AI Slop Is Killing Your Marketing: The 3C Framework That Fixes It

Most businesses don’t have an AI marketing tool problem.

They have an AI process problem.

In this episode of AI Marketing Research Radar, Dr. Eva Wolf breaks down why so much AI-generated marketing feels generic, empty, or like “AI slop” — and what the research suggests marketers, consultants, educators, and business owners should do instead.

The big idea: better AI marketing depends on the 3C Framework:

💪🏼Capability — choosing the right AI model for the creative job 
💪🏼Context — giving AI the right background, personality, and work-style profile 
💪🏼Competence — training people to use AI inside real marketing workflows

If you are using ChatGPT, Claude, Gemini, or other AI tools for marketing and still wondering why your content sounds generic, this episode will help you think differently about prompting, AI workflows, creativity, and team training.

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Learn more about Dr. Eva Wolf and Big Plans Media:
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This episode reviews three AI marketing research papers on:

• measuring AI creativity 
• personalized AI collaborators 
• generative AI training for small business marketing 
• how to avoid AI slop 
• how to build better AI marketing workflows 
• how consultants and educators can turn AI training into a stronger offer 

The money move: stop selling AI as a tool. Start selling AI as a capability-building system.

Businesses do not need another list of shiny AI apps. They need a repeatable way to choose the right model, give it the right context, train their team, review the outputs, publish strategically, measure results, and improve over time.

That is where real AI marketing advantage begins.

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WHAT YOU’LL LEARN

• Why AI-generated marketing often sounds generic 
• How to choose the right AI model for creative work 
• Why better prompts start with better context 
• How to build an “AI recipe card” for your work style 
• Why small business AI training needs feedback, practice, and workflow design 
• How to turn AI workshops into a stronger consulting or training offer 
• Why AI marketing advantage comes from capability, context, and competence 

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📚Research Mentioned in This Episode ====

Beaty, R. E., Deshpande, V., Lai, C. K. Y., Attuch, A., Shivagunde, N., Roy, S., Pujari, R., DiStefano, P. V., Muckatira, S., Stevenson, C. E., Gronas, M., & Rumshisky, A. (2026). AGC-Bench: Measuring Artificial General Creativity. arXiv. https://arxiv.org/abs/2607.01152

Kelley, S., De Cremer, D., & Riedl, C. (2025). Personalized AI Scaffolds: Synergistic Multi-Turn Collaboration in Creative Work. arXiv. https://arxiv.org/abs/2510.27681

Putra, H. D., & Azizah, J. (2026). Generative AI in Digital Marketing Strategy: Transforming Brand Communication and Consumer Engagement. Indonesian Journal of Business and Entrepreneurship Research, 4(1), 1–13. https://doi.org/10.62794/ijober.v4i1.23

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==== ABOUT DR. EVA WOLF ====

Dr. Eva Wolf is a marketing professor, AI marketing researcher, consultant, and founder of Big Plans Media. She helps business owners, educators, consultants, and marketing teams translate AI marketing research into practical strategy, content systems, workflow automation, and smarter business decisions.

AI Marketing Research Radar reads the research so you don’t have to — and turns academic findings into plain-English insights, evidence checks, and practical marketing moves.

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SUBSCRIBE

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If this episode helped you rethink your AI marketing workflow, subscribe and share it with someone who is tired of AI slop and ready to build real AI capability.

#AIMarketing #GenerativeAI #MarketingStrategy

Thanks for listening to AI & Marketing Research Radar by Big Plans Media.

I’m Dr. Eva Wolf, and I help marketers, educators, consultants, and business owners turn AI marketing research into practical strategy, smarter workflows, and real business opportunities.

More episodes: https://bigplans.media/ai-marketing-research-radar/
Consulting: https://bigplans.media/ai-marketing-consulting/

Big Plans Media — Where Big Ideas Meet Smart Marketing.

