The Human-Centered Algorithm: Marketing in the Age of AI

AI is changing far more than productivity. It’s changing how people make decisions, form opinions, build trust, express creativity, and even understand themselves. In this episode of AMS Illuminations, Stefano Puntoni joins the conversation to explore what happens when algorithms become deeply woven into everyday consumer life — and why marketers need to think beyond efficiency and automation.

As The Sebastian S. Kresge Professor of Marketing at The Wharton School and Co-Director of Wharton Human-AI Research, Puntoni discusses why AI must also be understood as a social science issue, not just a technological one. The conversation explores how AI is reshaping creativity and innovation, what it means when consumers increasingly outsource decisions to algorithms, and the growing responsibility organizations have to design systems that are genuinely human-centered.

From consumer wellbeing and identity to trust, influence, and ethical design, this episode challenges marketers and researchers to think more critically about the future they are helping create.

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Transcript

SPEAKER_00 0:06

Welcome to AMS Illumination, the podcast where we explore the ideas, research, and trends shaping the future of marketing and academia. I'm Brad Carlson, and each episode gives us a chance to have real conversations with scholars and industry leaders who are pushing our field forward. We talk research, career insights, emerging trends, and occasionally challenge some of the assumptions shaping where marketing is headed next. And today, we're diving into a small, quiet topic that absolutely no one seems to be talking about right now, artificial intelligence. So naturally, we felt it deserved an entire episode. But this conversation goes beyond the usual AI headlines and productivity hacks. We're not here to debate whether ChatGPT can write better emails or help you finish your slides faster the night before class. We're talking about the much bigger questions. What happens when consumers increasingly outsource decisions to algorithms? How does AI reshape creativity and innovation? What happens when machines begin influencing identity, trust, and consumer well-being? And what role should marketing scholars play in helping organizations build AI systems that are actually human-centered? Today's episode is called The Human-Centered Algorithm: Marketing in the Age of AI. And I'm thrilled to be joined by Dr. Stefano Puntoni. Stefano is the Sebastian S. Craig Professor of Marketing at the Wharton School at the University of Pennsylvania, where he also serves as co-director of the Wharton Human-Centered Technology Initiative. His research focuses on the intersection of technological innovation and human behavior, particularly automation, algorithmic decision-making, consumer identity, creativity, and the future of work. His work has appeared in many of the leading journals in our field, including the Journal of Consumer Research, Journal of Marketing Research, Journal of Marketing, Journal of Public Policy and Marketing, International Journal of Research for Marketing, Journal of the Academy of Marketing Science, and Harvard Business Review. Stefano, welcome to AMS Illuminations. Really excited to have you here.

SPEAKER_01 2:06

Thank you, Brad. Great to be here. Thank you.

SPEAKER_00 2:08

I want to start with the bigger framing of your work. A lot of the public conversation around AI is dominated by computer science, engineering, and technical capability. But you've argued that AI also needs to be understood as a social science issue. So what does this shift mean and why is it so important for marketing scholars in particular?

SPEAKER_01 2:29

Yeah, I think that this is basically one of the foundational assumptions in my uh research program is to say that basically for many organizations, technology is becoming table stakes, almost like a commodity, you know, your ChatGPT is as good as mine, kind of. And what is going to make the difference in the way that we integrate it into processes, into products, that we basically blend the human expertise with these systems. And I think that's a social science question, as much, if not more, of an engineering question. And then from the point of view of society, I think the impact that this technology is going to have on consumer well-being, on the functioning of markets, on economic growth, it's going to depend on user adoption, it's going to depend on user experience, it's going to depend on the fair deployment of this technology. All of these questions touch upon social science. So I don't think that we can get this technology right if we have this single technical focus. But we need to have a comprehensive focus that also has to bring in the social sciences for sure.

SPEAKER_00 3:30

I think that's a great starting point because it pushes us beyond the usual what can the technology do? question. So one line from your work that really stood out is the idea that AI is not just about automating the work we already do, but about automating work we aren't doing at all. Can you unpack that a little? You know, what kind of new possibilities does that create for marketers, researchers, organizations?

