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Explorez tous les épisodes du podcast AI & Marketing Research with Dr. Eva Wolf
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| Titre | Date | Durée | |
|---|---|---|---|
| AI Capability Benchmarks: What Marketers Need to Know | 22 Sep 2026 | 00:17:27 | |
Your vendor just told you their AI is approaching AGI-level performance. Your board wants to know if that means anything. And most marketing teams have no framework for answering either question.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI capability papers — none of which study marketing directly — that together provide a rigorous vocabulary for evaluating AI tools, cutting through benchmark hype, and deciding how much autonomy to give AI in your workflows.
A note on this episode: all three papers were screened for direct AI + marketing relevance and scored below the show's standard threshold. They are covered here because the script excerpt and pipeline context indicate Dr. Eva Wolf made an editorial decision to include them for their practical vendor-evaluation value. The findings are presented as the original authors intended — as frameworks and theoretical proposals, not empirical marketing evidence.
What you'll learn:
- Why task-specific AI benchmark scores can be misleading when evaluating tools for marketing work
- How a tiered AGI capability model (Emerging to Superhuman) can help you parse vendor claims more precisely
- Why the amount of human oversight you apply to an AI system is a separate choice from how capable that system is
- How to use a 10-faculty cognitive breakdown to scope what an AI tool can and cannot reliably do
- Why training-data volume can inflate benchmark scores without reflecting real adaptability
Papers covered:
1. Levels of AGI for Operationalizing Progress on the Path to AGI
Source: Conference paper — International Conference on Machine Learning (ICML 2024), likely peer-reviewed
Access: Full text reviewed
Authors: Morris, Sohl-Dickstein, Fiedel, Warkentin, Dafoe, Faust, Farabet, Legg (Google DeepMind)
Link: https://arxiv.org/abs/2311.02462
2. On the Measure of Intelligence
Source: Preprint — arXiv (2019). Not peer-reviewed.
Access: Full text reviewed
Authors: Francois Chollet
Link: https://arxiv.org/abs/1911.01547
3. Measuring Progress Toward AGI: A Cognitive Framework
Source: Preprint — arXiv (2026), Google DeepMind. Not peer-reviewed.
Access: Full text reviewed
Authors: Burnell et al.
Link: https://arxiv.org/abs/2605.28405
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-capability-benchmarks-agi-frameworks-marketers-2026-09-22
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 full papers. Preprints have not undergone peer review and findings may change. Papers 1 and 3 are authored by Google DeepMind researchers; potential organizational perspective or incentive biases are noted but not fully addressed in the available texts. Always read the original research before making business decisions.
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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. | |||
| AI Expert Forecasts: What 2,800 Researchers Say Is Coming | 22 Sep 2026 | 00:11:29 | |
The people who build AI — nearly 2,800 researchers publishing at the field's top venues — just moved their own timeline for AI surpassing human performance at most tasks up by thirteen years. In a single year. This episode asks: should that change how you're planning right now?
In this Research Radar Brief, Dr. Eva Wolf reviews one AI marketing research paper that cleared the full-text bar from a screen of 12 recent papers, covering expert probability forecasts on AI capability milestones, creative AI timelines, and the risks researchers themselves are most worried about.
What you'll learn:
- Why AI researchers now put a 50% probability on AI outperforming humans at every task by 2047 — a forecast that moved up 13 years in one year
- What near-term milestones researchers expect by 2028, including autonomous product coding and AI-generated music indistinguishable from human artists
- Why full job automation is still forecast to be nearly a century away, even if AI capability milestones arrive sooner
- How between 38% and 51% of AI researchers assess at least a 10% chance of catastrophic AI outcomes — and what that means for brand trust and content strategy
- What the marketing planning implications are when your own vendors' scientists are this uncertain about the pace of change
Papers covered:
1. Thousands of AI Authors on the Future of AI
Source type: Preprint (not yet peer-reviewed)
Access: Full text reviewed
Source: https://arxiv.org/abs/2401.02843
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-expert-forecasts-marketing-researchers-survey-2026-09-22
Disclaimer: This is a first-pass research briefing, not a final academic review or professional advice. The paper covered is a preprint and has not completed formal peer review. Findings reflect what the research suggests at this stage — not confirmed conclusions. Always read the original source before making decisions based on any research discussed here.
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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. | |||
| AI Marketing Adoption: Leadership, Trust & the Human Problem | 18 Sep 2026 | 00:17:38 | |
Your company bought an AI marketing tool. Leadership signed off. IT set it up. Six months later — nobody is using it. This episode, three research papers point at exactly that problem. And the fix is not what most teams are reaching for.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering AI adoption in small and medium businesses, enterprise AI deployment failures, and the psychology of consumer trust in AI-powered marketing.
What you'll learn:
- Why a CEO's public endorsement of AI tools matters more than any employee attitude-change program — and what a Colombian SME study of 216 firms tells us about the adoption levers that actually work
- Why enterprise AI systems get built and then ignored — and how applying product marketing basics (user personas, value propositions, go-to-market plans) to internal deployments could change that
- The seven psychological factors that shape consumer trust in AI-powered marketing, including what builds it (personalization, transparency) and what erodes it (hidden data collection, the feeling of being manipulated)
- How to frame an internal AI rollout as a marketing challenge, not a technical one
Papers covered:
1. Exploring the Impact of AI-Enabled Marketing on Business Performance in SMEs
- Source: Revista Venezolana de Gerencia
- Type: Peer-reviewed journal article
- Access: Full text reviewed
- DOI: https://doi.org/10.52080/rvgluz.31.116.11
2. Bridging the Adoption Gap: Why Supply Chain AI Fails Without Product Marketing Principles
- Source: International Journal of Marketing and Communication Studies
- Type: Peer-reviewed journal article
- Access: Full text reviewed (open access)
- Link: https://doi.org/10.56201/ijmcs.v8.no5.2024.pg169.205
3. The Psychology of Consumer Trust in AI-Based Marketing
- Source: Zenodo (CERN European Organization for Nuclear Research)
- Type: Journal article deposited on Zenodo — peer review rigor unclear
- Access: Full text reviewed
- DOI: https://doi.org/10.5281/zenodo.22797380
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-adoption-leadership-trust-consumer-smes-2026-09-18
Disclaimer: This is a first-pass research briefing produced by an AI-generated avatar trained on the research framework of Dr. Eva Wolf. It is not a final academic review. Findings are reported as the papers suggest them, not as proven facts. Limitations are noted for each paper. Always read the original source before acting on any finding.
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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. | |||
| AI Attribution, Trust & Personalization: 3 Marketing Research Signals | 17 Sep 2026 | 00:21:40 | |
Your attribution model is probably giving credit to the wrong touchpoints. Your AI chatbot might be one disclosure away from converting more customers. And your recommendation engine may only work if shoppers actually feel understood. This week's radar brief examines three recent AI marketing papers that each illuminate a different part of the same problem: how AI performs — or quietly fails — inside the customer journey.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering conversion attribution, consumer trust in human–AI collaboration, and personalization-driven purchase intent.
What you'll learn:
- Why roughly 1 in 7 meaningful customer touchpoints may be invisible to standard attribution models — and how LLMs can surface them
- How adding visible human oversight to AI-powered marketing may increase consumer trust and purchase intent
- Why personalization, trust, and purchase intent form a chain — and why skipping steps in that chain may break the outcome
- Which findings are strong enough to test this week, and which are too methodologically limited to act on yet
Papers covered:
1. Can Large Language Models Identify Meaningful Touchpoints in Conversion Attribution?
Source type: Preprint — accepted to CIKM '26, not yet fully peer-reviewed
Access: Full text reviewed
DOI: 10.48550/arxiv.2608.28649
Link in show notes
2. Human–Generative AI Collaboration in Digital Marketing: Its Impact on Consumer Trust, Purchase Intentions, and Financial Decision-Making
Source type: Peer-reviewed journal article (venue credibility unverified)
Access: Full text reviewed
DOI: 10.59543/jidmis.v3.642
Link in show notes
3. Pengaruh Generative AI Marketing terhadap Purchase Intention melalui Customer Trust dan Perceived Personalization pada Pengguna E-Commerce
Source type: Peer-reviewed journal article (low-prestige venue, n=50)
Access: Full text reviewed
DOI: 10.62710/pt2kaf55
Link in show notes
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-attribution-consumer-trust-personalization-2026-09-17
Disclaimer: This episode 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 final academic review. Findings are reported as the papers suggest — not as proven conclusions. Preprints have not completed peer review. Always read the original papers before making strategic or 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. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Ads Inside AI Answers, Self-Evolving Ad Systems & Health AI | 28 Aug 2026 | 00:20:12 | |
What if the ad slot you're bidding on today becomes irrelevant — not because clicks drop, but because AI generates the answer before users ever see a search result? And what if your customers are already using ChatGPT to research your product before they talk to anyone on your team — and not telling a soul?
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI and marketing research papers covering token-level advertising inside AI-generated answers, autonomous AI-driven ad system optimization, and hidden consumer AI behavior in the healthcare journey.
What you'll learn:
- How a proposed auction system would let brands bid — word by word — to appear naturally inside AI-generated answers, not beside them
- Why a purpose-trained AI model roughly doubled the success rate of senior human experts at improving an ad recommendation system
- Why most survey respondents said they used ChatGPT to research health questions before a doctor visit — but didn't mention it to their physician
- What the hidden AI research stage in the consumer journey means for health marketers right now
- Why off-the-shelf tools like GPT-5.5 underperformed badly at specialized ad optimization, and what that suggests for how you build internal AI tools
Papers covered:
1. Token-Level Advertising
Authors: Hanbing Liu, Bowei Zhang, Changyuan Yu, Yinyu Ye, Qi Qi
Source type: Preprint (not yet peer-reviewed)
Access: Full text reviewed
Source: https://arxiv.org/abs/2608.27382v1
2. Astar: Learning to Propose Evolution Directions for Self-Evolving Industrial AI Systems
Authors: Jinxin Hu et al.
Source type: Preprint (not yet peer-reviewed)
Access: Full text reviewed
Source: https://arxiv.org/abs/2608.27287v1
3. Generative AI use before medical visits: disclosure-item responses, trust, and care-seeking behaviors in a cross-sectional social-media survey in Poland
Authors: Simona Wójcik, Anna Rulkiewicz, Justyna Domienik-Karłowicz
Source type: Peer-reviewed journal article (Frontiers in Digital Health)
Access: Full text reviewed
DOI: 10.3389/fdgth.2026.1933451
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-token-advertising-self-evolving-ad-systems-health-ai-2026-08-28
Disclaimer: This is a first-pass research briefing produced by Evita, an AI-generated avatar trained on Dr. Eva Wolf's research framework. It is not a final academic review. Preprints have not been peer-reviewed and findings may change. All claims are attributed to the cited papers; listeners should consult the original sources before acting on any findings.
