Marketing is overflowing with data. It informs and guides nearly every decision we make as marketers. AI, and, more specifically, machine learning, can help. But, ultimately, we are just people trying to create meaningful connections with other people who are making purchasing decisions based on very human factors.

 Just like a copilot, AI assists but is not in command. You are. In episode 1 of season 2 of Emodo’s FIVE podcast, Everybody Gets a Copilot, Jake Moskowitz and his guests explain how AI assists and elevates the thinking and roles of marketers.

Hear from Shelly Palmer, CEO of The Palmer Group; Rishad Tobaccowala, author and senior advisor to the Publicis Groupe; and Michael Stich, Chief Business Officer at agency VMLY&R.

Jake and his distinguished guests explore how AI fits into the marketing org chart and address timely questions. When is the machine your partner, as opposed to the tool you’re using to accomplish your goal? What happens when machine learning replaces the chain of human roles — and training — in a marketing creative department? What are the biggest gaps to implement AI in marketing?

Explore the human-AI connection with them.

Episode Notes

Host Jake Moskowitz and his guests explore the human side of AI in this first episode of the new AI series. Shelly Palmer shares some common, compelling examples of machine learning, muses about how to be an “AI co-worker” and offers some sharp advice to marketers on how to vet the AI claims of vendors. Rishad Tobaccowala cautions about AI as a buzzword and describes the three turds on the table (a reference to one of the chapters in his book): Ignorance, fear and science fiction. Michael Stich returns in the new season to talk about AI as the marketer’s co-pilot and how viewing the AI-powered future through that lens can help marketers see AI in a different light. Jake and Jeremy Lockhorn talk about the movies that have shaped our perceptions of AI and weigh the accuracy of Hollywood’s depictions. 

And, of course, Jake hits the FIVE List.

The FIVE List: The human side of the AI discussion

  • Marketing is about ideas, the domains of people. 
  • Marketing’s job is to connect with people.
  • Scale and complexity are so great, marketers need help (from AI). 
  • People make purchase decisions based on very human factors.
  • Algorithms are programmed by people. 

Jake’s guests:

Rishad Tobaccowala, Author, speaker and Senior Advisor to the Publicis Groupe.

Shelly Palmer, CEO of The Palmer Group, columnist at AdWeek and regular commentator on CNN and CNBC.  

Michael Stich, Chief Business Officer, VMLY&R, frequent speaker and writer on a variety of digital marketing topics.

Jeremy Lockhorn, Global Head of Partner Solutions at Ericsson Emodo, speaker, mobile marketing expert.

In this episode, Rishad Tabaccowala references his book: Restoring the Soul of Business: Staying Human in the Age of Data and Shelly Palmer references free weekly courses and gatherings offered by The Palmer Group.

The Five podcast is presented by Ericsson Emodo and the Emodo Institute, and features original music by Dyaphonic and The Small Town Symphonette. This episode was edited by Justin Newton and produced by Robert Haskitt, Liz Wynnemer and Jake Moskowitz.

Transcript of S2 E1: Everybody Gets a Copilot

Everyone gets a copilot in their life, whoever you are and whatever task you have. As marketers we get copilots.

Let’s talk AI.

Welcome to FIVE, the podcast that breaks down the big transformations for marketers. This is the start of season two. This season is all about AI in marketing. Here’s where we start, episode one: Everybody Gets a Copilot.

I’m Jake Moskowitz.

There’s a lot of talk about AI, and not just among techies, data scientists, and sci-fi fans. AI is already transforming the ways businesses operate, how today’s products and services work, and how careers are defined and built. And the pace of AI driven change is only accelerating. The impacts of AI among consumers, brands, and agencies will be significant. As marketing is one of the most likely industries to be impacted by AI. But how? What do marketers need to know now, and why? That’s what we’re setting out to explore.