SPEAKER_01 3:53

Yeah, the background to that idea is that when you talk to organizations, you hear CEO pronouncements or see a lot of the kind of work that's been done, including in academia. The focus has been so much on productivity. And what does that mean? Well, it means that basically you have a pot of work to be done, a set of tasks that need to be executed, and then we are thinking about the ways in which we can do the best. And by best, we keep the criteria, but basically it's faster, cheaper, and hopefully also better. But essentially you're thinking about the work that we're doing and how we can execute that better. Now that's great, and there's nothing wrong with that. I think certainly we ought to try to be as productive and as efficient as we can. I mean, this is actually a responsibility of business to allocate the capital as efficiently as possible. The problem that I find in those conversations is that that focus on productivity tends to dominate so much that it doesn't really leave a lot of oxygen for anything else. And in the end, this technology is only going to deliver the kind of promise that people expect it to deliver, and just have to look at the kind of investments and evaluations of these companies to think that this is going to have to be big. It can have that kind of impact only if we think beyond current tasks. The job shouldn't be to see what we're doing and doing a little bit better, a little bit cheaper, maybe you know, without a lot of from people, the implications for headcount reduction, all of that, this is what dominates the conversations around jobs and everything else. I think the big question is what kind of innovation can we unlock? What kind of new product, new customer experiences, new customer relationships, new industries can be generated off the back of this technology? And that maybe means thinking about not automating what we're doing, but automating what we're not doing. Much harder. It takes imagination. Much harder task. It's going to take us a long time. It's not going to be an 18-month roadmap kind of thing that you see all companies aiming for. I mean, it's way beyond that.

SPEAKER_00 5:44

Are consumers simply using AI as a better tool, or are we starting to see consumers become comfortable with AI making decisions for them? And when that happens, how should we think about it? As empowerment, as convenience, as loss of autonomy, manipulation, or some messy combination of all those?

SPEAKER_01 6:01

My guess is that probably the answer is going to be contingent. And so you'll have different task contexts in which those agents are being deployed. Sometimes it's going to be very routine. So you can imagine an AI agent being a little more than a script in that sense. So if you go on Amazon and you have that current purchases for consumables, for example, it starts looking like that, meaning I told you that I run out of shaving blades every six months. And the AI will basically make a note of that and then will ship it to me at the right time and then charge my credit card. And I don't have to do anything. It's a gray area, right? So it doesn't go from zero to fully agentic. It's all this process of deploying innovations in a way that supports decision making, make our life better and easier. But it's basically happening. And for those routine tasks, I don't think trust and delegation are going to necessarily be a very big issue, especially if you trust the e-commerce vendor that is going to handle the transaction and the payments. But imagine financial services, imagine healthcare, huge potential there for improving decision making. Right now, basically, wealth advising is a privilege that only the wealthy get, because for banks, there's just no margin that could justify putting an expert financial advisor in front of a random person. They all have massive bank accounts. And so it's a very unequal access to good financial advice that we have today. But what if we could develop agents that can provide good, if not very good, financial advice to everybody?

SPEAKER_00 7:33

Well, so let's go ahead and make this more practical. So where do humans still clearly outperform AI in marketing today? And where do we think we're fighting battles that we've already lost?