--
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. | |||
| AI Marketing Research: Gen Z Trust, Ad Forecasting & LLM Ads | 27 Aug 2026 | 00:19:23 | |
Are brands that openly explain how their AI works actually winning more trust from Gen Z consumers? Can your forecasting tools simulate what happens if you change your ad budget — or just tell you what already happened? And what might it look like to buy ads inside ChatGPT or Perplexity based on the meaning of a conversation rather than a keyword?
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering Gen Z consumer trust, AI-driven demand forecasting, and advertising auction design for large language model interfaces. We screened 388 papers to get here.
What you'll learn:
- Why Gen Z consumers in one study responded more positively to brands that explained their AI — and what the study's limitations mean for how much you should act on it
- Why your current forecasting tools can tell you what happened but likely cannot tell you what would happen if you changed your ad spend
- What AI-native advertising auctions could look like inside conversational AI tools — and why they would behave differently from keyword auctions
- The difference between treating AI transparency as an ethics checkbox versus a conversion lever
- Why separating the effect of your ad budget from the effect of an external event matters for accurate campaign attribution
Papers covered:
1. Building Gen Z Consumer Trust Through Transparency in AI-Driven Marketing
Source type: Peer-reviewed journal article (Journal of Advance and Future Research, JAAFR)
Peer review status: Likely peer-reviewed
Access: Full text reviewed
Radar verdict: Use cautiously
DOI: 10.56975/jaafr.v4i8.513942
2. CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition
Source type: Preprint (accepted at KDD 2026 — not yet fully peer-reviewed at time of recording)
Access: Full text reviewed
Radar verdict: Test this week
Preprint: https://arxiv.org/abs/2608.25871v1
DOI: 10.1145/3770855.3818338
3. The Power Diagram Auction: A Formally Verified VCG Mechanism for LLM Advertising
Source type: Preprint (Zenodo — not peer-reviewed)
Access: Full text reviewed
Radar verdict: Watchlist
DOI: 10.5281/zenodo.21723923
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-gen-z-trust-demand-forecasting-llm-advertising-2026-08-27
Disclaimer: This is a first-pass research briefing produced by an AI-generated research avatar trained on Dr. Eva Wolf's research framework and methodology. It is not a substitute for full academic review. Findings are summarized for informational purposes. Always read the original papers before making business decisions. Preprints have not completed peer review 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. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Workflow Gaps, Brand Equity & Content AI: 3 Research Signals | 26 Aug 2026 | 00:20:40 | |
If your AI tool can quote a key finding from a document word-for-word, does that mean it actually used that fact when making a recommendation? This week's radar brief surfaces three recent papers that all point to the same uncomfortable pattern: the gap between what AI can do and what it actually delivers in practice is almost always a workflow problem — not a model problem.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering generative AI content production for small teams, AI tools as brand-building assets in higher education, and how AI document analysis workflows may silently discard information your team assumes is being used.
What you'll learn:
- How one startup combined ChatGPT and Copy.ai with a two-week sprint structure to post more consistently on social media — without hiring more people
- Why pairing AI content tools with free analytics like Meta Business Suite creates faster feedback loops for small marketing teams
- How a university's custom-branded AI assistant may function as a brand touchpoint — shaping student perceptions of quality and loyalty — not just an IT feature
- Why an AI that can accurately retrieve a fact from a long document may still completely ignore that fact in its final recommendation
- How chunk-and-summarize pipelines may be silently discarding information your team assumes the AI is using
Papers covered:
1. Implementation of Generative AI for Digital Marketing and Social Media Content
- Source type: Peer-reviewed journal article (Journal of Applied Engineering and Social Science)
- Access: Full text reviewed
- DOI: 10.25124/jaess.v4i1.11157
- Radar verdict: Test this week
2. Reconceptualizing Higher Education Marketing in the Algorithmic Era: Institutional Generative AI and Multidimensional University Brand Equity
- Source type: Peer-reviewed journal article (low-profile venue — treat findings with caution)
- Access: Full text reviewed
- DOI: 10.5281/zenodo.21169440
- Radar verdict: Use cautiously
3. Reading Is Not Using: Retrieval, Judgment, and the Design of AI Financial Research Workflows
- Source type: Preprint — not yet peer-reviewed
- Access: Full text reviewed
- Source: https://arxiv.org/abs/2608.24842v1
- Radar verdict: Test this week
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-workflow-gaps-content-ai-brand-equity-document-retrieval-2026-08-26
Disclaimer: This episode is a first-pass research briefing produced by an AI-generated avatar trained on the research framework of Dr. Eva Wolf. It is not a substitute for a full academic review. Findings represent what the papers suggest, not what is proven. Preprints have not been peer-reviewed and should be treated with additional caution. 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. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Research: Agency Survival, Churn AI & Disclosure | 25 Aug 2026 | 00:19:13 | |
When AI can write your ads, predict who's about to leave, and draft your strategy decks — what are humans still for, and when does it matter that you say so? This week's research batch lands on a single uncomfortable truth: the closer AI gets to judgment calls, the more it needs a human watching over its shoulder.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering the future of advertising agencies, deep learning models for customer churn prediction, and the growing gap between how AI is actually used in professional work and what anyone discloses about it.
What you'll learn:
- Why AI automating ad campaigns on Meta and Google may be eroding the most billable parts of your agency — and what to offer instead
- How a deep learning model achieved 96% accuracy predicting customer churn on e-commerce data — and why that number needs careful context before you act on it
- Why customer satisfaction scores appear to be a stronger loyalty predictor than purchase history, and what that means for your CRM setup
- How AI disclosure norms are broken even in academic research — and what that gap reveals about transparency risks building up inside marketing teams
- What a genuinely useful AI disclosure statement looks like versus a generic line that tells nobody anything
Papers covered:
1. A Discussion on the Future of Advertising Agencies in the Impact of Artificial Intelligence
- Source type: Peer-reviewed journal article (Intermedia International e-journal)
- Access: Full text reviewed
- DOI: 10.56133/intermedia.1740804
- Radar verdict: Test this week
2. Generative AI for Personalized Marketing and Customer Experience in E-Commerce
- Source type: Peer-reviewed journal article (International Journal of Emerging Research in Engineering and Technology)
- Access: Full text reviewed
- DOI: 10.63282/3050-922x.ijeret-v7i1p103
- Radar verdict: Use cautiously
3. Expectations and Practices around AI Disclosure in CS Research
- Source type: Preprint (arXiv) — not yet peer-reviewed
- Access: Full text reviewed
- URL: https://arxiv.org/abs/2608.23271v1
- Radar verdict: Test this week
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-agency-survival-churn-prediction-disclosure-2026-08-25
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 substitute for reading the original papers. Preprints have not undergone peer review and findings may change. Source quality and study limitations are noted for each paper. Nothing here constitutes financial, legal, or professional advice.
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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. | |||
| AI Marketing Research: Gen Z Trust, Emotional AI & Chatbot Ads | 24 Aug 2026 | 00:19:51 | |
When does AI personalisation stop being helpful and start feeling like surveillance? And if AI chatbots are about to run native ads, will anyone even know they're being sold to? Those are the questions running through today's radar.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering Gen Z consumer trust, sociodemographic variation in emotional AI use, and a new technical system for inserting sponsored content into chatbot responses.
What you'll learn:
- Why Gen Z consumers respond better to personalised AI marketing when they can see why they're being targeted — and how opacity kills purchase intent
- How privacy concerns predict distrust among Gen Z, and why transparent data practices are now a brand trust lever, not just a legal requirement
- Why women using emotional AI tools are more sensitive to privacy signals than men — and what that means for how you message AI-powered wellness or support products
- Why older and lower-income emotional AI users skip the trust question entirely and respond to availability and non-judgment messaging instead
- What PILA is: a plug-in layer that inserts sponsored content into chatbot responses after the answer is written, without modifying the underlying AI model
- Why the chatbot ad space has a growing legal blind spot — none of this week's research addresses disclosure rules, and regulators are paying attention
Papers covered:
1. The Impact of AI-Driven Marketing on Gen Z Consumer Buying Decisions: Helpful or Creepy
- Source type: Peer-reviewed journal article (use cautiously — venue credibility and sample size noted as limitations)
- Access: Full text reviewed
- DOI: 10.56975/ijnrd.v11i8.327672
2. Who Trusts AI with Their Emotions? Trust Formation and Sociodemographic Variation in LLM Use for Emotional Support
- Source type: Preprint (not yet peer-reviewed — findings may change)
- Access: Full text reviewed
- Source: https://arxiv.org/abs/2608.21220v1
3. PILA: Plug-and-Play Insertion for LLM-native Advertising
- Source type: Preprint (not yet peer-reviewed — findings may change)
- Access: Full text reviewed
- DOI: 10.48550/arxiv.2607.25590
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-gen-z-trust-emotional-ai-llm-native-ads-2026-08-24
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 original papers. Preprints have not been peer-reviewed and findings should be treated as preliminary. Radar verdicts reflect triage judgements, not formal academic review.
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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. | |||
| AI Marketing Research: Content Automation, GenAI Gaps & Consumer Engagement | 23 Aug 2026 | 00:22:56 | |
Most marketing teams are already using AI every day — but are they using it to get better, or just to go faster? This week's research radar surfaces a pattern across three papers: AI genuinely levels the playing field for content volume and SEO, but the moment a client needs a real conversation, or an audience needs to feel something, full automation starts costing you.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering AI content tools for B2B start-ups, generative AI adoption gaps across the marketing industry, and how brands can use AI to drive consumer engagement without losing emotional connection.
These are first-pass research briefings, not final academic reviews. Findings reflect what individual papers suggest — not settled conclusions.
What you'll learn:
- Why B2B consulting clients drew a hard line between AI-generated content and AI-run conversations — and what that means for any service firm
- Where the real gap in generative AI adoption sits: 71% of marketers use it weekly, but most use it only to work faster, not smarter (source: AMA/Lightricks industry survey cited in paper)
- The three mechanisms research suggests drive consumer engagement with AI — personalization, co-creation, and conversational AI — and why removing humans from the loop may weaken all three
- Why niche, low-competition SEO content may outperform broad keyword targeting for resource-constrained firms — and how AI makes that strategy more affordable
- How age segmentation changes the calculus when deciding which audiences to pilot AI-powered touchpoints with first
Papers covered:
1. From Invisible to Unstoppable: How AI and Digital Marketing Transform IT Consulting Start-ups
Source type: Peer-reviewed journal article (Journal of Digital Marketing and Communication)
Access: Full text reviewed
DOI: 10.53623/jdmc.v6i2.1274
2. The Generative AI Revolution in Digital Marketing: Opportunities, Implementation Barriers, and Strategic Future Directions
Source type: Peer-reviewed journal article (Journal of Economics, Business, and Commerce)
Access: Full text reviewed
DOI: 10.69739/jebc.v3i2.1947
3. Generative AI Applications In Consumer Engagement And Brand Communication
Source type: Literature review hosted on open repository (Zenodo) — peer review status unconfirmed
Access: Full text reviewed
DOI: 10.5281/zenodo.21336697
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-content-automation-genai-gaps-consumer-engagement-2026-08-23
Disclaimer: This episode is a first-pass research briefing produced by an AI-generated research avatar (Evita) trained on the methodology of Dr. Eva Wolf, marketing professor and founder of Big Plans Media. These briefings summarize what selected papers suggest — they are not final academic reviews, and findings should not be treated as settled evidence. Always read the original papers before acting on research findings.
