How to Make a Banner with a Neural Network: AI for Advertising

--- title: "Banners via Neural Networks: AI for Advertising — What It Can Do and Where the Risks Are" description: We break down which types of banners neural networks generate well and which ones poorly, how to build a pipeline, and how to avoid risks with text and brand guidelines. tags: [AI, advertising, banners] ### ⚡ The Gist in 5 Seconds - AI lets you create ad banners in under an hour, including approvals — an alternative to manual work and expensive designer commissions. - All the neural networks discussed are available on the BotHub platform. - AI handles static images and adaptive formats confidently, but is weak at video banners, precise typography, and exact brand-color matching. ### 🔍 What Was Found In an article by DrArgentum, published on the BotHub company blog on Habr, the process of creating banners with AI is examined. The author notes that a banner is not an illustration but a tool with requirements and KPIs: clicks, conversions, brand recognition. So the prompt should be written like a marketer, not an illustrator. A campaign typically needs 8–15 variants for different segments and placements, and here AI helps generate a series, but selection and refinement remain with the human. For static images, neural networks confidently generate backgrounds, abstract compositions, object scenes without complex geometry, and portraits with controlled lighting. Background replacement and atmospheric backdrops work well, but precise typography and complex product scenes do not. For adaptive formats, AI offers huge savings: if a creative is needed in 10–15 sizes, combining master-image generation with automatic adaptation via templates cuts time dramatically. Video banners, however, are a weak spot: frames are inconsistent, there are motion artifacts, and there is no way to control timing relative to text. The author also dwells on limitations. Small text is a frequent problem: even GPT Image 2 makes mistakes, so headlines and disclaimers are best overlaid in post-processing. The neural network also fails to hit brand colors: the model interprets color as a concept, not a value, so post-processing is needed to match the brand book palette. Legally significant elements — promotion terms, license links, disclosures — should not be left to AI. Additionally, human faces and trademarks must be checked: a mistake can lead to lawsuits worth hundreds of thousands or millions of rubles. ### 💡 Why It Matters Banner advertising is a field where results in numbers matter, not a self-sufficient image. AI saves time and budget at the prototype and creative-series generation stage, but does not replace the marketer: it is the human who defines the quality criteria, selects the working variants, and polishes them to the final result. Understanding the strengths and weaknesses of neural networks lets you build a pipeline where AI speeds up the routine while risks with text, colors, and legal issues remain under control. ### 🧩 Context Banner advertising has come a long way from static JPEGs in the 2000s to real-time personalization. Neural networks are the next wave of transformation, but they are often perceived simplistically, as ordinary image generation. The article

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