Marketing AI CEO Chats Welcomes Christopher Penn of

Marketing AI CEO Chats Welcomes Christopher Penn of

Transcript of the podcast:

John  (00:02): Hello, my name is John Cass, and I’m here with AIContentGen, I’m here today with Chris Penn. And we’re going to be talking about AI and marketing. Chris, thank you so much for joining today on the podcast. I think first met you at one of your pod camps many years ago, and, you know, I think you’re just such an inspiration. You were definitely an inspiration to me. I think the way that you approached marketing, you helped to teach the entire community here in the Boston area and across the country, actually. You know, what social media is, the value of it is. And then also you also had that connection to the PR industry. So thank you for joining me today.

Chris (00:49): Thank you for having me.

John  (00:54): You were one of the sort of the early pioneers in the industry with PodCamp and those, and those sorts of things. You know, what, what’s your sort of philosophy you know, in looking at the latest, latest in technology? Cuz now you’re in ai?

Chris (01:14): You know, I don’t know that I necessarily have a philosophy so much as it’s just what I’m interested in. I like to, to see what machines and tools and stuff can do. I am notorious for finding uses unintended uses of any technology. Just yesterday, I was cleaning out the toaster in my kitchen with the leafblower because I didn’t feel like seeing, I was sitting there shaking this thing and opening those little trays. Just take the leaf floor stick, unplug it, take the leaf floor, stick it in there, just blow all the, the, the crap out. And the same thing applies to, you know, all these things that we’re doing with data science and ai as, as an industry, just taking a look at what’s possible, what the tools can do, and then say, well, what are the, what are the applications that we wish we had solutions for? What kinds of intelligent automation should we be doing? What kinds of answers can we get that previously might have been inaccessible or might have been, you know, overly laborious to get? So that’s, that’s sort of just how things have pivoted over the years, you know, podcasting. Yeah, we were all early on, you know, I started my first podcast in 2005 and, you know, marketing over coffee has been on the air continuously since 2007, but the industry’s evolved and, and ideally everybody evolves with it.

John  (02:33):No, that’s very true. That’s very true. I mean, I also think it’s interesting cuz I mean, do you, do you call yourself a, a digital guy or a, a marketer or, because I think what’s interesting is that you spent quite a bit of time in the PR industry working for a PR agency. But what was fascinating to me is that you worked in tech pr, right? And that was a, you, you worked with a group of people who were very savvy and understood that it wasn’t just about the traditional ways that PR has done, even though it was done in digital context. It is being done in a digital context. So you have to bring in analytics and those sorts of things. How does that sort of color your perception of coming, having worked in the PR industry in relation to you know, digital marketing and ai?

Chris (03:27): I mean, AI really is, it’s just a tool. It’s, it’s, a lot of it is just math. And so the, the question to ask is what problems does any industry, public relations, advertising, marketing, what problems do you have that are mathematical in nature that you need answers for? One of PRS biggest problems is how do we measure this stuff, right? How do we know that public relations is working? And the limitations in the, in that industry have been largely because statistical modeling techniques for what PR does have been around for about 40 to 50 years. These are, these are not new things. However, no one in the PR industry has had the, the, the mathematical background to bring those to life. And so having this arsenal, or this toolkit of other things could bring into the industry helped for a period of time in improving measurement.

Chris (04:28): There are, you know, certain metrics and measures that I think should be in every PR person’s toolkit. And very, very few folks do it. Partly because, again, it requires some expertise and it requires stepping outside your comfort zone and part of it, because sometimes one of the trends that I’ve seen, particularly in the last five years, maybe the last six years, is people, and this stems from general society and politics. People only like data when it says what they want, right? People do not like answers that are uncomfortable. People do not like being told they’re wrong. People don’t like hearing bad news and or hearing something that does not agree with their perspective. And so when you do something like attribution analysis on a PR campaign, and the news is it did nothing, right? You spent 20 grand for nothing, right? People don’t like hearing that <laugh>

John  (05:31): <Laugh>. Well, well, Chris, that, that, that’s one reason why I think as a marketer and in the profession I’ve worked, you know, I don’t know if you know, but I, I worked in the agile marketing community for, you know, quite a few number of years. And part of it is, I think it reframes, it gives another mindset not just for the marketers. And it’s this mindset of, oh, we’re, we’re testing things, right? And so it also, but it also, but the real be, I mean, it definitely helps with the teams and also the leaders in marketing to think like that, right? But then it also gives a methodology that if you explain to the other stakeholders, you know, the C-suite and the other departments, they can say, oh, that’s what this market is doing. You know, they’re, they’re coming to me with this different mindset and they’re, so I think, I think something like a framework or a methodology like agile then re recast the frame. I mean, do you think that is kind of helpful? I know we weren’t going to talk about agile, but you, you, you brought that issue up.

Chris (06:33): It can, if the person understands the point of agile or understands the point of any framework, and it’s aligned with their goals, and this is the problem that, you know my c e o, Katie, Roberta and I have been tr trying to tackle around analytics and data in general for the existence of our company which is data and analytics and stuff. These are like cooking methods, right? You know, broiling, frying, et cetera. If the c e o wants McDonald’s, it doesn’t matter how good, you know, a, a, a stake chef you are or how good a sushi chef you are, if the, your stakeholders have a predefined, preset, inflexible rigid point of view, right? Unless you are aligned with that point of view, you’re not gonna make any headway. So someone who is, we call it opinion driven, someone who is opinion driven, isn’t going to become data driven, right?

Chris (07:33): That’s, it’s like asking somebody who is you know, one religious faith to become another religious faith. It’s very difficult. Something, something has to happen to, to motivate that change. And motivating change in human beings is really hard. It’s much easier to change code, right? Than it is to, to change humans. And so that combined with the systems and structures in businesses is very challenging because a lot of systems and structures reward the wrong things. You know, real simple example, if you are a publicly traded company, your reward is your stock price, right? And so you measure everything to your quarterly earnings. You measure everything to, you know, how much can you goose the stock price this quarter and keep investors happy? Not can I build a, a sustainable business for the long term and make bets that maybe money losing bets and, and not have it be a punishment, you know, for all of the issues and objections and ethical problems that I have with, with Facebook now, now meta you know, mark Zuckerberg trying to do the whole, you know, metaverse virtual reality, that’s a big bet.

Chris (08:43): And so far it’s really not paying off. But I have to at least acknowledge that they’re taking a big bet, even though they’re a publicly traded company. Most other publicly traded companies do not want to rock the boat. They’re unwilling to make those big bets. And as a result, when you start bringing things like analytics and stuff in and saying like, yeah, here’s what’s going on. They’re like, Nope, this, this, this data doesn’t help us make our quarterly numbers. Like, well, <laugh> then, then, you know, if, if that’s your point of view, that’s gonna be a lot harder to make change. It’s one of the driving forces why you see a lot of companies, there’s, you know, reasonably successful companies going back private, right? Privately held investment because they and their investors think, we know we can make some big moves, but we can’t do it if our, if our horizon of vision is measured by the quarter instead of by the decade.

John  (09:36): Right? Right, right, right. So ai, you know, how did you get into this area of, of marketing, you know, and what, and what are you doing in the area at the moment at at the company?

Chris (09:50): So I got into AI through analytics, right? So I, I started really paying attention to analytics and, and embracing it really, like 2004, 2005, you know, as websites started producing a lot of data and we all needed ways to analyze it. Google analytics became available to the public in 2005 after the Google bought a company called Urchin. 2011 is when they first introduced multi-tech attribution and assisted conversions. And it was at that point when like, okay, I guess I need to, you know, shrug off the shame and embarrassment of failing statistics in college and, and relearn this stuff. So when I joined the old public relations parameters with part of that you from 2013 on was reteaching myself, statistics, data science and stuff, and, and, and analytics. And, and AI was a natural outgrowth of that because that’s where machine learning, specifically classical machine learning is where data science and statistics have been going.

