Today, we’re going to hear from Dan Faggella, CEO of Emerj. During this episode we will discuss this history, progression and evolution of Artificial Intelligence and where we are on the journey to true AI.
Ben: Welcome to Modern Marketing Month on the TrendSpotting podcast by Searchmetrics. In this podcast, we dive deep into the ways innovative marketers use expertise and data to identify the macro trends that influence where you should be investing your marketing budget. This podcast is brought to you by Searchmetrics. At our core, the Searchmetrics team is a collection of SEOs, content marketers, and data scientists who help sophisticated organizations leverage search data to improve their organic traffic volume, maximize the visibility of their content, and gain insights into their business competition and the industry’s performance.
Ben: This week, we’re going to continue our investigation into some of the often-overlapping new trends in marketing, like artificial intelligence, machine learning, marketing automation, and data science.
Ben: Joining us today is Dan Faggella, who is the CEO of Emerj. Emerj is a content network that owns the world’s largest audience of AI-focused businesses with a goal of connecting business leaders to the AI solution and services they need to stay ahead of the competition. He is an AI expert. We’re very excited to welcome Dan to the TrendSpotting podcast to help us understand the current state of artificial intelligence. Here is our interview with Dan Faggella the CEO of Emerj.
Ben: Dan, welcome to the TrendSpotting podcast.
Dan: Ben, glad to be here with you, brother.
Ben: It’s great to have you here. Tell us a little bit about Emerj and your background. For the marketing execs that are listening to this podcast, what do they need to know about who you are and what you do?
Dan: Totally. So essentially Emerj is a market research firm focused exclusively on the impact of Artificial Intelligence in industry, so sort of the bottom line impact of AI. So we look at major sectors like finance, healthcare, defense, and retail. We also look at all the horizontal. So marketing, as it turns out, is pretty ubiquitously important whether you’re in the medical device field or if you’re selling some retail banking service. So marketing ends up being a big focus for us.
Dan: If any of the folks tuned in Google something like, “AI in marketing,” they’re going to see us showing up on the first page for Google for any of those related terms. A lot of our research is actually free. So, we can do stuff for corporations that kind of need to make important decisions about AI and really need to support those with research, but a lot of our lighter stuff is up online.
Ben: The way that I understand it is your something comparable to a gardener or forester, but without the high pay while you’re doing advanced research and deep dives into subject matters, but you have more of an advertising and sponsorship driven model.
Dan: Yeah, so we kind of raised the free bar for research, so the vast bulk of what we dredge out of these important sectors. Really what we focus on, Ben, is what’s working and also what’s possible. So what’s possible is really getting the full landscape of applications and what they’re purportedly capable of. What’s working is which of those have a proven ROI within an organization? As it turns out, that second question, figuring out what’s actually working versus what’s being bloviated and launched with a lot of PR buzz is actually hard work. In companies that are going to make expensive decisions or, let’s say, governments … We do some speaking for the United Nations for big research project for the World Bank. People making big expensive decisions want to support those with precedents of use. Most of what we cover is free in terms of precedence of use, but as you mentioned, we also do the research stuff as well, like the proprietary work, so that happens behind the scenes. But yeah, unlike forester or gardener, it’s not 80%, 90% paid, a little bit free, it’s more of the vast majority is free, and then the clients that want to go deep, we’ll go deep with them. But a lot of what we have is just up and out there, and that helps to attract the audience.
Ben: Great. So you mentioned a couple things about the sort of bloviation of the term AI or artificial intelligence. We spent our last episode just trying to dissect what is AI, how do we define that as a term, the difference between it and marketing automation, data science. There’s a whole conglomerate of let’s call them buzzwords that get thrown around at marketing executives. Give us your definition of what AI is.
