The latest marketing news and practitioner insights in AI Marketing, updated daily.
Can an AI marketing agent sell artwork? A semi-autonomous setup with artist approval works best. Permission tiers, budget splits, testing and legal points.
Instacart’s AI shopping assistant Clementine uses familiar food scenes to turn product capabilities into a memorable meal-planning and ordering habit.
AI adoption hasn't freed marketing teams because the bottleneck moved, not vanished. The four human checkpoints in every AI workflow — instruct, inform, review, release — and what it takes to go AI-driven.
When AI tools become universal, where does competitive advantage come from? Two Korean platforms arrived at the same answer through opposite failures — and found where AI still loses to people.
OpenAI began piloting ads in ChatGPT on Aug. 11 across the U.K., Mexico, Brazil, Japan and Korea, serving free and Go tier users while excluding paid subscribers. What the conditions mean for advertisers.
An analysis of 9.5 million AI citations found 75% of LinkedIn citations originate from individual member profiles and only 25% from company pages. What that means for B2B AI visibility programs.
Kakao's first agent integration connects suggestion, authentication, ordering and payment inside a conversation, targeting 10 million monthly active users by year-end.
White-on-white text no longer works. Semantic embedding, multimodal attacks and vulnerable autonomous agents now target brand assets, customers and LLM referral share.
A ChatGPT ad format in testing replaces the landing page with a business-specific AI agent. Chatbot-native advertising makes the conversation, not the website, the destination.
Anthropic hit 34.4% enterprise AI adoption against OpenAI's 32.3% — the first reversal. This is not a share shift but a change in what the market measures.
OpenAI, Anthropic, AWS, and Google are pouring billions into forward deployed engineers, not models or GPUs, to make enterprise AI actually work.
88% of enterprise AI projects never reach deployment. The real cause of enterprise AI failure is missing real-time data pipelines, not model quality.
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