SPEAKER_01 7:43

I think my sense is that right now, if you are really an expert in almost every job, you do better than AI. And perhaps that will always be the case. Perhaps not. And maybe it will be the case in some domains, but not others. The reason why I got into this field, and actually, you know, I realized this years later. Basically, I ended up in marketing because what I liked about it was a combination of left side and right side brain activities, so to speak, you know, using undated metaphor. This idea that you have a lot of data, a lot of analytical kind of tools, but you also have the need to stand out, be creative, be different, be in order. I don't think there are many disciplines where you have that same combination. You know, say finance, you don't want a creative part. I get scared when you have like creative accounting. Yeah, that gets you to jail. You know, like you don't want that. Right. So but uh in other domains is all the other way. You lack maybe the formalistic uh analytical kind of side. And I think marketing is one of the few. Actually, I wanted to be an architect. Architecture is the same because it's kind of structural, it's a bit close to engineering, but then you know, yeah, just not about building a box, it's about making it beautiful, enticing people on a living. And I think marketing is a little bit like that. I think uh architecture is a good metaphor for marketing action. I think that because of that combination of analytical and creative, I think a lot of marketing jobs are actually quite difficult to replicate easily, whether that is coming up with an amazing brand that stands out and resonates, or uh create that campaign that really breaks through the clutter and gets people interested. But the truth is that a lot of marketing activities are pretty copy and paste. And I think for a lot of the stuff that we do, whether that is programmatic or emails or display, whatever, that is kind of you'd expect AI not only to be able to do it, but we do it doing better. In the end, what AI can do is to interpolate data and make predictions based on past patterns, probably can do that much better than a human can. So anything that looks like we should create a predictive machine to learn from the past, to predict the future under some kind of stability assumption, why would you have a human do that? Obviously, AI should be better. But not all marketing is like that. In fact, the interesting bits of marketing are not like that. Right. You know, it's about radical innovation, it's about changing the rules of the game, it's about standing out in a cloud environment. You're not gonna stand out in a cloud environment by doing what you did yesterday, right? Precisely the opposite. In fact, it might be that as AI gets deployed more and more, there is more room for a creative human to really move the needle a lot.

SPEAKER_00 10:16

If algorithms are helping decide what we buy, what we watch, what we listen to, where we go, and maybe even how we present ourselves, what happens to consumers' sense of self?

SPEAKER_01 10:28

Yeah, it's gonna change. And I think uh technology is always uh one of the biggest drivers of changes in the way that we leverage material possessions and our context for identity work and purpose. And so that was the case in the past, it's the case today, gonna be the case of the future, and so that's nothing new. But I think what is interesting about AI is that it is asking us to answer some sometimes difficult questions. What's the value of my professional expertise now, AI can do this? Am I still really cooking when I have a machine that can do this? Lots of questions of that sort. And I think for me, the way to look at it is to say lots of research in marketing, academia, and practice is going to looking at what can AI do and what do consumers think about what AI can do. Equally interesting and important question is to think about how does AI change the way that we think about ourselves, not what people think about AI, but how AI shapes what we think about ourselves. And I think there's lots of interesting questions there for interesting branding. And in a way, to me, it really brings it to the value of human labor. If you think about automation in companies, it's always a supply-side decision. But if they're looking at the technology, the thinking what it can do, the thing of the status quo, how much it costs them to do something, then it's basically a cost-benefit trade-off, is it good enough, how much it costs, and I do a pull a trigger, yes or no. And I think maybe companies increasingly should think about the demand side and say, how does the presence of a human in this workflow, whether it's a service provision, it is customer service, or even product design and execution, what is the value of human labor in that workflow? And they might find that sometimes having a human there is actually adding value to the offering that is well kind of compensating for the additional cost that you may have to have when you have a human working applicable function. The question shouldn't be, can we automate it, but should we automate it? Doesn't make sense from a demand standpoint. Sometimes you'll say, you know what, that phone call from a real human to either apologize for that issue or to offer me this uh option or to reassure me about this concern is actually totally worth the cost.

SPEAKER_00 12:34

Do you think we're moving from algorithm aversion to something more quiet, like dependence, where people may still distrust AI, but increasingly rely on it anyway?

