy, in terms of trustworthiness, and ultimately, in terms of performance?

James Soto: 23:47

I think that's a great point. It's, it's, if if this world and the ability to put out content, you know, not is no longer a differentiator, it's a capability, it's an expectation, we can use this machine assisted technology to create copies of great content. And that's, you know, available to all, you know, how do you win? How do you go up and beyond, and to the extent that you have your niche here, and you're focused on marketing, you know, because it has a different standard, everything from the brand, to the audience, and people who are really trying to, you know, consider those constituents. And essentially, you guys are connecting the dots to make sure that you can drive real value engagement, entertainment, you know, education, to drive, you know, to inspire people to take action. And then as those systems work, you can continuously improve it to improve outcomes. And that's the beauty of AI. It's hard for humans to replicate that, you know, in terms of, you know, iteratively, you know, continuously improving content. And so, you know, so that, that, you know, there's a there's a big issue here and kind of to get into the third issue here, and curious to see how it's in pack With us, the third area we've seen, you know, you know, outside of the bias side of it is the impact on employment. So as we look at, we look at employers, right now in manufacturing. There's about 800,000 open jobs in the US manufacturing, manufacturing has been going through a lot of change with automation, we've seen displacement of jobs, because you can, you know, where robot can do it better, you should be having a robot where you need humans to operate those robots and have created opportunities. But I think the writing's on the wall, the combination of, of automation, you know, of AI, and you know, the ability for that AI to get smarter and smarter and smarter, is really going to have displacement. And so when we look at this in the context of us as marketers, and how do you really see that impact on employment?

Perry Malm: 26:02

Yeah, I mean, certainly, if we go back to the cotton mills in Lancashire, in the Victorian era,

James Soto: 26:12

The Industrial Revolution, that's where it started the spinning jenny's. And they made those so eight people could do the work of one, that's literally what we we start the Industrial Revolution story on the car.

Perry Malm: 26:22

Yes, absolutely. And you had the Luddites, who came into the factories, and they tried to bash the machines going, it's going to disrupt our way of life. And like, sure, you know, a few of the artisans who had you know, lose their front room, had to retrain, find a different living, whatever. But it created so many more jobs afterwards. And that's how these things work. If you think about the, like, what 1995 or something when the internet really started proliferating. A lot of people in newspapers said, Oh, this is going to is going to kill our jobs. And it didn't like there's more people writing news content now than there was back then. So like, when people worry about specific jobs being lost, usually it's because they have a vested interest in perpetuating the status quo. What automation is going to do what AI is going to do is it's just going to mean that will lead to have millions of people in the workforce, retrained and focused on doing something else. I don't believe the scare mongering of people saying that we're going to have you know, 20% structural unemployment in the future. It just doesn't make sense. Like, history doesn't indicate that's going to happen. There will be some job categories that AI will probably threatened. And it's going to be probably low level administrative work. And hopefully, some high level consultancies

James Soto: 28:03

Is that a dig?

Perry Malm: 28:06

The thing is, what these large language models can do, is they can aggregate let's say best practice, right? It control across millions of websites, and people who have written about topic X pick any business strategy or any business conundrum. And it can give you the vanilla ice cream tried and tested answer. Right? It's probably not the most effective answer, but it's not going to be the worst answer. It'll be the vanilla ice cream answer. And that's what a lot of consultants, do. They give the vanilla ice cream answer because they've read the textbooks. But no, you don't need them. So I think it's going to be quite interesting in 1520 years time to see what the role of consultancies are, there will still be a role for them, but it's not going to be giving pithy advice on business challenges. Yeah,