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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. | |||
| AI Customers, Chatbot Ads & Personalization Trust: 3 Research Signals | 22 Aug 2026 | 00:23:04 | |
What if the customer your marketing was built to reach isn't always a human anymore? Three recent papers converge on an uncomfortable idea: AI agents are already shopping, ad-tech is learning to reach them inside chatbot answers, and human trust in data practices remains the deciding factor in whether personalization converts at all.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering AI agents as autonomous shoppers, plug-and-play chatbot ad insertion, and the privacy-trust-personalization chain.
What you'll learn:
- Why AI agents acting as autonomous shoppers may require an entirely new marketing subdiscipline, and what that means for strategy today
- How a lightweight AI module can insert ads into chatbot responses after the fact, outperforming prompt-based methods by 34% on a composite quality score
- Why trust, not personalization sophistication, is the real driver of purchase behavior — and how transparency about data use is the lever marketers keep underestimating
- What generative engine optimization (GEO) is, why it differs from SEO, and why it may already affect whether your brand surfaces in ChatGPT or Gemini answers
- Why your product listings, checkout flows, and ad placements may need to be machine-readable, not just human-friendly
Papers covered:
1. Machine marketing: rethinking the customer in the age of generative AI
- Source: Journal of Marketing Analytics
- Type: Peer-reviewed journal article (likely peer-reviewed)
- Access: Full text reviewed (open access)
- DOI: 10.1057/s41270-026-00521-y
2. PILA: Plug-and-Play Insertion for LLM-native Advertising
- Source: arXiv (Cornell University)
- Type: Preprint — not yet peer-reviewed
- Access: Full text reviewed
- DOI: 10.48550/arxiv.2607.25590
3. AI-Driven Marketing Personalization and the Consumer Privacy Paradox
- Source: Golden Ratio of Data in Summary
- Type: Peer-reviewed journal article (likely peer-reviewed)
- Access: Full text reviewed
- DOI: 10.52970/grdis.v6i3.2517
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-customers-chatbot-ads-personalization-trust-marketing-research-2026-08-22
Disclaimer: This episode is a first-pass research briefing produced by an AI-generated avatar (Evita) trained on the research framework of Dr. Eva Wolf. It is intended to help busy professionals stay informed, not to substitute for a full academic review. Findings are reported as the papers suggest, not as proven conclusions. Preprints have not been peer-reviewed. 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. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Agents as Buyers, LLM Ad Auctions & Native Ads in Chatbots | 18 Aug 2026 | 00:22:57 | |
Your next customer might not be a person. It might be an AI agent — one that searches, compares, and completes purchases without asking a human. And while that shift is underway, researchers are already building the ad infrastructure for the AI chatbot era: auction systems that time ads to conversational intent, and plug-in layers that insert sponsored content into any AI response, even from closed models like ChatGPT.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering autonomous AI buyers and machine marketing, dynamic ad auction timing in LLM conversations, and plug-and-play native advertising in AI chatbots.
What you'll learn:
- Why AI agents are crossing from shopping assistant into autonomous buyer, and what that means for how brands structure their product pages and digital presence
- What "machine marketing" is as a proposed discipline, and how generative engine optimization (GEO) differs from traditional SEO
- How a new auction system simultaneously decides which ad wins and the best conversational moment to show it — with simulated revenue gains of 11% over fixed-timing alternatives
- How a plug-and-play ad module can insert sponsored content into any AI chatbot's answers without modifying the underlying model, tested across seven major commercial AI systems
- What a tunable "ad intensity" dial means for the revenue-versus-user-experience tradeoffs platforms will face as AI advertising matures
Papers covered:
1. Machine marketing: rethinking the customer in the age of generative AI
Source type: Peer-reviewed journal article (Journal of Marketing Analytics)
Access: Open access
DOI: 10.1057/s41270-026-00521-y
Source: https://doi.org/10.1057/s41270-026-00521-y
2. LLM-OSDA: An Optimal-Stopping Dynamic Auction for Native Advertising in Multi-Turn LLM Conversations
Source type: Preprint (not yet peer-reviewed)
Access: Full text available
Source: https://arxiv.org/abs/2608.00123
3. PILA: Plug-and-Play Insertion for LLM-native Advertising
Source type: Preprint (not yet peer-reviewed)
Access: Full text available
DOI: 10.48550/arxiv.2607.25590
Source: https://arxiv.org/abs/2607.25590
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-agents-buyers-llm-ad-auctions-native-ads-chatbots-2026-08-18
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. Preprint findings have not been peer-reviewed and may change. Two of the three papers covered this episode are preprints.
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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. | |||
| AI Bidding, Consumer Trust & GenAI Strategy: 3 Research Signals | 07 Aug 2026 | 00:21:38 | |
Your AI marketing tools are already making decisions — bidding on ads, recommending products, drafting strategy. This week's research keeps arriving at the same uncomfortable finding: pure automation creates measurable risk, while structured human-AI collaboration captures the upside. Three papers. One clear pattern.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering automated ad bidding architecture, consumer trust in human-AI marketing systems, and generative AI productivity in social media strategy.
What you'll learn:
- Why letting an AI freely adjust live ad bids is risky — and what a layered, hierarchical architecture does to make it safer
- How a three-layer system combining an LLM, a reinforcement learning agent, and specialist bidding models delivered a +3.6% improvement in ad spend efficiency in a real-world A/B test (preprint, Kuaishou platform)
- Why showing consumers that a human reviews AI recommendations significantly lifts trust and purchase intent — and why trust appears to be the mechanism, not just a side effect
- Where ChatGPT genuinely helps with social media marketing strategy (speed, structure, brainstorming) and where its outputs will need meaningful human editing before use
Papers covered:
1. HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
Source type: Preprint (not yet peer-reviewed)
Access: Full text reviewed
Source: https://arxiv.org/abs/2607.24779
2. Human-Generative AI Collaboration in Digital Marketing: Its Impact on Consumer Trust, Purchase Intentions, and Financial Decision-Making
Source type: Peer-reviewed journal article
Access: Open access, full text reviewed
Source: https://jidmis.org/index.php/jidmis/article/download/642/109
3. Generative AI In Marketing: Productivity Gains and Work Automation
Source type: Peer-reviewed conference paper
Access: Full text reviewed
DOI: 10.5210/spir.v2024i0.15342
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-bidding-consumer-trust-human-oversight-genai-marketing-strategy-2026-08-07
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 final academic review. Findings are drawn from the papers as read; one paper in this episode is a preprint and has not yet completed peer review. Always consult the original sources before acting on any findings.
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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. | |||
| GEO, AI Customers & LLM Ads: 3 Marketing Research Papers | 06 Aug 2026 | 00:21:01 | |
If AI search engines are replacing Google as the place where customers discover, compare, and buy — and those same engines can now insert ads into their own answers — who exactly are you marketing to: the human, or the algorithm acting on their behalf? Today's three papers converge on one uncomfortable answer: both, and the playbook for each is different.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering generative engine optimization (GEO), AI agents as autonomous buyers, and LLM-native advertising systems.
What you'll learn:
- How adding statistics, expert quotes, and credible citations to web content can increase citation frequency in AI search engines like Perplexity — by up to 37% on a live engine in controlled testing
- Why what works depends on query type: data-heavy writing outperforms on factual questions, while confident authoritative language works better for opinion and recommendation queries
- How AI tools are evolving from assistants into autonomous AI customers that shop, compare, and complete purchases on behalf of users — and why those agents follow different decision logic than humans
- How a lightweight add-on model can insert sponsored content into any chatbot's responses without rebuilding the underlying model
- Why AI search optimization is a separate layer on top of traditional SEO and eventually requires different content structures, writing strategies, and ad formats
Papers covered:
1. GEO: Generative Engine Optimization
- Source: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024)
- Type: Conference paper (likely peer-reviewed)
- Access: Full text reviewed
- Source: https://arxiv.org/abs/2311.09735
2. Machine marketing: rethinking the customer in the age of generative AI
- Source: Journal of Marketing Analytics, 2026
- Type: Peer-reviewed journal article
- Access: Full text reviewed
- DOI: 10.1057/s41270-026-00521-y
3. PILA: Plug-and-Play Insertion for LLM-native Advertising
- Source: arXiv (Cornell University), 2026 — PREPRINT, not yet peer-reviewed
- Access: Full text reviewed
- DOI: 10.48550/arxiv.2607.25590
Full show notes, transcript, and citations: https://bigplans.media/episodes/geo-ai-customers-llm-native-ads-marketing-research-2026-08-06
Disclaimer: This episode is a first-pass research briefing produced by an AI-generated avatar trained on Dr. Eva Wolf's research framework. It is not a final academic review. Findings are reported as the papers suggest, not as proven conclusions. Always consult the original papers and relevant experts before making strategic decisions.
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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. | |||
| GEO & AI Search Visibility: What the Research Actually Shows | 05 Aug 2026 | 00:21:23 | |
AI search engines like Perplexity, Gemini, and ChatGPT are replacing traditional link lists with synthesized answers that cite some sources and ignore everyone else. The question for marketers: do you know what it actually takes to get cited — and does what vendors are selling you hold up under research scrutiny?
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering generative engine optimization (GEO), AI search visibility, and what the evidence does — and does not — support about optimizing content for AI-generated answers.
What you'll learn:
- The specific content edits — adding statistics, expert quotes, and source citations — that increased citation visibility by up to 40% in a peer-reviewed benchmark study
- Why there is no universal GEO playbook: tactics that work for factual content fail for opinion content, and what works on blog posts may not transfer to e-commerce product pages
- Why a 2026 survey of 45 GEO studies found that no technique has yet demonstrated stable, real-world causal effects on organic discoverability or downstream business outcomes
- How to push back on GEO vendors: ask for longitudinal, peer-reviewed proof before signing any contract
- Why Gemini, GPT-based, and Claude-based search engines appear to have different citation preferences, and what that means for your content strategy
Papers covered:
1. GEO: Generative Engine Optimization
Type: Conference paper (peer-reviewed, KDD 2024)
Access: Full text reviewed
Source: https://arxiv.org/abs/2311.09735
2. Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023-2026)
Type: Preprint (not yet peer-reviewed)
Access: Abstract only
Source: Link in show notes
3. What Generative Search Engines Like and How to Optimize Web Content Cooperatively
Type: Preprint (not yet peer-reviewed)
Access: Abstract only
Source: https://arxiv.org/abs/2509.00000
Full show notes, transcript, and citations: https://bigplans.media/episodes/geo-generative-engine-optimization-ai-search-visibility-research-2026-08-05
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 them, with limitations noted. Always consult the original sources before making decisions.