SPEAKER_01 12:44

I mean, I think you should give credit to people. They're not gonna rely on something they don't trust unless they are basically in an algorithmic cage, so to speak, where they really don't have an option, which might happen. So that your experience every time that you're trying to talk to a customer service, and there's no way you can speak to a person, there's only this chatbot. That's all you get. You know, might not be happy about it, but you know, the only alternative is switching company, and sometimes you don't have that option. Certainly, I do think that there are situations where consumers engage with AI when they do not trust it. But more by and large, when they can do that engagement out of their own volition, they will give it the trust it deserves, so to speak, and uh recalibrate as they learn. I I see trust as basically a bit like a treadmill. You start and then uh you know gets harder, maybe make you more inclined when uh you you learn to try. You see what I mean? You basically it's an escalational commitment in that sense. But there are domains where you might see dependence. Okay, and I think the one that maybe is most relevant to me is the context of AI companions and basically AI applications that are not designed to be a productivity tool, but just some kind of social bot, like either a friend or a partner. And in those situations, it's quite easy to see how people might develop dependency. I heard one example that was really shocking. I don't remember the company name now, it was basically an anecdote I heard someone saying. There's a guy who was getting messages from, so I started chatting with a girl on Instagram after a painful breakup. And they chat for months, and she's fantastic, and he really likes talking to her, but it is always been only online. And then at some point uh she says to him, Oh, I'm uh in London too for uh this event at the Art Gallery, why don't you come and meet me there? And he was so excited to all his friends, we've got to change our plan. I have to go there, I have to meet her. And then after a while, when he said, Yes, I'm coming, then a message saying, I'm an AI system, if you want to keep talking to me, you have to start paying 100 bucks a month or something like that. Very dystopian. And so I think you're gonna get examples like this where people are gonna be basically subjected to some sort of emotional blackmail by, you know, companies who don't care or don't understand what they're doing. And I think that's something that as marketing academics we can try to document. So we can try to inform both company decisions and maybe also regulation.

SPEAKER_00 14:58

That story that you shared, in addition to being frightening, gets us into creativity, which feels like one of the most interesting and uncomfortable areas right now. So you've written about different types of gen AI use, including convergent versus divergent thinking. Can you explain that distinction and why that matters for marketing?

SPEAKER_01 15:17

Yeah, it's a similar to this distinction that people make often in management between exploration and exploitation. You know, you basically uh you have a phase where you are considering broadly what option might be there, and then uh you basically have to make some kind of commitment to say, I'm going to pick on this and just trying to understand how you make it work and execute it. And I think it's a little bit like that, though. It's the same in the context of creativity. You basically can think about the creative process as you know, a process that goes wide and narrow, wide and narrow. It's kind of think, okay, let me think of like a brainstorming thing will be more like come up with things and see what happens. And then, okay, which ones are the bad ones. It refers also to the topic of innovation. Typically, innovation is uh defined as new and useful. Like this is a standard definition in innovation management courses that many of us teach. Well, I mean, new is about generating instances of ideas, interventions, uh, you know, imagery, whatever it might be. That could be a good solution to a problem you face. But then the question becomes: is that piece of information any good? And so that's a useful component. Basically, AI can be valuable for both. I think that actually one of the most interesting areas of research around generative AI has been just uh how useful it can be in ideation. I don't know if you remember, but I remember a decade ago. When people were talking about what will AI never do? If jobs of the future for humans are gonna be, it's always something to do with creativity and emotion. Turns out actually that AI can do a lot of stuff related to both.

SPEAKER_00 16:48

Right.

SPEAKER_01 16:48

Um the other hand, people are also thinking, oh, anything cognitive analytical is gonna be for machines only. And that's also not true. I think basically you have to think about what are the capabilities of systems, how are they architected, how are they engineered, what room does it provide for humans to make a contribution? And my hope is that in most domains, it's not gonna be human or AI, but it's gonna be some shape of human and AI will be my hope.

SPEAKER_00 17:13

Yeah, and that's uh so I I share that view, and I share with my students that I don't believe marketing jobs are gonna be replaced by AI in the future. I believe that marketing jobs are gonna be replaced with people who know how to use AI effectively. I think as we look forward, you know, one of the things that's interesting in your work, you also touch a lot on the future of work. So, what jobs in marketing do you think are generally vulnerable right now, and what human capabilities become more valuable as AI advances?