Chris (10:45): Well, they, they’ve been there for 50 years, but the computational power has been available to us over the last 20 years. You know, your laptop can do things now that 50 years ago where theoretical only. And so my career progression really started taking that turn into the machine learning space, or starting in 2013, predictive analytics, you know, forecasting regression analysis, complicated regression analysis and things. And then in 20, late 2017 Katie my c e o and I, we, we decided the, the agency we were working with at the time was going one direction. The, we wanted to go in a very different direction. So we, we split off and started our own company and focused around the, the more intelligent use of, of data and analytics. And part and parcel of that is using artificial intelligence. I think it’s, you know, worth pointing out that AI is kind of a, a umbrella term.

Chris (11:42): There’s, there’s three things in machine learning that you’re, you’re three fundamental tasks, right? There’s regression classification in generation regression is supervised learning. Can we figure out, you know, what happened? Classification is, Hey, I’ve got a big bucket of data. Can I make so sense of it? Can I sort it? And, and, and classify it and understand what’s in this giant bucket, which is a problem that many marketers have. And then generation is, Hey, I want machines to make something from the data that we have. This has been the, the talk of the town really for the last 18 months with services like open ai, Dolly and Dolly two stable diffusion mid journey and, and stuff like that. You know, making pictures of dogs and tutus on skateboards and such writing blog posts, you know, with, you know, one click blog post writing. But the underlying technologies are all pretty much the same thing, you know, regression classification and generation.

John  (12:38): While you were at that PR company, were you also looking at social listening? Because, and, and the reason I asked that question, and I had wondered was because I was director of blogging strategies at a company called Backbone Media back in, I think it it’s about 15 years ago. And, you know, we were heavily into that. We weren’t a PR agency, we’re a digital marketing agency, but I, I think PR was some of the first people to look at social listening. And that, to me seemed to be a really ready a application and an early application of machine learning.

Chris (13:13): Yeah. Listening and search listening were the, were the two things that were worth paying attention to. And search listening I think is actually more valuable which is seeing what people are typing into search engines. I mean, anyone can do that. You go to Google Trends, you know,, type in search queries. You can see search volume of, of a given set of terms over time. You know, you don’t need any technical skill for that. But there was a book, I want to say it’s like five or six years ago called Everybody Lies. And it was a, a book they’re talking about from the perspective of search engines, the billions and trillions of questions that people ask surgeons, they would never ask another person, another living human being because it’s too embarrassing, too private, or something that, you know, even in a professional context, if you are in a marketing role and you, and you know, your, your, your stakeholder says, Hey, let’s let, let’s get some attribution analysis going.

Chris (14:06): And you don’t wanna admit, I have no idea what that means, <laugh>, right? You’re probably not gonna say it in public social media unless you have like a throwaway account, but you will absolutely Google it and say, okay, what is attribution analysis? Social listening is really good for qualitative analysis to understand the, the language space of our problem. It’s really poor at quantitative analysis because there’s, generally speaking people only have conversations about things when something’s either very wrong or very right. Most people don’t have conversations about things that were okay, right? You know, if you, if you think about like a restaurant experience, you either leave a one star review or a five star review. You, Jim, people don’t leave three star reviews. It’s like, service was good, food was fine, it was good, right? <Laugh>, that’s, you’re not, not compelled to leave review. It’s like, you know, but the, the waiter threw my food on me and lift the table on fire and <laugh>, you know and you see this like on Amazon too. There are tons of either one or five star reviews on things. It’s, there really is not a, not a lot of middle ground. And so social listening provides you the qualitative context, and then you need to use other methods like surveying and things to quantify the questions that you’ve developed from social listening.

John  (15:22): Right? You know, it, it is interesting cuz I’ve actually used reviews to pull together insights for customer journey mapping where I wasn’t able to initially do surveying. So I’d like to see more tools in, in those areas. Now for trust insights, what, what are you doing in that? How are you, how are you working with clients? What’s, what’s the area of coverage?

Chris (15:49): So, with a lot of our clients, our, we’re a management consulting firm that focuses right now on marketing. Although, you know, the, the techniques can be used to anything. Many of our clients come to us to make better use of the data they have or fix the, you know, the infrastructure problems they have or get insights that they’re not able to extract otherwise. For example, we have one client where we, we process a lot of their NPSs scores. We look at the, their, their net promoter score data, and then do some, you know, fancy math to say, here are the things they’re probably driving, you know, this score that’s rising or this score that’s falling. We had another client in the food and beverage industry that said, here’s, here’s the inbox of our, of our customer service department, right? Here’s all the emails coming in, do some text analysis and tell us if we’ve got any blind spots.

Chris (16:43): And we did. We found, you know, this was 20 20 19, they had no formulation. They made thickeners, they had no formulation for oat milk, right? And, and it was, it was becoming a very hot topic. And so being able to dig into their, their existing data, people asking them, emailing them, Hey, do you have, what’s your solution for oat milk? Was a valuable insight for them. We did some work with a, a recruiting agency, and we, we sh they were like, we can’t get people to, to, you know, fill out the application forms as much as we want. So we, again, we did some analysis. Here’s all the things that you say in your 5,000 job listings, here are the transcripts of 17,000 calls of candidates with your recruiters. Notice that the conversations, the questions candidates are asking are not any answered anywhere in any of your job listings.

Chris (17:29): Like, you know, well, you know, what’s for the vacation time? What’s the, the starting pay and stuff like that. And we said, if you just put those common questions into your job descriptions, you’ll do better. They did. And they literally increased their conversion rates 40% within, like, overnight, just, just saying, okay, here’s the answers that people are, are really after a lot of our other clients. Right now Google Analytics four is, is the hot topic. You know, the universal analytics system is coming to an end on July one of the, of next year. And a lot of people are realizing that it’s not just a a, an upgrade. It’s not like going from Microsoft Word 2020 to, to Word 2022. It’s a totally new piece of software. And so there’s a lot of marketing operations changes we’re helping companies make to, to, to make that pivot to the new system and make it useful because there are a legion of gotchas that, that are not obvious. So that’s, that’s kind of who, who we try to help.

John  (18:25): Great. Great. Great. how do you see the future of AI marketing? Where do you think it’s gonna go? You know in relation to where we are today and in, in the next well, two years or five years.

Chris (18:40): <Laugh>, if I knew that we would not be having this conversation because I would already be retired, <laugh>, phenomenally wealthy. Here’s what we, here’s what we see and, and what you can see in the marketplace, machine learning of all kinds is just getting baked into products, right? Very few companies other than like really big companies with really big budgets are building their own. Most companies are waiting for vendors to build machine learning into their products. And it’s in, it is in everything now, right? We’re using Zoom. Zoom has, you know, live captions, which is using text speech to text transcription, which is AI based. That is, you know an example of AI just kind of snuck its way in there. We’re gonna see much more of that much more intelligent automation, just machinery being used to, to speed things up.

Chris (19:33): The thing that’s captured people’s imagination right now is generation. You know, all of the, the AI generated artwork pe we are seeing a mechanistic automation of certain parts of marketing, you know, content generation is, is, is a big part of it. And there’s a real serious dangers with that. Particularly on the intellectual property side. I just did an almost hour long interview with attorney Ruth Carter. They specialize in IP law, and they were saying like, yeah, there’s, there’s a whole bunch of, of legal issues with AI generated content that nobody seems to be aware of, but it’s gonna bite some people really, really, really hard. So those questions are gonna need yet resolved to the ex to some extent in the next couple years. There are some there, what’s happening in the industry overall right now is there is a trend towards privacy.