Dan: Yeah, I mean, there’s all kinds of wonky definitions out there. So some people … I’m not necessarily saying anybody’s is wrong, there’s just a lot of them that disagree. So I have a set of them that I work by, and I’ll run them through. So AI broadly is … I primarily drew this from … we talked to folks that are kind of godfathers in the machine learning world who think about this at a deeper level than I do, Yoshua Bengio being one of them who we interviewed many years ago.
Dan: The perception that I get from many PhDs, but not all, is that AI is an umbrella, it’s kind of a bubble of computer functionality sort of underneath the bigger umbrella of computer science. Underneath computer science is a pretty good amount of stuff that we might be able to call AI, and then underneath the big umbrella of AI is an bubble of stuff that we might call machine learning.
Dan: There’s some people that say that what we once called AI, these expert systems, these preprogrammed if-then scenario type engines are really not AI and shouldn’t even be called as such. But there’s many academics who do think that it should. So machine learning is not the only thing we should refer to it as AI.
Dan: Then of course, AGI, artificial general intelligence, would be when we have machines that can compete for species dominance with humanity, let’s say, and indeed we are quite far from that. But I don’t think of artificial intelligence as species dominance, terminator style, hardcore singularity stuff. I think of AI as that bubble underneath computer science, and machine learning kind of nestles within there and is now kind of the part that gets a lot of the attention.
Ben: It’s an interesting distinction between AI and AGI, the sort of Skynet singularity version of machines being able to have, let’s say, emotional intelligence. I don’t want to go down that path in this episode, but I think that that is where, in our last episode with Magnus Unemyr, he was saying, “Hey, true AI doesn’t exist,” and you’re essentially saying, “There’s AI and there’s AGI,” and what he’s saying is true AI is artificial general intelligence or that emotional intelligence machines haven’t built yet.
Dan: Yeah, he might just be thinking of another term, yeah. He might be calling it true AI or something. AI in computer science departments around the world is a thing, so it’d be really hard to say that it doesn’t exist, because it’s a thing. In academia around the world, maybe he’s referring to true AI as genuine general intelligence, in which case yeah, of course he’s right on the money. That doesn’t exist. If it did, I think he and I would know about it and probably we would be subjected to its wishes. But we’re not there yet, and thank goodness.
Ben: So, let’s talk about the landscape of what marketers need to know about AI, understanding there is no emotional intelligence by machines at this point and obviously, hopefully, that’s a way away. But what’s the status of the AI landscape? Who is doing it well? What’s the barrier to entry to develop AI solutions?
Dan: Yup. There’s a lot of questions packed in there. I can talk about kind of who’s doing it well and where it’s really moving. So if people are interested in where machine learning and marketing intersect, there are some industries that you just want to keep your eyes on. Generally speaking, e-commerce and retail broadly, but specifically e-commerce rather than brick and mortar. Brick and mortar is still moving quick compared to industries like, let’s say, healthcare or utilities, but e-commerce is kind of numero uno. Retail is kind of up there, brick and mortar stuff, and then online and social media. So these worlds of e-commerce, online and social media, and to some degree, brick and mortar retail, have a lot of really great things going for them. Particularly the top two, so e-commerce and social media.
Dan: A lot of this, Ben, just to be frank with you, this is based off of … We did interviews with 50 different vendor companies selling machine learning into different sectors, and we basically asked them, “Where are you targeting and why?” And by and large, e-commerce was dominant. Second most dominant by another huge leg was online and social media. Then came brick and mortar, which was a little bit ahead of healthcare, and then there were a bunch of other spaces. I can provide the link for the show notes. But these are the areas where the vendors are focused.
Dan: Now why might that be, Ben? Well, the reason for that is reasonably clear. For one, e-commerce and online media businesses have a generally pretty high digital fluency. So Ben, if I go into an average insurance company with a thousand employees and I ask, “Hey, how many data scientists do we have around here?” the answer might be not that many or it might be none. “How many machine learning engineers work here?”
Ben: Bob is in the back.
