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Paper A

AdSpark: A Multimodal Generative AI Framework for Automated Digital Advertising and Marketing Asset Synthesis

Saurabh Keni — 2026 — International Journal for Research in Applied Science and Engineering Technology

peer reviewed journal article  ·   ·  test this week

Key findings

• AdSpark generated an advertising poster plus marketing copy in 15–25 seconds, compared to 4–6 hours for a human designer and copywriter — a speedup of over 99%.
• The estimated cost per campaign asset using AdSpark was under $0.50 in AI API fees, versus $150–$300 for the equivalent manual human work — a cost reduction of roughly 99.8%.
• The AI-generated marketing copy showed relevant thematic focus: for example, when given a smartwatch product, the system automatically generated captions emphasizing health tracking and lifestyle — suggesting the AI can identify and amplify key selling points without human direction.
• The system produced a complete 'campaign kit' (poster image, taglines, social media captions, and target audience profile) from a single product photo and description, replacing the need to use multiple separate tools.

Marketing implications

• If you run a small business or agency that produces a high volume of social media creatives, the architecture described here (product image + description → AI poster + copy + captions) is something you could replicate today using Google Gemini's API — no custom platform required.
• The idea of running image analysis and text strategy generation in parallel (not sequentially) is worth borrowing if you're building any AI creative tool — it cuts wait time significantly.
• Before investing in expensive creative production for early-stage product ideas, try using multimodal AI tools (like Gemini or GPT-4o) to generate a quick prototype ad kit first — the cost is under $1 and takes under a minute.

Paper B

THE EFFECTIVENESS OF AI-POWERED MARKETING STRATEGIES IN SUSTAINABLE TOURISM

Marko Trišić, Dušan Garabinović, Maja Staletović — 2026 — AGORA INTERNATIONAL JOURNAL OF ECONOMICAL SCIENCES

peer reviewed journal article  ·   ·  watchlist

Key findings

• AI-powered marketing tools — such as personalized recommendations, chatbots, and predictive analytics — can make tourism promotion more effective and improve the experience for travelers, but only when they are used responsibly alongside concerns about privacy, the environment, and fairness.
• Generative AI is seen as a particularly promising tool in tourism marketing because it enables highly personalized content and communication at scale, which fits with how modern travelers expect 'smart travel' experiences.
• The benefits of AI in sustainable tourism are conditional: AI alone does not automatically produce sustainable outcomes. Ethical oversight and careful management are required for AI to actually support — rather than undermine — sustainability goals.
• AI-based strategies can help travel destinations stay competitive, but the paper signals that unchecked AI adoption could conflict with environmental and social sustainability if not governed properly.

Marketing implications

• If you work in travel or tourism marketing, use AI personalization tools (like dynamic itinerary builders or AI chatbots) but pair them with a visible sustainability message — the paper suggests tourists respond better when AI-driven experiences feel ethically grounded.
• If you're pitching AI tools to a destination marketing organization (DMO), lead with both the efficiency gains and the ethical guardrails. Showing you've thought about data privacy and environmental impact will likely land better than a pure tech pitch.
• Don't deploy generative AI for content or recommendations in tourism without a human review layer — this paper's core warning is that AI benefits in this sector depend on active oversight, not set-and-forget automation.

Paper C

Predictive Analytics in Retail: Impact of AI on Inventory Optimization and Marketing Performance

R. Sakthivel, Dr D Santhanakrishnan — 2026 — International Journal of Creative Research Thoughts (IJCRT)

peer reviewed journal article  ·   ·  use cautiously

Key findings

• 72% of retail managers said AI tools improved how accurately they tracked inventory, and 65% said it reduced the problem of running out of stock at the wrong time.
• A statistical test (Chi-square, p=0.006) showed that stores using AI were meaningfully more efficient in their supply chains than those that were not — this was not just a coincidence in the data.
• A second statistical test (ANOVA, p=0.002) found that stores making inventory decisions with AI support also tended to get better marketing results — things like higher sales and better return on their promotional spending.
• Despite these benefits, high setup costs, lack of trained staff, and difficulty connecting AI tools with existing systems are stopping many smaller retailers from adopting AI.

Marketing implications

• If you run retail promotions, check whether your AI inventory tools are feeding into your marketing planning — this study suggests stores that connect the two tend to see better ROI on promotions.
• If you are a small retailer thinking about AI, be realistic about the upfront costs and the need to train your team — jumping in without addressing those two things is the most common failure point, according to the managers surveyed.
• If you are pitching AI tools to a retail client in an emerging market like India, lead with inventory accuracy and stockout reduction as the clearest benefit — 65-72% of managers in this study reported those improvements, making them your strongest talking point.

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