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

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AI & Marketing Research with Dr. Eva Wolf

Dr. Eva Wolf

Business

Technology

Science

Frequency: 1 episode/2d. Total Eps: 68

Evidence-led briefings that translate peer-reviewed studies and important preprints on artificial intelligence, generative AI, marketing, advertising, consumer behavior, and business strategy into practical insight. Dr. Eva Wolf explains what the evidence actually says, what deserves a deeper read, and what marketers, consultants, educators, and business leaders can do next.

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https://bigplans.media/ai-marketing-research-radar/
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https://bigplans.media/ai-marketing-consulting/
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https://arxiv.org/abs/2311.09735
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https://doi.org/10.48550/arxiv.2605.11582
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https://doi.org/10.62794/ijober.v4i1.23
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AI Expert Forecasts: What 2,800 Researchers Say Is Coming

Tuesday, September 22, 2026 • Duration 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. -- This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions. AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts.

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

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

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

Big Plans Media — Where Big Ideas Meet Smart Marketing.

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AI Capability Benchmarks: What Marketers Need to Know

Tuesday, September 22, 2026 • Duration 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. -- This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions. AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts.

AI Marketing Adoption: Leadership, Trust & the Human Problem

Friday, September 18, 2026 • Duration 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. -- This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions. AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts.

AI Attribution, Trust & Personalization: 3 Marketing Research Signals

Thursday, September 17, 2026 • Duration 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.

AI Ads Inside AI Answers, Self-Evolving Ad Systems & Health AI

Friday, August 28, 2026 • Duration 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.

AI Marketing Research: Gen Z Trust, Ad Forecasting & LLM Ads

Thursday, August 27, 2026 • Duration 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.

AI Workflow Gaps, Brand Equity & Content AI: 3 Research Signals

Wednesday, August 26, 2026 • Duration 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.

AI Marketing Research: Agency Survival, Churn AI & Disclosure

Tuesday, August 25, 2026 • Duration 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. -- This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions. AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts.

AI Marketing Research: Gen Z Trust, Emotional AI & Chatbot Ads

Monday, August 24, 2026 • Duration 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. -- This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions. AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts.

AI Marketing Research: Content Automation, GenAI Gaps & Consumer Engagement

Sunday, August 23, 2026 • Duration 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. -- 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.