Generative AI for marketing content is software that writes marketing copy and makes marketing images with AI models, trained on one brand's own style and checked so that every result stays on brand. Its most common job is making many versions of one design: the same banner in forty sizes, three languages and several offers.
Why is this a job for software? A single campaign needs a version for every place it runs. A shopping app wants a wide banner. A social media feed wants a square image. A story wants a tall one. Each language needs its own text, and each city may get its own offer. Making these versions by hand used to take up much of a creative team's time.
The software starts with the brand kit. This is the set of things that make the brand recognisable: its logo, its colours, its fonts, approved product photos, and rules about tone, such as "never use slang". The AI model is trained on, or given, this kit. A model that makes images from a typed description is an image model. A large language model writes the text. Both are foundation models: general models trained on huge amounts of data, which a company then adapts for its own use.
Then come the checks. Before a version is shown to anyone, the software checks it against the rules. Is the logo the right size and in a corner? Are the colours the brand's exact colours? Does the text avoid banned words and claims the brand cannot prove, such as "the cheapest in India"? A version that fails a check is fixed or thrown away.
Take, for example, an electronics retail chain in Chennai preparing its Pongal sale. The designer makes one master banner in graphic design software. The software then makes 40 sizes of it, in Tamil, English and Hindi, so 120 versions in all. For each of its 12 cities, it puts in that city's best offer on televisions. It checks every version against the brand kit. Then the designer looks through them, changes two and approves the rest in an afternoon. This used to take the team a week.
The designer's job is changing. The designer now sets the direction and approves the result, instead of resizing banners. This way of working is called human in the loop. Content made by models raises questions of its own, such as who owns it. These are covered under AI-generated content.
The approved versions go out in different ways. Dynamic creative optimisation (DCO) picks which version each viewer sees. A customer engagement platform uses the copy in its push messages and emails.