My name is Jake Moskowitz, I’m the head of the Emodo Institue, an orginazation that focuses on research and education of data challenges in marketing. I’ve been involved in marketing and data analytics for over 20 years. The Emodo Institute is part of Emodo, a wholly owned subsidiary of Ericsson, the global telecommunications company.

To some people, AI sounds scary. You know, cyborgs, robots, the end of humanity. To others, AI’s just a distant, irrelevant topic, the boring chatter of nerds and futurists. To most, AI just so unds complicated. But if you’re a business leader, or a career minded marketer, AI really shouldn’t be any of those things. It’s simply a set of capabilities that if well understood, can make your business better and your marketing more effective. And maybe most important, understanding AI will help you become more successful, because AI will impact a wide range of marketing jobs, capabilities, and functions. So understanding AI means opportunity, or put another way, not understanding AI, is likely to mean lost opportunity. Somehow, someway, AI is powering just about every industry you can name. Whether it’s automotive, telecommunications, or education, finance, gaming, healthcare, logistics, or countless other sectors, there’s some AI algorithm doing something in the background to make it work, probably lot’s of them. For example, AI is playing a huge role in world wide efforts to fight the covid-19 pandemic, it’s being used to determine early diagnosis of people who might be covid-19 positive. It’s being used to project infection rates, ICU capacities, and death rates in likely hot spots. AI’s also being used to shape and identify potential vaccines to push forward through the trial process, so lots of different uses and applications in just that one particular area. AI can be applied to all kinds of tasks to make them more scalable, smarter, faster, more accurate, make it better in numerous ways and even make some tasks possible for the first time. That sounds pretty good, right? Well not so fast. Throughout the first few minutes of this episode, I did what a lot of people do. I talked about AI as this big sweeping, singular thing without any degree of specificity or any real details. But when you hear sweeping, glowing statements like that, it almost sounds like AI is this infallible ingredient that produces solely positive outcomes and incredible benefits like something you might see promoted on a cereal box like: Cheerios, now made with real AI.

Just so we’re all starting on the same page, let’s take a minute to talk about what we’re talking about. AI, what is it really?

The term AI gets thrown around a lot, without definition or specificity. So it really is kind of a vague meaning, the truth is that artificial intelligence takes many forms, can be fraught with a variety of issues, as different models and depending on the application, can mean different things. And every company applies it and approaches it differently in just about any application, and in all those variations there are all kinds of potential issues. In your business you may have noticed a growing trend of vendors touting their proprietary artificial intelligence of their super smart algorithm. After this season of FIVE, you’ll know whether or not something’s of value beneath the surface, whether or not there’s something meaningful behind the vendor claims. You’ll know how to spot red flags, what those flags may be telling you. You’ll know where to probe, what questions to ask, and what you’re looking for behind the buzzwords. In fact let’s take a few steps beneath the surface, behind the buzzwords now.

In most marketing scenarios when people talk about AI, they’re talking about machine learning, which is essentially an algorithm that gets better as you feed it more data. Generally, uses and applications of machine learning are only growing more and more prominent in marketing and beyond. Some applications of AI have become pretty high profile. For example, autonomous vehicles. It is fair to say that AI makes automated driving possible, but you have to step down a little bit to understand how that actually works. Automated driving is made possible by three categories of machine learning algorithms: one that identifies the objects around your vehicle, one that determines what those objects are doing or are about to do, and one that decides on behalf of your vehicle what to do about it. Note that each of those categories involves multiple algorithms that have very specific roles, there’s no all in one algorithm that’s just programmed to drive a car.

To some degree AI, particularly machine learning, already enhances a wide range of marketing solutions including things like hypersegmentation, dynamic creative, inventory quality filtering, dynamic sites and landing pages. But there are lots of things that can get in the way of an algorithm’s success. If you don’t know much about AI, you might not even know why things went south or even that they went south at all. So it’s these issues where we’ll put a lot of our attention in upcoming episodes.