James Soto: 29:06

you know, so you hit two things here that I think are really important. So one, you know, you know, there's a consultancy, and I'll talk about frameworks a little bit, but then there's this, you know, first thing he said, on the Industrial Revolution, so, so we should look at this with optimism, because when you look at that displacement that had in the early Industrial Revolution, there was humanity were like, they needed X amount of men to do a job to harvest in an agricultural society, you know, and you would work there and you would work seasonally, a lot of seasonal work, the world was a lot different and what the Industrial Revolution unlocked and yes, it may have displaced folks in you know, in the textiles industry. But what it did is a credit the factory in the first time, you know, you would literally be able to go to a place and work year round. You know, what factories were impacted by the weather and the conditions and, and so you can literally go and it created, in essence, the middle class and a place where you can go to hopefully get an apprenticeship and work and, and have a career and really create a generational, you know, work. So I do think that displacement created the ability for folks to work like never before and know here the Homestead Act in the United States took people and they traveled, they traveled west, right. And that failed. But what happens is they came back to the urbanization movement, and they came back to the city centers where the factories were. And so that is a very, very true tale, a very interesting story. So for every disruption, you know, 100 years ago, 100, a little over 100 years ago, the US economy was 97%. Agricultural. And now that's down to three. What displaced it the Industrial Revolution, then what's this? And then now we look at, you know, that was a primary part of our economy, and now it's hovering around 11%. What displaced it, you know, information and technology, so you've got to make your way of living life and doing business obsolete before generational technology and market forces, you know, do do for sure. So I do think there's this really great opportunity moving forward to really, you know, look at these, you know, opera opportunities of the of the future, because there's, there is going to be change, for sure. And for people to like, hold on to those constituencies and, you know, have those worries, you know, we have to, you know, understand where we're going to be and something I heard, I think Gary Vee said it was that, you know, 10 years ago, I was saying like, you know, if you're a yellow page ad salesman, you better be looking at what Google and Yahoo are doing. You know, when they're first starting Yahoo, formed in 94, Google in 98, and I worked for an industrial directory, which is the biggest print industrial directory, and they wanted me to help them really kind of evolve their team and their go to market model, especially their sales organization, to be digital leaning. So we're all going through these disruptions. And so, you know, as we, you know, look at these same tools and this disruption, how do you see that playing out, you know, for marketers, you know, because there's just so many things, you know, yes, there's a redundancies? Yes, there's things that can be automated, but like, where's the writing on the wall? What areas?

Perry Malm: 32:29

Yeah, you know, I came up as a marketer. And I think what's happened to marketing in the last 20 years, is really sad. Where marketers used to be, you know, Don Draper types, sip your whiskey in the corner office and come up with crazy big ideas and go hard with them. And now 90% of marketers time is spent in a whole bunch of different software platforms and Excel spreadsheets. And they're expected to, to just execute, you know, they're just operators. That's all they are. There's very little thinking done in marketing. You know, there was a four P's right, like product, price place promotion, I think it is marketing. No, it's promotion, and product, price and place are sort of divvied off elsewhere. And I think it's a real shame, because good marketers can contribute a huge amount of value to the entire business into the products, which are being built into, you know, how you actually distribute it, all this kind of stuff. And yet know the majority of marketers time is like, like deciding, oh, should we up our bid on this Google Ad by 20 cents or something? It's just, it's taken a very interesting job, and has made it very boring. And I think that's a real shame. So hopefully, what some of these new technologies will do is get rid of a bunch of the boring stuff, automate, owed a whole bunch of the boring stuff, and make marketing. Interesting again, I love

James Soto: 34:20

it. So you're talking about the manufacturing experience, right? We have to automate out, remove repetitive work and go to high creative, high cognitive skills. You know, like, that's what we want to do as humans, like, everyone. Some people say they're creatives. And those people are not creatives that straightener, there's data and studies that say, like, No, we're trained out of that every kid's creative, they play with their kids, and you can recapture that creativity. And I do think, you know, that's what's happening. I believe the writing's on the wall, that if there is a process or something you repeat, those things need to be you know, really considered for candidates for these AI you know, To empower technologies related to marketing sales data, you know, the whole mix there. And that is going to be framed to folks, but it's true. How much of our time are we spending doing things that, you know, only humans can do. And that's the imagining, you know, our head of studios says, I need time to dream, James, where's our dream time, and whether your draw you, you know, you're Draper or you're, you know, you're just the, you know that you're the marketing, art director somewhere, you really want to be able to do those things. Because these technologies, if we use them, right, can really free up and capture our imagination. You know, I'd hate to see a world where AI is being creative for us completely. And we're devoid and detached from the process. And then lastly, you mentioned the displacement of consultancies, I find that very interesting, because I think that when you look at a lot of consultancies, like McKinsey and others and Deloitte, they have frameworks and students study, they're frameworks they use, and they're well vetted, they're very robust. And, you know, I do see that same issue happening with consultancies, where you can use these, you know, AI to really disseminate like really good frameworks that you can make your own. And that you can walk through whether you're going to go to market strategy, you're doing a transformation effort, you're looking at Business Model Generation, or you know, positioning, there's frameworks for these things, and that I believe, through these technologies and the ability to create these things. You can also co pilot that with AI and Bureau research, to really create great ways that you can get humans because frameworks you have to put a human through so a business can make decisions about it to really impact how that will affect audiences or marketers can do the same. We all have frameworks, and I think the the age of the consultancy, we all want to be paid based on our thinking, not just making things or doing things or doing something by the hour. It's really about where do we bring that strategy, that creativity, that dreaming to the to the forefront? And I think AI could be opening up a whole new set of possibilities. And, you know, to your point, Perry, I just want to key in on, I think you just have some really great points is that if we're going to resist this, you know, from the standpoint of our our biases and holding on to the past, I think we're missing the point, we're going through yet another set of evolutionary forces that we really have to contend with. So yeah, totally Yeah, to,