Chris (20:26): And this is not gonna let up anytime soon. We have legislation popping up all over the place next year in California. The California the consumer privacy act, C P R A, it takes effect January 1st, 2023. That’s gonna restrict companies from sharing personal, personal information of consumers, but it also allows consumers to know when there data’s being used by machines for decisioning, a k a machine learning, and to opt out of it. So there’s gonna be a lot of frantic scrambling to become compliant once the first lawsuits start rolling in a about the improper use of people’s data for ai. So a big portion of what is likely to happen in the next couple of years will be increased interest in things like synthetic data that’s modeled off of real data, consumer data, but is not using the, the consumer data to, to build, you know, functional models.

Chris (21:26): And there will be a lot of focus on behavioral data, because behavioral data doesn’t contain personally identifying information. I don’t care who you are, I just know that if you visit my website, you go to the services page, the about page and the contact page, you’re probably gonna convert. So anything I can do to nudge that along, again, with the no personal information at all, is gonna be a money maker. So those are kind of the, the, the areas where there’s definitely gonna be growth coming along. But there’s, there’s also so many unknowns. There’s a whole thing on from stability AI this morning from the founders saying we are holding off and releasing the next model be of our, our image generator, because we have a whole bunch of very serious unanswered questions about what people have done with the existing open source model. Some people are doing really bad things with it because it’s open source, right? It’s, it, it is like, you know, putting out a pile of kitchen appliances and somebody, yes, there is one person who’s running around with a cleaver hacking people to bits. You don’t have control over that once you open source something. So they’re, they’re struggling with that right now to figure out, okay, how do we live up to the ethos of open source, but also reduce harm?

John  (22:41): No, that makes sense. And I think you’ve seen that happen with some other players where they’ve taken a much more careful approach. And I also thought in this discussion, it was interesting you were talking about how companies are thinking about how AI applies to their setting, right? I I very much, I’m starting to see that as well. You know, you know, you, you might be I, I think what’s interesting, you, you bring up AI content generation, but I think that you know, you might be in one particular industry where you’ve already got industry players who are really successful, and then all of a sudden they’re saying, oh, we might have a competitive advantage if we start developing it. But what do you do? Do you build, do you start building it yourself? Or is it better to go and partner with someone or buy someone? You know, what, what, what are your thoughts about that in terms of AI content generation and the opportunities that apply to existing leaders in their particular industry?

Chris (23:41): You know, one of the things we have, we have a five part framework we call the five Ps purpose people, process, platform performance. And the big question for any company that’s looking at AI is, you know, number one, are you using it as, as, you know, incremental improvement, efficiency building and things like that? If that’s, if that’s your purpose, it’s probably safe to just buy it from a vendor. If it is gonna be part and parcel of your secret sauce as a company, you probably should own it because you don’t want your, the, the fate of your company in another vendor’s hands, right? Unless you’re just outright by that vendor. The second question you have to ask is, do you have the people with the right skills internally to support a build decision? If, if you go the buy route, then you don’t have to worry about that.

Chris (24:26): That’s the vendor’s problem to, to acquire the necessary talent. Do you have the right processes in place, you know, data, governance, operations, et cetera, to not only construct AI models successfully, but also adhere to the labyrinth of regulations that are cropping up around it, right? They’re, there are some regulations about the use of consumer data around the world that have intimate personal consequences, right? China has, its P I P L law, people’s internet privacy law. If you violate that law and you’re, you’re, you’re, you’re found guilty of it. Not only do you get whopping fines from the government of China, but if your executive set foot on Chinese soil, they go to jail, right? So, you know, the, the process and the governance is, is really important. Do you have the right platforms and technology stack to, to support ai? Some companies do, some companies don’t.

Chris (25:18): And then, you know, ultimately, can you, can you successfully use AI as to generate the outcomes you want? There are whole swaths of problems that AI is really bad at solving, right? Because they’re, they’re problems that either don’t have a lot of data, they have very sparse data, very poor quality data or they’re just a problem that you can’t solve with AI because it’s not a, it’s not a, a mathematical problem, right? Think about how, you know, the, the most common examples, think about something like getting people to wear masks, right? That is not a, a, a mathematical problem. That is a cultural problem, right? There’s a an education problem. You’re not gonna solve that with ai. There’s no way to, to automate or to, to even, to, to effectively measure why something as simple as, you know, stick this cool looking thing on your face is, is happening or is not happening. So that, you know, to, to look at the adoption of AI and, and making decision on it. You gotta look at all five of those factors.

John  (26:26): Great. Great. So what about your thoughts on AI content generation? And you also mentioned images, cuz I, I think to a certain extent, what’s interesting to me over the last couple of months is that that’s the aspect of AI generation, content generation. That’s really blown up partly because of that, that incidence in Colorado where you know someone was using one of the AI tools to develop an image that they put into an art competition and, and they won the art com competition mm-hmm. <Affirmative>. And there’s been a, a lot of blowback on that. But there’s also text generation as well. And I think what’s interesting to me is that there are different you know, there are different approaches. A lot of these companies, they might succeed on blog writing or general article writing, right? But then you’ve got companies like the PR industry where I don’t see companies doing as well as that type of content. And I, I wonder if, if that’s partly because there isn’t the demand for PR agencies in, in, in terms of what they’re asking. Although I’ve, I’ve, I’ve spoken to some that, that do. So what are, what are your thoughts about AI content generation, where that’s going and, and then maybe specifically that example with pr. Cause it goes back to your, you know, your work over time.

Chris (27:55): As the tools become easier and more accessible, PR will be probably one of the first consumers to use extensive text content generation, because a lot of the tech, they generators, boiler plate, right? Machines can already very capably write press releases that are better than what your average junior account coordinator is gonna crank out. The, the challenge with the content generation in general is that these models are trained on very large data sets, right? Eluthra, AI’s the piles 800 million documents, basic mathematics, most, most content is mediocre, right? The most content’s, okay, it’s not great, it’s not bad. Like there, there are are a few, you know, winners there a few like total losers, and it’s a whole bunch of meh in the middle. And all of these models are trained on as much data as can be acquired.

Chris (28:52): Most of that data is gonna be in the middle. So the, what the models produce right now is perfectly adequate mediocre content, right? The you, you use any of the major tools right now that they’re on the market and they create readable, coherent, okay, content it fits what Google calls. Nothing wrong, but nothing special, right? And that’s problematic because if everyone and their cousin is creating nothing wrong and not, but nothing special, nothing makes you stand out, right? You don’t have that unique creative edge that humans tend to bring. Now here’s the bigger issue for all of these companies and all these, these companies using AI generated content. Again, I talked to, to attorney Ruth Carter about this AI generated content cannot be copyrighted, period, because copyright can only be held by a human. Even if you are using the tool, the tool did the work.

Chris (29:54): And there are a number of court cases that we went through that illustrated very, a very clear chain of evidence by a court saying, if you use AI degenerate content, you cannot copyright it. And if it if someone rips it off, you can’t do anything about it because it is inherently in the public domain. There was a case, Naruto versus peta where a chimpanzee took a camera, took a selfie, and the photographer tried to copyright it, and the court ruled human didn’t create the content. No copyright. That image is, is in the public domain. So the, the person who created the mid journey image that won that art contest, that’s public domain. You can use that image as much as you want, and the artist can do nothing about it because they cannot hold a copyright on it. And this is one of the challenges that people don’t realize when it comes to AI generated content is legally it is a very different than human led content.

