AMS Illuminations
AMS Illuminations brings together fellows from the Academy of Marketing Science (AMS) for deep dives into the ever-changing world of marketing. Tune in to each episode for insider career tips, strategies for forging successful research partnerships, explorations of groundbreaking marketing studies, and discussions on the game-changing impact of new technologies.
AMS Illuminations
From Prediction to Creation: Rethinking Marketing in the Age of Generative AI
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Generative AI is changing marketing fast — but what does that really mean for strategy, creativity, ethics and consumer trust? In this episode of AMS Illuminations, host Brad Carlson talks with Dr. Erik Hermann, Interim Professor of Marketing at European University Viadrina, about the shift from predictive AI to generative and agentic AI.
Together, they explore AI as both collaborator and competitor, why consumers form emotional attachments to machines, and how marketers can use AI responsibly to create value for customers, organizations and society.
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Welcome to AMS Illuminations, the podcast where we explore the ideas, research, and trends shaping the future of marketing in academia. I'm your host, Brad Carlson, and each episode gives us a chance to have real conversations with scholars and industry leaders who are helping redefine where our field is headed. Today we're talking about a technology that seems to be evolving faster than our ability to create conference tracks, special issues, LinkedIn think pieces, and emergency syllabus updates about it, generative AI. More specifically, we're exploring what happens when AI moves beyond prediction and optimization and starts creating, interacting, persuading, and maybe even behaving a little more like a consumer or a marketer itself. Because the conversation around AI has shifted fast, we're no longer just talking about systems that analyze data or automate repetitive tasks. We're talking about AI generating campaigns, interacting socially, acting autonomously, and increasingly participating directly in value creation. This raises some fascinating questions. What happens when AI becomes a collaborator in marketing strategy and creativity? Why do consumers form emotional attachments to machines? Could consumers eventually prefer AI interactions in some situations? And how do we balance innovation, personalization, ethics, autonomy, and trust in a world increasingly shaped by intelligent systems? Today's episode is called From Prediction to Creation: Rethinking Marketing in the Age of Generative AI. And I'm really excited to be joined by Dr. Eric Herman. Thanks for being here with us, Eric.
SPEAKER_00Thanks a lot for having me. A pleasure to be here.
SPEAKER_01Well, for those who don't know, Eric is interim professor of marketing at the European University Via Drena in Frankfurt. He previously served as permanent affiliate professor of marketing at ESCP Business School and has also worked extensively in industry, including roles involving digital solutions, marketing strategy, investor relations, and advertising policy. He currently serves as social media editor for the Journal of Marketing, and his research focuses on generative AI, AI agents in marketing, AI ethics, consumer psychology, and vulnerable consumers. His work has appeared in leading journals, including Journal of Marketing, Journal of the Academy of Marketing Science, International Journal of Research and Marketing, Journal of Business Ethics, Journal of Public Policy and Marketing, and Harvard Business Review. Again, thank you for being here with us today. Really glad to have you. I want to start with your path into this space for a minute. Your work sits at this really interesting intersection of AI, ethics, consumer psychology, and social good. How did you end up working in this area?
SPEAKER_00Yeah, that's a little bit of an unusual story, as you mentioned. I worked in industry since I had to leave academia in 2019, and one role was project manager for SME digitalization trading, and I was focusing on information and cybersecurity. And I found an intriguing comment on trust in AI and cybersecurity as a double-edged thought. And one of the authors was Luciano Floridi back then, a professor in Oxford, now he's in Yale. And he had also several other intriguing papers on this AI for social good perspective, where they lay out five ethical principles which should be followed. And then I was curious and checked the marketing literature and realized that, of course, in some papers, some principles were addressed, but not comprehensively. And I decided to write a paper on it, which was eventually published in Journal of Business Ethics, quite well received. That's how this whole research path started. Then I worked on more conceptual papers on AI and marketing and always took the social ethical perspective on it.
SPEAKER_01Very interesting backstory. Now, before we dive deeper into AI, let's start with some terminology because AI researchers seem to be committed to inventing new categories faster than the rest of us can define them. We hear predictive AI, generative AI, now agenic AI, and we hear these things all the time. So for listeners trying to make sense of all this, what actually matters in those distinctions?
