October 28, 2025
Generative AI is a type of artificial intelligence that can create new and original content, rather than just analyzing or processing existing data. It can produce a wide variety of outputs, including text, images, music, and code, that are often different from content created by humans.
How does it work?
Generative AI learns by studying huge amounts of data such as countless books, articles, images, and songs. During this training process, it learns the underlying patterns, styles, and structures within that data.
Once trained, it doesn't just copy what it has seen. Instead, it uses its learned knowledge to generate something entirely new. For example, after analyzing thousands of cat photos, it doesn't just show you an existing cat photo; it creates a brand new image of a cat that has never existed before. This process is powered by complex algorithms and deep learning models, particularly neural networks.
What can generative AI do?
Generative AI has a fastly growing list of applications across many different fields.
Content creation
It can write emails, blog posts, marketing copy, and even poetry or fiction. Chatbots like ChatGPT are a prime example of this.
Art and design
It can generate stunning, realistic, or stylized images from simple text descriptions (prompts). Tools like Midjourney and DALL-E are popular examples.
Music and audio
It can compose original music in various genres, create sound effects, or generate realistic human-like speech.
Coding and development
It can write, debug, and explain code, significantly speeding up the software development process.
Scientific research
It can help design new molecules for drugs or create simulations to test complex scientific theories.
Key technologies behind generative AI
While the field is complex, a few key technologies are at the heart of most generative systems:
Large language models (LLMs)
These are the engines behind text-based generative AI. They are massive neural networks trained on huge amounts of text data, enabling them to understand and generate human-like language.
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Generative adversarial networks (GANs)
Often used for image generation, a GAN consists of two competing neural networks: a generator that creates content and a discriminator that judges its authenticity. They work against each other, with the generator getting better at creating realistic outputs.
Transformers
This is a specific type of neural network architecture that has proven exceptionally effective at handling specific data like text. It's the foundational technology for most modern LLMs, including those that power ChatGPT.
Brands that use Generative AI
Generative AI has moved beyond being a tech demo and is now being used by major brands to create viral marketing campaigns and offer hyper-personalized experiences. Here are some interesting examples of how Generative Ai can be used:
Coca-Cola
Coca-Cola took AI marketing to the next level with its “Create Real Magic” campaign. Instead of keeping AI behind the scenes, the brand invited fans to co-create. Using a custom platform powered by DALL·E and GPT-4, people could remix classic Coca-Cola assets, such a the contour bottle and iconic logo, into their own AI-generated art. The best creations didn’t just stay online; they lit up Times Square in New York and Piccadilly Circus in London, transforming a creative experiment into a global showcase.
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Mattel
Mattel, the company behind Barbie and Hot Wheels, is using generative AI to speed up its design process. Instead of spending weeks sketching ideas by hand, designers can type a prompt like “a Barbie car in a futuristic mermaid style” and instantly see dozens of detailed concepts with tools such as Adobe Firefly. What once took weeks of back-and-forth can now happen in a single afternoon, allowing teams to experiment quickly, refine ideas on the spot, and bring new toys to life faster than ever.
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Stitch Fix
Stitch Fix is taking fashion a step further by using generative AI to design brand new clothes. Instead of only recommending outfits, its AI studies what customers already love, including sleeves, colors, patterns, and necklines, and combines those insights into fresh designs. For example, it can create a blouse with the sleeves of a bestseller, the neckline of a trending item, and a new popular pattern. The result is data driven fashion that feels personalized and is more likely to become a hit with customers.
Why should you use Generative AI?
Using generative AI can make creating content faster and easier. It helps you come up with ideas, write text, design images, or even produce music, all in less time than it would take manually. Businesses can use it to create personalized marketing, design unique products, or explore new ideas without starting from scratch. For creators, it is a tool to experiment, iterate, and bring concepts to life more quickly. By using generative AI, you can save time, work more efficiently, and open up new opportunities for creativity and innovation.
Intern Content Marketing
Marija is a 21-year-old content marketing intern in Zwolle, originally from Lithuania. She’s in her final year of a Creative Business bachelor’s at NHL Stenden University. She loves writing and creating content for social media, but she’s also curious about the bigger world of digital marketing and enjoys picking up new skills along the way. Maria’s international background makes her adaptable and open-minded, always ready to bring fresh ideas to every project. Outside of work, Maria loves animals. She’s more of a cat person, but she also likes dogs, so she feels right at home in our dog-friendly office :)