One more thing about machine learning, throughout our series, we’re primarily focused on supervised machine learning, that just means that the algorithms learn to predict outcomes by learning from specific, intentional data inputs. Essentially, you show a computer a bunch of examples, you tell it the right answer for each example, and then based on these examples it’s seen, the computer starts to predict the right answer on its own. Feed it more examples, and the predictions get more and more accurate, well that’s the goal anyway.

Okay before we go any further, we need to talk about something else. Up to this point I have done something a lot of marketers do. It’s not intentional and it’s not good. You know we as marketers are constantly chasing metrics and conversions, optimizing creative and media, tracking ratios and percentages. How many impressions, clicks, views, visits. You know what we don’t talk about? People.

Seriously, we’re an industry of euphemisms like audience, consumers, visitors. We define personas, targeting segments, talk about personalized this and that, and we buy and sell impressions. So often we talk about programmatic innovation, demand sides, sell side, viewability, fraud, safety, that the human element, the real human element gets a little lost. In the day-to-day grind it’s easy to forget that we’re actually just an industry of people, trying to make a meaningful connection with people. Most AI in marketing is there for the purpose of either helping or reaching people. So in my view people-based themes are essential for framing our perspectives and discussion around AI, and there are actually quite a few of those. In fact to be specific there are five.

Marketers are people, and in an industry so complex and so dependent on scale, the people who make it work need help. 

Marketing is about big ideas, ideas of the domain’s of people.

Marketing is about connecting, connecting with people. When we lose sight of that, AI is worthless.

People buy stuff, they make purchase decisions, that stuff may include razors or it could include AI powered marketing tools. Algorithms may even inform those decisions, but smart investment and purchases are based on very human factors.

Algorithms are programmed by people. We’ll talk a lot about these issues that may hinder AI, most of them are actually about people.

Marketers are people, and in a complex high scale business of marketing, people need help. AI can provide a great deal of assistance, the kind of things that can elevate the thinking and the roles of a marketer. Marketers shape strategy, ideas, and creativity. AI provides actionable insights, predicts outcomes, and powers new ways to execute bigger ideas at scale. Shelly Palmer is the CEO of the Palmer Group, a tech solutions and strategy firm, and host of the podcast Think About This with Shelly Palmer and Ross Martin.

Shelly Palmer:

The way you learn to be an AI coworker is to first identify what it is you do best as a human, and what it is the machine does best as a machine. There are programs you can work with from Autodesk, AutoCAD programs, and the program knows your drawing or designing a car. So you start to draw a wheel and it knows that you mean a tire, and it completes the tire for you. You go to draw the fender, and it knows that fenders go above wheels, and it starts to draw the fender. And, in some cases you are selecting where the light is going to be, well of course it understands where the shadows are going to fall so you don’t have to do the shading. And you can be in a style, so you have to get AI out of your head, and you have to start thinking about where are humans and machines partners and where do machines stop and humans take over? 

In the case of an AutoCAD program, there is a lot of artificial intelligence or machine learning going on there and it is in fact coworking with you as you design. You’re doing what you do and the tool is doing what it does. The question is, when does the concept of cognitive nonrepetitive work (which is what a white collar executive would believe they do) when does that idea and the idea of cognitive nonrepetitive work done by a machine, where do those two things sort of get together and when is the machine your partner as opposed to a tool you’re using to accomplish your goal?


Marketing’s about big ideas. Ideas are the domains of people. Truly the currency of ad agencies is the big idea. No agency is just asking a machine what it should do or could do strategically, or how to define it’s big visions. But agencies are getting answers and direction on a more tactical level from AI powered solutions.


