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Paper A

Ad Genie: A Multimodal Generative AI Framework for Automated Marketing Campaign Creation Using Product Images, Textual Prompts, and Web Intelligence

MS. GOUTHAMI, K. Saanvi, M. Divya Bharathi, Arekanti Mercy et al. — 2026 — Zenodo / International Journal of Engineering Research & Technology (IJERT)

peer reviewed journal article  ·   ·  watchlist

Key findings

• The team built a working prototype called Ad Genie that takes a product photo plus a short description as input, then automatically produces: social media posts, a blog idea, a short video script, a target audience profile, a summary of current market trends, and a sentiment summary — all in one workflow.
• The system combines several AI techniques at once: it reads the product image to understand what the product looks like, searches the web for current trends and reviews, and uses a large language model to write the actual campaign content. Chaining these steps together produced more context-aware output than text-only AI tools.
• The authors argue this approach solves a real gap: most existing AI writing tools ignore the product image and don't check what's trending online, so they produce generic copy. Ad Genie tries to fix both problems simultaneously.
• The paper identifies future improvements needed — including support for multiple languages and the ability to learn a specific brand's tone of voice — suggesting the prototype is not yet production-ready.

Marketing implications

• If you're a freelancer or small business owner spending hours writing social media posts from scratch, this paper is a blueprint for how a single AI tool could do that automatically from just a product photo — that workflow is coming, even if this specific tool isn't ready yet.
• Next time you evaluate an AI copywriting tool, check whether it reads your product image and pulls in current trend data, not just your text prompt — the paper shows those two inputs make a real difference in output quality.
• If you build or buy marketing tools, watch for systems that chain image understanding + web search + LLM writing together; this architecture is likely to become the new baseline for AI campaign tools within the next 1–2 years.

Paper B

Impact of Generative AI on Content Marketing Quality and Efficiency: A Comparative Study Between AI-Assisted and Human-Created Content

Amrendra Kumar — 2026 — International Scientific Journal of Engineering and Management

peer reviewed journal article  ·   ·  use cautiously

Key findings

• AI tools can produce content much faster and at lower cost than humans working alone — they're good at the 'factory' side of content creation.
• AI-generated content on its own tends to feel less authentic and emotionally flat to consumers — people notice when a brand's voice seems generic.
• The best results came from a hybrid approach: humans guiding and editing AI-generated drafts outperformed either AI alone or humans alone on content quality measures.
• Despite being faster, purely AI-produced content struggled to maintain brand voice and connect with audiences emotionally.

Marketing implications

• If you're using AI to write content, don't publish it raw — have a human editor review it specifically for brand voice and emotional tone before it goes out.
• Use AI to handle the first draft and the volume work (product descriptions, social variants, email templates), then spend human time on the parts that need personality.
• If a client or boss asks whether to go all-AI or all-human on content, this study gives you a data point for 'neither — do both together.'

Paper C

A conversational generative AI-driven advertising recommendation framework for personalized travel planning

Hsing-Tung Ho, Wei-Yu Chen — 2026 — IET Conference Proceedings

peer reviewed journal article  ·   ·  test this week

Key findings

• The new AI framework got 10–18% more ad clicks compared to the baseline systems it was tested against — meaning more people clicked the ads it recommended.
• It also improved two standard measures of how well a recommendation system ranks results (AUC and NDCG@10), suggesting the ads it surfaced were more relevant and better ordered.
• In user studies, the framework increased purchase conversion rates by 7%, meaning more people who saw the ads actually bought something.
• Users reported higher satisfaction, suggesting that when ads feel more relevant to an ongoing conversation about travel plans, people find them less annoying and more helpful.

Marketing implications

• If you work in travel advertising, this is a signal to pilot conversational ad placements inside AI travel planners (e.g., ChatGPT plugins, Gemini travel features) — ads embedded in trip-planning conversations may outperform standard display ads.
• When briefing your ad tech or martech team, ask specifically about reinforcement learning for ad ranking — the paper suggests that systems which learn from ongoing user behavior during a conversation outperform static recommendation engines.
• If you're building or evaluating a chatbot for a travel brand, consider whether it can use the conversation history (past questions, expressed preferences) to time and personalize ad insertions — this paper suggests that's where the performance gains come from.

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