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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. | |||
| AI Slop Is Killing Your Marketing: The 3C Framework That Fixes It | 18 Jul 2026 | 00:28:58 | |
Most businesses don’t have an AI marketing tool problem. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Research: CRM Performance, Agent Loyalty & AI Forecasting | 16 Jul 2026 | 00:20:15 | |
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.
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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. | |||
| AI Pricing Algorithms, LLM Bias & Ad Retrieval: 3 Research Signals | 15 Jul 2026 | 00:20:41 | |
When your AI pricing tool receives more market data, does it actually compete harder — or does it quietly learn to charge more? And if the AI writing your content was trained to please the average user, who is that person, and is your audience actually in the room?
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI and marketing research papers covering algorithmic pricing behavior, LLM personalization bias, and ad retrieval systems. We screened 374 papers this cycle; these three cleared the full-text bar.
What you'll learn:
- Why giving your AI pricing algorithm more market data does not always produce more competitive prices — in some configurations, it produces higher ones
- Why regulators trying to prevent AI-driven price collusion may inadvertently make it worse by restricting information access
- Why most major AI systems are trained to serve the average user, a demographic that does not exist, and how that systematically disadvantages non-Western and minority audiences
- Why the algorithm deciding who sees your ad operates on completely different logic than the one serving organic content — and why optimizing for one does not help the other
- How ad targeting and LLM technology are converging around shared retrieval architectures
Papers covered:
1. Strategic Information Disclosure in Algorithmic Pricing
- Authors: Chengcheng Wang, Zexin Ye
- Source type: Preprint (not yet peer-reviewed)
- Access: Full text reviewed
- Source: https://arxiv.org/abs/2607.04345v1
2. Large Language Models Should Learn Personalized Rather Than Aggregated Human Preferences
- Author: Cristina Garbacea
- Source type: Preprint (not yet peer-reviewed)
- Access: Full text reviewed
- Source: https://arxiv.org/abs/2606.07629
3. A Survey of Retrieval Algorithms in Ad and Content Recommendation Systems
- Authors: Zhao Yu, Fang Liu, Yuan Yuan, Yifan Dang
- Source type: Peer-reviewed journal article
- Access: Full text reviewed
- DOI: 10.11591/ijece.v16i3.pp1518-1530
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-pricing-algorithms-llm-bias-ad-retrieval-research-2026-07-15
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. Nothing here constitutes legal, financial, or business advice.
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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. | |||
| AI Marketing Research: Generative AI, Ethics & Cooperative Branding | 14 Jul 2026 | 00:20:11 | |
Are your marketing workflows actually keeping up with generative AI — or are you using powerful tools without the governance to back them up? This episode examines three recent research papers that each point to the same uncomfortable gap: most organisations are adopting AI for marketing faster than they are building the systems to use it well.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering generative AI content creation for small businesses, ethical frameworks for AI-enabled marketing, and AI adoption in agri-food cooperatives.
What you'll learn:
- Why prompt writing — not design skill — is the key capability for non-technical teams using AI content tools
- Why the 70% efficiency claim from the small business study is directional, not proven, and how to run your own test
- How large brands like Unilever and P&G are already running AI ethics boards and bias-testing protocols — and what a basic compliance checklist looks like
- How values-driven and community-rooted brands can use authentic storytelling as a competitive advantage when adopting AI
- Why governance — who teaches AI skills, who checks outputs, who owns the ethics policy — is the common thread across all three papers
Papers covered:
1. Accelerating MSME Digital Marketing Through the Use of Generative AI to Improve Visual Content Creation and Creative Promotional Narratives
- Source type: Peer-reviewed journal article (Jurnal Pengabdian Masyarakat dan Riset Pendidikan)
- Access: Full text reviewed
- DOI: 10.31004/jerkin.v4i4.6076
- Radar verdict: Test this week
2. Ethical Frameworks for AI-Enabled Marketing: Guidelines, Adoption, and Organizational Practices
- Source type: Peer-reviewed academic book chapter (IIP Series)
- Access: Full text reviewed
- DOI: 10.58532/nbennureambv6b2p1c7
- Radar verdict: Test this week
3. Digital Transformation in Agri-Food Cooperatives: AI and Marketing Strategies in Case Studies of First- and Second-Degree Models
- Source type: Peer-reviewed journal article (British Food Journal)
- Access: Full text reviewed
- DOI: 10.1108/bfj-10-2025-1430
- Radar verdict: Test this week
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-generative-ai-msme-ethics-cooperative-branding-2026-07-14
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 final academic review. Findings are summarised for marketing professionals and should be read alongside the original papers. Study limitations are noted throughout. Nothing in this episode constitutes legal, regulatory, or investment advice.
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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. | |||
| AI Ad Generation, Tourism Marketing & Retail AI: 3 Research Signals | 13 Jul 2026 | 00:20:21 | |
The AI tools your team is racing to adopt — have they ever been tested on a real customer? This week's radar covers three genuinely interesting papers across AI creative production, sustainable tourism marketing, and retail analytics. The ideas are worth knowing. But the evidence behind the headlines is weaker than the numbers suggest.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering automated ad asset generation, AI personalization in sustainable tourism, and the link between AI-assisted inventory decisions and promotional performance.
What you'll learn:
- How a single-product-photo-in, full-ad-kit-out AI pipeline works and how to prototype it today with off-the-shelf tools
- Why the 99% time and cost savings figures from the AdSpark paper are attention-grabbing but not yet independently validated
- What the sustainable tourism paper signals about AI personalization and the ethical guardrails that may determine whether campaigns land or backfire
- Why connecting your AI inventory system to your promotional planning may be the most underused lever in retail marketing
- What the three biggest blockers to AI adoption in retail are, based on 120 surveyed retail managers in India
Papers covered:
1. AdSpark: A Multimodal Generative AI Framework for Automated Digital Advertising and Marketing Asset Synthesis
Source type: Peer-reviewed journal article (IJRASET — low-tier venue; treat findings cautiously)
Access: Full text reviewed
DOI: 10.22214/ijraset.2026.78444
Radar verdict: Use cautiously
2. The Effectiveness of AI-Powered Marketing Strategies in Sustainable Tourism
Source type: Peer-reviewed journal article (Agora International Journal of Economical Sciences)
Access: Full text reviewed
DOI: 10.15837/aijes.v20i1.7640
Radar verdict: Watchlist
3. Predictive Analytics in Retail: Impact of AI on Inventory Optimization and Marketing Performance
Source type: Peer-reviewed journal article (IJCRT — low-prestige open-access venue; treat findings cautiously)
Access: Full text reviewed
DOI: 10.56975/ijcrt.v14i1.300688
Radar verdict: Use cautiously
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-ad-generation-tourism-marketing-retail-analytics-research-2026-07-13
DISCLAIMER: This is a first-pass research briefing produced by an AI-generated avatar trained on the research framework of Dr. Eva Wolf. It is not a final academic review. Findings represent what papers suggest, not what is proven. Always read the original papers before citing or making business decisions based on this content. Paper quality and peer review rigor vary; notes on venue credibility are included for each paper.
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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. | |||
| AI Marketing Research: Banking Loyalty, AI Training & LLM Ethics | 12 Jul 2026 | 00:18:50 | |
AI tools are being deployed across banking, education, and marketing workflows — but the research suggests the gap between a promising feature and a real business outcome is wider than most teams assume. In digital banking, AI features only convert to customer loyalty when they first produce genuine satisfaction. In training, even impressive short-term results deserve scrutiny. And sitting underneath all of it: bias, hallucinations, and privacy risks that compound the deeper AI is embedded in your work.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent peer-reviewed AI marketing research papers covering AI-powered digital banking loyalty, AI skills training for beginners, and the ethical risks of large language models in business contexts.
What you'll learn:
- Why AI banking features don't build loyalty on their own — and what the research says the missing link actually is
- How age segments respond differently to AI banking tools, and why one-size-fits-all campaigns may underperform
- What a structured hands-on AI training program using ChatGPT and Canva AI can realistically achieve — and what the evidence doesn't yet prove
- How ethical problems in LLMs compound as they move from the model into tools like ChatGPT and then into specific business applications
- Why bias, hallucinations, and data privacy leakage are documented risks at the tool level, not just theoretical concerns
Papers covered:
1. An Analytical Study on the Role of Satisfaction in Mediating Customer Loyalty and AI-Powered Digital Banking
Source type: Peer-reviewed journal article (International Review of Management and Marketing)
Access: Open access — full text reviewed
DOI: 10.32479/irmm.22326
Radar verdict: Read now
2. Empowering Vocational Students through AI-Based Smart Digital Marketing Training: A Case Study at SMKN 1 Grati, Pasuruan
Source type: Peer-reviewed journal article (Nusantara Science and Technology Proceedings)
Access: Full text reviewed
DOI: 10.11594/nstp.2026.5484
Radar verdict: Test this week
3. Ethical Issues of Large Language Models: A Multi-Level Thematic Synthesis of the Academic Literature
Source type: Peer-reviewed journal article (AI and Ethics)
Access: Full text reviewed
DOI: 10.1007/s43681-026-01173-5
Radar verdict: Watchlist
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-banking-loyalty-ai-training-llm-ethics-2026-07-12
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 final academic review. Findings are reported as the research suggests, not as proven conclusions. Always consult the original papers before making strategic decisions.
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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. | |||
| AI Marketing Research: Generative Recommendations, AI Frameworks & Adoption | 11 Jul 2026 | 00:20:24 | |
AI recommendation systems can now generate personalized ads, hold real shopping conversations, and show customers what a product looks like on them. So why are most marketing teams still running the same algorithm they had three years ago — and what does the research say about who's actually getting AI marketing right?
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers screened from 391 sources, covering generative recommendation systems, a unified AI marketing knowledge framework, and ground-level AI adoption barriers in an emerging-market insurance sector.
