Article
AI explained: What is generative AI?
19/1/2025
min read
AI
In the year 2025, we’re all well-aware that artificial intelligence is reshaping entire industries and changing our daily lives. And among the many branches of AI, one stands out for its creativity and transformative potential: generative AI.
Generative AI (or GenAI) refers to a subset of AI that can create new content – text, images, audio, video, and even code. Unlike traditional AI models that are designed to analyse data and make predictions, generative AI learns patterns from existing data to produce original outputs. This unique ability sets it apart as a more creative and innovative branch of AI. Essentially, it doesn’t just interpret the world; it generates something new.
How does generative AI work?
Generative AI relies on deep learning, a type of machine learning that mimics the way humans learn. Specifically, it uses neural networks – complex mathematical models inspired by the human brain – to process and understand vast amounts of data. Two key technologies drive generative AI:
Generative Adversarial Networks (GANs)
Introduced in 2014, GANs consist of two neural networks: a generator and a discriminator. The generator creates new content, while the discriminator evaluates it. Imagine an art forger (the generator) trying to create counterfeit paintings, while an art expert (the discriminator) identifies which paintings are fake. Over time, the forger becomes better at creating convincing fakes, and the expert becomes more adept at spotting them. Through this back-and-forth process, the generator improves, producing outputs that increasingly resemble real-world data.
Transformer models
These models excel in processing sequential data, such as text. Transformers use mechanisms like attention, which allows them to focus on different parts of input data, enabling them to understand context and generate accurate outputs. Take this sentence as an example: “The dog chased the cat, which climbed a tree”. Attention enables the model to know that it was the cat that climbed the tree, not the dog. This ability to dynamically prioritise different parts of the input makes transformers exceptionally powerful in tasks like language translation or summarisation.
Examples of generative AI in the business world
In customer support
One of the best uses of generative AI is for chatbots and virtual assistants that provide instant, accurate responses to customer queries. Unlike traditional bots, these AI models can understand nuances and maintain conversational flow. So it feels more like you’re talking to a human than to a machine, which is great for customers because they can usually get answers or support 24/7. It’s also great for businesses because it lessens the burden on human support teams to answer easier queries so they can focus on solving more complex issues.
See how Visma companies are using AI to elevate customer support
For content automation
Another great use case is in marketing. Marketing teams can use generative AI to produce blog posts, ad copy, and social media content tailored to specific audiences, outcomes, and goals. It’s a simple way for teams to produce more content at scale, and generate new ways of thinking outside the box to reach broader audiences.
In software development
Generative AI is also revolutionising how software is built. With its ability to auto-complete code, identify bugs, and suggest optimisations, developers can move faster and more efficiently. It cuts down on routine tasks, leaving developers with more time to focus on creative and complex challenges, making development processes smoother and quicker overall.
Learn how AI is transforming Visma’s software development
For data insights and reporting
Most businesses have a huge amount of complex data, that they may or may not know what to do with. But generative AI can help. It can make sense of large, complex datasets by transforming them into clear, actionable insights. It can also automatically generate easy-to-understand reports, create visualisations, and even write narratives that explain key trends or forecast future outcomes, making it easier for businesses to make better data-driven decisions.
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