AI use cases are specific tasks in your work where a language model such as ChatGPT or Claude takes over part of the job. Think of writing up a meeting, summarising a document, drafting an email or translating a text. Each use case differs in what AI does well, which errors it makes and which check it needs.
This page lists those use cases, with what to watch for in each task before you use the output. At the bottom you will find all published articles in the use cases cluster, one of the sections of the AI knowledge base.
What are AI use cases?
An AI use case is a task you use AI for, not a tool. ChatGPT, Claude and Copilot are tools; taking meeting notes, summarising and translating are use cases. The same use case works in several tools, and the same tool can handle dozens of use cases.
Almost all use cases for office work run on generative AI, which creates new text, images or audio based on an instruction. The underlying system is usually a large language model that predicts which text logically follows the input. That explains both the strength and the weakness of every use case: the output almost always sounds convincing, even when a detail is wrong.
That is why, in any use case, what matters most is how quickly you can check whether the result is right. A summary you can hold up against the source is quick to check. An analysis whose source you do not have is not.
Which AI use cases work best in the office?
The AI use cases that come up most often in the office are taking meeting notes, summarising, writing and translating. What they have in common is a source you can compare the output with, which makes them a good place to start.
Taking minutes and writing up meetings
When you take meeting notes with AI, speech recognition turns the meeting into a transcript, after which a language model extracts the decisions and action points. The best-known error pattern is a proposal that ends up in the minutes as a decision, or an action point that gets a deadline nobody agreed. Then there are the rules on recording, such as the GDPR and, in the Netherlands, the works council’s right of consent. For a single recording outside a meeting, such as an interview or dictation, converting audio to text is the first and often the only step.
Summarising documents
Using AI to summarise a document saves the most reading on long pieces, such as reports, contracts and policy memos. The risk lies in what drops out: an exception, a caveat or a negation easily disappears, so the summary says something other than the original. During the period I worked intensively with ChatGPT, I consistently came across summaries that sounded plausible but differed in substance from the source.
Writing and rewriting
In the office, AI writing is mostly about emails, draft letters and internal memos. A fixed instruction for each type of text works better here than a new question every time; a collection of ChatGPT prompts for work for recurring tasks is a practical starting point. The check is mainly about facts, names and commitments the model adds of its own accord.
Translating
Language models translate more fluently than classic translation tools, because they process the sentence in its context rather than word for word. How well translating with ChatGPT works depends on the type of text: a general email usually goes well, while specialist terminology and legal wording need a reader who knows both languages.
Which AI use case should you start with?
Start with a use case that comes up often, whose result you can check quickly and where no confidential data ends up in a tool your organisation has no agreements about. Setting up one task properly delivers more than doing five halfway.
Three questions help you choose:
- How many times a week do you do this task?
- Is there a source you can compare the output with?
- What data goes in, and is that allowed in the tool you use?
A task that scores well on all three is a logical starting point. Having meetings summarised and drafting emails faster are the best-known forms of working smarter with AI, precisely because they score well on all three questions.
What goes wrong with AI use cases?
With every AI use case, the same thing goes wrong as with the underlying model: the output can contain a fact that is not based on anything. Such an AI hallucination is least noticeable in output that looks polished, such as neatly formatted minutes or a fluent translation.
That error looks different in each use case. In minutes it is an invented owner of an action point, in a summary a missing caveat, in a translation a technical term that means something slightly different. If you know the error pattern of a use case, you also know where the check needs to be.
From my years at major banks I bring the four-eyes principle to AI work: nothing goes out without a second check. For an internal memo that check can be light; for minutes that record agreements or a text that goes to a client, it cannot.
When does a use case become automation?
A use case becomes automation as soon as the task no longer starts with a question you type yourself. Instead, it starts on its own, for example with every incoming email or every new document. The difference between AI and automation is that automation carries out fixed rules, while AI can also handle varying input.
The step from a single use case to a workflow requires different agreements about checks, because nobody is watching the output at the moment it is created. With AI agents and automation, the check therefore moves to the moment just before an action, such as sending something or saving a final record.
If you would rather build a fixed way of working than test use cases one by one, LearnLLM’s AI courses offer an approach for each tool, with checkpoints that apply to every use case.