Transcript

– Welcome & what's in this episode

SPEAKER_00 0:08

Hi, let's be clear. Most businesses are not struggling with AI marketing because they picked the wrong app. They're struggling because it's like they're walking into this very expensive digital kitchen and they're tossing one vague sentence into their prompt pot. And then they expect the five-star meal to appear just 30 seconds later. Well, here's the uncomfortable truth about AI marketing in 2026. Consumers can now smell it. And in fact, we've even coined a term for it. We're calling it AI Slop. And increasingly, they just don't want it on the menu, people. They're tired of AI Slop. So you know the routine, we've all done it. We open Chat GPT, Claude, Gemini, whatever tool we happen to have available at that time. And then we just put in one quick prompt and we get something polished, but that feels strangely empty, like transform your business. And then we wonder why does it sound so generic? What's going on? Well, we're creating a lot more content, but not necessarily doing a better job at marketing. Shouldn't we be honing our craft now as marketers in the age of AI? Well, AI marketing is a lot like cooking. You can own a professional blender, a beautiful five burner biking range, and all these expensive knives. But at the end of the day, if you don't know what you're making or what your customers want or how the ingredients uh fit together, the dinner is still gonna be so-so, like slop. The equipment matters, the recipes matter, and the people using it matter even more.

– Paper 1: AGC-Bench — measuring artificial general creativity (Penn State / UMass Lowell)

SPEAKER_00 1:40

So that's what today's three research papers point to. Better AI marketing depends on A, the right capability, B, supplying the right context, and C, uh, having the right competence. Hi, and welcome. I'm Dr. Ava Wolf, and this is my roundup. I review AI marketing research papers so that you don't have to. I love reading research papers and conducting studies, but I know most business owners don't have that time to work through 30 pages of methodology tables, academic language, and so forth just to find one idea that might improve their business on Monday morning. So, with me, there are no random tool lists, there are no affiliate marketing links, and I'm not gonna serve up recycled AI hype. That's what I'm what I'm here to do. I just offer research-backed ideas that are translated for marketers, thought leaders, and business leaders. So today's three studies ask three practical questions. And they couldn't have come at a better time. In light of this consumer backlash on AI Slop, can AI creativity actually be measured? Is one of the questions. Is it better if you partner it with an AI that understands you? And what happens when small business owners receive real AI marketing training instead of simply just being handed a log on and told good luck. By the end of this episode, you're gonna know which model to choose for your creativity work and how to give your AI a useful personality and work style recipe card. And you're also gonna know how uh how to use real training results responsibly when you're pitching workshops and consulting. Let's get started with the first C, which stands for capability. So, not every model is good at the same creative job. Okay, the first paper is called ACG Bench, measuring artificial general creativity. And the lead author is Roger Beattie from Pennsylvania State University and Vegeta de Spande from the University of Massachusetts Lowell. I love this paper. And I know the title sounds super academic, but stay with me. Because the question that they're asking is Is AI creativity a real capability? Or are we just lucky in seeing some really good answers? Most marketers have experienced both, right? Sometimes you ask AI for a campaign idea and it gives you something surprisingly small but cool. Other times it produces a grammatically correct perfect paragraph and it sounds good, but it sounds like it belongs in a conference brochure, right? Nobody wants to read it. Well, it's not exactly wrong, but it just feels

– The creativity leaderboard: which AI is best for each job

SPEAKER_00 4:28

maybe empty. Okay, so this paper suggests that AI creativity is more measurable than we may think. Imagine this giant digital test kitchen where 83 AI models were put through all sorts of challenges across 67 different data sets. Okay, in one station they did brainstorming, another one they did scientific ideation, another one had to write figurative language, another one had to do humor. The researchers built that test kitchen by screening more than 3,100 papers. Yes. And then they developed a benchmark for measuring creativity on their AI. They called it the AGC bench and artificial general creativity benchmark. That's the name they came up for it. And out of the 83 AI models that battled it out on creativity, they got some interesting results actually. A broad creativity factor they came up with, which the researchers now call C, explained 81% of the variation across the six domains. What does that mean? Well, in plain English, that means that the models performed well in one creativity area, and then oftentimes were just as strong in several other areas too. Right? Surprise, surprise, yeah, it makes sense. If it's good at this, it might also be good at this other type of creativity function. Right? I get it. So that's interesting because that means creativity is not random across the board. But interestingly enough, the models still have