SPEAKER_00Predictive AI, as this term implies, is about analyzing past data to predict certain outcomes like the next purchase. It can be used for pricing, traditional examples, recommendation systems like collaborative filtering systems, like we know from this quite well-known big e-retailer or e-commerce firm. We have generative AI, which is able to create seemingly, and that is quite important, seemingly novel content like text, video, audio, code, and quite interestingly also data, synthetic data. I will come to that a little bit later. And then agentic AI or AI agents, which are advanced AI systems, which are able to perceive, act, and reason on behalf of so-called principles, which could be firms or customers. And that is currently discussed as maybe the future, agentic commerce, and so on and so forth.
SPEAKER_01I do note that you really emphasize seemingly novel, and I'm sure we'll unpack that a little bit. But before we unpack that, do you think marketers fully grasp how significant this shift really is from predictive to agenic AI? Or are we still mostly treating gen AI like a productivity tool instead of something that fundamentally changes how marketing works?
SPEAKER_00Depends a little bit of whom you ask. There are so many reports out there, uh, asking CEOs, asking leaders. Interestingly, a lot of them see the potential, like there's this Gardner report, where 65% of CMOs say that it will dramatically change their role as the CMO chief marketing officer, but only 32% say that it also requires significant changes to their own skills. So there's a certain AI blind spot, or mostly the leaders acknowledge the importance of AI, Gen AI, but they also express the feeling that they are not really prepared for that. And some of them also say that they do not see the return on investment now. And what I also found uh very interesting was a report by PWC. Their main finding was that uh 74% of AI's economic impact is just captured by 20% of the organizations. And these are basically large organizations. So awareness is there, but implementation seems to be still an issue.
SPEAKER_01That response naturally raises the anxiety question. Are we moving toward AI as a collaborator or as a competitor? And where do you think the real pressure points are for marketers right now?
SPEAKER_00I think it could be both. And this is actually related to this debate about human enhancement versus human replacement. So it can be a competitor in regard to really basic repetitive routine tasks where humans can be replaced, like advertising copy or creating visuals. It could be also a great collaborator for content production or creativity tasks where we have already studies showing Gen AI can create quite good ideas, but when it comes to implementation, humans still excel. For basic routine tasks, there might be the risk of human replacement and competition, whereas for higher level tasks, higher order tasks, there could be the potential for Gen AI to become a great collaborator.
SPEAKER_01Do you think there's anything specific about those higher order things? Is it a creativity link? Is there something that you see as a clear advantage that we as humans have over AI right now?
SPEAKER_00Yeah, I would say so, definitely. And it's also a critical thinking and so on. There's in the creativity literature, there's this interesting aspect of convergent thinking and divergent thinking. So convergent thinking is a domain specific, a quite narrow task where Gen AI could be quite good since it's trained of tons of data. And divergent thinking is a domain general, which is at the core of really creative tasks, where you need critical thinking abilities to really judge a novelty of an idea. And that is something where I think that humans still excel and will excel. We heard the term AI slot that is getting more average and average. There are also uh discussions and already opinion pieces that Gen AI will homogenize the content, our attitudes, and so on. So if you take this into account, then for those what I called higher order or higher-level tasks, um, humans will excel.
SPEAKER_01From a personal standpoint, this intrigues me. I just wonder what your own thought is. Do you think creative individuals and critical thinkers who use AI frequently, do you think it enhances their creative abilities? Or do you think it is potentially reducing overall creativity for those people?
SPEAKER_00I think it really depends on how you use it. And that is also a heated debate, how the impact on learning and so on, critical thinking is yeah, if you start using it frequently for all kinds of tasks and do not reflect about the output which is provided, and we do not use your own brain to say it really in simple terms, then I think there's more backlash than benefit.
SPEAKER_01Another really interesting part of your work involves anthropomorphized AI, if I said that correctly.
SPEAKER_00So I do not know, I hate this word.
SPEAKER_01It looks great in text. Basically, it's consumers forming emotional attachments to machines. Why do you think this happens so easily?
SPEAKER_00We have this interesting phenomenon even before Gen AI and so on, of parasocial interaction, this feeling that you are connected and have a reciprocal relationship between a mediate persona, which was celebrities, but also social media, people on social media, and now we have it for Gen AI since those LLM chatbots, for instance, are increasingly humanized, and there are all kinds of apps, AI companions. They try to emulate those human capabilities in regard to uh conversation, empathy, personality, and therefore over time, users, consumers might get the feeling that they are talking to real human. Basically, humans are wired to anthropomorphize. So we as humans naturally ascribe or try to ascribe human traits to non-human entities, not just technology, but also to pets, for instance, and some people even to objects.