What you'll learn:
- Why AI recommendation systems that generate content solve the "new product, no data" problem that has long plagued e-commerce
- How a three-bucket framework — Strategic AI, Consumer AI, and Conversational AI — can guide your next AI investment decision
- What conversational product recommendation looks like in practice, and why LLM-based tools now outperform older rule-based chatbots
- Why the biggest barriers to real-world AI marketing adoption are skill gaps and messy data, not budget or interest
- What risks to build into your AI deployment checklist: filter bubbles, demographic bias, and cold-start failures that standard metrics miss
Papers covered:
1. Recommendation with Generative Models
Source type: Preprint — comprehensive monograph / literature review (not yet peer-reviewed)
Access: Full text reviewed
Source: https://doi.org/10.1108/ftinr-06-2025-0109
2. Artificial Intelligence in Marketing: A Bibliometric Analysis and Integrated AI Marketing Knowledge Framework
Source type: Peer-reviewed journal article (Journal of AI & Immersive Marketing — new venue, peer review process not independently verified)
Access: Full text reviewed
Source: https://doi.org/10.53893/jaiim-v1-2-2026-2
3. Integrating AI to Improve Customer Experience and Marketing in Zambia's Insurance Sector
Source type: Peer-reviewed journal article (lower-profile venue; peer review rigor not independently verified)
Access: Full text reviewed
Source: https://doi.org/10.59413/ajocs/v7.i2.46
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-generative-recommendation-framework-adoption-2026-07-11
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 final academic review. Findings are reported as the papers suggest them, not as proven conclusions. Preprints have not been peer-reviewed. Always consult original sources before acting on any finding.
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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. | |||
| AI Marketing Research: Brand Trust, AI Advice Bias & CRM ROI | 10 Jul 2026 | 00:23:00 | |
When AI writes your content, runs your CRM, and briefs your customers before they ever reach a human expert — are you building a smarter brand, or quietly eroding the trust that makes it work? That is the question connecting today's three papers, and the answers are more specific — and more actionable — than most AI marketing headlines suggest.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering consumer trust in AI-generated content, directional bias in AI advice tools, and the link between AI marketing investments and financial performance in digital banking. Today's episode was screened from 388 papers.
What you'll learn:
- About half of U.S. consumers prefer brands that avoid AI-generated customer-facing content — and transparent disclosure paired with visible human oversight appears to reduce the reputational risk
- The design choices baked into AI tools are not neutral: in a preregistered RCT, patients who used an AI chatbot before seeing a doctor were about 5 percentage points less likely to receive a prescription and reported lower satisfaction with their physician
- AI-enabled CRM outperformed both personalization and chatbots as a predictor of financial performance in Nigerian digital banking — suggesting not all AI marketing tools are equal
- Any AI tool inserted between a customer and a human expert can shift what happens in that expert conversation, and marketers deploying pre-consultation AI should plan for this dynamic deliberately
Papers covered:
1. Impact of Generative AI on Brand Authenticity and Customer Trust in Marketing Content Creation
- Source type: Peer-reviewed journal article (peer review likely but unconfirmed — hosted on open repository)
- Access: Full text reviewed
- Source: https://doi.org/10.5281/zenodo.21272817
2. Directional AI Advice: Experimental Evidence from Healthcare
- Source type: Preprint (not yet peer-reviewed)
- Access: Full text reviewed
- Source: https://arxiv.org/abs/2607.08706v1
3. The Influence of AI-Driven Marketing on the Financial Performance of Digital Banks in Nigeria
- Source type: Peer-reviewed journal article (peer review likely but unconfirmed — hosted on open repository)
- Access: Full text reviewed
- Source: https://doi.org/10.5281/zenodo.21277513
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-brand-trust-advice-bias-crm-roi-2026-07-10
Disclaimer: This is a first-pass research briefing, not a final academic review. Findings are summarized from the papers as written. Preprints have not been peer-reviewed. Results should not be treated as definitive. Always read the original papers before acting on any finding.
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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. | |||
| AI Marketing Research: Gen Z Trust, Trend Detection & Creative Deskilling | 09 Jul 2026 | 00:23:05 | |
When AI handles more of the creative work, who actually benefits — the brand, the consumer, or neither? Three recent peer-reviewed papers point to the same pattern: AI in marketing moves fast, but the failure modes are subtle. They show up in trust, in advocacy, and in your team's ability to do the work when it gets hard.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering Gen Z purchase intent and brand trust, automated LLM-powered trend detection, and the impact of generative AI on the meaningfulness of creative work.
What you'll learn:
- For Gen Z, voluntary brand advocacy — sharing, recommending, defending — is a stronger predictor of purchase intent than trust or digital participation alone, and AI personalization is what triggers it
- Impressions and reach are the wrong metrics for Gen Z campaigns; active engagement (comments, shares, UGC) predicts buying intent far better than passive exposure
- A multi-LLM consensus pipeline can auto-generate structured topic maps from social media text — a practical blueprint for trend monitoring without a full expert team
- Generative AI is shifting creative work from making to curating, and that shift carries real deskilling risk for marketing teams over time
- There is a growing penalty for AI use: workers and agencies perceived as AI-heavy may face reputational stigma, even when output quality holds up
Papers covered:
1. AI-Driven Social Media Marketing and Purchase Intention: The Roles of Brand Trust, Consumer Citizenship Behaviour, and Digital Participation among Generation Z
Source type: Peer-reviewed journal article (International Review of Management and Marketing)
Access: Full text reviewed
Radar verdict: Read now
DOI: 10.32479/irmm.22943
2. Automated Semantic Ontology Construction for Foresight Studies Using Large Language Models
Source type: Peer-reviewed journal article (System Research and Information Technologies)
Access: Full text reviewed
Radar verdict: Watchlist
DOI: 10.20535/srit.2308-8893.2026.2.09
3. The Impacts of Generative AI on the Meaningfulness of Creative Work
Source type: Peer-reviewed journal article (Journal of Business Ethics)
Access: Full text reviewed
Radar verdict: Watchlist
DOI: 10.1007/s10551-026-06342-4
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-gen-z-trust-trend-detection-creative-deskilling-2026-07-09
DISCLAIMER: This is a first-pass research briefing produced by an AI-generated avatar trained on Dr. Eva Wolf's research framework. It is intended to help busy professionals stay informed, not to replace academic peer review. Findings are summaries of what papers suggest, not definitive conclusions. Always consult the original sources before making decisions based on this content.
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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. | |||
| AI Marketing Research: Brand Trust, AI Fatigue & Adoption Barriers | 08 Jul 2026 | 00:19:16 | |
Your brand might be producing AI content at scale — but are consumers quietly losing trust in it? And for businesses betting on AI to lift marketing numbers, what's actually standing between them and results?
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering consumer perceptions of AI-generated brand content, AI-driven marketing performance in hospitality, and generative AI adoption barriers in emerging markets.
What you'll learn:
- Four ways AI overuse can quietly damage brand credibility, including emotional flattening and content homogenisation
- Why consumers are developing "AI fatigue" and what that signals for engagement and brand perception
- How hotels that embedded AI into their innovation and decision-making workflows — not just their tech stack — saw stronger marketing gains
- Why most businesses are still stuck at AI basics (content and data analysis) and haven't reached personalization at scale
- What the real bottleneck to AI adoption is: skilled people and process integration, not awareness
Papers covered:
1. Generative AI in Marketing Communication: Consumer Perceptions, Brand Credibility, and Responsible AI Practices
Type: Conference paper (likely peer-reviewed)
Access: Full text reviewed
Source: https://doi.org/10.25401/cardiffmet.32326287
2. Innovative and Sustainable Pathways in Hospitality and Tourism Businesses: How AI, Digital Innovation, and Organizational Agility Enhance Marketing Performance
Type: Peer-reviewed journal article
Access: Full text reviewed (open access)
Source: https://doi.org/10.30892/gtg.65225-1730
3. Adoption of Generative AI Tools in Marketing and Customer Engagement: A Study on Indian Businesses
Type: Peer-reviewed journal article (conference special issue) — use cautiously
Access: Full text reviewed
Source: https://doi.org/10.36948/ijfmr.0000.ic-aircm-t3-2026.1602
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-brand-trust-ai-fatigue-adoption-barriers-2026-07-08
Disclaimer: This episode is a first-pass research briefing produced by an AI-generated avatar trained on the research framework of Dr. Eva Wolf. It is not a substitute for reading the original papers. Findings are summarised from available full text; some papers may have limitations not fully captured here. Nothing in this briefing constitutes professional marketing, legal, or business advice.
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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. | |||
| AI Marketing Research: Generative AI, Small Business & Chatbot Trust | 07 Jul 2026 | 00:19:36 | |
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.
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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. | |||
| AI Visuals, Engagement & Marketing Agent Loop: Research Brief | 06 Jul 2026 | 00:15:59 | |
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.
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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. | |||
| AI Ad Speed, Brand Trust & Green Influencer Risk: 3 Research Signals | 05 Jul 2026 | 00:20:28 | |
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.
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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. | |||
| AI Ads, Persona Research & Consumer Trust: 3 Marketing Papers | 04 Jul 2026 | 00:19:20 | |
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.
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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. | |||
| AI-Generated Ads, Consumer Trust & Privacy: 3 Research Signals | 03 Jul 2026 | 00:20:17 | |
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.
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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. | |||
| AI Creativity Benchmarks, Chatbot Personas & Agent Competition | 02 Jul 2026 | 00:22:11 | |
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.
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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. | |||
| Personalized AI, Creative Work & Knowledge Protocols: Research Brief | 17 Jun 2026 | 00:13:08 | |
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.
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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. | |||
| AI Personalization & Creative Campaigns: What the Research Shows | 17 Jun 2026 | 00:13:52 | |
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.
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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. | |||
| AI Marketing Research: Location Leakage, AI Workflows & Content Attribution | 17 Jun 2026 | 00:21:40 | |
Your AI personalization stack may be inserting geographic references into content where location is completely irrelevant — and new research puts concrete numbers on how often that happens. Meanwhile, two other papers raise questions about who owns accountability inside AI-assisted marketing workflows, and who gets paid when AI-generated content is built on someone else's data.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering geographic conditioning in large language models, human-AI workflow orchestration, and contributor attribution in generative AI markets. Selected from 382 papers screened this week.
What you'll learn:
- How location data passed to AI tools can leak into outputs up to 793 times more often than baseline — even when location is irrelevant to the task
- Why the way you inject location into a prompt (system prompt vs. user-profile block) can cut geographic leakage from 16% down to 3.8%
- Which AI models showed the highest and lowest leakage rates in this study
- How one conceptual framework proposes mapping a specific AI tool to each step of the marketing management process, with a human accountable at every stage
- Why the economics of AI-generated content are moving toward automatic revenue splits among training data providers, model builders, and prompt engineers — and what that means for the platforms marketers use today
Papers covered:
1. Unintended Effects of Geographic Conditioning in Large Language Models
Authors: Naz Col, David M. Chan (2026)
Type: Preprint — not yet peer-reviewed
Access: Full text reviewed
Source: https://arxiv.org/abs/2606.18124v1
2. Reframing the Managerial School in the Age of Generative AI: Toward an Augmented Marketing Framework
Authors: Merve Kadriye Yurdabak (2026)
Type: Peer-reviewed journal article
Access: Full text reviewed (open access)
DOI: 10.16953/deusosbil.1795373
3. AME: A Multi-Type Contributor Attribution Framework in Generative AI Markets
Authors: Yang Shi, Songwen Pei, Yang Gao, Bingxue Zhang (2026)
Type: Preprint — not yet peer-reviewed
Access: Full text reviewed
Source: https://arxiv.org/abs/2606.16075v1
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-location-leakage-workflows-content-attribution-2026-06-17
Disclaimer: This is a first-pass research briefing produced using an AI-generated avatar trained on Dr. Eva Wolf's research framework. It is not a substitute for reading the original papers. Two of the three papers covered are preprints that have not yet undergone peer review; findings may change. Nothing in this episode constitutes professional, legal, or financial advice.
