AI basics: what the technology actually does

Three terms worth separating

Artificial intelligence is a broad field concerned with tasks such as recognising patterns, interpreting language and making predictions. Machine learning builds models from examples rather than requiring a person to specify every rule. Generative AI produces new content, such as text or code. A tool that forecasts demand and a tool that drafts an email may both use AI, but they solve different problems and need different checks.

What a language model knows

A large language model learns patterns during training and generates a response from the context supplied at use time. Fluent output is not proof of truth. A model does not automatically know your current stock position, customer commitments or internal policies. An application must give it appropriate information and enforce access to that information.

A useful first business conversation

Consider a fictional operations team receiving long supplier updates. The immediate opportunity might be drafting a short summary for a reviewer. Posting a revised delivery date into the ERP is a separate decision. Agree which part needs language interpretation, which part needs an exact calculation and which part requires a person’s approval. This avoids buying a broad tool before understanding the work.

How to judge whether it helps

Choose one recurring task and compare the existing effort with an assisted version. Look at corrections, missed details and time to a usable result, not just how fast the first draft appears. Ask staff whether the result helps them make a decision. Our existing article on better context goes deeper into the practical habits behind a useful Microsoft 365 request.

Microsoft sources and further reading

• Microsoft Learn: introduction to large language models