AI & Marketing Research with Dr. Eva Wolf – Details, episodes & analysis

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AI & Marketing Research with Dr. Eva Wolf

Eva

Business

Frequency: 1 episode/1d. Total Eps: 53

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.

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🇫🇷 France - marketing
19/07/2026
#84
🇫🇷 France - marketing
18/07/2026
#72
🇫🇷 France - marketing
17/07/2026
#56
🇫🇷 France - marketing
16/07/2026
#38
🇫🇷 France - marketing
15/07/2026
#17

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https://bigplans.media/ai-marketing-consulting/
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https://doi.org/10.48550/arxiv.2605.11582
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https://www.youtube.com/hashtag/generativeai
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https://www.youtube.com/hashtag/marketingstrategy
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AI Slop Is Killing Your Marketing: The 3C Framework That Fixes It

Season 1 · Episode 1

Saturday, July 18, 2026 • Duration 28:58

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.

==== FREE RESOURCE ====

Build your own AI collaborator profile:
https://bigplans.media/ai-collaboration-profile/

Learn more about Dr. Eva Wolf and Big Plans Media:
https://bigplans.media/about-me/

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

Subscribe for research-backed AI marketing strategy, generative AI for marketing, AI adoption for small business, psychographic marketing, AI workflows, and practical insights for consultants, educators, and business owners.

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.

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AI Marketing Research: CRM Performance, Agent Loyalty & AI Forecasting

Thursday, July 16, 2026 • Duration 20:15

When your AI says it's predicting the future, is it actually forecasting — or just remembering? And what happens to brand loyalty when an AI agent, not a human, is the one making the purchase? Three papers this week examine whether we are actually getting what we think we are getting from AI in marketing. In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering AI-driven CRM and financial performance in digital banking, AI agent loyalty loops in autonomous commerce, and a method for evaluating whether AI forecasting tools are genuinely predicting or retrieving memorized answers. Note: Evita is an AI-generated research briefing avatar trained on the research framework of Dr. Eva Wolf. Every Friday, Dr. Wolf records a live weekly roundup with her own analysis. What you'll learn: - Why AI-powered CRM outperformed both personalization and chatbots as a financial performance driver in a Nigerian digital banking study, and how to use that R-squared value in a budget conversation - How to tell whether an AI forecasting tool your vendor sells is actually predicting anything, or retrieving answers it already knows from training data - Why emotional brand loyalty may stop mattering once an AI agent takes over purchasing decisions — and what marketers need to put in its place - What machine-readable brand signals means in practice, and why loyalty programs need to be legible to algorithms, not just humans - Why noisy or speculative retrieval sources make AI predictions worse, not merely less precise Papers covered: 1. The Influence of AI-Driven Marketing on the Financial Performance of Digital Banks in Nigeria Source type: Peer-reviewed journal article (likely peer-reviewed, published via Zenodo) Access: Full text reviewed DOI: 10.5281/zenodo.21277512 2. The Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) Model and the Net Human-Agent Score (NHAS) in Autonomous Commerce Source type: Preprint (not yet peer-reviewed — treat findings as early-stage and theoretical) Access: Full text reviewed Source: https://arxiv.org/abs/2607.13998v1 3. Hindcast: Replaying Prediction Markets to Evaluate LLM Forecasters Source type: Preprint (not yet peer-reviewed) Access: Full text reviewed Source: https://arxiv.org/abs/2607.14051v1 Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-crm-performance-agent-loyalty-forecasting-2026-07-16 Disclaimer: This is a first-pass research briefing produced by an AI-generated avatar trained on Dr. Eva Wolf's research framework. It is not a substitute for reading the original papers. Preprints have not been peer-reviewed and findings may change. Correlation findings do not establish causation. Always consult the original source before citing or acting on any research discussed here. -- This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions. AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts.