I’d have a senior art director, a couple apprentices or junior art directors and then a 50 person room, filled with people who do art. The senior executive would call the art director and say, art director! I need an ad, and I want the ad to do blank blank blank blank blank, and the senior art director with years of experience would go to the drawing board, draw a couple ads, comp them up. Ask for a meeting, go show three things to the senior executive who would go, I want that one. They’d go back, they’d tune it up, come back, get approval, yes I want that. And then they’d get a page of deliverables. In the old days, what you’d do is as a senior art director, you say to your juniors and the people who worked for them, “okay guys, we’re approved, go.” And they’d mock stuff up, comp stuff up, get you whatever you wanted, they’d hand it to you, you’d make some tweaks and in the process of tweaking it, you’d be teaching your aesthetic, you’d be teaching your methodology. And now, today, you finish the first part of that process where you get approval on the ad that you made, and you press a button and AI creates the entire deliverables list. And you as the senior art director still tweak it a little bit here and there, and you’re not training a person you’re training the model to your aesthetic.


Marketing is about connecting with people. When we lose sight of that, AI is worthless. When we talk about people based marketing, we’re talking about targeting, data accuracy, creative versioning, testing, and measurement, all at an unprecedented scale. AI is central to realizing that marketing future.


I think the way to think about it is this; if I took any marketer, any business person really, and I gave them a 10 by 10 matrix, and I said, here’s 10 rows and 10 columns that describe your business, almost any competent executive will look at that matrix and go, wow I know what this is, this is, double income no kids and this is… They’ll all understand what they’re looking at because they know their business, they are subject matter experts, and they’ll be able to understand and explain to you what they’re looking at in the context of their business rules and value creation.

If that spreadsheet was 25,000 columns by 250 million rows I don’t care how smart you are, there’s nothing you can do about it. So you need to translate what it is you think you knew about that 10 by 10 matrix into a much larger data set. So it’s this translation of what you’re thinking and what you know into what a machine can actually interpret and do for you. These are the real, real best use cases for data scientists and for marketers. You speak with a data scientist, they write you a good training set, you train against your bigger model, you test, fail, and learn and then you go, you know what, I can let the computer go now. Now I’m going to just get all this data in, all day long. It’s coming in from the web, it’s coming in from mobile, it’s coming in from our shopper marketing, it’s coming in from our CX, and UX teams. All of a sudden I’m starting to let the computer really, really continuously improve the user experience, the consumer experience, my margins, optimize my media bias. That’s where this starts to get fantastic because really, when you think about it, what can you do with the data? If you think about it deeply, you can transform it, you can learn from it, and you can make some predictions. That’s what you can do with the data set. So from a marketers perspective this is heaven.

Michael Stich:

So an oversimplification is, everyone gets a copilot in their life, or whoever you are, and whatever task you have, as marketers, we get copilots.


That’s Michael Stich, Chief Business Officer at agency VMLY&R.


We get copilots to help us price, we get copilots to help us figure out what we’re going to sell, through what channels, and where. We get a copilot to help us find new consumer insights to help drive brand planning. We get a copilot to help us with building media plans. I’m making it up right, but I’m describing specific marketing tasks, that now the application of AI will come on to help us apply that to those tasks. And if you think about it as a copilot, and that really, I think takes away some of the fear around Elon Musk and aliens. I think if anything, if we architected right, which is our challenge, but also our opportunity. And what we have is someone giving us guidance on what we should do. And much more task level basis within you know, in my world, the marketing world. Everything I just said is also true for the finance world, for the supply chain world, for the manufacturing world. And I think that’s part of the beauty of this is the application of that.


We have a stat from new marketer that surveyed: What are the biggest gaps to implementation of AI within marketing? And the number two answer was a lack of full understanding of what AI even is.


Yeah, I totally agree.


To me data is, here are some numbers, you as a human, look at those numbers, think about them, and then figure out what to do with them. And in some ways, AI is the exact opposite of that. It’s like, I’m not going to bother showing you the numbers, I’m just going to figure it out myself what to do with the numbers. And so it’s almost like the opposite of measurement. But you’re saying that you can’t think about it that way because you can’t take anything for granted. You always have to question.

Rishad Tobaccowala:

You always have to question the other is, by the way, nobody actually looks at the spreadsheet, only crazy people look at the spreadsheet.


That’s Rishad Tobaccowala, the Senior Advisor to the Publicis Group, and author of the book Restoring the Soul of Business: Staying Human in the Age of Data.


