AI & Marketing Research with Dr. Eva Wolf
Not another AI news podcast. This is a research radar — a twice-weekly briefing that surfaces peer-reviewed studies on AI and marketing, tells you what the evidence actually says, and helps you decide what's worth a deeper read.
AI & Marketing Research with Dr. Eva Wolf
AI Marketing Research: Ai And Marketing — 5 Papers
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Here's the uncomfortable question this week. If your AI is quietly reinforcing gender stereotypes in every ad you publish, would you even know?
SPEAKER_00That's the thread. AI bias in creative work, consumer trust in AI-made ads, and whether our measurement systems are actually reliable.
SPEAKER_01We screened 115 papers. Five made the radar.
SPEAKER_00Quick caveat. This is a first pass research briefing, not a final academic review. We'll tell you what the papers suggest, what they don't prove, and which ones deserve a deeper read.
SPEAKER_01Okay, let's get into it.
SPEAKER_00Paper one gender bias in AI-powered marketing. And I want to be specific. This isn't about some abstract future system.
SPEAKER_01Right. These are the tools people are using right now.
SPEAKER_00The researchers ran semi-structured interviews, 11 marketing pros, art directors, agency owners, creative directors, all with hands-on AI experience.
SPEAKER_01So qualitative, what did they say?
SPEAKER_00Three things stood out. First, there's no consistency in how seriously people take this. Some treated bias as a PR risk, others as a real social harm. Almost nobody had a formal system to check outputs.
SPEAKER_01That tracks its vibes-based review if it happens at all.
SPEAKER_00Second finding the bias isn't always coming from the AI.
SPEAKER_01Wait, that matters. Say more.
SPEAKER_00If a team has always shown women in domestic roles, the AI learns to keep doing that. Nobody's consciously choosing it. The bias is baked into the creative process first.
SPEAKER_01That's the piece I care about. You can't just blame the algorithm.
SPEAKER_00Third thing. The fixes practitioners actually trust are human, diverse teams, outside feedback before publishing. More specific prompts.
SPEAKER_01Money move. A monthly AI bias audit, even 10 minutes reviewing recent outputs is a service nobody is formally selling yet. Try this by Friday. Pull your last five A. I generated images. Check who appears in what role. See a pattern you wouldn't consciously choose. Rewrite the prompts.
SPEAKER_00Evidence check, 11 interviews, all German speaking context. Theory building, not statistical proof. Don't generalize to your whole industry.
SPEAKER_01Radar verdict deep dive. The idea that bias lives in your creative habits, not just your tools, changes how you run a creative review.
SPEAKER_00Paper 2. What happens to consumer trust when people know an ad was made by AI?
SPEAKER_01And this one has actual numbers.
SPEAKER_00It does. Two by two experiment. 214 participants each saw one ad with a disclosed creator, human, AI, or hybrid. Two product categories, fast food and cosmetics.
SPEAKER_01Okay, so what happened?
SPEAKER_00Human made ads scored 5.63 on trust out of seven. AI-made ads scored 4.24. Statistically significant gap.
SPEAKER_01So more than a full point of trust gone, just because people knew AI made it.
SPEAKER_00Purchase intent followed the same direction. Human ads 5.62. AI ads 4.65.
SPEAKER_01What about the hybrid?
SPEAKER_00Hybrid recovered some trust. 5.08. But here's the interesting part. That trust recovery didn't translate into meaningfully higher purchase intent over fully AI-made ads.
SPEAKER_01So trust and buying are moving separately. That's the thing I'd have gotten wrong. Plain English payoff, telling people a human was involved can recover their trust, but trust alone doesn't get you the sale. Try this by Friday. For your next AI visual in a personal category, skincare is the obvious one. Add human creative involvement and consider flagging it. See if disclosed human review moves your engagement.
SPEAKER_00Evidence check, small sample, likely skews toward university students in India. And this experiment used clear disclosure labels. Real ads almost never say made by AI. The trust gap in practice could look very different.
SPEAKER_01Radar verdict use cautiously. Most rigorous design we have this week, but the sample and that artificial disclosure condition limit how far you can take it. Test it in your own context before drawing conclusions.
SPEAKER_00Paper 3, a proposed framework combining marketing, mix modeling, attribution, and incrementality testing into one AI-driven system. The author calls it AIMX.
SPEAKER_01Hold on, is this a real study or a white paper with citations?
SPEAKER_00Fair Challenge. It's design science with simulation. No real company data. The author built a conceptual model and tested its internal logic.
SPEAKER_01So it's a framework proposal.
SPEAKER_00Correct, but the problem it's solving is real. Most teams run MMM attribution and incrementality as completely separate projects. They produce conflicting signals. Nobody reconciles them before moving budget. That's the argument. One system, continuous feedback loop, faster budget decisions. The simulation suggests more stable allocation than running each tool in isolation.
SPEAKER_01I buy the problem.
SPEAKER_00Fair flag. The performance numbers, conversion rates doubling, marketing costs dropping 40% are compiled from secondary sources, not measured in a controlled study. Illustrative, not evidence.
SPEAKER_01What's still useful, the five-step sequence is a solid mental model. Get your customer data in one place, run AI across it, automate decisions, execute across channels, measure, and improve.
SPEAKER_00Evidence check, no original data. Metrics of uncertain providence, framework only.
SPEAKER_01Verdict skim later. Good checklist framing for a team just starting to think about AI in retail. Don't cite the numbers.
SPEAKER_00Paper 5.0 Marketing Professionals in India. How AI affects strategy across five dimensions. Regression analysis. The AI factors explained about 55% of the variation in self-reported outcomes.
SPEAKER_01Okay, what were the big predictors?
SPEAKER_00Data-driven decision making and personalization, not efficiency, not automation. Those two out in front.
SPEAKER_01Plain English payoff. If you're picking where to put your AI budget first, this study says data infrastructure and personalization tools outperform everything else in the eyes of practitioners.
SPEAKER_00Evidence check. Self-reported survey. Correlation, not causation.
SPEAKER_01Verdict used cautiously. Directionally useful for budget conversations. Don't build a business case on it alone. Okay, so here's what I think is actually happening this week. Five papers, all circling the same anxiety. Marketers are adopting AI faster than they're building systems to govern it. No bias checks, no unified measurement, no clear answer on what AI-made content does to the people who see it.
SPEAKER_00And the evidence quality reflects that. More AI in the back end can improve performance, while more AI in the creative output damages trust. Not the same lever.
SPEAKER_01That distinction matters. AI as infrastructure. Yes. AI as the author of your brand voice and your visuals. Tread carefully. Here's the playbook from this week. One, audit your last five A. I generated images for role stereotyping. Rewrite any vague prompts with specific casting. Two, for high trust categories, skincare, health, financial, keep a human visibly in the creative loop. Consider whether you can disclose that. Three, when your MMM and incrementality tests disagree, hold a meeting before you move budget. Don't let either tool win by default. Four, before you cite any AI marketing performance number from a framework paper, ask where it came from. If the answer is secondary sources, say so. Five, personalization and data infrastructure first. That's where the evidence says to put your AI budget, not automation for its own sake.
SPEAKER_00Quick note: two of today's papers are available in full text. The other three are abstract only or from venues with limited methodology detail. Links are in the show notes. Read the originals before making major decisions.
SPEAKER_01See you Thursday. And if the bias finding made you look differently at your own creative process, I genuinely want to hear about it.