AI Marketing Research: Generative AI, Small Business & Chatbot Trust

Tuesday, July 7, 2026 • Duration 19:36

Access to AI tools isn't the same as knowing how to use them — and assuming your AI chatbot's output is safe to ship may be a risk your brand isn't tracking. This episode examines what the research actually says about generative AI adoption in small businesses, the reshaping of marketing content pipelines, and a striking finding about how often AI chatbots cite sources you'd never want attached to your brand. In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering generative AI training for small businesses, AI-driven content and influencer disruption, and a source-trust audit of AI chatbots using open web search — drawn from 385 papers screened. What you'll learn: - Why structured training and mentoring matter as much as the AI tools themselves for small business adoption - How generative AI is shifting who does creative work — and what that means for marketing teams - Why roughly 1 in 3 AI chatbot answers using open web search cited at least one source flagged as untrustworthy by domain experts - Why telling your AI to "prefer trusted sources" in the system prompt barely works — and what the more reliable fix actually is - What a pre/post measurement framework for AI training programs looks like in practice Papers covered: 1. Generative AI in Digital Marketing Strategy: Transforming Brand Communication and Consumer Engagement - Authors: Halim Dwi Putra, J. Azizah (2026) - Source type: Peer-reviewed journal article (likely peer-reviewed) - Venue: Indonesian Journal of Business and Entrepreneurship Research - Access: Full text reviewed - DOI: 10.62794/ijober.v4i1.23 - Radar verdict: Use cautiously 2. How Generative AI is Disrupting Marketing, Branding & Content Creation for Modern Businesses - Author: Shaan Garg (2026) - Source type: Peer-reviewed journal article (likely peer-reviewed) - Venue: International Journal For Multidisciplinary Research - Access: Full text reviewed (open access) - DOI: 10.36948/ijfmr.2026.v08i01.64298 - Radar verdict: Use cautiously 3. Curated Retrieval versus Open Web Search in Public AI Information Services: A Coverage-Trust Trade-Off - Authors: Hafsteinn Einarsson, Hafsteinn Birgir Einarsson, Jon Gunnar Olafsson, Jon Gunnar Thorsteinsson (2026) - Source type: Preprint — not yet peer-reviewed - Venue: arXiv - Access: Full text reviewed - Source: https://arxiv.org/abs/2607.05217v1 - Radar verdict: Watchlist Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-generative-small-business-chatbot-source-trust-2026-07-07 Disclaimer: This is a first-pass research briefing produced by an AI-generated research avatar trained on Dr. Eva Wolf's methodology. It is not a final academic review. Findings are summarised for informational purposes. Always read the original papers before making decisions based on this content. Preprints have not been peer-reviewed and should be treated with additional caution. -- This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions. AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts.

AI Visuals, Engagement & Marketing Agent Loop: Research Brief

Monday, July 6, 2026 • Duration 15:59

AI tools are cheap enough now that even a shoestring team can produce polished visuals — but does that actually close the competitive gap, or does it just mean everyone looks polished while the power imbalance stays exactly the same? And when AI agents start making pricing, promotion, and relationship decisions inside your marketing org, who is still accountable for auditing the results? In this Research Radar Brief, Dr. Eva Wolf reviews 2 recent peer-reviewed AI marketing research papers covering AI-generated visual content on social media, disclosure practices, photorealistic AI imagery, and a proposed framework for AI agent integration in marketing workflows. What you'll learn: - AI-generated visuals boost social media engagement — but the lift is roughly equal for all players, which means it may not deliver the competitive edge many marketers expect - Disclosure practices are uneven: better-resourced organizations label AI content more consistently, and that gap is emerging as a credibility signal - Photorealistic AI imagery paired with negative or emotionally charged messaging drives higher engagement — with real implications for brand safety and platform scrutiny - The Marketing Agent Loop (sense, generate, interact, learn) offers a four-stage mental map for deciding where AI agents fit in your workflow — and where human oversight is still essential Papers covered: 1. Party Equalization or Normalization Through Visual Generative AI in the 2025 German Federal Election - Authors: Simon Kruschinski, Fabio Votta (2026) - Source: Media and Communication (peer-reviewed journal) - Access: Full text reviewed - DOI: https://doi.org/10.17645/mac.11859 - Radar verdict: Deep dive 2. Agent-Oriented Transformation of Marketing Functions in the Generative AI Era: Introducing the Marketing Agent Loop - Authors: Merve Kadriye Yurdabak (2026) - Source: Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi (peer-reviewed journal) - Access: Full text reviewed — open access - DOI: https://doi.org/10.24889/ifede.1788481 - Radar verdict: Watchlist Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-visuals-engagement-agent-loop-2026-07-06 Disclaimer: This is a first-pass research briefing produced by Evita, an AI-generated avatar trained on the research framework of Dr. Eva Wolf. It is not a substitute for reading the full papers. Findings are reported as the research suggests — not as proven facts. Always consult primary sources before making strategic decisions. -- This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions. AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts.

