エピソード

  • AI Creative Workflows, AI Influencers & Marketing Analytics

    AI Creative Workflows, AI Influencers & Marketing Analytics
    2026/06/09
    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. -- 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.
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    19 分
  • AI Chatbot Ads, Cultural Bias & GenAI Content: 3 Research Signals

    AI Chatbot Ads, Cultural Bias & GenAI Content: 3 Research Signals
    2026/06/08
    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.
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    18 分
  • AI Chatbot Ads, Neuron Auctions & Agent Security: 3 Research Signals

    AI Chatbot Ads, Neuron Auctions & Agent Security: 3 Research Signals
    2026/06/07
    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. -- 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.
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    23 分
  • AI Ethics, SME AI Wins & LLM Pipelines: 3 Marketing Research Signals

    AI Ethics, SME AI Wins & LLM Pipelines: 3 Marketing Research Signals
    2026/06/06
    ``` === AI MARKETING RESEARCH RADAR === === TODAY'S RADAR QUESTION === 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. === PAPERS COVERED === 1. "With great power comes great responsibility": A meta-narrative review of ethical considerations and implications in the cro -- 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.
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    22 分
  • AI Marketing Research: Campaign AI, Content Quality & Conversational Ads

    AI Marketing Research: Campaign AI, Content Quality & Conversational Ads
    2026/06/05
    ``` === AI MARKETING RESEARCH RADAR === === TODAY'S RADAR QUESTION === 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. === PAPERS COVERED === 1. Ad Genie: A Multimodal Generative AI Framework for Automated Marketing Campaign Creation Using Product Images, Textual Prompts, and Web Intellig -- 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.
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    19 分
  • AI Ad Targeting Research: LLMs, Knowledge Graphs & Ad Auctions

    AI Ad Targeting Research: LLMs, Knowledge Graphs & Ad Auctions
    2026/06/02
    ``` === AI MARKETING RESEARCH RADAR === === TODAY'S RADAR QUESTION === 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. === PAPERS COVERED === 1. Fine-Tuned LLM as a Co -- 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.
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    20 分
  • AI Marketing Tasks, LLM Ads & Brand Visibility: 3 Research Signals

    AI Marketing Tasks, LLM Ads & Brand Visibility: 3 Research Signals
    2026/06/01
    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.
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    19 分
  • AI Chatbot Trust, Cold-Start Ads & AI Disclosure: 3 Research Signals

    AI Chatbot Trust, Cold-Start Ads & AI Disclosure: 3 Research Signals
    2026/06/01
    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. -- 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.
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    17 分