SPEAKER_01Do you think that we're entering into a world where consumers may actually prefer AI interactions in certain situations? And if so, what kind of situations are those?
SPEAKER_00We are now in a situation that we have enough evidence to draw conclusion across several years of research, which is called meta-analysis for the people which are interested to get more insights. Basically, we already had this phenomenon of AI aversion, that people prefer human providers or humans in marketing context over AI. One of those meta analyses has shown that this AI aversion became less and less over the years and even turned into AI appreciation. And there was one point in time, and there was the release of ChatGPT, where this really changed this picture. So people are now accepting AI to a much higher extent than some years ago. Another meta analysis showed that uh even when consumers are skeptical, they still choose and buy from AI and rely on AI similarly to humans. And this preference is, however, context-dependent. So for certain tasks which are very objective and more utilitarian, not the hedonic pleasure context, they are more willing to rely on AI. And interestingly, also in an embarrassing consumption and purchase context. They are also more willing to rely on AI than humans.
SPEAKER_01Let's move into ethics for a minute. So, where do you see the biggest ethical blind spots right now and how companies are deploying AI?
SPEAKER_00Yeah, that brings me back to one of the points we mentioned before. Uh, this is uh over-reliance and so-called miscalibrated trust, meaning that you trust AI, although the capabilities uh should not justify this trust. There are already studies showing that people using AI to combat loneliness become even more lonely when using it, since they're just interacting with Gen AI and therefore do not interact with humans. So some studies have shown there are short-term positive effects, but in the long run, there could be huge risks. I think there's also this risk of manipulation. There was quite well-received and debated study in science showing that AI conversation can reduce conspiracy beliefs. And now when you think about conspiracy beliefs, which are really stable and conversation with Gen AI can change it, then think what Gen AI conversations could change else. I already mentioned sicker fancy. There's also research showing that it leads to a certain confirmation bias and overconfidence of users, even to more extreme attitudes. And the last one we have not mentioned yet is uh sustainability. There's Sasha Lucioni and company Hugging Face, they're doing great work to bring in more transparency here. But of course, on the other hand, the big tech and AI providers try to hide the real amount of emissions, environmental impacts. Also, Google no figures about their data centers, about the emissions, and so on and so forth.
SPEAKER_01So when when companies talk about ethical AI, do you think that's becoming a real operational priority, or is it still mostly aspirational language for a lot of organizations?
SPEAKER_00Yeah, it depends a little bit uh how comprehensively they really account for the different ethical principles which are out there. Just take privacy. That's such a huge issue. Just to take privacy seriously is actually quite a big endeavor then to guarantee autonomy, to guarantee justice and fairness, so that really every consumer is treated equally, that every consumer has equal access. I think that's a huge task which needs uh self-regulation by AI providers and also regulation. But regulation, of course, given this task developments, lags behind.
SPEAKER_01So if I'm in an organization and I'm trying to figure out how to navigate this ethical AI framework, it just so happens that you've proposed the assurance framework as a way to think more systematically about this. Can you walk us through that framework in a practical way and explain how marketers can actually apply it?