– Just say "be creative" — the one prompt change that boosts AI output

SPEAKER_00 5:56

specialties. Yes, for example, Chat GPT 5.4 ranked first for brainstorming, STEM ideation, and narrative work. Whereas Kimmy K 2.5 led in problem solving. Okay, and Kimmy 2.5, by the way, is a Chinese model. Cloud Opus 4.6, the fast version, it led in figurative writing. And Claude Opus 4.7 was the funniest. It ranked first for humor. Okay, interesting, right? So that changes the question marketers should ask. Instead of asking what is the best AI model, maybe we should be asking, What am I really trying to cook here? What am I, what kind of content am I really trying to create, right? Because you know a blender is an excellent tool for making a smoothie, but it's a terrible tool for slicing tomatoes. So the tool only makes sense in relation to your job at hand. In this is in the same way that the model that helps shape a long-form brand story may not be the one that's gonna give you the most interesting product name. Okay, or it might not be the same model that's gonna produce polished copy, right? Or one that is gonna deliver that strange, unexpected, viral campaign that breaks through, you know, a crowded category. So it really depends on what it is that you're trying to do with your digital marketing or your marketing as a whole. And I know it's convenient to stay in that one model that you're used to and keep stick with it. I do it too, but convenience isn't necessarily the same thing as having creativity fit. So a better move is to match your model to your job, okay? And then there's this other finding in the paper that I found fascinating, and I think every marketer should know. It's in a separate test, they also discovered that explicitly telling the model to be creative made a big difference. Yes, the creativity instruction produced a much larger shift, roughly four times the magnitude of the reasoning mode effect

– Paper 2: Personalized AI scaffolds for creative collaboration (Northeastern)