Perry Malm: 37:42

like, people can be Luddites, and they can pretend it's not happening and they can fight it. That's fine. But like, what happens the Luddites?

James Soto: 37:55

Know what to believe and know what a Luddite is. I actually write about it, because that's one of the things I talked about in teaching folks about the Industrial Revolution. And then like how that form the, you know, the unions and the weekend was great it because of the fact Yeah, that's a byproduct of the Industrial Revolution. There wasn't even a weekend such a thing of the weekend. So, you know, I think there's not going to be a lot of Luddites that, you know, you know, would want to, you know, make a bigger workweek or, but yeah, there's, there's a lot, there's a lot for us to upon us now. And COVID had a huge impact on that.

Perry Malm: 38:31

Like, like, there's even a world. Yeah, I'm not sure if this is gonna happen or not. I learned a long time ago to like, not make bold predictions, because the world is unpredictable. And that's one of the wonderful things about the human experience, right. But there is a world where we become very homogenized in the world experience becomes very boring. That's one of my fears. So like, no, like back in 1998, my brother and I backpacked around Europe for three months. And we were really surprised and delighted by so much we saw like we saw all sorts of new things crazy things like her different languages even use different currencies back then it was before the Euro was adopted, the Treaty of Maastricht. But no, if you go on holiday, first of all, the experience is very homogenized, because every city has a McDonald's and a Starbucks and an Irish Pub. But you also like research it before you go away, so you already know what you're going to see before you go. And I think one of the risks with AI, is it it makes the entire human experience like that, where we, we we optimize everything in automate, automate everything in our lives. which means that the world becomes one big blob. And I think that we as people, as humans, and as members of society, need to ensure that there's still things that surprise and delight us. If we ever get to the point where we're never surprised or delighted. You may as well just jump off a bridge.

James Soto: 40:25

Yeah. And to the extent that something is so predictive, prescriptive and personalized every time. And that becomes our world, right? We expect that and we open up Netflix writes, predictive, prescriptive and personalized. Where are those things that you never would have ever thought to do? Or run into? Where spontaneity? Where's that serendipity in our lives? And to the extent that this becomes an overlay? It could be soul crushing? I completely agree with that. And I think as we look at the ethical use of AI, you know, personalization and privacy, those are big issues. But I think the net outcome is how does it affect the human condition? You know, I think as we look at, you know, you know, even the workforce, as you know, as you look at, you know, tell us, like, how big is your team? By the way, it crazy how many, how many humans do you got,

Perry Malm: 41:18

you have roughly about 100 people? So 100

James Soto: 41:22

people, you know, do you have this conversation on how this, all these things are going to impact them? And, you know, is it the responsibility of your organization to really kind of consider that, and mitigate potentially some of these impacts on them?

Perry Malm: 41:39

Yes, and no. So, ultimately, you know, a company's objective and fiduciary duty is to its shareholders, which is a very depressing way to think about business, but that's ultimately the truth. So like, with that said, we do have a duty of care to our employees, also, we need to make sure that what we're doing, Will, we'll advance them in their careers and expose them to things that they that they can carry forward in 10 years time and when, you know, maybe Freezy will be will be like, on the NASDAQ or maybe maybe will just cease to exist, you never know what's gonna happen in 10 years. But like, do we do we talk to them deeply about like, how AI is gonna affect their jobs and whatnot? No, because we we live and breathe it. And people don't join crazy with the expectation that AI is not going to affect them. They join crazy because they know that AI will affect them. So I'm probably not well placed to, to answer that question. Because we live and breathe this stuff every day.