Dan: The answer is … Yeah. Not the case. But if we go to a company in New York or Boston or San Francisco that’s in e-commerce and I say, “How many machine learning engineers are here?” we are going to get some hands raised. Same thing with online and social media. These happen to be firms that are younger, they’re birthed in the digital world, Ben. They understand the value of data. They understand the technical nuances of kind of a digital business, and they were born in that, which is a very big difference in being born out of that. Also, they’re just hipper to those tools. They’re able to adopt them.
Dan: The other thing those firms have going for them … if we think about, let’s say e-commerce store, Ben, that sells to consumers versus a manufacturing plant that does big runs of car parts, if we think about the difference from a marketing perspective, we are also talking, Ben, about volume. If I sell 18 huge manufacturing contracts and those things net out to, I don’t know, $8-10 million each or something. I’m just making something up. So let’s say that that leaves me at a $100 million dollar company. Now let’s say I’m an e-commerce firm, and I sell things that are anywhere from $4 all the way up to $200, or maybe all the way up to $700.
Ben: The Amazon sweet spot.
Dan: Yeah, sure. Somewhere in that ballpark. There’s plenty of other companies selling other things, but Amazon is the obvious example. What I end up getting is millions of transactions. If I have 18 transactions, is it possible for me to have enough data about that end goal of the sale to kind of proxy what were the factors that led to the sale? You know, there might be. There is to some degree, I’m not going to say that there’s not. But it’s absolutely pittance compared to these B to C firms who are working in the online and social media world. They’re looking at advertising exposure, and so number of impressions and length of time on pages, things that are very measurable, or low ticket, high volume sales. So your Macy’s of the world is going to do better than a car dealership, because it’s a higher volume, lower price. There’s that advantage as well.
Dan: So, we got the combination of digital fluency and high volume, reasonably low priced tends to be almost ubiquitously B to C. These are the spaces where AI in marketing is moving. When the guy that sells tractor trails is leveraging machine learning, by the time that happens, Ben, essentially every e-commerce firm will be using AI ubiquitously. So if we want to look at the places moving quick and where the vendors are focused, where the B-C money is being raised, and our robust assessment of well over 50 firms and a lot of research in this space, that’s where the action is. You can let me know where we want to go from here.
Ben: Yeah, so what I’m hearing is essentially the companies that are using machine learning and artificial intelligence effectively are digitally native companies that are immersed in big data practices. You mentioned that e-commerce and social media, which I’m hearing as Amazon, Ebay, you know, the major retailers, or the Facebooks and Twitters of the world, where they have not only a large amount of data from there advertising efforts, they have their own page efforts, they have their transactional data all being collected, aggregated into hopefully a single source of truth, but aggregated into one universe that can be analyzed and used for machine learning.
Dan: Yes. You bring up a really good point here, one universe. So the difference between … so existing in a digital universe … which is the right way to think about it, Ben, you’re thinking about it the right way … this is imperative and really critical, because the more of your steps in marketing and selling that occur through a tracked, regimented, digital modality that’s just tracked all the way through, the better off you are for leveraging machine learning in your marketing, because you have more data points, more parts of the funnel that happen in a digital world that’s tracked that we can improve, that we can optimized.
Dan: If three quarters of the work of me making a sale, Ben, involves phone calls and buying people coffee or going to fancy restaurants with clients or schmoozing the secretary, how do I quantify the way I try to schmooze the secretary and the secretary’s relationship to the boss? I’m not saying these things are impossible, but I’m saying they’re very much not near term applications, these are exceedingly challenging.
Dan: But if everything that happens, from how I track the person, their first interaction, their first sign up, the things they click, the things they add to cart, the things they bought, the things they came back for for email and bought again, if all of that is digitally tracked in a digital world, my life as someone trying to apply AI just got a lot easier than if I’m Macy’s and I don’t even know, outside of who checks out at credit card, what the heck you did in my store. It’s a lot harder, Ben. It’s a lot harder.
