What is generative AI? How it differs from analytical AI, how it works, how businesses use it and what the rules require

Definition

Generative AI refers to AI models that create new content – text, images, audio, video or code – based on patterns learned from large amounts of data. Unlike analytical AI, which classifies and predicts, generative AI produces something that did not exist before.

Since ChatGPT launched at the end of 2022, generative AI has gone from research project to everyday tool in marketing departments, customer service and development teams. This guide explains what sets generative AI apart from other AI, how it works in broad terms, where it adds most value in a business and which risks and rules you need to know about.

What is generative AI – and how does it differ from analytical AI?

Generative AI produces something new, whereas analytical (also called discriminative) AI sorts, classifies and predicts based on existing data. A spam filter, a credit check or a model that predicts customer churn is analytical AI: it answers the question "which category does this belong to?" A generative model answers a different question: "what would a plausible continuation look like?" – the next word in a sentence, the next pixel in an image, the next line of code.

The difference decides where the technology fits. Analytical AI is usually the right choice when the question has a correct answer: is this invoice anomalous, which leads are warmest, which customer is likely to leave? Generative AI fits when the task is to draft, summarise, translate, sketch or vary something – work with not one right answer but many acceptable ones. Microsoft draws the same line in its introduction: analytical AI interprets, generative AI creates.

How does generative AI work in broad terms?

Generative AI is built on models that are trained on enormous amounts of data to learn statistical patterns, and then use those patterns to produce new material. Two architectures dominate today:

• Transformers (introduced in 2017) are the foundation of every modern LLM – the large language models that power ChatGPT, Claude and Gemini. The model predicts the next word based on the whole context of your prompt, word by word, until the answer is complete.
• Diffusion models are used for images and video. They are trained to remove noise from degraded images, and in doing so learn to build an entirely new image out of noise, guided by a text description. IBM has a clear technical walkthrough.

Older methods such as GANs and VAEs live on in niche uses but have in practice been replaced by these two. What matters to you as a buyer is not the architecture but its consequence: the model calculates probabilities, not facts. It does not know what is true – it knows what usually follows what. That is why it is often connected to a knowledge base using RAG, where answers are retrieved from your own documents via a vector database, or to tools and systems via MCP when it needs to carry out tasks as an AI agent.

How is generative AI used in marketing, on websites and across the business?

Marketing and sales is the most common use of AI in Swedish companies, and it is also where generative AI adds most value today. According to Statistics Sweden (SCB), 35 per cent of Swedish companies used AI in 2025, up ten percentage points in a year, and marketing/sales was the most common purpose (41.7 per cent). The gap between company sizes is wide: about 31 per cent of small companies compared with about 72 per cent of large ones. Note that SCB measures AI in the broad sense – not just generative AI – but the growth since 2023 has come from the generative side.

In practice, use often looks like this:

• Marketing: draft copy and ad variants, summaries of customer interviews, translation, image sketches and personalised mailings. People set the strategy, angle and quality bar; the model provides the speed.
• Website: AI-assisted development means a coded website with a headless CMS now takes less manual work to build than before – we describe how we work in Building a website with AI and a headless CMS. On websites, generative AI also powers chatbots that answer questions from the company's own documentation.
• Operations: meeting notes, first-line replies in customer service, searching internal documents, coding help and automating routine tasks.

Globally, the picture is even clearer: in McKinsey's State of AI (March 2025), 71 per cent of responding organisations say they regularly use generative AI in at least one business function. To find out where your company stands, read Is your company ready for AI?, which walks through AI readiness in practice. How to prioritise between use cases is covered in AI strategy for companies.

What are the risks of generative AI?

The four risks that most often cause problems are incorrect answers, unclear rights, data leakage and failure to comply with EU rules. None of them is a reason to avoid the technology, but all four call for clear routines.

• AI hallucination. The model can state completely made-up details, sources and figures with the same confidence as correct ones. Anything that will be published or used as a basis for decisions needs to be fact-checked by a person. RAG reduces the problem by tying answers to a verified set of sources, but does not eliminate it.
• Copyright. The legal position is still partly unclear, both for the material the models were trained on and for who owns the output. Copyright protection generally requires human authorship, so purely AI-generated material may not be protected. Use services with clear contract terms, and have a person rework anything business-critical.
• Confidential data. What you paste into a public tool may be stored and used for training, depending on the service and the agreement. Set a clear policy on which data may leave the organisation.
• The EU AI Act, Article 50. The transparency requirements have applied since 2 August 2026. Providers of generative systems must label AI-generated content in a machine-readable way, chatbots must tell users that they are interacting with an AI, and manipulated images and videos (deepfakes) must be openly disclosed. Systems already on the market before the rules took effect have a transition period until 2 December 2026. Breaches can lead to fines of up to €15 million or 3 per cent of global turnover. There is a summary from Cooley. Get legal advice on what applies to your specific use – a customer service bot on your website, for example.

What does generative AI mean for SEO and AEO?

To search engines, how content is produced does not matter – quality does. Google has been clear since 2023 that AI content is not penalised as such, but that content produced at scale primarily to manipulate rankings breaks the spam policy whether it was written by a person or a model. The same applies in AI search features such as AI Overviews: Google's guidance on AI features points back to the same basic principles of usefulness, originality and E-E-A-T – experience, expertise, authoritativeness and trustworthiness.

That gives a simple rule of thumb: AI-assisted content, where the model provides the speed and a person provides the angle, subject knowledge, examples and fact-checking, works well. Mass-produced, unreviewed content does not, and that usually shows in the results. We describe how the division of labour works in practice in Writing SEO content in 2026.

Generative AI is also changing how search works. As more people search via ChatGPT, Gemini and Google's AI modes, your website becomes source material that language models retrieve and cite – that is the core of AEO and of what is often called AI SEO. If clear information about what you offer is missing, the model tends to answer "no" rather than "don't know", and the deal is lost without you ever noticing. So clear answers, well-structured pages and content worth citing are the same job whether the reader is a person or a model.