SPEAKER_00 8:09

in those tests. So let me say that again. Okay, simply telling any capable model to be creative actually matters. I I know it sounds crazy, but that doesn't mean that a lazy prompt such as be creative and write me a campaign is suddenly gonna make it brilliant. Okay, it doesn't work that way. But the model still needs a goal, an audience, useful constraints, and a definition of what creativity means for a task. Okay, so instead of saying write five campaign ideas for my business, okay, which we all do, right? We get lazy and maybe that's all we ask for. Instead, try this. Generate five campaign ideas that are original, useful, and meaningfully different from the conversations in my category. Be creative, explore unexpected customer attention, unusual analogies and approaches my competitors are unlikely to ever use. Try that first. So that phrase, be creative, is like telling the chef not to serve that basic chicken dinner again, or to use and you're giving it permission to use the ingredients, but like take me somewhere that I'm not expecting, or is sort of like what the instructions are that you're giving it. And a lot of marketers accidentally season creativity out of their prompt over time. We ask the model to be professional, accurate, concise, and on brand, right? And then we're shocked when the answer is bland. So sometimes you need to say, now surprise me. Now give me the version my competitors would never write. And that actually brings us to the second C in this podcast. And that one stands for context. Okay? So let's go into the second paper here. Because everybody needs a recipe card in their digital kitchen, correct? So the second paper is called Personalized AI Scaffolds, Synergistic Multi-Turn Collaborate Collaboration and Creative Work. Wow, that's a mouthful. And the author is Sean Kelly from Northeastern University. And the practical question he asks is much easier. He asks, Does AI become a better creative partner when it gets to know you? Interesting, huh? Well, picture this 331 people entering this digital test kitchen to develop a campaign for a fictional startup. One group received a generic AI assistant like ChatGPT. It's like a chef who knew the assignment, but nothing about the person beside them. And a second group received an AI informed by the participants' own psychological profile, including personality, creative tendencies, professional experience, problem-solving style, values, and collaboration preferences. Then a third group received an assistant carrying somebody else's recipe card, somebody else's personality profile. And the participants working with the correctly personalized AI produce stronger campaigns than those using generic assistance. Among the highest quality campaigns, personalization also increases creativity. That matters because marketers are not just looking for 50 average ideas. We're looking for the ones that make the customer stop and say, yes, that's exactly my problem. You get me. The most interesting contrast came from the campaigns produced by AI alone. They were polished, organized, and generally very high quality, but they were also much more similar to one another. Like a tray of technically perfect cupcakes, but they all came out of the exact same mold. Well, that's what we call the sameness problem. AI can make everyone's marketing cleaner and smoother while slowly making everyone sound alike. We don't want that. And audiences have noticed in 2023 when AI generated uh creator content was still novel and new, a billion-dollar boys surveyed found that 60% of consumers actually preferred AI contents and ads. Hmm. However, in their latest survey of 6,000 consumers, creators and marketers, that number dropped to 26%. Okay, so it's more technology, but we've lost the appetite for AI Slop. The novelty's worn off. And now we have a word for it, and we're calling it AI Slop. And that's what we started calling it in 2025. The new word. That's what we crowned it AI Slop. And it's not a compliment, people. You don't want to be putting out AI Slop. So what keeps your brand from becoming interchangeable? Well, your taste, your lived experience, the examples you remember, the values that you won't compromise, and even those little odd observations from actual customer conversations. It's those little details. It may feel small, but those are the ingredients that are going to make your work uniquely yours. Okay. And you want those to leverage your AI. Personalization is not merely saying, oh, make it sound like me. No, no, that's too shallow. This goes deeper. Okay. And the stronger goal and message for your AI is more like this. Understand how I think, where I am strong, where I get stuck, what I value, what good work looks like in my business. And then use this information to complement me and my work, my marketing efforts. You see? The study found that some of the most influential personalization factors were learning orientation, problem solving, and core values. So your AI should know what kind of evidence changes your mind. Do you want to be challenged early or do you prefer to organize your thinking first? Okay, where do you overcomplicate things? What principles are you not willing to sacrifice? Now, armed with those answers, now the AI knows how much spice to add, when to ask a question, when to stop hovering over the stove and actually just go do the work. In this research study, personalization also sort of changed the division of work. Okay. For example, with the generic AI, the assistant spent more time asking and coordinating while the human kept feeding it information. Have you ever felt like that? Like you keep answering question after question after question, you don't really get anywhere. Well, with personalized AI, the human did more strategic direction and the AI performed more execution. So in the long run, this is a big time saver. In essence, the human became the head chef, and the AI became the sous-chef. Okay. And that's the kind of relationship I want in marketing, where the human sets the menu and makes the judgment calls and decides what belongs on the plate. The AI just comes and helps me do research, draft, and maybe just execute faster. Okay. Um, it wasn't the benefit, it wasn't that simply that people trusted the personalized AI more. Trust increased, yeah, but the stronger explanation here is that it improved attention and reasoning. So the human and the AI focus more on the relevant issues and work through the task more effectively

– Paper 3: Generative AI in digital marketing strategy (IJoBER)