James Soto: 42:52

It let me let me ask you this. So we have a lot of industrial marketers, we have a lot of executives of, you know, industrial companies that, you know, in our sphere and our audience, these are marketers, sellers, people on the digital side of the organization, trying to bring together marketing sales, customers experience, you know, what would you say to marketers, who are really trying to grasp with what's the role, you know, machine assisted whatever, you know, in their roles, like, how do they? What would you have to say to them in terms of how they need to really reconcile AI? What, if anything, should they be doing right now?

Perry Malm: 43:30

I would say, figure out the things that you like doing the least. And then use AI to do those things. You know, I mean, a great example is like writing loads and loads of marketing content over and over and over is quite arduous. Especially the leg short stuff, like subject lines, right? Who wants to sit there and write subject lines every day, or Google ads or feed like Facebook ads, when you're doing this stuff at scale? Who wants to do that? It sucks. Like it's not, this is not what Don Draper does, you know, he doesn't sit there on Madison Avenue writing a subject line. Nobody wants to do that. So automate it, that's one phrase he does. But if you like doing some stuff, then keep doing it. I think, you know, there's so many different tools and so many different opportunities to automate and optimize every part of the value chain in business, and particularly in marketing. So I think marketers should be a little bit selfish. Do the stuff you want to do. And then Vice offered to do the stuff you don't want to do? I mean, what's the downside?

James Soto: 44:47

Yeah, yeah. Minding that balance that you were talking about before, where you're not just over automating it and your job becomes soulless and let it free you. But if it's not freeing you, you know, then You know, what is it? You know? What is it doing for you if you're caught up, you know, in the minutia of the technology, I think the chief martech a list of all the marketing related software technologies, I've seen it grow from 500 to 1000. And you know, just last year reach 10,000, marketing, automation. And now obviously, growing AI related technologies, it's so massive, it's, it's almost impossible to keep up with all the distinct categories of technologies that are out there. So as we wrap up, you know, get give us that yet again, plug for, you know, for what information listeners can use to follow learn more about Freezy. You know, what are you guys up to? What is the cool events? But, you know, I know everyone's gonna be dying to learn more about you. And frankly, how do we how do we connect with

Perry Malm: 45:54

you? That's super easy. frasi.co. And I'm the only Perry mallam in the world. Weirdly, I looked on LinkedIn the other day, and there's a Perry Milne spelled p e r ry, in North Dakota. So I don't know how I feel about that I feel like 80% Less unique than I did before. But Perry with a male, I'm the only one. And as far as freezing goes, look like, if the problem you've got is you need to produce highly effective content at scale, then Freezy is a good solution for you. And we should talk. If that's not your problem, we can still talk I just want to try to sell you anything.

James Soto: 46:42

Yeah. Well, that's great. And, you know, thank you so much for making, you know, time to be with us here today. You know, I think that, you know, what you've built is an amazing company. The technology really works. I love the fact that, you know, seeing and seeing using the technology, that we're not in the best guest game, we really can use AI to really get to better outcomes. And as we review the amazing outputs, because you know, we're ultimately making decisions around those outputs, we're getting to a better product, and not just like the marketers, like not just the business or their brand likes, but the audience's like, and it really gives them value that compels them to do something that really gets them, you know, hopefully a real well thought out and great valuable proposition that they can consider, and whether it's a message or a product. That's what's really great about the technology. So so so Perry with an egg. Thanks for being on the show. Great, listen, thank you. All right, awesome. So on that note, I just want to thank you guys in the audience so much for making time in your day to listen to the industrial strength Marketing Show. I just hope you heard even one thing that inspires you to make marketing, technology and people even more strength of your business. So for more insights from industrial marketers, or if you just like to reach out to us visit industrial strength marketing.com You'll see us on the podcast. You'll see us all over the internet and online on YouTube. And we'd love to hear from you thumbs up subs. Let's have a great time. We'll catch you next week.