Apple Podcasts  ·  Spotify  ·  Buzzsprout

Paper A

The Impact of Generative AI on B2B Marketing Processes: Evidence from Industrial Firms

Mikko Vesterinen, Joel Mero, Mika Skippari, Heikki Karjaluoto — 2026

peer reviewed journal article  ·  unknown  ·  read now

Key findings

• GenAI is used unevenly across B2B marketing: it is having the biggest effect on execution tasks (like writing content, creating ads, and personalizing messages), but companies are barely using it for research or planning — most of that is still done by humans without any systematic AI help.
• As AI takes over more execution tasks, the marketer's job is shifting toward higher-level work: understanding the market, setting strategy, and managing the AI tools and processes — rather than producing content directly.
• Companies are not applying GenAI in a structured or consistent way across their marketing operations — adoption is patchy and often reactive rather than planned.
• Industrial B2B companies (manufacturers, engineering firms, etc.) are behind many other sectors in adopting AI for marketing, meaning there is still significant untapped potential in this space.

Marketing implications

• If you work in B2B or industrial marketing and you're only using AI for writing blog posts or email copy, you're doing what everyone else is doing. The gap — and the opportunity — is in using AI for market research and campaign planning, which almost nobody is doing systematically yet.
• Start planning now for your team's roles to shift: the people who used to write content will need to become good at briefing AI tools, reviewing AI output, and thinking about strategy — not just execution.
• If you sell marketing services or tools to industrial companies (manufacturers, engineering firms), this is a relatively early market — these buyers are behind on AI adoption and likely open to guidance and structured solutions.

Paper B

Mapping Research Trends in AI-Based Tourism and Hospitality Marketing: A Bibliometric and Thematic Review

Pankaj Kumar Tyagi, Priyanka Aggarwal, Priyanka Tyagi, Asokan Vasudevan et al. — 2026 — F1000Research

peer reviewed journal article  ·   ·  watchlist

Key findings

• Research on AI in tourism and hospitality marketing exploded between 2017 and 2020 — the number of papers published grew sharply during that period, showing the industry was rapidly experimenting with smart tech in marketing.
• The review identified four main topic clusters in the research: (1) how digital platforms and data shape tourist behavior, (2) AI-powered tools for selling travel and tourism online, (3) tech-driven improvements to hotel and hospitality guest experiences, and (4) using data and AI models to predict travel demand.
• AI tools like recommendation engines, sentiment analysis of reviews, dynamic pricing, and personalized campaigns are the most-studied applications — these are already being used by hotels and travel companies to make marketing smarter.
• Concerns about data privacy, transparency, consumer trust, and whether AI makes service feel less human are recurring themes in the literature — researchers flag these as unresolved challenges.

Marketing implications

• If you work in travel, hotels, or tourism marketing, the most research-backed AI applications to explore right now are recommendation engines (suggesting trips or hotels based on browsing history), sentiment analysis of online reviews, and dynamic pricing tools — these have the most academic attention and real-world deployment evidence.
• Don't just automate — the research consistently flags that over-relying on AI in hospitality can make guests feel like they're talking to a machine, which hurts loyalty. If you're adding AI to customer touchpoints, keep a human option available for high-stakes moments (complaints, booking changes).
• If you're pitching AI tools to a hotel or travel brand, lead with data privacy and transparency — these are the top trust concerns in the literature, and addressing them upfront will help close deals faster.

Paper C

A Single Neuron Is Sufficient to Bypass Safety Alignment in Large Language Models

Hamid Kazemi, Atoosa Chegini, Maria Safi — 2026 — ArXiv.org

peer reviewed journal article  ·  open access  ·  watchlist

Key findings

• Turning off just one specific neuron inside an AI model is enough to make it answer dangerous questions it was trained to refuse — like instructions for making weapons or drugs. This worked on 7 different AI models, achieving a 91.7% success rate on average at getting the models to produce harmful content.
• The AI models' safety systems are not spread evenly across the whole model — they depend critically on just one or a handful of specific neurons. This is like a building's entire fire suppression system depending on a single switch: flipping it off breaks all the protections.
• The same logic works in reverse: turning one neuron UP can cause the AI to inject harmful content (like suicide-related text) into completely normal, innocent conversations — without any hacking of the prompt.
• These 'safety neurons' exist in the model even before safety training is applied, which means safety training is tweaking neurons that were already there, not building something new — making them easier to find and exploit.

Marketing implications

• If your company uses AI tools that run on self-hosted or open-source models (not just APIs), ask your AI vendor or IT team specifically whether they monitor for attempts to manipulate model internals — not just prompt-level abuse.
• If you're building AI-powered marketing content tools, chatbots, or customer-facing agents, this paper is a reason to layer safety checks at the output level (e.g., a separate content filter checking what the model actually says), not just rely on the model's built-in refusals.
• Brand safety buyers and AI compliance teams should know that 'safety-aligned' on a model card does not mean the model is robustly protected — especially for open-weight models anyone can download and modify.

Apple Podcasts  ·  Spotify  ·  Buzzsprout