SPEAKER_01 17:45

Yeah, I think the many marketing jobs are under pressure. They're under pressure in terms of the need for people to be performing, or simply under pressure in the sense that they have to change. Whether that is in the creative side, you know, if you are uh creative nowadays, it is really a must that you learn to use tools like I don't know, Flora or whatever, that helps you create workflows that benefit from both language and image generation. If you are in market research, same story. I think you have analytic tools that enable you to do things that would have taken much more time before. There are even questions about whether sometimes we can enrich, augment, or maybe even partially replace human-provided data with synthetic data. And I think there's some interesting, that's one very interesting area of research, actually. What does that mean for the established marketing services industry? I think there's gonna be a limit of the marketing services industry that is gonna be lost because basically companies are gonna bring it back in. Imagine the creative process. Before, as a brand manager, I might go to an agency, and then I tell them my objectives, and basically I get the agency to pitch me ideas, and I get them to narrow it down, then I ask them to make some schematics and even prototype, and then eventually we decide to go and do it. You could imagine a lot of that process being brought back into the firm where now you go to the agency and say, this is a basically a nicely already quite realistic executed copy of some sort, uh, like a TV clip or something, and say, go and do it for real. And so only the last part now would be the way you need more technical execution than the company would want.

SPEAKER_00 19:27

If consumers are increasingly relying on AI agents to make purchases on their behalf, are brands still marketing to humans? Are we eventually marketing to algorithms?

SPEAKER_01 19:37

I think one of the most interesting questions today for marketing academics is to explore the question: what is the future of branding? You could argue that basically the main effect of the deployment of personal agents is gonna be commodifying markets. Because basically the agent will be instructed on how to satisfy a need. That kind of customer need is gonna be then translated into product specs, and then the agents will basically seek out the best fit given the specs required to fulfill the need of that particular customer in regard to price points and attributes and attribute levels and configuration. The other view, which is maybe actually brands are gonna be a moat, because if consumers are going to have a strong brand preference, they might actually query the agent to go find that brand and prevent competition from even having a go. Or maybe it's gonna be more subtle. Maybe the brand is shaping how the consumer thinks about the category. And even though the consumer is always structural the agent to find that brand, the way that the consumer thinks about the category is shaped by your branding, and the query will basically align with whatever you think you win on. So basically, by shaping, we should think about this category around this attribute. Oh, that's my attribute. I don't know. I think it's gonna be probably a combination. And there's gonna be also areas where consumers love shopping and they're still not gonna say clothing. So some good people would basically say, I need the A and Y just finding, just I don't wanna, I don't want to spend any time on that. Other people just love exploring and trying things on and think about it and have creative style. I mean, those people are not gonna you might benefit from AI support, but I'm not gonna give up that fun activity.

SPEAKER_00 21:21

This has been an incredibly interesting conversation. I would love to keep it going, but I also want to be understanding and respectful of your time. So before we wrap, I do want to switch gears, do a quick lightning round. Real quick, biggest myth about AI in business.

SPEAKER_01 21:36

Productivity should be the end goal. That it should be about doing what we're doing now cheaper instead of thinking about innovation.

SPEAKER_00 21:42

What's one thing AI already does better than humans? Lots of things.

SPEAKER_01 21:47

Everything that has to do with uh basically correlations.

SPEAKER_00 21:51

A first AI tool or capability every marketing professor should understand. I think today probably is uh coding agents. Last question. Last time you personally used AI, what did you use it for?

SPEAKER_01 22:04

Yesterday, I was um this afternoon, I have an executive session, and I wanted to illustrate this idea that AI could help you explore a wider set of uh possible solutions. So I wanted to create a surface plot that had local optima and a global optima. And then I asked it to define what function that might look like, give some feedback, or the optima were all at the edges or didn't like that, make it more in the middle, or make the maximum a bit bigger. So defined a math function. Then I asked it to execute and make a beautiful visual and use it on the slide. I mean, it would have taken me forever to do that, maybe with MATLAB or something, but you know, it would have taken some time. We would have gone through calculus text, we'd have figured out well what would be a good function. But anyway, no, it took me two minutes.

SPEAKER_00 22:44

It definitely has value as long as we don't let it guide all the decision making. Stefano, this was fantastic. Thank you for helping us think about AI not just as a technology story, but as a consumer from organizational, ethical, and human stories as well. And to everyone listening, thanks for joining us for AMS Illuminations. If this conversation gave you something to think about or made you slightly more optimistic, skeptical, or professionally anxious about AI, share it with a colleague and follow AMS Illuminations for more conversations with the people shaping the future of marketing. Thank you so much for being with us today. Thank you, Brad. We'll see you next time.