Generative AI creates text, images, code and audio from a prompt.
Generative AI is AI that creates new content — text, images, code or audio — rather than only classifying or predicting. Large language models such as GPT are the best-known example.
Generative AI is software that produces new content (text, images, code, audio) from a prompt, rather than classifying or predicting from existing data. Large language models are the best-known kind, but the category is broader.
The commercial point is not that it writes. It is that it turns unstructured work into something automatable: reading documents, drafting replies, summarising calls, extracting fields from a scan. Those are the jobs that previously needed a person simply because the input was messy.
Four things, reliably: drafting text a person will review, extracting structured fields from unstructured documents, summarising long inputs, and classifying messy text into categories. Extraction and classification are the underrated ones: far less glamorous than writing, far more valuable in a business process, and much easier to measure.
It produces fluent output regardless of whether it knows the answer, which is the root of most business risk. It cannot reliably do arithmetic, it does not know your internal facts unless you supply them, and it has no concept of whether a source is current. Fluency reads as confidence, so errors survive review longer than they should. Grounding it in your own content (see RAG) addresses the second problem, not the first.
Predictive AI answers questions about what will happen, which customers will churn, when a machine will fail, how much stock to order. Generative AI produces content and handles language. They are not competitors, and the best SME projects often combine them: a model predicts which invoices will be paid late, and a generative model drafts the chasing email.
Using ChatGPT costs a subscription. Building generative AI into a business process costs more, because the work is integration, grounding and evaluation rather than the model itself. Crux Digits prices this as a €2,500 audit, a €20,000 Production-ready MVP in 4–6 weeks, and production from €50,000. If an off-the-shelf tool solves your case, the audit will tell you.
Mostly drafting, extracting, summarising and classifying. In practice that means answering support questions from your documentation, pulling fields off invoices and delivery notes, summarising meetings, and sorting inbound email. The pattern is always the same: work that was manual because the input was unstructured.
ChatGPT is one product built on generative AI. The term covers the whole category of models that produce content, while ChatGPT is a specific consumer and business application of it. Other examples include Claude, Copilot, and the models companies embed directly into their own systems.
Yes, if you set it up properly. Keep the data in Europe, get it in writing that your prompts are not used to train their models, share only the fields you need, and write down what you are doing. A consumer subscription and a business setup are not the same thing here.
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