AI Ad Speed, Brand Trust & Green Influencer Risk: 3 Research Signals

Sunday, July 5, 2026 • Duration 20:28

When AI runs your ads, writes your content, and picks your influencers — are you gaining an edge, or quietly building a trust problem you haven't spotted yet? This episode looks at both sides of that question at once, drawing on three recent AI and marketing research papers that together point to the same tension: AI is removing friction from ad delivery at exactly the moment consumers are developing resistance to AI-generated content. In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering LLM-powered ad delivery, consumer perceptions of generative AI content, and AI and AR tools in sustainability influencer marketing. We screened 358 papers this cycle; these three cleared the full-text bar. What you'll learn: - How a compressed large language model was reported to run real-time ad delivery at over 1.8x its original speed — and what that means for ad tech teams still treating LLMs as too slow and too expensive - The four ways consumers signal distrust when they sense a brand is leaning too hard on AI: verification burden, emotional flattening, content homogenisation, and AI fatigue - Why AI-generated visuals and AR experiences in sustainability campaigns can amplify greenwashing risk rather than credibility - What inference latency and competitive precision actually mean for marketers evaluating AI ad vendors - Why a hybrid human-AI content approach may now be a measurable brand safety decision, not just an ethical preference Papers covered: 1. Efficient LLM-based Advertising via Model Compression and Parallel Verification Authors: WenXin Dong, Chang Gao, Guanghui Yu et al. (2026) Source type: Preprint (not yet peer-reviewed) Access: Full text reviewed Source: https://doi.org/10.48550/arxiv.2605.11582 2. Generative AI in Marketing Communication: Consumer Perceptions, Brand Credibility, and Responsible AI Practices Authors: Tahir Mushtaq, Onder Kethuda, Antje Cockrill, Ahmed Almoraish (2026) Source type: Conference paper (likely peer-reviewed — AMI Conference 2026) Access: Full text reviewed Source: https://doi.org/10.25401/cardiffmet.32326287.v1 3. Sustainable Influencer Marketing: Leveraging AI and AR Tools to Promote Green Lifestyles Authors: Lina Tio, Salamiah Muhd Kulal, Dorris Yadewani (2026) Source type: Peer-reviewed journal article (use cautiously — see episode notes on venue credibility) Access: Full text reviewed Source: https://doi.org/10.54099/ijibmr.v5i2.1624 Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-ad-speed-brand-trust-influencer-greenwashing-2026-07-05 Disclaimer: This is a first-pass research briefing produced by an AI-generated research avatar trained on the methodology of Dr. Eva Wolf. It is not a final academic review. Findings are reported as the papers suggest, not as established facts. Preprints have not been peer-reviewed. Always consult the original papers before making business decisions. -- This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions. AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts.

AI Ads, Persona Research & Consumer Trust: 3 Marketing Papers

Saturday, July 4, 2026 • Duration 19:20

What if two of the biggest bets your team is making right now — building detailed customer personas for AI research and trusting AI to write your ads — are both quietly working against you? This episode looks at what the latest research actually says about where AI-native advertising is breaking, and whether the fixes are simpler than most teams assume. In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering LLM ad auction design, the accuracy of AI-simulated customer personas, and consumer trust in generative AI advertising. What you'll learn: - Why filtering ads for topic relevance before the auction — not after — may earn AI platforms more revenue while annoying users less - Why elaborately detailed customer personas tend to make AI simulations of human behavior less accurate, not more - Why a simple Age + Gender persona consistently outperformed a 10-attribute Ideal Customer Profile in AI audience research - Why perceived authenticity — not visual quality — is the stronger predictor of whether AI-generated ads drive engagement - Which industries (healthcare, finance, luxury) face the sharpest consumer trust penalty for AI-generated creative Papers covered: 1. Mechanism Design for Quality-Preserving LLM Advertising Authors: Jiale Han, Xiaowu Dai (2026) Source: arXiv (Cornell University) Type: Preprint — not yet peer-reviewed Access: Full text reviewed DOI: https://doi.org/10.48550/arxiv.2605.10964 2. How Well Do Large Language Models Capture Human Personality? Authors: Aanisha Bhattacharyya, Yaman Kumar Singla, Rajiv Ratn Shah, Changyou Chen, Jitendra Ajmera (2026) Source: arXiv (Cornell University) Type: Preprint — not yet peer-reviewed Access: Full text reviewed Source: https://arxiv.org/abs/2606.18263 3. Generative AI Applications in Advertising Author: Yifei Wang (2026) Source: Frontiers in Computing and Intelligent Systems Type: Peer-reviewed journal article (lower-tier venue — see episode notes for caveats) Access: Full text reviewed DOI: https://doi.org/10.54097/4tggde31 Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-advertising-persona-accuracy-consumer-trust-llm-2026-07-04 DISCLAIMER: This is a first-pass research briefing produced using an AI-generated research avatar trained on Dr. Eva Wolf's methodology. Two of the three papers covered are preprints that have not yet been peer-reviewed. Findings are presented as signals to investigate, not as settled conclusions. Always read the original papers before making business decisions based on this content. -- This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions. AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts.

