AI & Marketing Research with Dr. Eva Wolf

By Dr. Eva Wolf

Evidence-led briefings that translate peer-reviewed studies and important preprints on artificial intelligence, generative AI, marketing, advertising, consumer behavior, and business strategy into

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FAQs about AI & Marketing Research with Dr. Eva Wolf:

How many episodes does AI & Marketing Research with Dr. Eva Wolf have?

The podcast currently has 64 episodes available.

AI & Marketing Research with Dr. Eva Wolf episodes:

August 05, 2026 GEO & AI Search Visibility: What the Research Actually Shows
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Type: Preprint (not yet peer-reviewed)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Type: Preprint (not yet peer-reviewed)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.
--
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.
...more
22min
July 18, 2026 AI Slop Is Killing Your Marketing: The 3C Framework That Fixes It
Most businesses don’t have an AI marketing tool problem.

They have an AI process problem.

In this episode of AI Marketing Research Radar, Dr. Eva Wolf breaks down why so much AI-generated marketing feels generic, empty, or like “AI slop” — and what the research suggests marketers, consultants, educators, and business owners should do instead.

The big idea: better AI marketing depends on the 3C Framework:

💪🏼Capability — choosing the right AI model for the creative job
💪🏼Context — giving AI the right background, personality, and work-style profile
💪🏼Competence — training people to use AI inside real marketing workflows

If you are using ChatGPT, Claude, Gemini, or other AI tools for marketing and still wondering why your content sounds generic, this episode will help you think differently about prompting, AI workflows, creativity, and team training.

==== FREE RESOURCE ====

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

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

This episode reviews three AI marketing research papers on:

• measuring AI creativity
• personalized AI collaborators
• generative AI training for small business marketing
• how to avoid AI slop
• how to build better AI marketing workflows
• how consultants and educators can turn AI training into a stronger offer

The money move: stop selling AI as a tool. Start selling AI as a capability-building system.

Businesses do not need another list of shiny AI apps. They need a repeatable way to choose the right model, give it the right context, train their team, review the outputs, publish strategically, measure results, and improve over time.

That is where real AI marketing advantage begins.

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WHAT YOU’LL LEARN

• Why AI-generated marketing often sounds generic
• How to choose the right AI model for creative work
• Why better prompts start with better context
• How to build an “AI recipe card” for your work style
• Why small business AI training needs feedback, practice, and workflow design
• How to turn AI workshops into a stronger consulting or training offer
• Why AI marketing advantage comes from capability, context, and competence

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📚Research Mentioned in This Episode ====

Beaty, R. E., Deshpande, V., Lai, C. K. Y., Attuch, A., Shivagunde, N., Roy, S., Pujari, R., DiStefano, P. V., Muckatira, S., Stevenson, C. E., Gronas, M., & Rumshisky, A. (2026). AGC-Bench: Measuring Artificial General Creativity. arXiv. https://arxiv.org/abs/2607.01152

Kelley, S., De Cremer, D., & Riedl, C. (2025). Personalized AI Scaffolds: Synergistic Multi-Turn Collaboration in Creative Work. arXiv. https://arxiv.org/abs/2510.27681

Putra, H. D., & Azizah, J. (2026). Generative AI in Digital Marketing Strategy: Transforming Brand Communication and Consumer Engagement. Indonesian Journal of Business and Entrepreneurship Research, 4(1), 1–13. https://doi.org/10.62794/ijober.v4i1.23

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==== ABOUT DR. EVA WOLF ====

Dr. Eva Wolf is a marketing professor, AI marketing researcher, consultant, and founder of Big Plans Media. She helps business owners, educators, consultants, and marketing teams translate AI marketing research into practical strategy, content systems, workflow automation, and smarter business decisions.

AI Marketing Research Radar reads the research so you don’t have to — and turns academic findings into plain-English insights, evidence checks, and practical marketing moves.

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If this episode helped you rethink your AI marketing workflow, subscribe and share it with someone who is tired of AI slop and ready to build real AI capability.

#AIMarketing #GenerativeAI #MarketingStrategy
Thanks for listening to AI & Marketing Research Radar by Big Plans Media.

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

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

Big Plans Media — Where Big Ideas Meet Smart Marketing.
...more
29min
July 16, 2026 AI Marketing Research: CRM Performance, Agent Loyalty & AI Forecasting
When your AI says it's predicting the future, is it actually forecasting — or just remembering? And what happens to brand loyalty when an AI agent, not a human, is the one making the purchase? Three papers this week examine whether we are actually getting what we think we are getting from AI in marketing.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering AI-driven CRM and financial performance in digital banking, AI agent loyalty loops in autonomous commerce, and a method for evaluating whether AI forecasting tools are genuinely predicting or retrieving memorized answers.
Note: Evita is an AI-generated research briefing avatar trained on the research framework of Dr. Eva Wolf. Every Friday, Dr. Wolf records a live weekly roundup with her own analysis.
What you'll learn:
- Why AI-powered CRM outperformed both personalization and chatbots as a financial performance driver in a Nigerian digital banking study, and how to use that R-squared value in a budget conversation
- How to tell whether an AI forecasting tool your vendor sells is actually predicting anything, or retrieving answers it already knows from training data
- Why emotional brand loyalty may stop mattering once an AI agent takes over purchasing decisions — and what marketers need to put in its place
- What machine-readable brand signals means in practice, and why loyalty programs need to be legible to algorithms, not just humans
- Why noisy or speculative retrieval sources make AI predictions worse, not merely less precise
Papers covered:
1. The Influence of AI-Driven Marketing on the Financial Performance of Digital Banks in Nigeria
Source type: Peer-reviewed journal article (likely peer-reviewed, published via Zenodo)
Access: Full text reviewed
DOI: 10.5281/zenodo.21277512
2. The Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) Model and the Net Human-Agent Score (NHAS) in Autonomous Commerce
Source type: Preprint (not yet peer-reviewed — treat findings as early-stage and theoretical)
Access: Full text reviewed
Source: https://arxiv.org/abs/2607.13998v1
3. Hindcast: Replaying Prediction Markets to Evaluate LLM Forecasters
Source type: Preprint (not yet peer-reviewed)
Access: Full text reviewed
Source: https://arxiv.org/abs/2607.14051v1
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-crm-performance-agent-loyalty-forecasting-2026-07-16
Disclaimer: This is a first-pass research briefing produced by an AI-generated avatar trained on Dr. Eva Wolf's research framework. It is not a substitute for reading the original papers. Preprints have not been peer-reviewed and findings may change. Correlation findings do not establish causation. Always consult the original source before citing or acting on any research discussed here.
--
This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts.
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.
...more
21min
July 15, 2026 AI Pricing Algorithms, LLM Bias & Ad Retrieval: 3 Research Signals
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.
--
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.
...more
21min
July 14, 2026 AI Marketing Research: Generative AI, Ethics & Cooperative Branding
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.
--
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.
...more
21min
July 13, 2026 AI Ad Generation, Tourism Marketing & Retail AI: 3 Research Signals
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.
--
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.
...more
21min
July 12, 2026 AI Marketing Research: Banking Loyalty, AI Training & LLM Ethics
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.
--
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.
...more
19min
July 11, 2026 AI Marketing Research: Generative Recommendations, AI Frameworks & Adoption
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.
--
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.
...more
21min
July 10, 2026 AI Marketing Research: Brand Trust, AI Advice Bias & CRM ROI
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.
--
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.
...more
23min
July 09, 2026 AI Marketing Research: Gen Z Trust, Trend Detection & Creative Deskilling
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.

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.

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