Chris (30:47):Now you can do things, for example, like have a machine provide you an outline, and then you write the content from that. You can take a first draft from a piece of con of AI generated content and substantially rewrite it and improve it. And generally speaking, if you think of AI generated content as being in the middle, literally it’s the, the, it’s the definition of, of average. If all of your staff are below average writers, then guess what? AI is actually going to improve your business to, to get it to mediocre. But if you, if you aspire to anything above mediocre, you’re going to need to, to continue investing in humans to not only deal with the copyright issues, but also to stand out. Because right now, again, machines create stuff that is okay, even in the image generation side. You know, there are still enough artifacts and enough oddities that you can tell the difference between machine generated and human generated.

Chris (31:46): Now, the machine generated stuff some way it’s pretty darn and cool. It is, it’s fun to look at and think, we’re starting to see models generating images and animations and stuff. But we are a long way off still from providing a prompt, having a machine write a novel that is, you know, coherent or producing a motion picture that will, it’ll be a little while for what companies should be thinking about though. A, you should be talking to your lawyers A S A P to figure out how to integrate AI processes in your company without endangering your ability to protect your intellectual property. That’s a big deal. And, and expect to spend a lot of money with your legal team to do that. And then b, figuring out from a purpose perspective, what problem does this does, does this technology solve for you?

Chris (32:35): And does it actually solve that problem? Like if, you know, if you’re in the example of a PR firm, if you wanna relieve an account coordinator of doing the first draft on press releases, that’s a good application, right? You can take that person and then have them become an editor and, and level up those, those releases. Or maybe not. I mean, if no one reads press releases anyway but if you are, you know, creating a content shop you’ve gotta create content that is helpful and useful. One of the things that has happened recently is Google pretty much straight up said, Hey, we’re not gonna allow content that is low quality to rank anymore machine generate or human generated, which we’re, we’re, we’re trying to curtail any benefit that that content mills and spam farms generate. And that bar isn’t just a one-time announcement that’s, they’re gonna keep ratcheting that up to say, okay, we expect your content to be higher and higher quality and AI won’t solve that for companies.

John (33:28): Right? And, and Chris, you know, a point there, it doesn’t mean that Google is saying you know, you can’t use AI generated content. But I think the point that you are making and is in the industry, and as I spoke, speak to people across the industry, you have to have that editor. You have to have that marketer, that communicator there who is coming up with additional ideas. I mean, that’s really the value, right? With the with these tools. It’s, to me it’s that initial process of what do I need to write? You know, you, you made that earlier point about SEO and search analytics. So I, I, I think you’re making a mistake if you’re, you’re only, you know, you’re only using the tool cuz the production and the content that you get back is, is just not gonna work. You have to have a human involved. But the thing is, it’s a cultural change, right? For those writers, you know, in AI content donation. Have you have you seen reluctance on the part of companies to do this? Not for some of those issues, but for the problems with, you know, that digital transformation on the part of employees,

Chris (34:38): People, you know if this was before March of 2020, I would say that companies are, you know, were obviously being held back, but beginning into March of 2020, digital transformation was kind of forced on the entire planet, right? We spent three months all trying to figure out how to take every possible business and make it work from our living rooms because we had to, and if you look at the history of digital transformation stuff, what happened during the early years of the pandemic was we basically accelerated some companies in some industries, 10 or 25 years in three months, right? You take old school, like manufacturing companies, like, okay, now you’re gonna learn how to be a hybrid company because your accounting team can’t work in the office anymore. And, you know, seeing something, companies that were a little more forward thinking say, yeah, you know what, our company doesn’t need an office.

Chris (35:30): We can, we can still get work done from wherever. And that bleeds over into the use of other things like AI and things. Because the more accepting you are of change and the more tolerant you are of, of change in new technologies, the faster you’ll get benefit out of any kind of machine learning tools, right? If you are willing to adopt, you know data-driven attribution models for your a your analytics, you will pivot faster and deal with unexpected changes in the market faster than the competitor that is purely opinion led that doesn’t see the, the, you know, the ground changing underneath them. The, the last three years have been massively disruptive to every single company, you know, regardless of, of industry. But those companies that had the resilience and the agility to deal with it are also inherently the kinds of companies that will benefit more from ai.

John  (36:30): Great. Well, Chris, I, I really appreciate you spending some time with me today in the marketing ai chat podcast. Thanks so much.

Chris (36:42): Thanks for having me.






Marketing AI CEO Chats with GoCharlie AI Co-Founders Kostas Hatalis & Brennan Woodruff

Marketing AI CEO Chats with GoCharlie AI Co-Founders Kostas Hatalis & Brennan Woodruff

Let’s chat AI Marketing! Welcome, Kostas and Brennan of GoCharlie to our inaugural podcast!

Transcript of the CEO Chat

Scott (00:04): Hi, and welcome to the AIContentGen video podcast. And today we’re delighted to host Kostas,  the CEO of  GoCharlie, and also Brennan, the COO of GoCharlie. And we’re gonna be talking about how GoCharlie helps marketers get their work done. And so thanks for joining us. My name is Scott Sweeney. I’m one of the founders of AIContentGen.

John (00:33): And I’m John Cass, one of the co-founders of AIContentGen. Thanks so much, Scott. Well, it’s nice to see you, Kostas and Brennan. Perhaps we can start off by asking you to tell us about yourself and also your AI journey.

Kostas (00:50): Yeah. I’ll introduce myself and then Brennan. I think it’d be for him to introduce himself. I founded GoCharlie about a year ago, it’s our first birthday coming up soon. We founded coming outta my PhD, where I saw a massive opportunity that only a few short years ago, content generation of AI, whether it’s text now, images and audio and video was really considered science fix. And it’s still in the realm of academia and universities. And now we’re seeing, we’re starting to see the early days of this explosion and it’s only gonna pick up from here. So that was my main inspiration for starting the company.

Brennan (01:39): Yeah. So Brennan Woodruff here, I’m COO co-founder of My AI journey was, a little bit different than Kostas who’s obviously had a decade worth of experience in the field, I personally joined SoftBank back in 2019 working on the vision funds because I wanted to learn more about artificial intelligence.  I saw that that’s where the world was going whether or not it be AI replacing humans or where I think we like to place is the enablement of human capabilities through artificial intelligence type technologies. What I found when I was at the vision funds is that there were a lot of different flavors of artificial intelligence but generative AI, I think has the potential to be the most transformative of any of the AI technologies I saw during my time there. And so when Kostas and team offered me the opportunity to jump aboard, it was a bit risky, but I wanted to take the plunge and learn as much as I could from some forefront thinkers in the space

Kostas (02:42): To mention our third co-founder could make the call. She’s she also has a PhD in AI and together we’re, we’re building our own technology from the ground up. So that’s one unique aspect about GoCharlie, is that unlike many of the other players in the space, especially for writing and for marketing it’s rare to find a company that’s developing their own technology, whereas instead of just plug and play from open AI or Google or IBM or existing technology is.

Scott (03:15): That’s really exciting. So does that mean that you don’t actually use any of the other technologies just your own AI?

Kostas (03:23): We do use some of the other technologies for three purposes. One is experimental to see if our AI meets or surpasses two for data augmentation, we do use, for instance, GPT-3 to help create data to train our own models. And we do also use a few other tools to augment our capabilities. Our plan by end of the year is to become a hundred percent self-sufficient. But that this, does take time. And as you guys may know that GPT-3, which we see as one of our competitors took a team of 30 PhDs years and I think 5 million to train. So we have a pretty big, you know mountain to climb.

Scott (04:14): Absolutely great. Well, that’s exciting to have a goal of being 100% your own AI by the end of the year. 