AI-Generated Ads, Consumer Trust & Privacy: 3 Research Signals

Friday, July 3, 2026 • Duration 20:17

When your AI writes the ad, the email, or the product page — does it matter whether customers know it's AI, or does what they feel about it matter more? This radar brief follows the trust trail: from authenticity signals to privacy gates to the uncanny valley of AI creative, and asks what actually moves a consumer from "I see this ad" to "I'm buying." In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering AI-generated content and purchase intent, consumer responses to AI advertising, and how privacy trust shapes the effectiveness of AI personalization across cultural markets. What you'll learn: - Why perceived authenticity — not credibility — is the single strongest driver of purchase intent from AI-generated content, and why credibility only converts when it first builds trust - How consumers can feel both impressed and unsettled by AI ads at the same time, and what determines which reaction wins - Why disclosing AI involvement doesn't automatically hurt ad performance — and when it may actually help - How privacy trust acts as an on/off switch for AI personalization, especially in GDPR-regulated markets - Why cultural localization means adapting visuals, flow, and tone — not just language — and how that amplifies AI personalization effects Papers covered: 1. The Impact of AI-Generated Marketing Content on Consumer Behavior Source type: Peer-reviewed journal article (International Journal of Multidisciplinary Research and Analysis) Access: Full text reviewed DOI: 10.47191/ijmra/v9-i6-60 2. Decoding AI-Generated Advertising: Consumer Responses, Advertising Outcomes, and Strategic Leverage Source type: Academic book chapter (IntechOpen, likely peer-reviewed) Access: Full text reviewed DOI: 10.5772/intechopen.1016226 3. Cultural Adaptation and AI-Driven Marketing in the Context of Trust in Data Privacy Source type: Academic book chapter (Routledge, likely peer-reviewed) Access: Full text reviewed DOI: 10.4324/9781003757719-14 Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-generated-ads-consumer-trust-privacy-personalization-2026-07-03 DISCLAIMER: This is a first-pass research briefing produced by an AI-generated avatar (Evita) trained on the research framework of Dr. Eva Wolf. It is not a substitute for reading the full papers. Findings are described as the studies suggest them — not as proven facts. Sample sizes, methodologies, and limitations are noted where relevant. Always consult the original sources before making strategic decisions. -- This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions. AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts.

AI Creativity Benchmarks, Chatbot Personas & Agent Competition

Thursday, July 2, 2026 • Duration 22:11

Are you choosing AI tools on instinct, setting your chatbot to maximum-friendly, and hoping your AI agents figure out the rest? Three new preprints suggest those defaults are leaving real performance on the table — and that small, specific changes to how you prompt and configure AI can produce meaningfully different outcomes. In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI and marketing research papers covering AI creativity measurement, adaptive chatbot personality design, and the competitive dynamics of AI agent labour markets. What you'll learn: - Adding "be creative" to your AI prompt meaningfully improves creative output — and takes about five seconds - The best AI model for writing taglines is probably not the best model for brainstorming campaign concepts - A chatbot with a medium-intensity personality outperforms both flat and over-enthusiastic bots on trust and likeability - Context-specific prompting for AI agents changes outcomes — a single generic prompt leaves performance on the table - Platform design choices (like whether pricing is public or hidden) shape whether AI agents race to the bottom or compete on quality Papers covered: 1. AGC-Bench: Measuring Artificial General Creativity Authors: Beaty, Deshpande, Lai, Attuch, Shivagunde, Roy, Pujari, DiStefano, Muckatira, Stevenson, Gronas, Rumshisky (2026) Type: Preprint — not yet peer-reviewed Access: Full text reviewed Source: https://arxiv.org/abs/2607.01152v1 2. Behavior-Adaptive Conversational Agents: Toward a Fluid Personality Framework Authors: Rahman, Desai (2026) Type: Preprint — not yet peer-reviewed (presented at AAAI-2026 Bridge Program) Access: Full text reviewed Source: https://arxiv.org/abs/2607.01034v1 3. When AI Agents Compete for Jobs: Strategic Capabilities and Economic Dynamics of AI Labour Markets Authors: Chiu, Zhang, van der Schaar (2025) Type: Preprint — not yet peer-reviewed Access: Full text reviewed Source: https://arxiv.org/abs (link in show notes) Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-creativity-benchmarks-chatbot-personas-agent-competition-2026-07-02 Disclaimer: This episode is a first-pass research briefing produced by an AI-generated avatar trained on the research framework of Dr. Eva Wolf. All three papers discussed today are preprints that have not yet completed peer review. Findings are preliminary and may change. Nothing in this episode constitutes professional marketing, legal, or financial advice. Always read the original papers before making strategic decisions. -- This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions. AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts.

