Generative AI (Artificial intelligence), also known as GenAI, is a branch of artificial intelligence that aims to create new and original content. In contrast to traditional AI systems, which mainly analyze and classify existing data or make predictions, generative AI goes one step further: it learns patterns and structures from extensive training data sets in order to independently generate new data instances with similar properties. This includes a variety of formats such as texts, images, videos, audio files or Software code.
How generative AI works
The basis of generative AI is formed by sophisticated Machine learning models, deep learning architectures such as neural networks. These models are trained with huge amounts of data to identify and internalize the underlying statistical patterns and relationships. When a user submits a query (Prompt) in natural language, the generative AI system uses its learned knowledge to generate a suitable and creative response in the form of new content.
The best-known model architectures of generative AI include:
- Generative Pre-trained Transformers (GPTs): These models are primarily known for their ability to generate text and form the basis of many large language models (LLMs).
- Generative Adversarial Networks (GANs): Two neural networks (generator and discriminator) compete with each other to produce increasingly realistic results, often for images.
- Variational Autoencoders (VAEs): These models learn a compressed representation of data and can then generate new examples from this latent space.
- Diffusion models: Particularly successful in image generation by removing noise from an image step by step to synthesize a clear image.
Areas of application and relevance
Generative AI is transforming the dynamics of content creation, analysis and delivery across industries. Its applications are diverse and offer significant productivity gains for companies and individuals. The technology is being used in numerous sectors, including software development, healthcare, financial services, media and Marketing.
Specific application examples include:
- Text creation: Generation of articles, blog posts, marketing texts, scripts or e-mails.
- Image and video generation: Creation of visual art, photorealistic images or videos from text descriptions as well as image editing and design assistance.
- Code generation: Support with the Software development by writing, completing, checking and debugging software code.
- Synthetic data: Generation of artificial data sets for training other AI models, particularly useful for scarce or sensitive real data.
- Customer interaction: Drive chatbots and virtual assistants that can conduct human-like dialogs.
- Product design: Development of new product designs based on market trends and customer preferences.
In the year 2025, the acceptance of generative AI in companies accelerating further, with technology acting as the central driver of digital transformation. It is increasingly working alongside humans, taking over repetitive or data-intensive processes and allowing people to focus on creativity, judgment and leadership.