SPEAKER_00 16:31

together. Okay. The personalized assistant got it closer to the final perfect dish a lot sooner. Um, and that's why I'm putting a downloadable personality to AI questionnaire in the show notes. Okay, so it's free, download it. And the goal is not to give your AI every private detail about your life. No, no, the study itself raises real concerns about privacy, profiling, and treating people as fixed labels. Okay, but use it, use the minimum useful context, and train it on how you make decisions, what kind of feedback helps you, and where you are the strongest, where you get stuck, and what your audience expects, and how you want your AI to challenge you. Okay, and for example, here is a line that I use. Okay, I say uh I write, uh do not simply imitate me, use this profile to compliment me, help me identify my blind spots, strengthen my weak ideas, and make better decisions. Preserve my strategic control while you do most of the research, you do most of the comparison and the development and the execution. But when a task calls for it, be creative. And this isn't just a personality gimmick, it is a practical recipe card for collaboration with your AI. So download it, fill it out, put it in your AI, use it. And that brings us to the third C, the final paper here, and that stands for competence. The third paper is called Generative AI in Digital Marketing Strategy: Transforming Brand Communication and Consumer Engagement. The author is Halim Dwey Putra from Polytechnic Nagari Benkalis in Indonesia. Okay. And the study involved 40 business owners and managers of micro and small businesses in the Benkalis Islands. Yes, many were already using social media, but their marketing was still basic, you know, just posts that were inconsistent or messages that weren't necessarily always clear. Their brand stories were sometimes weak, and um they had very little experience with generative AI. Okay, they're just basic, they're using it as a basic tool. So the researchers didn't simply put Chat GPT on their counter and walk away. They did a four-week program covering digital marketing fundamentals, responsible AI use, caption and product description writing, brand storytelling, content planning, hands-on work with Chat GPT, Gemini, and most importantly, they gave them feedback on their revisioning, publishing, and evaluation. Okay, so they taught, practiced, revised, published, measured, and improved over four weeks. That was competence building. So instead of just buying professional set a set of Japanese knives, they actually taught them how to use them. Okay, somebody still needs to know how to use it and how to use the knife and how to cut in our digital kitchen, right? And how to finish this dinner without losing a finger. After the program, the reported changes were substantial. The portion of participants able to create promotional content more than doubled, okay? It moved from 35% to 82%. And the ability to use AI assistid tools moved from 15% to 85%. Regular posting rose from 42% to 76%, while using the use of structured marketing messages went from 30% to 79%. Wow, those are huge improvements. Why does this matter? Well, the paper also reported stronger brand communication and short-term platform results, including engagement that went up 45%. 45% engagement increase. I can't, I it was that to me that's pretty good. But okay, here's the responsible research note. There was no control group to compare them to.