John (04:24): So next, what’s the what does the software do, perhaps you can explain that to the folks.

Kostas (04:32): This is where it gets really unique. We’re proud to say that we’re one of the first to be able to analyze images, to create content. So first off we’re a platform to help create digital marketing content, primarily co caps text. So sort of a co-writing tool for the time being we focused initially on social media posts and ads, and now we’re slowly expanding into almost every use case. And as I mentioned in the beginning, we pride ourselves in that we are trying to be the first to be multimodal, to incorporate other types of media into the content process. For instance, we can take an image and create an entire or, or post out of it. And one of the first ones to do that, we’re working on prototypes with video and with audio, and soon towards the end of the year, next year, we’re also doing the opposite, which is to go from text to an image. You guys have definitely heard of Dolly too, so there’s another mountain to climb there. And that’s definitely a unique obstacle in the sense that those are great artistic pictures, like paintings, but none of them are quite there yet for professional, like marketing purposes. Brennan, I think you definitely have a lot of ideas there. A lot of opinions there too.

Brennan (06:01): Yeah. Yeah. I mean, the way that we’ve approached generative AI is transformative technology. And while we see a bunch of competitors having success in purely the writing space, we think that that’s almost a disservice to the technologies capabilities. You, as you’ve seen with DALL·E 2  and a couple of other WebOs dream apps. There’s definitely an appetite for apps where you can go from text to an image. We also have seen a significant appetite for having a video and being able to turn that into text content. You know, we really want to make the starting point wherever the user is. So maybe the user has a photo or they have a video, but they have no idea what to say to optimize their content and engage their target audience. We want to be that bridge between the modalities with our AI. So we’re looking a little bit beyond writing, but we’re finding that that’s a good space to start out in because the learning there applies to so many different other mediums of media.

Scott (07:06): Great. Our next question is kind actually, you may have felt like you already answered it, but I’m gonna ask you anyway, is what’s the strength of your software

Kostas (07:19): And in its current state, I would say three main strengths first is that we’re trying to be driven by, our customers, our users, like building this in public, getting instant feedback. Again, we’re very small. We have several hundred users but we listen to every single one of them. And to that end, because we’re building a lot of this ourselves, we are going this multimodal aspect. So, and we hear a lot of, you know, demand, Hey, I want to use this image or this video and create content out of it. Another thing is it, blogs, Brennan and I were discussing the other day are like, Hey, can you take this blog and gimme 10, you know, tweets or, or Facebook posts out of this single blog? So that’s, our core strength is being able to innovate very fast with our own ideas and implement them from the ground up.

And the third is not to be that strength, but something I would say is that we try to be fun. And this is actually Brendan’s idea which I absolutely love this is that we have, for instance, these tones. So when you’re creating content on our platform, you can choose a tone and we’ve added some fun tones like a pirate, Brooklyn, and Shakespeare. And, when you start playing with ’em, they’re so incredibly fun and you can’t just help yourself, but giggle a little bit or just smile. So that’s a philosophy that we’re adapting is that when you’re using our tool so not just help you be more productive, but it should be fun to play with and just make you happy to use it.

John (08:57): Yeah, almost like the old Groupon model where they had that different style. That was the whole approach. Wasn’t it using a content style?

Brennan (09:07): definitely. And I think Kostas hit the nail on the head, you know, in a post-pandemic world where everyone’s experiencing burnout. We’re trying to introduce tools that make work feel as fun as playing with a puppy. So that’s, that’s GoCharlie. But we think some of those new tones are definitely a massive step towards that in making work feel like play.

John (09:29): So how do you support the client’s content design approach, you know perhaps in the areas of ideas and research briefing, actually writing the content, optimizing the content, you know, you talked about doing some of those extra things, even expansion metrics, you know, do you have how do you, how do you follow that content design teams sort of framework for how they do things?

Kostas (09:56): So, Brennan, you wanna talk about the customer aspect and I’ll talk more about the technical aspect next.

Brennan (10:01): Yeah, yeah, definitely. So John, I, I think you’re hitting the nail on the head, as you know, like when we think about a land and expand strategy, we’re, we’re starting to think about all right. Yeah. We’re generating the content, but, the creative process for humans and marketers and anyone that needs to create content really is like, you have to start from an idea. And so for some people that idea, we see more with like an influencer type customer. That idea is more life experience. It’s something that’s like really created this learning. They want to share with people to engage their audience in a more organic storytelling-driven way. For marketers, it seems to be a little bit more driven about like, what’s trending. So while it’s not there yet we actually have a hashtag and recommendations and trending functionality that’s being developed to really start you at that ideation point based on what’s performing what’s working well in your industry what topics are trending, which hashtags are trending that gets us a little bit more into that SEO space.

Brennan (11:03): So it’s still to be determined if we’ll grow that through partnerships or not. But then as we kind of go from ideation into that content creation piece, that piece, I feel like we’re completely addressing right now, but expanding the number of use use cases we go across then the next piece which I think is a huge differentiator for us is that we have content scoring. So content scoring too, if you’re unfamiliar is the ability to assess the content that we’ve created against the industry’s most engaging pieces of content that we’ve analyzed indexed, and fed through our models and really give you actionable insights as to how you can improve it. So not only are we giving you insights on how to improve and edit, but then if you think about the application of this in an enterprise setting that can, our content scoring can become part of your review process.

Brennan (11:52): So rather than living independently through a bunch of different emails, you just check the box that, Hey, you’ve scored an on GoCharlie’s content scoring. Therefore it’s good by me. And then the last piece obviously is, you know, publishing that content. And we’re, we’re happy to say that in the next month and a half, we’ll have the ability to post directly from GoCharlie into 10 different platforms that are most commonly used by our marketers that we’ve talked to. So that’s Facebook, Instagram, Twitter, YouTube TikTok. So we’re really trying to land and expand from just the content generation piece to a full sort of content creation platform methodology.

Kostas (12:31): Excellent.

Scott (12:32): That’s great. I, I have a question about that. How are customers doing with the scoring you know, one of the areas I’m always interested in being several businesses over the years is metrics? So is there a way to are there customer success stories or are there proof points on how that works?

Kostas (12:56): So still in the early days, we’re actually in the process of creating use cases now and hopefully band of the summer, we’ll be able to publish a few white papers on those use cases. Content scoring is, is becoming quite popular with our users. There’s still a lot for us to figure out what’s the best way to deliver it. We have quite a big UI. So we’re trying to take out an approach of optimizing for mobile, which we’re realizing almost all of our customers are on their mobiles. And that’s something none of our competitors are doing either is, okay, how can you deliver an immense amount of value on a much smaller screen? And we’re also seeing quite a bit of demand in addition to the quality of the writing is to what audiences will, you know, a content resonate with specific demographics, gender location, so forth.

Kostas (13:48): And part of our multimodality aspect is to analyze we’re building right now, the ability to analyze the content and tell you, okay, this is great for gen Z, or this is great for millennials, but also given an in image or video, okay, will this also appeal? So it’s more of a complete all-in-one packet analyzing quality than analyzing the appeal. And we’re in the process of seeking out a few partnerships with something data marketing companies to then start getting more performance statistics and say, okay, we can give you a quality score and audience score, but now let’s start predicting how many clicks, how many likes we will. We also start getting with your content especially as it gets mixed up with videos and images in different modalities, that’s something we’re also cooking up in our, in our MADLAB, <laugh>,

Scott (14:38): It’s exciting. So I think that leads us to our, our final question for today. And it’s one that I always find very insightful from business leaders, in general, is if there’s one thing that you believe that most people believe about AI content generation that most people think is true, but you actually take a contrarian view. You don’t think it’s actually that true.

