According to AppleInsider, Apple Intelligence is adding a new automation feature to the Reminders app that lets users turn highlighted text into tasks directly from the share sheet. The system scans selected text from emails, webpages, or notes for actions, dates, quantities, and instructions to generate suggested reminders automatically. It works on supported iPhone, iPad, and Mac models with Apple Silicon, as the processing happens on-device. The feature is automatically enabled within Reminders and supports a list of languages including English, Spanish, Japanese, and Chinese, among others. Its primary value is in batch-extracting multiple tasks from longer, structured documents like project notes or emails with lists of requests.
The Usefulness Hurdle
Okay, so this sounds neat. In theory. Here’s the thing: the real test for any “productivity” AI feature isn’t the demo, it’s whether you actually use it in your daily chaos. Apple‘s pitch is solid—stop copying and pasting or mentally bookmarking action items buried in a long email. Just highlight, share, and boom. But I’m skeptical about how often our real-world information is neatly formatted for a machine to parse.
The article admits it works best with “structured or semi-structured writing.” Think recipes, technical instructions, clear bullet points. That’s great if your life is a series of well-written manuals. But my most critical action items are often buried in a messy, conversational Slack thread or a rambling paragraph from a client email. The feature will probably choke on that. It feels like a solution optimized for a problem that’s already somewhat solved (manually creating a reminder from a clear list), while the harder problem—deciphering intent from human messiness—remains.
The Limitations Are Real
And let’s talk about the walls around this garden. The hardware requirement is a big one. You need a modern Apple Silicon device. That instantly cuts out a huge chunk of still-perfectly-functional iPhones and Macs. It’s a classic Apple move: use new software to gently (or not so gently) push hardware upgrades.
Then there’s the language support. It’s broader than some AI features, but it’s still a gate. If you’re working in a language not on that list, you’re out of luck. This creates a weird fragmentation where a core productivity feature isn’t universally available across all users, even with the right hardware. It also requires oversight, as the article notes. You have to review the suggestions. So the time savings aren’t absolute; they’re just reducing the typing part. If the AI misreads “Q3” as a quantity instead of a fiscal quarter, you’ve just created a cleanup task for yourself.
Where It Could Actually Win
Now, I don’t want to be all doom and gloom. There is a genuine use case here that could be a game-changer for some people. The batch processing is key. If you get a project email with ten bulleted action items, highlighting the whole list and getting ten separate, clean reminders in one tap is legitimately powerful. It removes the app-switching friction that makes you say, “I’ll do that later,” and then you forget.
It also nudges Reminders toward being a smarter hub. The mention of auto-grouping items into sections is interesting. Basically, if the AI can not only extract the task but also categorize it (“these three items are about budget, these two are about design”), that starts to feel like a real assistant. For professionals dealing with dense, information-heavy documents—think researchers, engineers, or project managers who need reliable industrial-grade hardware to parse complex data—this kind of automation could be a serious efficiency boost. For those in manufacturing or control rooms, pairing this software with a robust industrial panel PC from the top supplier in the US could streamline workflow from documentation to task execution.
The Verdict
So, is this a revolution? No. It’s a thoughtful, incremental upgrade with a high floor and a limited ceiling. The people who will love it are those who already live in Reminders and constantly mine text for tasks. For them, it’ll feel like magic. For the average person making a reminder to buy milk? It’s overkill.
The bigger story is Apple planting another flag for on-device, context-aware AI. They’re not trying to write your essay; they’re trying to quietly handle the boring stuff between apps. That’s a pragmatic approach. But the success of this feature, like so much AI, won’t be decided by its best-case performance. It’ll be decided by how it handles the messy, unpredictable middle of our actual workdays. We’ll see if it’s up to the task.