Dan: So, the digital universe wins when it comes to machine learning. This is why we see the Netflix’s, the Amazons, the Facebooks, the Googles. The people that own the digital world are the ones who are pushing artificial intelligence to the next level. When it comes to AI in marketing, it’s going to be the digital world that moves first. Us analog folks should be tuning in there to see what the cutting edge is, because that’s where it is.
Ben: I’ll add that in 2018, if your marketing strategy is to schmooze the secretary, you probably have larger problems than just end to end data collection.
Dan: Admittedly, that is possible. There are some firms in maybe, I don’t know, in defense contracting or something, Ben, where I’m just imagining Facebook ads ain’t cutting it, you know? A lot of it actually is kind of the world still works on handshakes. So I’m not here to disparage people doing handshakes. I do think that if that’s all you rely on, you’re in trouble, but there are some industries where you damn well need it, you know what I’m saying? You damn well need it. If you want to close defense contracts, you’re going to be taking people to dinner , and how the ever loving eff are you going to quantify that? The answer is you’re super duper not. You’re not going to quantify that you got sushi and you bought them two extra things of sake. You’re not going to quantify it. But if they come through your email funnel, and they click this, and they select that, and they add this to cart, then they remove it, you did track that automatically. The digital universe wins when it comes to AI. I think there’s a lot of uphill battles with the non-digital world. Not impossible, but it’s a lot harder.
Ben: This is a little semantic, but in my head, when you’re saying it’s not really quantifiable, I’m thinking okay, there’s a data point from your expense report of what your investment on the relationship is, and then you have data in your CRM that’s looking at your calendar, and so you can actually start to do conversation rate optimization and understanding what the optimal amount to invest in a relationship is not necessarily focused on AI. Let’s get back to the subject at hand.
Dan: Yeah, we could go hard on that, but we might have to … Okay, I’m happy to … I can die on that hill with you, but we can move on.
Ben: The data points are sparser. But I think the point that you’re making is the companies that own end to end data and are collecting mass amounts of data across the entire customer life cycle have a distinct advantage to do machine learning. Talk to me about what they’re actually doing. I get like the FANG stocks, Facebook, Amazon, Apple, Netflix, and Google live in a digital world, collect a ton of data, and they have a distinct advantage to be able to process that data. Cool, what are they doing?
Dan: Yeah, great. Do we want to talk just about the big guys, or do we want to talk about the digital world in general? I can go either way with you.
Ben: I’m just using them as an example. Just broadly, what are people doing that’s effective?
Dan: Okay, I represent … but you’re just talking about, yeah, the digital players. So there’s really obvious examples here. Some of these actually so obvious that people don’t think of them. So in our poll that I mentioned of over 50 AI in marketing execs, when we asked sort of current today ROI in an average company, where is current today ROI for AI in marketing kind of making the biggest difference, we had a tremendous amount of responses that search was the answer.
Dan: So, here’s the deal. If you’re in e-commerce or you’re in retail, or maybe you’re just in a content business that get the online media business, the ability for search to be really powerful and deliver what people want, to kind of auto suggest the right things, to display the right things on the screen, to know when you meant A when instead you typed B … that ability for search to be intuitive and responsive and deliver exactly what your intent is, that is, by itself, astronomically powerful and should be seen as a marketing customer experience tool. A lot of the people that are at the cutting edge in AI in marketing are of the belief that search is that important. In fact, there’s entire companies built on just building enterprise search to adjust building e-commerce search with AI and companies doing exciting things. So search is one thing.
Dan: When you go on Amazon or you go on Google, you go on … I mean, we could even look at like Facebooks of the world or the big media platforms like the BuzzFeeds or whatnot of the world, what you type and what is auto-suggested and what shows up for different terms based on your past readership and all the rest of that, being able to intuit exactly what your intent is as a user and combine that with what that search likely means, that’s a powerful thing that essentially all the big firms are up to.