What we always look at are the results or the summary that someone did of the spreadsheet, which by the way, is a strange form of AI. It’s called humans. Right? So my stuff is the same interrogation and someone says, these are the results, you ask them the question. So somebody when a machine comes up and says this, we sometimes interrogate humans, because we say humans are fallible, right? Other times, we interrogate humans, because we don’t like the results that they’re showing us so we fire them. But for some reason, when a machine, especially because of the idea of ignorance and anxiety, and everything else, we don’t want to like question the machine because the machine is computing millions of teraflops of data and has come up with this whole thing. And my whole stuff is no, you got to basically ask, right? What’s going on? And when you do that, two things happen, which are very, very powerful. The first is the AI gets better, because the AI loves whoever is training, the AI can get better, which is number one. Number two, is you feel better about the AI because you can always add value to it, and therefore you’re more likely to use it.


We talk about AI being a buzzword. So why is that important? Well, AI is where the innovation is happening. It’s a competitive differentiator. So when people, marketers, buy tools, use tools, trust tools, the details matter. People make purchase decisions based on the promises of vendors. They vet claims, compare features, and evaluate competitors. When comparing AI powered solutions, actual people need an intuitive understanding of AI.


Yes, so you know, to a great extent, AI is a very hard flavor that people are caring a lot about. And because it’s a buzzword, they put it on everything. So you basically have AI driven showers and AI driven bathrooms, and you would not be surprised you will have basically AI driven kombucha and coffee. But I think because at its heart, at the very lowest level, it is pattern recognition, and it is a base algorithm that can connect two sets of data. You could potentially say I’m doing AI because I’m connecting two sets of data. But that’s really, you know, not what machine learning and other kinds of stuff are. So there are actually three thirds of the table when it comes down to AI. So one is the broad ignorance of what it is and what it could be, which is number one. A fear of what it could be, both for jobs or for the future, which is the second one. And then the third is almost a science fiction, either utopian or dystopian model. So either the robots will set us free, or we will be the slaves of the robots, right? Those kinds of things. What I explain to people is, it’s really interesting, that you think about whether it is fear, or ignorance, or anxiety, there are things that AI doesn’t have, but we do. Right? Which basically means that’s the area that we have to use AI to both get us less anxious, less fearful, and less ignorant, but also recognize that there’s parts of anxiety, fear, and ignorance that makes us so special that we will live with AI versus be taken away. And so it is that balance, and I always, it’s the theme of my book, which is; it’s important to combine the story and the spreadsheet. It’s important to connect the analog and the digital, and the data driven silicon and the feeling based carbon.


I love that. So embrace your ignorance, embrace your fearfulness, because that’s what the algorithm can’t replace.


Yes, and what basically happens is because of that, we innovate and we think, and we imagine, and the algorithm cannot do that, right? Because the algorithm is pattern looking, and it can look through scenarios. But a scenario that does not make sense to its pattern looking, doesn’t make sense. But innovation by its very nature is fresh insight for connections. Innovation is not connections, innovation was connecting data to data, AI could do it, right? Innovation is fresh, insightful connection. I would feel safer with a self driving car that once in a while I have to override when the self driving car signals to me that they aren’t sure what they need to do. Not when I think that they’re not sure, because then I’ll override and become a human driver. But there is absolutely no doubt that most mistakes are made by humans, but there are things that sometimes come up in the real world. Because self driving cars work absolutely fantastic for a human who’s in a self driving car, as long as there are no more humans outside it, right? But self driving cars have programmed in that people don’t behave like idiots. But people do occasionally behave like idiots, right? And that’s where you basically have to sort of override it, because my whole basic belief is if we were all logical, we would all be wearing masks, but we’re not.


I really appreciate your time. This has been super valuable.


Absolutely. Thank you very much.