Kostas (15:08): I’ll go last cause as a Ph.D. in AI, I have a lot of opinions about <laugh> the world is different than actually is.

Brennan (15:15): So just to make sure I, I understand the question correctly, Scott, you, you wanna know what the masses think about generating content with AI that we don’t necessarily think is true? Correct.

Brennan (15:27): Got it. I think that there is a large portion of folks that look at AI content generation as a spam tool. And they, you know, they’ve cherry-picked validation of that belief through some of the Google policies about AI content and, and rather than delving into, you know, the science behind that and what was actually said, they just believe the headlines for what they are and, you know, we don’t, we don’t really subscribe to that, that idea. We believe that there could be bad users in, in abusers of such technologies to create content that is spammy or that is trying to be manipulative. But, but we believe that the majority of users of an AI content generation tour are, are really trying to pursue just making their dreams come true, whether that’s, you know, creating content to help grow their business or creating content, to create awareness around a passion that they care about, or, or maybe even just helping their friend grow their business so that they can be sustainable in, in this economically wild world that we’re living in. So, so for us, we, I, I would say that we don’t really subscribe to the AI content generation being a spam tool. It’s more an enabler, of people’s personal pursuits. And, and that’s how we choose to view AI is just, we’re enabling, what’s already there. We’re just giving you a way to unlock it.

Kostas (17:01): And I also add, cause that comes with two fears is one, AI is gonna spam you, but also AI may replace you. And that’s another belief we don’t have is that it, our whole philosophy is that it’s gonna augment your life, make your life easier, the same way that Photoshop 20 years ago made designers’ life easier instead of just doing things by hand. And that’s how we see AI. Maybe in 10, or 20 years, it’ll start replacing jobs, but we’re nowhere near there. And to be honest, we don’t wanna be in the business of replacing people’s jobs either. We wanna make them as productive and as fun as possible, really that that’s our whole core mission at GoCharlie.

Scott (17:43): I love the comparison to Photoshop and yeah. And especially for your application, I, I think that’s a really good analogy and you know, who knows what kind of jobs are gonna be around 10 to 20 years from now. Right, exactly. So so that’s a kind of a long time frame.

John (18:05): And, and I agree Kostas, I mean, isn’t it true when I’ve spoken to so many folks in the industry where I think these tools are, are helping those marketers and writers to get more out of their profession by doing more so I think it’s, I think that’s very true. So I think it’s a good idea. I think that that saying that you have about making it fun again is, is, is pretty insightful. So I really really appreciate Kostas and Brennan for joining us on the content video podcast. I also wanna thank the audience for supporting us. Thank you, Kostas. Thank you, Brennan. 

Scott (18:47): Thank you, guys. Take care. We’ll look forward to talking to you again sometime soon. <Laugh> thank you.





Is AI Marketing Legit?

Is AI Marketing Legit?

Is AI Marketing Legit?

Just Ask IBM Watson! Are brands like Toyota, Best Western, and CVS legit? They have all used AI-Powered IBM Watson to attract and engage prospects at critical times in their customer journeys resulting in impressive results. Wayfair has developed its own Machine Learning models to predict their consumers’ preferences concerning email marketing.2

So the simple answer to “Is AI Marketing Legit?”  is a 100% Yes!  AI for marketing is a legitimate way to use artificial intelligence to help with marketing tasks. 

It can automate tasks, such as customer segmentation, campaign management, and product recommendations. It can also improve accuracy and targeting and free up time for marketers. And it can assist in content creation.

What is AI for marketing?

AI helps with marketing by automating tasks since AI can process large amounts of data quickly and effectively. This can free up time for marketers to focus on other tasks. 

AI can also lead to more accurate and targeted marketing campaigns. 

Can AI-generated content be as good as human-generated content?

AI-generated content can be just as good as human-generated content, and in some cases, it can even be better. This is because AI can analyze data more quickly and accurately than humans, and it can also create targeted content that is more likely to resonate with readers.

Is AI Marketing becoming the new norm? 

As artificial intelligence (AI) continues to grow and evolve, technology is beginning to create content that writers would otherwise write. While some AI-generated content may be of poor quality, there are cases where it can be legitimate and even beneficial for businesses. In general, AI-generated content can be a helpful tool if used correctly; however, it is essential to remember that artificial intelligence is still in its early stages of development and thus should not be relied on exclusively. The use of AI in marketing is still a relatively new concept and one that not everyone is familiar with. 

AI is currently being deployed in personalization, targeting, PPC advertising, chatbots, you name it!. And, AI powered Predictive Analytics is helping companies to deliver the right content to the right customers at optimal times. Anywhere that big data or repetitive tasks are involved are a great place to apply AI for Marketing.

What is AI-generated content?

In short, it refers to any type of content that has been created with the help of artificial intelligence. This can include articles and blog posts, social media posts, and even product descriptions. While some people may be hesitant to trust the content that a machine has created, there are actually several advantages to using AI-generated content for marketing purposes.

For starters, AI-generated content can be highly accurate and informative. Because it is generated based on data and patterns, when writing certain types of content, such as reporting on last night’s sporting events,  financial results, or healthcare information, there is less room for error than if a human were creating the same type of content. This makes AI-generated content especially well-suited for businesses in industries where accuracy is essential. Additionally, because AI can generate large amounts of content quickly and efficiently, it can be a valuable tool for businesses that need to produce a lot of content regularly but may not have the staffing to do so manually.

Of course, it’s important to remember that artificial intelligence is still in its early stages of development. There are certain limitations to what AI can do when it comes to creating marketing content. For example, AI typically struggles with understanding context and subtleties such as tone and style; as a result, the quality of AI-generated content can sometimes be lacking compared to handcrafted copy written by humans. However, these limitations are gradually being addressed as technology continues to evolve; as more advances are made in the field of artificial intelligence. 

What is the Future of AI For Marketing? Learn Here! 

Will AI Content Generation Replace Human Writers?

In some cases it already has. The Associated Press began using AI in 2014 to automate the writing of news stories about corporate earnings. This freed writers from the drudgery of translating numbers into words allowing them to focus on stories that required higher levels of journalistic expertise.

While some people may be hesitant to trust the content that has been created by a machine, there are actually several advantages to using AI-generated content for marketing.

As artificial intelligence continues to evolve, it is becoming better at understanding context and subtleties such as tone and style. This means that the quality of AI-generated content is improving all the time, and it is beginning to rival copy written by professional copywriters.

Additionally, AI might struggle with understanding complex concepts or humor. So it’s essential to have a trained writer pulling the strings, putting on the finishing touches and reviewing the copy for bias, plagiarism and adherence to corporate style guides.  In the future, we can expect AI to become even better at creating high-quality content that engages and informs readers.

Is it time to Develop a Strategy for AI?

You betcha! 

If you’re a Content Manager, PR or Marketing Agency, Writer for hire,  CMO, Product Marketer, Ecommerce Company, you know what to do. Jump on the AI Bandwagon. Yes, AI for marketing is definitely legit! It’s a growing field that is already helping marketers to automate tedious tasks and improve their campaigns.

Need Help with Developing a Strategy, Plan or Jump Start in AI? 

We’re here for you with reports, custom AI Marketing training sessions and advice. All we do is AI for Marketing. 

Contact AIContentGen to learn how to get started.

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See the AI Content Generation ScoreCard and Analysis

Cover Page AI Contnent Generation Scorecard

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What Three Questions Marketers Should Be Asking About Artificial Intelligence | aicontentgen

What Three Questions Marketers Should Be Asking About Artificial Intelligence | aicontentgen

What three questions marketers should be asking about artificial intelligence

If you’re like most marketers, you’ve been hearing a lot about artificial intelligence (AI) lately. And if you’re not sure what it is, you’re not alone! AI is an evolving and complex field, and there’s lots of misinformation out there. 