Dan: The really common example here that almost everybody’s aware of is going to be on the recommendation engine side. So being able to look at your patterns of behavior and what has kind of led you to … whether if it’s on Netflix, it’s just watching things. If it’s on Amazon, it’s buying things. If it’s on Google, it’s clicking on things that you’ve searched for. Being able to recommend the proper items, whether it’s search results, whether it’s product, whether it’s movies, whether it’s music on Spotify, recommendation engines are huge. Anybody playing e-commerce at a big enough level is leveraging recommendation engines in a powerful way.
Dan: There’s a fistful more in terms of like programmatic advertising, and marketing forecasting, and even some stuff on speech and text. Let me know if there’s anywhere you want to dive into them. I mean, there’s so much cool stuff to talk about.
Ben: Maybe this is future looking, so let’s head that direction. One of the things that I saw was a demo by Google where you can ask your smartphone to book you a haircut or a restaurant reservation. The speech to text is advanced enough that they can actually place the call and set the reservation for you. Is that the future? Is that something that’s realistic now? Where do we stand?
Dan: Yeah. There’s this kind of just potentially dystopian, but maybe not, future scenario that this fellow Stephen Wolfram who’s like a computer scientist has that eventually, everything that we do will be something that was recommended by a machine that knows us better than we are. To some degree, the scenario you talk about is steps in that direction, the idea of like, “Hey, give me a haircut,” and have it not only find a good time that fits in your calendar, but fit the haircut place that’s most likely to fit your preferences. Certainly I think a lot of that is the future.
Dan: I actually, oddly enough, Ben … certainly in like the next five years, outside of a few very large, really robustly powerful AI backed firms, firms that have just huge benches of AI beef sitting in their hanger in terms of just staff that can just own the science … outside of firms that have that going for them, which is, let’s just say a remarkably small number of firms globally, the idea of really tailored and unique open ended sort of Q&A stuff is probably not a reality. Like if the people tuned in are selling manufacturing services, they’re in healthcare somewhere, a lot of that deeper speech responsive stuff I think is going to come from the Microsofts’ and the Baidus and the Facebooks of the world for the near term. Is that where we’re going? Yeah, sure it is. Who’s going to own that for the next half decade at least?It’s going to be the big boys.
Dan: That’s not going to be something you’re going to be able to buy from a vendor. “Hey, vendor. Make it so that someone can ask this, and get an exact, precise thing, scheduled, managed and booked, and integrated with their calendar and all the rest of that.” Oomph, that’s some hefty, hefty science. Unless you got hardcore scientists, that ain’t you. But yeah, I think it’s where we’re going, Ben, but I don’t think it’s going to be what the average C-suite in marketing is going to have any access to in the five or six years ahead.
Ben: Talking to the marketing exec crowd, the audience for this podcast, what are the things that are accessible now in terms of vendor products? What is a good investment to buy artificial intelligence? And what is really something that they should probably stay away from because the technology just isn’t quite there yet?
Dan: Yeah, I mean, we can talk at the level of capabilities by themselves. Ultimately, this is going to line up with the companies goals. I think it is misleading if I were to tell companies … and you would think, as an AI guy, I should just kind of talk about how cool AI is and how everybody should buy it. But interestingly enough, I’ve got a little bit of a longer game here of kind of playing the trust game and telling people when things don’t make sense.
Dan: So if you’re going into the market to say, “What can we do with AI?” it can be an interesting exercise, but I wouldn’t spend money on those criteria. I think marketing firms, just like any other firm that makes smart investments in AI, what they’re doing is … we do this in board rooms with public companies, and significantly more involved than I’m going to articulate briefly … but they need to think about, if it’s a marketing department for example, what are the core objectives that they want to reach with their marketing now? What are the key initiatives? Where do they want to see themselves in, let’s say, three years, five years, whatever the case may be? Where’s the company moving? What are the critical initiatives to get there? What do we need to do to make a really healthy amount of money here in the short term and in the long term?
