There is a trade you are making, and it is worth naming before the tool list.
Notes are useful for two different reasons. They store things, and making them changes you. AI is excellent at the first and structurally unable to help with the second, because the second depends on the work being done by you. A summary you did not write is a document you have read once.
So the rule underneath everything below: let AI handle what you would not have done anyway, and keep the part where the thinking happens.
Where AI genuinely helps
Transcription. The one unambiguous win. A recording of a lecture, interview or call becomes searchable text, and text you never had before is not a loss of anything. Accuracy on clear audio is now high, though see the caveats.
Retrieval across a large collection. Asking a question of two thousand notes and getting the four relevant ones is a real capability, and it beats keyword search when you cannot remember the words you used. This is the strongest case for AI in a mature system, and it makes the pile of notes you never reread partially worth having. See how to search your notes.
Turning a mess into a structure. Twenty fragments from a week, grouped into themes. Not the conclusion, just the sorting, which is tedious and which you were not going to do.
Generating questions from your notes. This one is underrated. Ask for ten questions your notes should be able to answer, then answer them from memory. You have converted passive material into active recall practice at almost no cost, and the AI never touched the retrieval, which is the part that has to be yours.
First-pass cleanup. Fixing the shorthand, expanding the abbreviations, formatting a wall of text. Mechanical work on material you already understood.
Where it quietly costs you
Summarising things you have not read. The most tempting use and the most expensive. The summary is fluent, you feel informed, and nothing was encoded. This is the illusion of competence with a much better interface than it used to have.
Replacing the writing. Writing a note in your own words is not a formatting step, it is the encoding step, which is the finding behind the generation effect and most of why writing helps memory. Delegating it removes the reason you were taking notes.
Meeting notes you never look at. An AI-generated summary of every call produces a large archive of documents nobody reads, at speed. It is the collector's fallacy automated.
The useful test before delegating anything: was I going to do this by hand? If yes, doing it yourself is probably the point. If no, the AI version is strictly better than the nothing that would otherwise exist.
Accuracy, specifically
Two failure modes, and they are different.
Transcription errors. Names, jargon and accented speech are where they cluster. Reported clean-audio accuracy sits in the mid-nineties, which sounds excellent until you notice that a wrong name in a medical or legal note is not a rounding error.
Hallucinated content. More serious, because it is invisible. Transcription tools have been documented inventing entire sentences that were never spoken, including in clinical settings. Grounded systems do better: one study of NotebookLM in journalistic research still found hallucinations in around 13 percent of responses, which was the best result in that comparison.
Practical consequences.
- Never let an AI summary be the only record of something consequential. Keep the transcript, and keep your own three lines.
- Check every name, number, date and quote against the source. These are exactly what gets fabricated.
- Be sceptical of action items in particular. The common failure is an AI promoting a passing remark into a commitment, which then propagates into someone's task list. See how to take meeting notes.
The consent part
Skipped constantly, and it is not optional.
Recording a conversation without telling people is a legal question in many jurisdictions and a trust question in all of them. Several US states require all-party consent. Class actions have been filed against AI notetaker vendors over automatic joining and voice data collection.
Some concrete rules that cost nothing.
Say it out loud at the start, not in a calendar description. Accept a no without negotiating; someone declining to be recorded is not an obstacle. Do not run a notetaker in one-to-ones, HR conversations, or anything about health, immigration or legal exposure. And know where the audio goes, whether it trains a model, and how long it is retained, particularly if the conversation includes anyone else's confidential information. See the best private notes app.
A workflow that keeps both halves
During: capture by hand, badly and sparsely. Structure, questions, what you did not follow. Let the recorder handle completeness. This is where the Cornell method's question column earns its place.
Immediately after: three sentences in your own words, before reading any transcript. What was decided, what surprised you, what you now have to do. Ninety seconds, and it is the note that will still be useful in a year.
Then: let the AI produce the full transcript and summary, and attach both. Never read them first.
Later: ask the AI for questions rather than answers. Then answer them cold.
The pattern holds across contexts. AI does capture and retrieval. You do selection and articulation. The moment those swap, you have a very efficient archive and a worse memory.
The short version
Use AI for the transcript, the search, the cleanup and the quiz. Write the three sentences yourself, always.
Check names, numbers and quotes. Tell people you are recording. And treat any summary of something you did not read as a document you have not read.
More in the best AI notes app, how Ask AI works, and how to take notes from a video.