So in this blog post, we’ll break down three key questions about AI that every marketer should ask. By the time you finish reading, you’ll better understand what AI can do for your marketing team and how to get started using it.

Let’s get started!

1. How can marketers begin to implement artificial intelligence into their strategies today?

As marketers, we’re always looking for ways to improve our marketing efforts. Every day we’re introduced to a new tool, technology, or strategy designed to help us do things better, faster, easier, or cheaper. It turns out that AI has been used to improve marketing. Here are some ways AI can be used in marketing: 


If you’re a writer, you know the importance of search engine optimization (SEO). But what if there was a tool that could help you optimize your writing without all the hassle?

Enter AI.

AI can help increase a website’s chances of ranking higher in search engine results.

In other words, AI can be your secret weapon for SEO. And the best part is that you don’t have to be a tech expert to use it. There are plenty of AI-powered tools out there that are designed for writers of all skill levels.

Here are just a few of the ways AI can help you with your SEO:

  1. Generating Better Headlines

One of the most important aspects of SEO is creating headlines that are both attention-grabbing and informative. After all, your headline is what will make people want to click on your article in the first place.

 Thankfully, there are now AI-powered tools to help you write better headlines.

  1. Identifying the right keywords for your content

Another important aspect of SEO is using the right keywords throughout your content. This can be tricky, as you don’t want to stuff your keywords in awkwardly or use them too much.

Thankfully, there are AI-powered tools that can help you identify the right keywords. 

  1. Formatting Your Text for Search Engine Optimization

Finally, AI can also help you format your text to rank higher in SERPs. Check out this list of AI writers to learn more about the best tools for SEO AI content generation. On the blog post, you’ll find AI writers with SEO tools, including Frase, Outranking, ScaleNut, ContentBot, MarketMuse, Jasper, CrawlQ, Longshot, and NeuralText.  

Chatbot Tools 

One area where AI tools are making inroads are chatbot tools that provide AI conversation. An example of a case study would be Amtrak. The company has 20,000 employees and carries 30 million people per year. gets over 375,000 visits per day. Through its chatbot called Julie, the company allows travelers to book train travel by simply telling where they want to go and when they want to arrive. AI-driven Julie answers five million questions every year. With Julie, Amtrak improved its booking rates by 25 percent. Through Julie the chatbot, bookings generate 30 percent more revenue. Julie has produced an 800% return on investment for Amtrak. 

Sales intelligence

Sales intelligence is the process of gathering and analyzing data about customers, competitors, and market trends to inform sales strategy and enable more effective selling. AI can play a valuable role in sales intelligence by providing insights that would be otherwise difficult or impossible to obtain.

For example, AI can analyze large amounts of data to identify patterns and trends. This information can develop sales strategies, potential target customers and optimize marketing campaigns.

AI can also monitor customer behavior and identify when a customer is likely to make a purchase. This information can be used to trigger targeted marketing campaigns or even personalized sales pitches.

Overall, AI can significantly boost sales intelligence by providing access to insights that would otherwise be difficult or impossible to obtain. When used effectively, AI can help marketers make better decisions, improve customer targeting, and optimize marketing campaigns.

An example of a sales intelligence tool for AI is Contentmasterai; it helps marketers with their sales intelligence strategies. It does this by providing access to insights that would otherwise be difficult or impossible to obtain.  Contentmasterai can help sales teams make better decisions, close more deals, and build stronger relationships with their customers by providing access to data-driven insights.

There is no doubt that artificial intelligence (AI) radically transforms the marketing landscape.

2. What are the potential implications of artificial intelligence on marketing in the future, both positive and negative?

 Marketers are always looking for new and innovative ways to reach our target audience. What happens when a new technology changes the game? Could AI become our new best friend? Or is it something to be feared? 

While there are many potential benefits to using AI in marketing, it’s essential to be aware of the potential negative implications as well. Data collected without consent, fake news, and manipulation of search engine results can all have harmful effects on individuals and society.

For example, AI can create “fake news” or false information that is designed to look like real news. This fake news can then be spread through social media and other channels, potentially reaching many people.

AI can collect data about people without their knowledge or consent. This data can then be used for marketing purposes or sold to third parties. For financial institutions, AI can gather data about a person’s financial habits and preferences. While this information can provide better service, it can also be used for less savory purposes, such as identity theft or fraud. 

While AI can benefit grocery stores, some potentially harmful implications should be considered.

One of the primary ways that AI is used in food marketing is through targeted ads and personalized recommendations. By analyzing past purchase history and other data, AI can provide customers with ads and offers for products tailored specifically to them. While this can be helpful in some cases, it also can create an “echo chamber” effect, where people are only exposed to ideas and products that confirm their existing beliefs. This could lead to people becoming more entrenched in their own bubble and less likely to try new things.

Another potential downside of using AI for food marketing is that it could advertise even more “junk food.” Because AI can target ads specifically to individuals, there is a danger that people who are vulnerable to junk food marketing will be bombarded with advertisements for unhealthy products. This could lead to people consuming more unhealthy food, which would negatively affect their health.

Overall, while AI can benefit grocery stores, some potentially harmful implications should be considered. When using AI for food marketing, it is vital to be aware of the potential risks.  

3. How will artificial intelligence impact marketing and advertising in the future?

With its ability to process large amounts of data quickly and accurately, AI provides marketers with invaluable insights into consumer behavior. This, in turn, is helping marketers create more targeted and effective advertising campaigns. This will have a profound impact on advertising. AI can target ads more effectively, based on a customer’s individual preferences and past behavior. And AI is increasingly being used to create and place ads. For example, Google’s AdWords system uses AI to automatically place ads on websites likely to generate the most clicks. Similarly, Facebook’s ad-targeting system relies heavily on AI to select the best placement for each ad.

There is no doubt that artificial intelligence (AI) will significantly impact marketing and advertising in the future. Here are just a few ways that AI will change the way these industries operate:

  1. Increased personalization: AI will allow marketers to personalize their messages to individual consumers more than ever before. This could include everything from targeted ads to customized product recommendations.
  2. Improved customer service: AI chatbots will be able to handle basic customer service inquiries, freeing up human agents to handle more complex issues. Additionally, AI will help businesses proactively address customer problems before they arise.
  3. Greater efficiency: AI can automate many marketing and advertising tasks, from email marketing to social media campaigns. This will free up human employees to focus on more strategic tasks.
  4. Deeper insights: AI can help businesses gather and analyze data more effectively, providing insights that can guide marketing and advertising decisions.
  5. Faster decisions: With AI-powered automation, businesses will be able to make real-time decisions about their marketing and advertising campaigns.AI will profoundly impact marketing and advertising in the future. By personalizing messages, improving customer service, and automating tasks, AI will help businesses operate more efficiently and effectively.

As we’ve reviewed, these are the three things marketers should ask: 

  • How can marketers begin to implement artificial intelligence into their strategies today?
  • What are the potential implications of artificial intelligence on marketing in the future, both positive and negative?
  • How will artificial intelligence impact marketing and advertising in the future?

Overall, AI will impact marketers in both positive and potentially harmful ways; the critical aspect to consider is to plan how to manage both the challenges and opportunities AI presents to marketers. Marketers will have to be prepared for those challenges by reviewing what’s happening with AI companies in such areas as AI writers. In the meantime, we’ll keep you updated here on this blog.

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What is the future of Digital Marketing and AI?

What is the future of Digital Marketing and AI?

What is the future of Digital Marketing and AI?

