Generative AI (Artificial Intelligence), also known as GenAI, is a subfield of artificial intelligence that aims to create new and original content. Unlike traditional AI systems, which mainly analyse, classify or make predictions based on existing data, generative AI goes one step further: it learns patterns and structures from extensive training datasets in order to independently generate new data instances with similar characteristics. This encompasses a wide variety of formats, such as text, images, videos, audio files and software code.
How generative AI works
Generative AI is based on sophisticated machine learning models, in particular deep learning architectures such as neural networks. These models are trained using vast amounts of data to identify and internalise the underlying statistical patterns and relationships. When a user enters a query (prompt) in natural language, the generative AI system uses its acquired knowledge to generate an appropriate and creative response in the form of new content.
The best-known model architectures in generative AI include:
- Generative Pre-trained Transformers (GPTs): These models are best known for their ability to generate text and form the basis of many large language models (LLMs).
- Generative Adversarial Networks (GANs): Two neural networks (a generator and a discriminator) compete with one another to produce increasingly realistic results, often in the form of 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 at image generation by gradually removing noise from an image to synthesise a clear image.
Areas of application and relevance
Generative AI is transforming the dynamics of content creation, analysis and delivery across all sectors. Its applications are diverse and offer significant productivity gains for businesses and individuals alike. The technology is being used in numerous sectors, including software development, healthcare, financial services, media and marketing.
Specific examples of applications include:
- Text generation: creating articles, blog posts, marketing copy, scripts or emails.
- 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 for software development by writing, completing, checking and debugging software code.
- Synthetic data: the generation of artificial datasets for training other AI models, particularly useful when real-world data is scarce or sensitive.
- Customer interaction: Powering chatbots and virtual assistants capable of conducting human-like conversations.
- Product design: Developing new product designs based on market trends and customer preferences.
In 2025, the adoption of generative AI in businesses will accelerate further, with the technology acting as a key driver of digital transformation. It will increasingly work alongside humans by taking on repetitive or data-intensive processes, enabling people to focus on creativity, judgement and leadership.
