"AI" is not one technology, it is an umbrella term. When somebody says it today, they usually mean one of three different things: the research field, the machine learning underneath it, or specifically the generative language and image models. The three are not synonyms, and most of the confusion comes from the people in a conversation meaning different things by the same word.
That confusion costs money. According to the EU statistical office, 19.95% of companies with ten or more employees used some kind of AI technology in 2025 — but the survey measures several technologies separately, from text mining to workflow automation. Anyone who "adopts AI" in practice always adopts one of those, never the whole thing.
The most common misconception is that AI thinks. It does not: it recognises patterns and continues them. That is not a put-down — it is exactly what makes it useful wherever a lot of similar cases have to be handled quickly, and exactly why it is confidently wrong wherever there were not enough similar cases to learn from.
For the three meanings pulled apart, and which one is worth using when, read What AI Really Is, a longer piece on the subject. Directly related terms: machine learning, LLM and generative AI.