Marketers, it’s time to panic. Just kidding…or am I? The digital marketing landscape is changing faster than we can keep up, and what worked yesterday may not work tomorrow. So where is digital marketing headed in the future? And more importantly, how do we prepare for it? Continue reading to find out!

How will AI Change Digital Marketing? 

The rise of artificial intelligence (AI) is one of the most talked-about topics in business today. It’s been called a “game-changer,” and for good reason: it has the potential to transform every industry on earth.   Paul Roetzer, CEO of the Marketing AI Institute recently wrote: 

“There will be three types of businesses in every industry: AI Native, AI Emergent, and Obsolete.”

And this includes all of us who call ourselves marketers. Mmm hmm, it’s here to stay.

But what exactly does this mean? And how can we leverage this technology to make our digital marketing more efficient and effective?

In this post, I want to take you through some of the ways AI is already changing the way we do things and where it could go from here.

How is AI Changing Digital Marketing Today?

Let’s start with the basics. What is AI?

Artificial Intelligence is simply a computer program designed to mimic human thought processes. It’s human-like, but it doesn’t show up to work hungover on Friday morning. These programs can learn and adapt over time, allowing them to solve problems or complete tasks better than humans alone. And they accumulate PTO, so they are working 352 24/7.

Artificial Intelligence is the ability of machines to perform tasks usually associated with human thought processes. This includes everything from speech recognition to image recognition to machine learning. And these capabilities are getting smarter by the day (literally).

As an example, let’s look at speech recognition. Siri and Alexa are using AI to help them determine what you are asking and provide the best responses. They draw artificial intelligence and machine learning based on huge datasets and choose the best answer. You may have noticed that your voice assistants have improved over time. They have learned the correct responses based on feedback from you and millions (billions?) of other people and responses to similar inquiries in the past.

Ok. But Here’s the question all marketers are thinking: Will AI Replace Human  Marketers?


Marketing involves appealing to logic and emotion. And, while AI can help you to understand your customer. It hasn’t evolved to understand the feelings of your customer.  Although AI programs embedded in programs like Grammarly can advise you on the tone of your writing, human marketers need to determine the emotions necessary in copywriting and other communications. 

Another essential skill in marketing, storytelling, is still part of the bailiwick of human marketers. Although some AI tools have rudimentary storytelling capability today, tales of woe and triumph are best reserved for human marketers. But be prepared; as AI capabilities evolve, the role of the human marketer will also evolve.  Marketing is not the only discipline evolving due to advances in AI. You’re not alone.

What Can AI Do Already?

A lot! 

Here are some of the functions that are already the purview of AI in Marketing

  • Automating routine and repetitive tasks and processes

    • Content curation – No more searching for articles to share!
    • Pay-per-click ad management – You may be kissing your Google Ads expert goodbye soon.
    • Automate some email replies – Who wants less email?
    • Chatbots – personally, not my fave.
  • Compilation and analysis of large data sets to

    • Identify patterns – How many marketers do you know that love statistical analysis?
    • Create predictions – Tell me 8 Ball – What does the future hold?
    • Improve customer experience by providing insights into your customers and tailoring experiences based on their preferences. – How did Amazon know I was out of TP?
    • Forecast future customer behavior – Sometimes before they know it themselves!
  • Develop Content

    • AI Content Generation can write blogs, ads, titles, outlines and SEO copy
    • AI SEO programs suggest keywords, topics, and content. They also develop writer briefs.
    • AI Video and other AI creative can create captions, suggest images, create video or using AI Generated Actors based on actual humans.

What do writers think about AI Writer technology?  See and read what they have to say here: Writers on AI Content Writing.

The Benefits of Using AI in Digital Marketing

The benefits of using AI in digital marketing are numerous. It is a great way to improve the user experience and increase conversions. Here are a few reasons you need to jump on board with AI and quick:

Improve User Experience with AI

One of the most important reasons people love using AI is because it improves their user’s experience. The more intuitive an interface is, the easier it is for users to navigate through it. This makes them feel comfortable with the product or service they may be using.

Increase Conversions with AI

When you have a good user experience, you also get better conversions. You can create a seamless flow between the two by ensuring no friction when customers interact with your products or services.

Reduce Human Error with AI

When humans make mistakes, they tend to do so repeatedly. With AI, however, you don’t need to worry about human error as much. You don’t have to give AI a verbal warning for messing up too many times!

Create Better Customer Experiences with AI interactive websites

You can tell whether visitors are interested in something on your website and then show them relevant content.

Automate Marketing Processes

Marketing automation is one of the most significant advantages of AI. When you automate processes, you save time and money. Time and money are about all we have. Who doesn’t need their own personal minion to automate their tasks?

You can start small with basic tasks like email campaigns and eventually expand into more extensive content creation and customer support projects.

The time saved can be redirected to higher-level marketing activities such as creative thinking and planning.  To make more time and more money and then, well, I think you get the point.

The Challenges of Using AI in Digital Marketing

First, don’t be afraid. AI’s your friend. It’s a fantastic technology that has been around for decades but this technology is different. Instead of always being behind the technology learning curve, AI is going to let you catch up!  AI is already here; there is no turning back the clock. It’s wise to invest time learning as much as you can about the tools and how AI might help you do more and be more effective. Knowledge is power. 

Second, AI’s not all that. Make sure that you understand the limitations of AI. There are many different types of AI, each with its strengths and weaknesses. For instance, bias (gender, racial, ageism, you name it)  can creep into AI.  Know how to spot it and keep it from invading your marketing. And AI won’t allow you to sit back on your laurels and just push buttons. AI is not going to do your work for you. Like your loyal labradoodle, you need to train it how to sit and fetch and get you a beer (wine) out of the fridge. And once you have it trained and understand how to best work with it, you will get the best results. It will take some trial and error, but in the end, you will come to love your big fluffy AI. 

AI can help you identify potential customers, but it won’t choose a content strategy for you. You are still 100% in control of your strategy, goals, and tactics. You are the great and powerful Oz!  And, AI is your trusted assistant. It will provide you with input and possible ideas on strategy and tactics, but it will not do it all for you.

Measurement is critical.  If you can’t measure it, then how do you know if it’s actually helping? Determine upfront how to measure the impact of your AI-powered campaigns. Never choose an AI technology and then force-fit it into your process. This is Bass Ackwards!  First, determine the best areas where automation and AI could potentially improve your results, and then go out and look for AI solutions.

What Does the Future Hold for AI and Digital Marketing?

Who knows? But here’s what I think…

We are just on the cusp of all of the possibilities for AI and Marketing. The future is limitless. Monotonous, repetitive tasks will disappear. Your AI technology will help you determine the best ways to attract and retain customers and improve your brand. 

Those companies that fully embrace AI now will have a distinct competitive advantage over those that do not. 

Writers and creatives that use AI technologies such as AI Writer tools and new AI art/media platforms coming from DALL-E , AI-powered art, and other technologies make you better and faster and get you greater success. 

Marketing executives like CMOs and Marketing Technologists will have dashboards powered by AI that will allow them to make adjustments to plans and strategies in an agile manner that can be expediently implemented throughout the organization to lead by data and predictive technologies. Leaders will lead with data and can share the all-important answer to the question, Why?

Marketers will reach new levels of sophistication and responsiveness to customer demand by receiving real-time data using analysis of large data sets to help determine the best possible strategies for effective marketing campaigns. And marketers will have the ability to adjust those campaigns in real-time to optimize success.

So marketers, is it time to panic? Never! You’ve got this. The future is now. And the future of Digital Marketing is AI.

What do writers think about AI Writer technology? See and read what they have to say here: Writers on AI Content Writing.


Image by PublicDomainPictures from Pixabay

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