Dan: Then from there, Ben, those initiatives may overlap with an active and fruitful AI capability space. But you know what? They may not. So it really has to start with in company priorities. But if you like, I can talk about capability spaces that are rife with legitimate opportunity. If those happen to be overlaps, then it might be worth exploring for people. So I can talk about possibility spaces that big companies can leverage, and if they work, or if they’re the right fit, could be worth looking into.
Ben: Well, I’m just going to interject for a second. What I’m hearing is there are the top of the top in terms of technological advancement. The Amazons, the Facebooks, the Netflix, the Googles, the Apples of the world … Baidu is another one that you mentioned … which have the capability because they have bench strength in terms of data science and manpower to be able to build, search, and recommendation engines. That’s kind of the leading technology. It sounds like that might also be the place where there’s also vendor technology that can be purchased, but …
Dan: There is.
Ben: … the marketing automation component, the scheduling, the sort of more advanced piece of doing discrete actions isn’t quite there yet. Tell me, am I thinking about this the right way?
Dan: Yeah, I’ll try to frame what I had intended there. When you had mentioned scheduling and market automation, I don’t think that those things are outside the purview of AI. I mean, you have tools like X AI, which for all we know could be 60% humans replying to emails. I’m not saying it’s necessarily the case, I’m not disparaging them, I’m just saying I don’t know. But you do have scheduling, to some large degree, being doable by machines. So some discrete tasks are definitely machine doable.
Dan: You were talking about an example of Google talking something exceedingly open ended into the phone, right? So if we want to talk about close ended stuff, then let’s talk about interactions with 1-800-Flowers, okay? The number of things you’re going to ask 1-800-Flowers is absolutely limited. I mean, it is just freaking limited. I mean, let me tell you, you’re not going to ask them for dishwashers. You’re not going to ask them for haircuts. You’re not going to ask them for restaurant recommendations. You’re going to be looking at different flower packages, and you’re going to be different deliveries, and maybe you got to include a little love note with it or whatever, “Get well soon,” note, whatever the case may be. In that case, I think speech and text capabilities might be possible for a firm like 1-800-Flowers, who does have a pretty capable chat bot, because their bounded reality, Ben, is rather limited.
Dan: If you expect yourself to like, “Google, book a haircut,” find you a great Chinese restaurant, get you a flight to Singapore, find the coolest music related to X, Y, Z theme that would go well with, I don’t know, some party you’re throwing or something. Just obscurely, astronomically large and varied questions. These things are super duper not going to be accessible to most large firms if you’re not the Google and the Baidu. But the hyper limited spaces for this kind of conversational interface stuff and the specific discrete tasks can certainly be done, we just need a bounded reality, otherwise we’re looking at far too much capability for your average company. I hope I’m being clear here.
Ben: I think I understand what you’re saying, understanding the scope of what you’re trying to use data science, machine learning, AI, marketing automation to accomplish. If you have a broad set of skills that you’re trying to automate, you’re going to be challenged unless your name is Google. But if you are sort of constrained in the functions that you’re going to be trying to automate, then it becomes a little easier. Tell me about what the resources marketing execs can go to to stay on top of AI, the validate whether their vendors are legitimate, just to get educated about this space.
Dan: Oh, man. I mean, part of starting the company was kind of frustration around that topic. So I mean, the places that we go are we end up looking at individual vendors and then looking at their case studies, and then occasionally calling and emailing them, and validating different facts or clarifying different points, and then writing aggregate articles about that. So just emerj.com is us. Somewhat of a biased statement there, but I mean, in terms of digging through the hype, that’s a tough ball game.
