AI & Marketing Research with Dr. Eva Wolf

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 Not another AI news podcast. This is a research radar — a twice-weekly briefing that surfaces peer-reviewed studies on AI and marketing, tells you what the evidence actually says, and helps you decide what's worth a deeper read. 

  1. 3d ago

    AI Agents as Buyers, LLM Ad Auctions & Native Ads in Chatbots

    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An Optimal-Stopping Dynamic Auction for Native Advertising in Multi-Turn LLM Conversations Source type: Preprint (not yet peer-reviewed) Access: Full text available Source: https://arxiv.org/abs/2608.00123 3. PILA: Plug-and-Play Insertion for LLM-native Advertising Source type: Preprint (not yet peer-reviewed) Access: Full text available DOI: 10.48550/arxiv.2607.25590 Source: https://arxiv.org/abs/2607.25590 Full show notes, transcript, and citations: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. -- 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.

  2. Aug 7

    AI Bidding, Consumer Trust & GenAI Strategy: 3 Research Signals

    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. -- 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.

  3. Aug 6

    GEO, AI Customers & LLM Ads: 3 Marketing Research Papers

    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. -- 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.

  4. Aug 5

    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.

  5. Jul 18

    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. ━━━━━━━━━━━━━━━━━━━━ 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  ━━━━━━━━━━━━━━━━━━━━ 📚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 ━━━━━━━━━━━━━━━━━━━━ ==== 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. ━━━━━━━━━━━━━━━━━━━━ SUBSCRIBE Subscribe for research-backed AI marketing strategy, generative AI for marketing, AI adoption for small business, psychographic marketing, AI workflows, and practical insights for consultants, educators, and business owners. If this episode helped you rethink your AI marketing workflow, subscribe and share it with someone who is tired of AI slop and ready to build real AI capability. #AIMarketing #GenerativeAI #MarketingStrategy Thanks for listening to AI & Marketing Research Radar by Big Plans Media. I’m Dr. Eva Wolf, and I help marketers, educators, consultants, and business owners turn AI marketing research into practical strategy, smarter workflows, and real business opportunities. More episodes: https://bigplans.media/ai-marketing-research-radar/ Consulting: https://bigplans.media/ai-marketing-consulting/ Big Plans Media — Where Big Ideas Meet Smart Marketing.

    AI Slop Is Killing Your Marketing: The 3C Framework That Fixes It

  6. Jul 16

    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.

  7. Jul 15

    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.

  8. Jul 14

    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.

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 Not another AI news podcast. This is a research radar — a twice-weekly briefing that surfaces peer-reviewed studies on AI and marketing, tells you what the evidence actually says, and helps you decide what's worth a deeper read. 

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