SPEAKER_00Yeah, sure. Assurance, by the way, I like acronyms since therefore you will find some acronyms in my paper. But since AI research is exploding, there's so many frameworms using acronyms, so that is not a Unix selling reposition anymore. But let's get back to assurance. Uh the first one is autonomy. So you should always keep humans in the loop on the side of the firm, of course, but also regarding consumers, they should have the possibility to intervene. So both firms and consumers, particularly in high-stakes contexts, just think of using AI, Gen AI for financial advice. Then you have security. Yeah, a lot of personal private data is involved, so you have to ensure the data protection that AI is not misused or hacked, something like that. Then the second S and the U is about sustainability. So keep an eye on AI's footprint, and yeah, let's put it simple, whether it's worth the environmental impact. Then representativeness relates to the input data, so that they should be representative of the population you are addressing. There's this issue of the so-called weird samples that a lot of LLMs have been trained on, so-called weird samples and underlying data, meaning Western, educated, industrialized, rich, and democratized. So that's a bias towards uh Western and industrialized countries. Then the A is about accountability. So who is held accountable if something goes wrong? That is, of course, an important question. Is it the AI provider? Is it the firm? Could it be the customer? Here you really have to trace, audit, and take responsibility for AI decisions. Then non-biasedness and non-discrimination is not about the inputs but the outputs. So, how are people treated by AI? Are the recommendations and predictions fair? Then uh crediting, which was really big debate when everything started, since LLMs have been trained on all kinds of documents, also on art, on newspapers, there are a lot of lawsuits now going on. Um, you should actually credit authorship when you use AI. Imagine as a firm you create a lot of visuals which use the work of uh artists. Actually, you should credit the, although even it's one data point, but still. And the last one is empowerment. So you really have to enable all stakeholders within the firm, but also consumers to be able to understand AI and to use it in a responsible way, which is often related to concepts like AI literacy. So you have to train people how to use Gen AI responsibly and also in the future AI ages.
SPEAKER_01Before we wrap up, I want to switch gears and do a quick lightning round. So here's how this works. I'm gonna throw out a handful of kind of rapid-fire questions, and you've got about 60 seconds for each answer. So it's long enough to say something insightful, but short enough to prevent either of us from accidentally turning this into a conference symposium. Here we go. First one. How much of the current Gen AI buzz is real utility versus pure fear of missing out?
SPEAKER_00I would say it's both. It's real utility at the specific task level, like uh content creation, analytics, and so on. But at this overarching strategic level, it might be more FOMO of so firms are experimenting, but they are not really redesigning their workflows to really implement it effectively and efficiently.
SPEAKER_01All right, number two, one AI hype trend you completely don't buy into.
SPEAKER_00That AI in the future will replace human creativity. So really original vision, taste, and so on, cultural walls. I do not think that AI is possible to do that.
SPEAKER_01One comment uh about that issue. I don't know if you've listened. There's some AI tools that take existing songs and recreate them. So for instance, there's some that take rap songs and then they recreate them in like a 50s blues. I don't know if you've seen or heard any of these, but some of these are incredible. They're incredible because the original songs are great, but the voices, the instrumentation, it's a little scary how good some of this is.
SPEAKER_00Yeah, that's true. But that is again then this question of creativity. What is creativity? Is it creative to more or less recombine and put it in another context? But yes, for some things they're incredibly good.
SPEAKER_01Yeah. Okay, number three: one human trait AI will never truly replicate in a marketing context.
SPEAKER_00I think that's really about genuine empathy, which is really grounded in lived experiences. AI can simulate it, but it can't feel, and you have to feel in order to show real empathy towards others.
SPEAKER_01All right, number four. Ten years from now, will we still call it AI marketing or will it simply be called marketing?
SPEAKER_00I would say it's a little bit like digital. So digital is now the default. Sometimes we still use this term digital marketing, although a lot of stuff related to marketing or almost everything is digital. I I would say the same could happen for AI.
SPEAKER_01All right, last one. One underappreciated opportunity in AI and marketing.
SPEAKER_00To come back to the introduction, uh, doing good by using AI. So, like this AMA definition, providing value not just for firms, but also for society at large and of course customers.
SPEAKER_01Yeah, that seems to be one area that we continually need more effort doing good. And it seems like AI gives an amplified opportunity to stray from that path or move closer to that path.
SPEAKER_00Yeah, actually, one problem, well, not problem, but it's often neglected. When we think about AI, we think about commercial interests and context and firms, but you could also leverage for charities, NGOs to optimize the campaigns and so on. And that is also doing good with AI.
SPEAKER_01Absolutely. Eric, this was fantastic. I really appreciate how grand this conversation was, not just in the technology itself, but in the human side of marketing, ethics, trust, vulnerability, and empowerment. And to everyone listening, thanks for joining us for another episode of AMS Illuminations. If this conversation sparked a new idea, challenged your assumptions, or made you slightly rethink your relationship with AI, share it with a colleague and follow AMS Illuminations for more conversations with people shaping the future of marketing. Thanks so much for being with us today, Eric.
SPEAKER_00Thank you. Thanks for having me. What's great?
SPEAKER_01All right, we'll see everyone next time.