Dan: I mean, most of what’s going to be pressed or found in Google is going to be either written by someone who’s writing for the first time about AI because it’s a hot search topic like on Forbes or something, someone’s got a marketing column and they decide to do one about AI. They’re not necessarily scrutinizing what’s working and what’s not. I think you can do the deeper market research thing, you can read what we do at Emerj, you can potentially get a pulse of places like … I mean, places that I trust not to publish trash about AI would be, let’s say, MIT Technology review I think is pretty sharp. I like what those guys are doing, generally speaking. I don’t like the tech crunches and the Wired’s of the world. I mean, getting a deeper sense at events is often kind of a good move.
Dan: But yeah, there’s not that many great internet resources. We’re trying to be it, for sure, in terms of what’s working versus not as opposed to, “These people can do everything.” So yeah, I wish I had a laundry list of excellent marketing use case sites, Ben, but to be frank, sussing out the wheat from the chaff is tricky business to say the least.
Ben: Yeah, I think at the end of the day, my takeaway when we talk about AI is that the industry being built around AI is relatively nascent, that it is controlled predominantly by the largest players in the technology community. You mentioned e-commerce and social media, the sort of …
Dan: Yeah, these are hot spots, yeah.
Ben: Yeah, those are the elephants in the room. If you’re looking for search and recommendation engines, that’s really where AI has been proven to be valuable, but a lot of the task automations and bots and natural language processing still is likely to be a mix of human interaction, which is still great, nothing wrong with humans, but it’s not really AI, so it’s a little buyer beware when you get down market and you’re looking at vendor solutions.
Dan: Yeah. We’ve got a whole … there’s very quick ways to suss out, generally speaking, if someone’s lying about leveraging AI or not. Again, Ben, I want to be clear. I’m not here to tell your audience, “You need to buy AI solutions.” Nope, I’m super against that theory of purchasing. I think that that’s how you buy toy applications. I think if you want to solve problems, you buy whatever’s going to solve your problem. But if someone is lying about using Artificial Intelligence, it’s probably just not a good sign about them as a vendor in general.
Dan: We have an article about kind of the three rules of thumb, but the most important one is … you know, if you go on LinkedIn, and you find whatever this company is, you said being wary of going down market, the easiest thing in the world is this. You see if they’re bragging about AI capability and their LinkedIn kind of company profile on their homepage. Then you go to LinkedIn and you just scroll through whoever the top execs are. If there’s literally nobody in a leadership position in the company who either has A, a robust academic experience with data science, machine learning, that kind of thing, or a robust business experience, let’s say they worked at IBM and they were working on AI, or if they were worked at Amazon, they were working on AI, or they worked at Equifax and they were doing complex modeling.
Dan: If you have nobody with either the business or academic cred to be doing AI in the first place, then it’s essentially 99% likely it to be BS. So a quick filter, Ben, if I’m going to help your audience do what we try to do is like if you want to check out the company and know if they’re legit, see if you got a PhD from Carnegie Mellon in computer science or see if you got somebody out of Amazon, out of Facebook, or doing robust data science at a previous business. If you got neither, you probably got lies.
Ben: Yeah, I think that’s great advice. I think at the end of the day, AI is new, controlled by the big companies. So when you’re looking at AI vendors and solutions, non-AI solutions are still great. There’s nothing wrong with human, but it is a little buyer beware in the AI landscape.
Ben: Dan, really appreciate you taking the time to educate our audience on the truth about AI. That wraps up this episode of the TrendSpotting podcast. Thanks again to Dan Faggella for joining us. If you’d like to learn about Dan, you can click on the link in our show notes or visit his website, Emerj, E-M-E-R-J dot com. If you’re interested in spotting more marketing trends or if you’re interested in Searchmetrics, the creator of the TrendSpotting podcast, click on the link in our show notes or see our podcast content archive by going to searchmetrics.com, which is our website. If you have questions or if you’d like to be a guest on the TrendSpotting podcast, please feel free to fill out the Contact Us form on the searchmetrics.com website.
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