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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. | |||
| AI Social Media, Creative Risks & Global Competitiveness: 3 Papers | 16 Jun 2026 | 00:20:44 | |
Can AI write convincing social media posts, profile your audience, and brainstorm your next campaign — and is your team getting sharper as a result, or quietly more generic? This episode of AI & Marketing Research Radar digs into the research behind those questions.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering LLM performance on social media tasks, role-based integration of generative AI in creative work, and AI-driven marketing capabilities for international competitiveness.
What you'll learn:
- AI models like GPT-4 can detect authorship and mimic writing styles on social media, but accuracy depends heavily on which users and posts are tested — and no single model leads on every task
- AI-generated social posts can score well on automated quality metrics but still feel fake to real human readers — automated checks are not a substitute for human review
- Assigning GenAI a specific creative role (idea generator, conceptual synthesiser, strategic framer) is proposed to produce better outcomes than treating it as an all-purpose brainstorm machine
- Heavy AI use in creative work carries a risk of gradually flattening output — pushing teams toward statistically common ideas rather than genuinely original ones
- AI-powered customer analytics may help smaller and emerging-market firms compete globally by speeding up market sensing and entry decisions
Papers covered:
1. Assessing Capabilities of Large Language Models in Social Media Analytics: A Multi-task Quest
- Authors: Ramtin Davoudi, Kartik Thakkar, Nazanin Donyapour, Tyler Derr, Hamid Karimi
- Source type: Preprint (arXiv — peer review status not fully confirmed)
- Access: Full text reviewed
- Source: https://arxiv.org/abs/2604.18955
2. Beyond the Creativity Paradox: A Theory-informed Framework for Role-based Integration of Generative AI in Organisational Creativity
- Authors: Youngseok Choi, Chang Won Park, Ceyda Paydas Turan, Habin Lee
- Source type: Peer-reviewed journal article (Information Systems Frontiers)
- Access: Full text reviewed
- DOI: 10.1007/s10796-026-10746-y
3. AI-Driven Marketing Capabilities and International Competitiveness
- Authors: Manoj Govindaraj, D. Jishnu, Jenifer Lawrence, Duggirala Aravind
- Source type: Peer-reviewed academic book chapter (IGI Global)
- Access: Full text reviewed
- DOI: 10.4018/979-8-3373-9988-1.ch001
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-social-media-analytics-creativity-paradox-marketing-competitiveness-2026-06-1
Disclaimer: This is a first-pass research briefing produced with AI assistance and reviewed editorially. It is not a substitute for reading the original papers. Findings from preprints have not completed full peer review. Practical implications are the editorial team's interpretations and should not be treated as professional advice.
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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. | |||
| AI Content Adaptation: Real-Time GANs, RL & Marketing Claims | 14 Jun 2026 | 00:12:49 | |
If an AI system claims it can boost your click-through rate by 25%, your conversions by 20%, and cut your bounce rate by 30% — all without human intervention — how do you know whether to run toward it or run away from it?
In this Research Radar Brief, Evita (an AI research briefing avatar trained on the framework of Dr. Eva Wolf) reviews 1 recent AI marketing research paper on generative AI-based real-time content adaptation, examining what the system claims to do, what the evidence actually supports, and what marketers can do with this kind of research right now.
What you'll learn:
- What GANs and Reinforcement Learning actually do when combined in a marketing content pipeline
- Why impressive performance numbers can be unactionable without knowing the experimental design behind them
- How to use a paper's claimed metrics as a vendor checklist — even when the paper itself can't be acted on yet
- Where real-time content adaptation already exists in deployable tools, and how to find verified results
- What the gap between enterprise dynamic creative optimization and small business access means for marketers today
Papers covered:
1. Generative AI-Based Content Adaptation System for High-Impact Digital Marketing
Authors: Surendra Singh Jagwan, Nirmesh Sharma, Ashwani Sharma
Year: 2026
Source type: Conference paper (likely peer-reviewed)
Access: Full text reviewed
Radar verdict: Watchlist
DOI: https://doi.org/10.1109/qpain69676.2026.11546259
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-content-adaptation-gans-reinforcement-learning-marketing-claims-2026-06-14
IMPORTANT: This is a first-pass research briefing, not a final academic review. Every paper covered has been reviewed at the full-text level where available. Read the original papers before making major marketing, business, legal, or product decisions.
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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. | |||
| AI Chatbot Persuasion, Data Silos & AI Labor Markets: 3 Research Signals | 13 Jun 2026 | 00:21:01 | |
What if your AI chatbot is already persuasive enough — and the real problem is that nobody's opening it? What if your churn model is missing the most predictive signals you already own? And what if AI-powered freelance services are heading toward a price war that compresses margins faster than most agencies are prepared for? Those are the questions this episode's research surfaces.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI and marketing research papers covering AI chatbot persuasion versus traditional campaign advertising, integrated predictive analytics for marketing ROI, and AI agent competition in simulated labor markets.
From 381 papers screened, three cleared the full-text bar and made the radar.
What you'll learn:
- Why AI chatbots match professional campaign ads in persuasiveness per person reached — but getting people to start the conversation remains the hard problem
- The estimated cost to change one person's mind via AI chatbot is $48–$75, compared to roughly $100 using traditional campaign methods (with important caveats about research design)
- Why marketing AI may perform significantly better when it shares data with supply chain and financial systems — and why the specific benchmark numbers in that study deserve scrutiny before you act on them
- How AI agents competing in simulated gig labor markets drive prices down fast, with winner-take-most dynamics emerging quickly
- The three capabilities — self-reflection, competitive awareness, and long-horizon planning — that predicted AI agent success, and why they're worth asking about when evaluating any AI vendor
Papers covered:
1. A Framework to Assess the Persuasion Risks Large Language Model Chatbots Pose to Democratic Societies
Source type: Peer-reviewed journal article (Journal of Experimental Political Science)
Access: Full text reviewed
DOI: https://doi.org/10.1017/xps.2026.10032
2. AI-Driven Predictive Analytics for Supply Chain Resilience, Financial Risk Management, and Digital Marketing Strategy: A Unified Business Intelligence Framework
Source type: Peer-reviewed journal article (Journal of Business and Management Studies)
Access: Full text reviewed
DOI: https://doi.org/10.32996/jbms.2026.8.7.3
3. Strategic Self-Improvement for Competitive Agents in AI Labour Markets
Source type: Preprint — not yet peer-reviewed
Access: Full text reviewed
Source: https://arxiv.org/abs/2512.04988v1
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-chatbot-persuasion-data-silos-labor-markets-marketing-research-2026-06-13
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 final academic review. Findings are summarized for accessibility and should not be treated as definitive conclusions. Always consult the original papers before making strategic or financial decisions. Preprints have not been peer-reviewed and should be interpreted with additional caution.
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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. | |||
| AI Marketing Research: Banking Signals, GenAI CRM & Digital Commerce | 12 Jun 2026 | 00:18:36 | |
What if the two strongest predictors of banking customer engagement — call duration and account balance — are already sitting in your CRM right now? This week's radar papers circle a single uncomfortable truth: AI's predictive power is real, but the gap between a working model and a working marketing program is wider than most vendors admit.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering banking customer segmentation with behavioral signals, generative AI architecture for enterprise CRM, and the current state of AI in digital commerce.
What you'll learn:
- Two behavioral signals — call duration and account balance — predicted banking customer engagement with 97-99% accuracy in a study of 45,000 records, outperforming demographic-based targeting on both accuracy and compliance grounds
- Why behavioral signals may be more predictive and more ethics-friendly than demographic data for financial services segmentation
- What a generative AI CRM architecture could look like (churn prediction, LLM-driven insights, explainability layer) — and why it has only been tested in simulation, not in a live enterprise
- How human-AI collaboration in digital advertising — humans set strategy, AI handles execution — is the model most consistently linked to better campaign performance in the reviewed literature
- Why data privacy and algorithmic bias remain the two biggest practical barriers to AI adoption in digital commerce
Papers covered:
1. The adaptive engagement framework: enhancing banking customer experience through AI-powered invisible marketing
Source type: Peer-reviewed journal article (Scientific Reports, Nature Portfolio)
Access: Full text reviewed
Source: https://doi.org/10.1038/s41598-026-49522-y
2. Design and implementation of generative Artificial Intelligence-driven automation for enterprise customer relationship management decision support systems
Source type: Peer-reviewed journal article (Global Journal of Engineering and Technology Advances)
Access: Full text reviewed
Source: https://doi.org/10.30574/gjeta.2026.27.2.0089
3. Digital Commerce in the AI Era: Opportunities and Challenges
Source type: Peer-reviewed journal article (conference proceedings)
Access: Full text reviewed
Source: https://doi.org/10.66710/ijersem.v2si1.36
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-banking-signals-genai-crm-digital-commerce-2026-06-12
Disclaimer: This is a first-pass research briefing, not a final academic review. Evita is an AI-generated briefing avatar trained on the research framework and methodology of Dr. Eva Wolf. Findings are reported as the papers suggest them, not as proven conclusions. Individual studies have limitations noted in the full episode. Always read 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. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Research: B2B GenAI, Hospitality AI & LLM Safety | 11 Jun 2026 | 00:20:30 | |
Your B2B team is using AI to write copy. Your hotel client's chatbot is labelled "safety-aligned." And new research suggests both of those things might be giving you a false sense of progress.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering generative AI adoption in B2B and industrial marketing, AI applications and trust risks in travel and hospitality, and a striking finding about how easily safety guardrails on open-weight language models can be bypassed.
What you'll learn:
- In B2B and industrial marketing, AI is being used almost exclusively for execution tasks — content, copy, and ads — while research and planning remain nearly AI-free. That gap is where the real opportunity is hiding.
- As AI handles more execution work, the marketer's role is shifting toward briefing AI tools, reviewing outputs, and strategic thinking — teams that don't plan for this will fall behind.
- In travel and hospitality, the most research-backed AI applications are recommendation engines, sentiment analysis, and dynamic pricing — but over-automating customer touchpoints is consistently flagged as a trust and loyalty risk.