Personalized AI, Creative Work & Knowledge Protocols: Research Brief

Wednesday, June 17, 2026 • Duration 13:08

Most marketing teams are running AI on generic settings — and the latest research suggests that may be the core problem. A new experiment finds that giving an AI tool a profile of the person it's working with produced measurably more creative and higher-quality marketing campaigns than using a generic AI setup. A second paper proposes a new framework for guiding how AI reasons through expert work — compelling in theory, but not yet tested in practice. In this Research Radar Brief, Dr. Eva Wolf reviews 2 recent AI marketing research papers covering personalized AI collaboration, human-AI creative synergy, and expert-guided AI reasoning frameworks. What you'll learn: - Why a personalized AI collaborator outperformed a generic one in a controlled experiment — and the three mechanisms that explain the gap - How human-plus-personalized-AI teams achieved a synergy effect that beat AI working alone — a result rarer than most assume - What Knowledge Protocol Engineering proposes as a step beyond RAG-based AI — and why it has not yet been validated - How to start loading your AI tools with collaborator context today, before any platform natively supports it Papers covered: 1. Personalized AI Scaffolds Synergistic Multi-Turn Collaboration in Creative Work - Authors: Sean Kelley, David De Cremer, Christoph Riedl (2025) - Type: Preprint (not yet peer-reviewed) - Access: Full text reviewed - Source: https://arxiv.org/abs/2510.27681v2 2. Knowledge Protocol Engineering: A New Paradigm for AI in Domain-Specific Knowledge Work - Authors: Guangwei Zhang (2025) - Type: Preprint (not yet peer-reviewed) - Access: Full text reviewed - Source: https://arxiv.org/abs/2507.02760v1 Full show notes, transcript, and citations: https://bigplans.media/episodes/personalized-ai-creative-work-knowledge-protocols-marketing-2026-06-17 Disclaimer: This is a first-pass research briefing, not a final academic review. Both papers covered are preprints that have not yet been peer-reviewed — findings may change before publication. Dr. Eva Wolf summarizes what the research suggests and where the evidence is limited. Nothing here should be taken as a definitive research conclusion. -- This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions. AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts.

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: Consulting: Big Plans Media — Where Big Ideas Meet Smart Marketing.

AI Personalization & Creative Campaigns: What the Research Shows

Wednesday, June 17, 2026 • Duration 13:52

Does it matter whether your AI tool knows anything about you before you start a campaign? A new experimental study suggests it does — and the reason why changes how you should think about every AI-assisted creative session you run. In this Research Radar Brief, Dr. Eva Wolf reviews one recent AI marketing research paper covering personalized AI, multi-turn creative collaboration, and what the research suggests about setting up AI tools for better campaign work. We screened twelve papers this cycle; one cleared the full-text bar. What you'll learn: - Why a short "creative brief about yourself" before any AI session may improve campaign quality — and what the research says is driving that improvement - The three collaboration mechanisms personalization appears to activate: shared memory, joint attention, and aligned reasoning - Why single-prompt tests of AI tools may miss the real performance gap — and where personalization pays off most - Why this study's evidence is directionally strong but not final — and what to keep in mind before overhauling your AI workflow Papers covered: 1. Personalized AI Scaffolds Synergistic Multi-Turn Collaboration in Creative Work Authors: Sean Kelley, David De Cremer, Christoph Riedl (2025) Type: Preprint (not yet peer-reviewed) Access: Full text reviewed Source: https://arxiv.org/abs/2510.27681v2 Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-personalization-creative-marketing-campaigns-research-2026-06-17 Important note: This is a first-pass research briefing, not a final academic review. Every paper covered has been reviewed at the full-text level where access was available. Read the original papers before making major marketing, business, legal, or financial decisions. Preprints have not yet completed peer review and findings may change. -- This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions. AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts.

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: Big Plans Media — Where Big Ideas Meet Smart Marketing.

AI & Marketing Research with Dr. Eva Wolf – Details, episodes & analysis

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AI & Marketing Research with Dr. Eva Wolf – Details, episodes & analysis

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