What is AI, really? (and how GenAI fits)

A plain-English map of AI, machine learning, LLMs, and generative AI — without jargon.

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Most AI confusion comes from using one word for multiple things. Here’s a simple way to think about it.

AI is a goal, not a single technology

“Artificial intelligence” is the umbrella goal: software that performs tasks that normally require human intelligence (pattern recognition, decision support, language, vision, planning).

Machine learning is a common way to build AI

Machine learning (ML) is a set of methods where systems learn patterns from data rather than being explicitly programmed for every rule.

Generative AI is a subset focused on producing new content

Generative AI produces new outputs: text, images, code, audio. Large language models (LLMs) are a popular GenAI approach for text.

What this means for your business

• Use classic ML when you want stable predictions (e.g., churn, fraud, forecasting) and you have good labels.
• Use GenAI/LLMs when language and content generation are central (e.g., support drafting, summarisation, retrieval-based Q&A).
• Always define the job-to-be-done and measurable success criteria first.

If you’re deciding where GenAI fits, the fastest litmus test is: does the user’s job depend on language and context? If yes, GenAI may help — but you’ll still need reliability controls.

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