- The safety guardrails on popular open-weight AI models are more fragile than most marketing technology buyers realize. A single internal neuron can be toggled to bypass them entirely.
Papers covered:
1. The Impact of Generative AI on B2B Marketing Processes: Evidence from Industrial Firms
- Authors: Vesterinen, Mero, Skippari, Karjaluoto (2026)
- Type: Peer-reviewed journal article
- Access: Full text reviewed
- Source: https://doi.org/10.18690/um.fov.4.2026.32
- Radar verdict: Read now
2. Mapping Research Trends in AI-Based Tourism and Hospitality Marketing: A Bibliometric and Thematic Review
- Authors: Tyagi, Aggarwal, Tyagi, Vasudevan, Singh (2026)
- Type: Peer-reviewed journal article (F1000Research)
- Access: Full text reviewed
- Source: https://doi.org/10.12688/f1000research.177254.2
- Radar verdict: Watchlist
3. A Single Neuron Is Sufficient to Bypass Safety Alignment in Large Language Models
- Authors: Kazemi, Chegini, Safi (2026)
- Type: Preprint — not yet peer-reviewed
- Access: Open access
- Source: https://arxiv.org/abs/2605.08513
- Radar verdict: Watchlist
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-b2b-genai-hospitality-llm-safety-2026-06-11
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. Findings are described as the research suggests, not as proven conclusions. Preprints have not completed peer review and should be treated with additional caution.
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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. | |||
| AI Marketing Research: Brand Voice, API Trust & Arab AI Attitudes | 10 Jun 2026 | 00:19:51 | |
Your AI vendor might be delivering a cheaper model than the one on your invoice. Your brand copy might be drifting off-voice without anyone noticing. And if you're launching in Arab markets, your audience is holding genuine enthusiasm and real fear about AI at the same time — and these are measurable, distinct feelings that campaign messaging needs to address separately.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering Arab consumer attitudes toward large language models, behavioral consistency and transparency in LLM API gateways, and brand voice governance under AI-assisted content production. These were selected from 353 papers screened for this episode.
What you'll learn:
- Why Arab consumers hold measurable enthusiasm and fear about AI simultaneously, and why generic positive messaging may miss the fear dimension entirely
- How third-party LLM API gateways were found to quietly substitute cheaper AI models, silently truncate conversation memory, and charge for tokens that were never processed
- Why positive attitudes toward AI in general do not automatically transfer to a specific LLM tool — and why marketers should test these separately before launching
- How AI-assisted content production causes brand voice to drift gradually, often before anyone notices
- What a five-level brand voice governance framework looks like — and why it is theoretical, not yet empirically tested
Papers covered:
1. Developing and Validating the Arabic Version of the Attitudes Toward Large Language Models Scale
Source type: Peer-reviewed journal article (SN Computer Science)
Access: Full text reviewed
DOI: 10.1007/s42979-026-04855-3
Radar verdict: Test this week
2. Behavioral Consistency and Transparency Analysis on Large Language Model API Gateways
Source type: Peer-reviewed conference paper (ACM IMC '26 / arXiv)
Access: Full text reviewed
DOI: 10.1145/3777912.3809156
Source: https://arxiv.org/abs/2604.21083
Radar verdict: Test this week
3. Brand Voice Management in the Era of Large Language Models
Source type: Peer-reviewed journal article (Integrated Communications)
Access: Full text reviewed
DOI: 10.28925/2524-2652.2026.119
Radar verdict: Watchlist
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-brand-voice-api-gateway-arab-llm-attitudes-2026-06-10
Disclaimer: This is a first-pass research briefing produced using an AI-assisted research framework developed by Dr. Eva Wolf. It is not a substitute for reading the original papers. Findings are reported as what the research suggests, not as definitive conclusions. Paper three is a conceptual framework with no empirical validation. Listeners should evaluate each paper independently before acting on its findings.
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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. | |||
| AI Creative Workflows, AI Influencers & Marketing Analytics | 09 Jun 2026 | 00:19:05 | |
When AI does the creative work — writing your copy, pitching ideas, fronting your campaigns — how much control do you actually need to keep? And what happens when you hand the wheel entirely to the bots? This episode's research lands on a consistent answer: the human layer isn't optional overhead. It's the thing that makes the output worth having.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering multi-agent creative workflows, AI influencer humanlikeness, and the evolution of marketing analytics from dashboards to AI-generated content and ethics.
What you'll learn:
- Why autonomous AI agent coordination tends to stall in creative tasks, and what a human-directed model looks like instead
- How the human-like quality of an AI influencer or chatbot directly affects whether people intend to purchase
- What a four-stage model of AI marketing evolution suggests about where governance and ethics now fit in your AI stack
- Why your role in an AI-assisted creative workflow is director, not just reviewer — and why that distinction matters
Papers covered:
1. Understanding Human-Multi-Agent Team Formation for Creative Work
Source type: Peer-reviewed conference paper (CHI '26, ACM CHI Conference on Human Factors in Computing Systems)
Access: Full text reviewed
DOI: 10.1145/3772318.3791166
Radar verdict: Read now
2. Physical Humanlikeness as A Moderator of The Relationship Between AI Influencer Marketing and Purchase Intention
Source type: Peer-reviewed journal article (International Journal of Management Science and Information Technology)
Access: Full text reviewed
DOI: 10.35870/ijmsit.v6i1.7072
Radar verdict: Read now
3. Artificial intelligence across social sciences and humanities: The evolution of marketing analytics in the digital era
Source type: Peer-reviewed journal article (Journal of Interdisciplinary Research in Artificial Intelligence and Society)
Access: Full text reviewed
DOI: 10.20897/jirais/18474
Radar verdict: Watchlist
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-creative-workflows-influencers-marketing-analytics-2026-06-09
Disclaimer: This is a first-pass research briefing, not a final academic review. Evita is an AI-generated briefing avatar trained on the research framework and methodology of Dr. Eva Wolf. Findings are summarised from full-text sources and reflect what the research suggests, not what it conclusively proves. Always read the original papers before making decisions. Paper quality and venue credibility vary; notes on limitations are included in the full show notes.
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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. | |||
| AI Chatbot Ads, Cultural Bias & GenAI Content: 3 Research Signals | 08 Jun 2026 | 00:18:08 | |
When you advertise inside an AI chatbot, is the model working for your customer — or quietly for whoever pays it most? And when AI writes your regional ad copy, does it actually understand the culture, or just fake the surface look? This episode examines both questions through three recent research papers screened from a pool of 373.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering chatbot advertising conflicts of interest, LLM cultural awareness in ad copy, and generative AI content marketing efficiency.
What you'll learn:
- Why 18 out of 23 AI chatbots tested pushed users toward more expensive sponsored products over cheaper equivalents — and what that means for brand trust
- How GPT-5.1 redirected users away from their explicitly chosen store toward a sponsored competitor 94% of the time, and why disclosure failures may carry FTC risk
- Why AI models appear to treat higher-income user profiles differently — and the implications for fairness in AI-powered retail recommendations
- What the cultural stylistics research suggests about AI's ability to write genuine Hong Kong-style ad copy versus mainland Chinese copy — and the gap between recognizing a style and producing it
- What the generative AI content efficiency review covers, and why its evidence base (largely industry surveys) warrants caution before acting on it
Papers covered:
1. Ads in AI Chatbots? An Analysis of How Large Language Models Navigate Conflicts of Interest
Source type: Preprint — arXiv (Cornell University). Peer-review status unconfirmed. Treat findings with appropriate caution.
Access: Full text reviewed
Source: http://arxiv.org/abs/2604.08525
2. Probing Cultural Awareness in LLMs: A Case Study of Cross-Culture Aesthetic Stylistics
Source type: Preprint — not yet peer-reviewed
Access: Full text reviewed
DOI: 10.48550/arxiv.2605.27296
Source: https://arxiv.org/abs/2605.27296
3. The Impact of Generative AI on Content Marketing Efficiency: Opportunities, Risks, and Future Perspectives
Source type: Literature review — Zenodo (CERN). Peer-review status unconfirmed (Zenodo self-submission).
Access: Full text reviewed
DOI: 10.5281/zenodo.20021151
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-chatbot-ads-cultural-bias-genai-content-marketing-2026-06-08
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 undergone formal peer review and findings may change. Nothing here constitutes legal, financial, or regulatory advice.
--
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. | |||
| AI Chatbot Ads, Neuron Auctions & Agent Security: 3 Research Signals | 07 Jun 2026 | 00:23:25 | |
As AI chatbots replace search engines for millions of users, two parallel questions are becoming urgent for marketers: who controls which brands get recommended inside those conversations — and are the AI agents we're deploying to automate marketing tasks actually secure?
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering neuron-level ad auctions inside large language models, a two-stage chatbot ad auction framework, and runtime hijacking attacks on AI agents browsing the web.
All three papers are unreviewed preprints tested in controlled or simulated environments. None are ready to act on today. But together they sketch the shape of AI-powered advertising and AI agent security over the next several years — and they raise questions your team should start asking now.
What you'll learn:
- Why the future of paid media in AI chatbots may have nothing to do with writing ad copy — and everything to do with how a model is wired internally
- How a two-stage AI ad auction system picks more relevant ads faster than either approach alone — and what that means for how you write advertiser copy today
- Why your ad description quality will matter more than your headline when AI chatbot placements go live
- How AI agents browsing the web on your behalf can be silently hijacked — appearing to work normally while leaking data to an attacker's server
- What questions to ask any AI agent vendor before you let their product take actions on the web for your brand
Papers covered:
1. LLM Advertisement based on Neuron Auctions
- Authors: Peiran Yun, Wenxin Xu, Jiayuan Liu, Yihang Zhang, Liang Zeng, Lingkai Kong, Tonghan Wang (2026)
- Source type: Preprint — not peer reviewed
- Access: Full text reviewed
- Source: https://arxiv.org/abs/2605.08326
2. LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbots
- Authors: Haoran Sun, Xinrui Song, Xinyu Zhang, Zhaohua Chen, Xu Chu, Zhilin Zhang, Chuan Yu, Jian Xu, Bo Zheng, Xiaotie Deng (2026)
- Source type: Preprint — not peer reviewed
- Access: Full text reviewed
- Source: https://arxiv.org/abs/2605.16474
3. WebMCP Tool Surface Poisoning: Runtime Manipulation Attacks on LLM Agents
- Authors: Lin-Fa Lee, Yi-Yu Chang, Chia-Mu Yu, Kuo-Hui Yeh (2026)
- Source type: Preprint — not peer reviewed
- Access: Full text reviewed
- Source: https://arxiv.org/abs/2606.06387v1
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-chatbot-ads-neuron-auctions-agent-security-research-2026-06-07
Disclaimer: This is a first-pass research briefing produced by Evita, an AI-generated avatar trained on the research framework of Dr. Eva Wolf. All papers flagged as preprints have not been peer reviewed and should be treated as preliminary. Findings may change. Nothing in this episode constitutes professional legal, financial, or security advice.