Some great insights from Rishad Tobaccowala. If you haven’t already, you should really check out Rishad’s book, Restoring the Soul of Business: Staying Human in the Age of Data. Again, here’s Shelly Palmer.


And, why is it a buzzword? Because people are just so non understanding what the limitations of this technology is.


I actually feel kind of the opposite. Like, I feel like it’s a buzzword because people don’t understand what it is, not so much what the limits are. But like, I almost think people project way too much to it, like they think AI, they think robots.


Yeah, that’s right. I think we’re saying the same thing. I think ultimately, we are saying the same thing. There are two kinds of executives in the world, like, you know, when you like to bifurcate the world, there are two kinds of executives, those who think AI is this magical black box that will do their work for them, and then those who know that that’s just completely stupid. And unfortunately, you read an awful lot of science fiction in the papers and in the trades and online. People are ascribing all kinds of ridiculous attributes to artificial intelligence, and it’s getting blamed for a lot of stuff. Oh, you know, the computer. Okay, that’s sort of like just the laziest explanation ever. So yeah, at the end of the day, we’re both saying the same thing. The problem with AI right now is that people literally do not know what it can and cannot do. And I think that’s the most important thing.


If you’re a marketer, and you’re overwhelmed by the options in front of you from companies that claim that they’re using AI, how do you pick the real from the fake?


You immediately discount, anytime you see AI or machine learning, just discount it and you’re looking at the efficacy of the tools. We do a digital skills class every Wednesday, and I’m surprised at how many people come and take it. It’s free. You can learn about it at We try to basically just empower people to understand that all you’re looking for is outcomes, and if the tool can give you the outcome, whether it uses smoke signals or carrier pigeons, or AI or space dust or pixie dust, it doesn’t matter. Anybody who tells you that they have an AI, whatever. They look ‘em straight in the eye and say, can you explain why this AI model is better than a statistical machine learning approach to this problem? And when they can’t answer you, you know, they’re completely full of shit, in which case you move on. But if their product does what it’s supposed to do, the salesman’s just using jargon, you know, it’s on the banned words list, you shouldn’t use it unless you really know what it is. By the way, if you don’t know what you’re asking about, then you’re bad.


Thank you, Shelly.


Thank you so much for the opportunity. We’ll talk soon.


Algorithms are programmed by people. They’re only as strong, accurate, efficient, and ethical as the people who create them. We’ll talk a lot about the issues that may hinder AI, most of them are actually about people. In fact, the next episodes will dive into specific issues you should know about AI algorithms, and how those issues can impact the accuracy and execution of your marketing efforts. All in, this season will essentially provide you with a cheat sheet of sorts for understanding, leveraging, and vetting AI driven marketing tools. We’ll cover topics like insufficient training data, overgeneralization of algorithms, the negative impact of outdated data, and the compromises that are often made when data and algorithms are integrated with other marketing solutions.

Before we go, we’ve talked about some of the fears that some people have around just the idea of AI. That’s probably sparked by all kinds of things, but you can bet that a fair amount of fear has been sparked by some of the classic depictions of AI in movies and TV shows. Jeremy Lockhorn, ad agency veteran, mobile expert, thought leader, let’s talk Hollywood AI for a minute.

Jeremy Lockhorn:

That sounds like fun, let’s do it.


Alright, I’ll go first. I think any list like this has to start with 2001. This movie is just a classic, partially because it’s 50 years old, and yet, it’s still totally relevant to include it on a list about AI. One thing I have to say that I don’t like about this movie is unfortunately, it depicts AI as making its own decisions in a way that I don’t think is realistic, now or anytime in the near future. And what I mean by that is there’s Hal 9000, and Hal 9000 finds out that the humans are going to turn it off, and therefore decides to avoid that happening by killing humans. “I’m sorry, Dave…”, an algorithm can only do the things that it has been trained, that it’s possible to do.

“…I’m afraid I can’t do that.” What about you?


