AI Marketing Research: B2B GenAI, Hospitality AI & LLM Safety

Your B2B team is using AI to write copy. Your hotel client's chatbot is labelled "safety-aligned." And new research suggests both of those things might be giving you a false sense of progress. In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering generative AI adoption in B2B and industrial marketing, AI applications and trust risks in travel and hospitality, and a striking finding about how easily safety guardrails on open-weight language models can be bypassed. What you'll learn: - In B2B and industrial marketing, AI is being used almost exclusively for execution tasks — content, copy, and ads — while research and planning remain nearly AI-free. That gap is where the real opportunity is hiding. - As AI handles more execution work, the marketer's role is shifting toward briefing AI tools, reviewing outputs, and strategic thinking — teams that don't plan for this will fall behind. - In travel and hospitality, the most research-backed AI applications are recommendation engines, sentiment analysis, and dynamic pricing — but over-automating customer touchpoints is consistently flagged as a trust and loyalty risk. - The safety guardrails on popular open-weight AI models are more fragile than most marketing technology buyers realize. A single internal neuron can be toggled to bypass them entirely. Papers covered: 1. The Impact of Generative AI on B2B Marketing Processes: Evidence from Industrial Firms - Authors: Vesterinen, Mero, Skippari, Karjaluoto (2026) - Type: Peer-reviewed journal article - Access: Full text reviewed - Source: https://doi.org/10.18690/um.fov.4.2026.32 - Radar verdict: Read now 2. Mapping Research Trends in AI-Based Tourism and Hospitality Marketing: A Bibliometric and Thematic Review - Authors: Tyagi, Aggarwal, Tyagi, Vasudevan, Singh (2026) - Type: Peer-reviewed journal article (F1000Research) - Access: Full text reviewed - Source: https://doi.org/10.12688/f1000research.177254.2 - Radar verdict: Watchlist 3. A Single Neuron Is Sufficient to Bypass Safety Alignment in Large Language Models - Authors: Kazemi, Chegini, Safi (2026) - Type: Preprint — not yet peer-reviewed - Access: Open access - Source: https://arxiv.org/abs/2605.08513 - Radar verdict: Watchlist Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-b2b-genai-hospitality-llm-safety-2026-06-11 Disclaimer: This is a first-pass research briefing produced by an AI-generated avatar trained on Dr. Eva Wolf's research framework. It is not a substitute for reading the original papers. Findings are described as the research suggests, not as proven conclusions. Preprints have not completed peer review and should be treated with additional caution. -- 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.