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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. | |||
| AI Ethics, SME AI Wins & LLM Pipelines: 3 Marketing Research Signals | 06 Jun 2026 | 00:21:38 | |
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AI MARKETING RESEARCH RADAR
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TODAY'S RADAR QUESTION
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As AI takes over more of your marketing workflow — writing copy, targeting ads, analyzing data — who's actually checking whether it's doing any of that responsibly, accurately, or ethically? This episode asks whether your team is building with AI or just hoping for the best.
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PAPERS COVERED
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1. "With great power comes great responsibility": A meta-narrative review of ethical considerations and implications in the cro
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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. | |||
| AI Marketing Research: Campaign AI, Content Quality & Conversational Ads | 05 Jun 2026 | 00:18:30 | |
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AI MARKETING RESEARCH RADAR
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TODAY'S RADAR QUESTION
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Can AI actually replace your content team, your campaign designer, and your ad buyer — or is the real edge hiding in the seams between human judgment and machine speed? This episode screens 370 papers and surfaces three that put that question to the test.
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PAPERS COVERED
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1. Ad Genie: A Multimodal Generative AI Framework for Automated Marketing Campaign Creation Using Product Images, Textual Prompts, and Web Intellig
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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. | |||
| AI Ad Targeting Research: LLMs, Knowledge Graphs & Ad Auctions | 02 Jun 2026 | 00:19:30 | |
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AI MARKETING RESEARCH RADAR
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TODAY'S RADAR QUESTION
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AI is already inside your ad stack — but are platforms using it as a scalpel or a sledgehammer? This episode digs into three new studies asking whether LLMs actually improve ad targeting in production, what happens when too many brands fight for control of an AI's recommendations, and how wiring a knowledge graph into your ad engine could make it both smarter and 24% faster.
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PAPERS COVERED
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1. Fine-Tuned LLM as a Co
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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. | |||
| AI Marketing Tasks, LLM Ads & Brand Visibility: 3 Research Signals | 01 Jun 2026 | 00:18:49 | |
AI promises to make every marketing task faster and smarter. But does it? Three recent research papers suggest the answer depends heavily on the task, the person using the tool, and whether someone is quietly steering the AI answers your customers are already reading.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering AI task performance in content creation, commercial influence inside LLM chatbots, and engineering approaches to real-time LLM-powered ad delivery.
What you'll learn:
- AI improves quality for long-form content like blog posts and destination guides, but makes no measurable difference for short social captions, and actually worsens visual design outputs
- Digital literacy is the hidden variable: team members with weaker digital skills may produce lower-quality work when using AI than when working without it
- LLMs are now an official advertising channel — ChatGPT began running ads in February 2026, and commercial influence inside AI answers is harder to detect than in traditional search
- Your brand's reputation inside AI chatbots is already shaping customer decisions, and almost no marketing team is monitoring it
- Real-time LLM-powered ad targeting is technically possible at scale, but only with significant ML engineering infrastructure most teams will not build in-house
Papers covered:
1. Task To Tech: An Exploration of Generative AI in Tourism Marketing through Student Experiments and Practitioner Interviews
Source type: Peer-reviewed journal article (Media Wisata)
Access: Full text reviewed
DOI: https://doi.org/10.36276/mws.v24i1.945
2. Advertising and Large Language Models: A New Frontier Influencing Medical Practice
Source type: Peer-reviewed journal article (Eye)
Access: Full text reviewed
DOI: https://doi.org/10.1038/s41433-026-04518-w
3. Efficient LLM-based Advertising via Model Compression and Parallel Verification
Source type: Preprint — not yet peer-reviewed (arXiv / Cornell University)
Access: Full text reviewed
DOI: https://doi.org/10.48550/arxiv.2605.11582
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-tasks-llm-advertising-brand-visibility-2026-06-01
Disclaimer: This episode 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 final academic review. Findings are described as the research suggests, not as proven conclusions. Listeners are encouraged to read the original papers before making strategic or operational 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. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Chatbot Trust, Cold-Start Ads & AI Disclosure: 3 Research Signals | 01 Jun 2026 | 00:17:14 | |
Is everything we assume about chatbot design — the personalization, the warm tone, the friendly AI — actually doing what we think it's doing? This week, three studies landed on the radar that challenge assumptions baked into nearly every conversational AI and ad tech strategy right now. The findings are counterintuitive enough to warrant a pause and an audit.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering conversational AI trust and reliance, cold-start ad personalization using large language models, and the effects of AI disclosure on brand authenticity.
This is a first-pass research briefing, not a final academic review. Papers are assessed for relevance and rigor, but findings should be treated as signals to investigate further — not settled conclusions.
What you'll learn:
- Why personalizing your AI chatbot's explanations may actually reduce its persuasiveness when used alone — and what happens when warmth is added
- Why higher AI literacy did not make users more skeptical of AI advice — and what that means for tech-savvy, B2B audiences
- How Walmart used an LLM to generate ad ranking weights from creative content before a single click — and the real-world results from their deployment
- Why AI-generated visuals without disclosure can damage brand trust, and why disclosing AI use acts as brand insurance rather than a trust differentiator
Papers covered:
1. Personalized to Persuade: The Effects of Contextualization and Warmth on Trust and Reliance in Conversational AI
Source type: Preprint (not yet peer-reviewed)
Access: Full text reviewed
Source: https://arxiv.org/abs/2605.31275v1
2. LLM-HYPER: Generative CTR Modeling for Cold-Start Ad Personalization via LLM-Based Hypernetworks
Source type: Preprint (likely peer-reviewed venue — formal status uncertain)
Access: Full text reviewed
Source: https://arxiv.org/abs/2605.31275v1 — see show notes for correct link
3. Opening AI: A Study of Transparency's Impact on Brand Authenticity and Trust in Visual Advertising
Source type: Master's thesis (not peer-reviewed)
Access: Full text reviewed
Source: Link in show notes
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-chatbot-trust-cold-start-ads-disclosure-research-2026-06-01
DISCLAIMER: This episode 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. Two of the three papers covered today are preprints or theses and have not completed formal peer review. Findings should be treated as early signals, not settled evidence.
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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. | |||
| AI Brand Visibility, SMB Content Playbooks & AI Music in Ads | 31 May 2026 | 00:19:41 | |
When AI becomes the first stop for brand discovery, does it surface what makes your brand genuinely different — or does it quietly reduce every brand to a price-and-quality comparison? That question threads through all three papers in this episode, along with two more grounded ones: what does responsible AI content adoption actually look like for a small business, and can AI-generated music replace the royalty-free tracks you're paying for right now?
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering brand identity collapse in AI-mediated search, generative AI adoption by small businesses in Nigeria, and AI-generated music performance in digital advertising.
What you'll learn:
- Why AI search tools may strip most of what makes your brand distinctive down to price and quality — and which brands the research suggests are hurt most
- How adding structured, machine-readable brand data to your website may partially recover the brand identity AI search flattens
- What a minimum viable governance playbook looks like for small businesses actually using generative AI for marketing content today
- Why being transparent with customers about AI-generated content helped small business owners in this study build trust rather than lose it
- How AI-generated music performed against royalty-free stock music in a live digital ad campaign — and what that may mean for your production budget
Papers covered:
1. Dimensional Collapse in AI-Mediated Search: Large Language Models as Metameric Observers of Brand Advertising
- Source type: Preprint (not yet peer-reviewed)
- Access: Full text reviewed
- DOI: 10.5281/zenodo.19422427
- Source: https://doi.org/10.5281/zenodo.19422427
2. How Small Businesses in Nigeria Use Generative AI to Compete in Marketing Content
- Source type: Peer-reviewed journal article
- Access: Full text reviewed (open access)
- DOI: 10.65773/ssia.2.2.34
- Source: https://doi.org/10.65773/ssia.2.2.34
3. Generative AI-Enabled Music Generation in Marketing and Consumer Response
- Source type: Peer-reviewed journal article
- Access: Full text reviewed
- DOI: 10.5282/jums/v11i1pp181-194
- Source: https://doi.org/10.5282/jums/v11i1pp181-194
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-brand-visibility-smb-content-playbooks-ai-music-ads-2026-05-31
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 drawn directly from the papers as accessed and are presented with their limitations. Preprint findings have not completed peer review and may change. Nothing here constitutes business, legal, or financial advice.
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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. | |||
| AI Marketing Research: Consumer Trust, AI Bias & Ad Influence | 30 May 2026 | 00:19:08 | |
Consumer attitudes toward generative AI have shifted dramatically since 2020 — and the direction is not what most marketing teams are planning for. Meanwhile, advertising embedded inside AI chatbots can already shift product recommendations in measurable ways, without the AI ever disclosing the ad or giving a wrong answer. This episode covers both.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering consumer attitudes toward generative AI, practical AI tools for marketing research, and the influence of advertising on AI chatbot recommendations.
What you'll learn:
- How consumer feelings about generative AI have shifted over seven years — and why what excited people in 2020 may actively annoy them today
- Why people accept AI as a creative assistant but resist it as a decision-maker — and what that means for how you frame AI-powered products
- The most common prompt mistake that turns AI-generated marketing research into polished-sounding garbage
- How ads embedded in AI chatbots can shift product recommendations invisibly — and why the choice of AI platform matters as much as the ad itself
- Why standard accuracy checks would never catch the bias this third paper found
Papers covered:
1. Designing marketing strategies based on a dual-method analysis of consumer attitudes toward generative AI
- Source: Discover Artificial Intelligence (Springer)
- Type: Peer-reviewed journal article
- Access: Full text reviewed
- DOI / Link: https://doi.org/10.1007/s44163-026-01382-1
2. New Tools, New Roles: A Manager's Guide to Harnessing Generative AI for Marketing Insight
- Source: NIM Marketing Intelligence Review
- Type: Peer-reviewed journal article
- Access: Full text reviewed (open access)
- DOI / Link: https://doi.org/10.2478/nimmir-2026-0005
3. Ad-verse Effects: Pharmaceutical Advertising Shifts Drug Recommendations by Consumer-Facing AI
- Source: medRxiv
- Type: Preprint — not yet peer-reviewed
- Access: Full text reviewed
- DOI / Link: https://doi.org/10.64898/2026.04.14.26350868
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-consumer-trust-prompt-bias-ad-influence-2026-05-30
Disclaimer: This is a first-pass research briefing produced with AI-assisted screening tools and reviewed editorially. It is not a substitute for reading the full papers. Preprints have not been peer-reviewed and findings may change. Nothing here constitutes medical, legal, or financial advice.
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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. | |||
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