Yeah, that’s a great one, you’re spot on with the limitations there, we’ve got a long way to go until we get to a world where we have sentient AI or conscious AI. And that’s sort of the example that I want to pick up on, which is from The Matrix. And there’s this great quote when Morpheus is telling Neo about sort of the origins of The Matrix. And he says something to the effect of, “We marveled at our own magnificence as we gave birth to AI, a singular consciousness that spawned an entire race of machines.” And that’s a terrible impersonation. So forgive me for that. But consciousness is a very appropriate word. And again, we’re a very long way from that, you know, AI today, to your point is very good at very specific tasks, it’s trained to do one thing. For example, an artificial intelligence algorithm that is trained on a fashion ecommerce site to make product recommendations about what matches with what, based on style and color is probably not going to be all that good at an ecommerce website that focuses on grocery.


Multitasking is definitely a big deal and shows up a lot in pop culture. I want to talk about Minority Report, which I think is an actually very responsible example that doesn’t fall into that trap of AI having multiple skills. In Minority Report, there’s a segment of the police force that cracks down on future criminals based on an algorithm predicting that a crime is going to happen, as opposed to a crime having happened in the past. And I actually really like that it’s a accurate depiction of what AI or machine learning really is, which is using historic data to predict future outcomes. The one thing that is unrealistic about this, is that in order for a machine learning algorithm to be accurate and effective, you need excellent training data, and it would be exceedingly difficult to get actual, real high quality, accurate, thorough training data to predict crimes. Because A, there’s the ethical limitations of being able to know all the things you’d need to know about every single person in order to be able to find the needles in the haystack. But also B because, by definition, you don’t know who’s going to commit a crime. Therefore, you don’t know who you needed all that information about, so it’d be very hard to create that data set. Outcomes that are very rare, in this case, a violent crime, or in marketing, it might be a conversion, for example, are very, very difficult to create machine learning algorithms on because it’s very hard to have enough training data of those positive outcomes.


Fantastic example. So my last example I want to pull from is, from the TV series, Star Trek The Next Generation. There’s another super advanced concept in the show, there’s this series of rooms called the holodeck, and it essentially is virtual reality without the need for headgear or a headset. It’s just a room that is able to create a virtual world that you can interact with, including human beings and other types of life forms. And, you know, they use it for entertainment, they use it for, you know, a bunch of other kinds of things, but they also use it to solve really challenging problems because the computer has units and databanks, the entire collected works of famous scientists who have passed away. And so, if a character in the show encounters a really challenging technical problem, they can go into the holodeck and ask the computer to create a virtual human being that has the entire knowledge base of a particular scientist who’s an expert in a certain field related to the problem that they’re trying to solve. So I recently started rewatching, the series, you know, during the pandemic and this particular episode where they were in the holodeck with the artificial scientists trying to solve a particular technical challenge struck me as super relevant at this time, given sort of the technology inflections that we’re about to witness you know, the intersection of 5g and artificial intelligence and virtual reality and others. And I think you know, putting on the marketers lens creates a really powerful set of technologies to create truly magical and breakthrough consumer experiences that really connect in powerful brand ways and I’m super excited to see those things come together.


Hey, I’d like to thank my guests, Rishad Tobaccowala, Shelly Palmer, Michael Stich, and Jeremy Lockhorn.

In the next episode of Five: AI for Marketers, we’re gonna zero into one of the very human issues that can make AI falter, and algorithms lose credibility. Join me and my guests for a revealing look at bias in AI, and how algorithmic bias can diminish marketing results, damage brands, and other touchy subjects. If you like the podcast, please let us know, we’d love it if you left us a comment. And if you want other people to hear it too, maybe even give it a high rating on your podcast app. Thanks for joining us.

The FIVE podcast is presented by Ericsson Emodo and the Emodo Institute and features original music by Dyaphonic and the Small Town Symphonette. Original episode art is by Chris Kosek. Social media and other promotional stuff is managed by Lyon Solntsev. This episode was produced by Robert Haskitt, Liz Wynnemer, and me. I’m Jake Moskowitz.