– The 3 C's: Recap & takeaways

SPEAKER_00 22:00

That many companies make is they buy software or they buy access to an AI like chat or perplexity or whatever, and then they announce to the team we are now AI powered and assume transformation now. Um, yeah, well, no, no, it doesn't work like that. You bought the kitchen and all the equipment, you're right. We got the nice stove in there, but your team still needs a menu, a process, practice feedback, and a reason to keep going back in there and trying again. Okay. Um, they still need the hand holding and the training and the workshopping and the feedback and all of that. So here's a takeaway for any one of you who is selling workshops, or if you're selling consulting, or you're selling educational services, this paper gives you useful supporting evidence. Okay, so use it carefully. Don't promise that your workshops are going to guarantee results overnight. We don't want to say that. However, you can say something like this: a published four-week training and mentoring program with 40 small business owners reported substantial before and after gains in promotional content creation, structured messaging, posting consistency, and AI tool use. Those gains came from instruction, practice, publishing, feedback, and mentoring, not from tool access alone. And that is the kind of capability building process our program is designed to support. Take that to the bank. And that's a much stronger message than oh, here are 10 cool AI tools. No, no, companies do not need another tour of the appliance aisle. They don't need another tool, okay? They don't they don't need that. They need to know how to run the kitchen that they already have. They need to know that their their employees and their staff are using the tools to the best of their abilities. So let's bring all three studies back to one table and do an overview and a review here. The first one tells us that AI creativity could be measured and that different AI models have different creative strengths. And explicitly requesting creativity can actually improve performance. And that is capability, choosing the right equipment and creative mode for your particular task at hand. The second study tells us that AI can become a stronger creative collaborator when it receives useful information about us, when it learns about our personality, when it learns how we solve problems, and when it understands how we define good work. That is context. Giving the recipe to an assistant and our flavor profile before it can begin cooking. The third study tells us that technology creates business value only when people learn to use it inside a real marketing process. And that is competence, C for competence. So knowing how to plan, practice, taste, adjust, and improve. Okay. Together, these three C's change the questions we ask. Stop asking what is the best AI tool, and ask, what specific job am I hiring this model to do? Stop asking, why does this not sound like me? And start asking what useful context have I actually supplied it. And um, before asking why your team is not adopting AI, ask what training, feedback, and workflow have you built around it for your team and your you know your marketing team. This is the shift from one button takeout to a fully working marketing kitchen. So, what do we do this week? Well, here are three practical moves. First, run a creative model taste test. Pick one real marketing task, like a real campaign concept, a product name, a LinkedIn post, a newsletter, subject line, something or some kind of lead magnet idea, and run it through two or three models using the same context. Then compare the originality, the customer understanding, the usefulness and similarity to what your competitors are already saying. So don't just merely ask which one sounds more polished. Ask yourself which one gives you an ingredient that you can actually keep and use as an advantage. Second, build your AI recipe card. So download the personality to AI questionnaire from the show notes and turn your answers into a more concise working profile. Tell the AI how you think and what you value and where you need support and what kind of feedback helps, and you know how you want it to challenge you, and then keep it relevant and avoid sensitive information, of course, that the system the system does not need. Third, practice uh one dish for 30 days, is what I call it. Choose one repeatable workflow, your newsletter, your LinkedIn content, sales emails, client reporting, or product descriptions, and run just one loop. Plan, generate, review, publish, measure, learn, and revise. Okay, just one loop. Try practicing that. AI becomes a capability through practice, not through one perfect prompt, not through one tool demo. If you perfect this craft through practice. So, what's the money move in this podcast? Well, this podcast is the money move is stop selling AI as a tool and start selling it as a capability building system. Okay, and uh a tool tour is easy to copy, but it's also super easy to forget. So a stronger offer teaches people how to select the right model, how to supply the right context, and how to apply marketing judgment. Um, maybe even review the output, publish it, and then measure the result, and then improve the performance the next round. And that's what companies are actually trying to buy. Even when they ask for an AI workshop, what they really mean is that they don't need another list of shiny appliances, they need a team that can reliably produce better work, is really at the end of the day, what they're trying to get out of a workshop. So frame the promise around the behavior and the workflow change, not access. Explain that your program is going to combine marketing fundamentals, hands-on AI use, guided practice, feedback, publishing, and most of all, most importantly, measurement, so that their AI team can turn AI access into this repeatable business capability over the long run. It's the right capability, the right context, and the right competence. Remember the three C's. And this is how you move an AI from a chat bot, people that occasionally use, to a real marketing advantage. So I'm going to include a personality to AI questionnaire in the show notes so you can build your own AI collaborator profile. And if you do run a workshop or some sort of educational programs, I'm also going to include a simple way to turn the third paper into a responsible pitch slide. Don't download 10 new apps this week, please. Just pick one workflow and test a few models, build one useful recipe card, and start running one learning loop around it. And that's enough to get your kitchen moved. So this is Dr. Ava Wolf, and this has been my AI research roundup and uh where I read research papers so that you don't have to, and I translate them into practical strategies for your business, for your marketing, and for your AI workflows. Today's message is simple. Match the model to the creative job, match the context to the person and to the business, and match the training to the workflow. Because the future of AI marketing is not going to belong to the companies with the longest tool list or the fanciest digital kitchen. It's going to belong to the people who know what they're making, who understands their ingredients, and who is willing to practice until the work is genuinely good. This has been my first podcast. Thank you very much for joining. If you enjoyed the information and you find it useful, please subscribe to my podcast on Bud Sprout, Spotify, YouTube, and wherever podcasts can be found. Thank you so much for listening.