A product announcement goes out. It says the team “worked tirelessly” to build the feature. It describes the benefit in a sentence that could apply to any product in the category. The one detail that makes this release interesting, the specific constraint it removes for a specific kind of customer, isn’t mentioned. It reads like an announcement. It communicates nothing.
This happens when AI writes the announcement and no one asks it the right question. The model knows what product announcements look like. It can produce the structure instantly: the opening claim, the benefit paragraph, the call to action. What it doesn’t know is why this release matters to the specific people who are going to read it.
That’s not a prompt problem. You can add more context and get a better draft. But the gap between a generic announcement and a specific one isn’t a matter of prose quality. It’s a matter of having the information at all.
What AI fills in when it doesn’t have the real thing
When AI writes an announcement without a detailed brief, it fills in the gaps with the most common version of whatever you’re describing. A performance improvement becomes “significantly faster.” A workflow change becomes “streamlined.” An edge case that drove the feature idea becomes invisible.
The result sounds like an announcement. It has the rhythm of one. Someone could read it and not notice anything missing because the shape is correct. The problem surfaces later, when customers read it and don’t understand why they should care.
A few specific places where the fill-in happens:
The benefit statement. AI will write “save time and work more efficiently” because that’s what benefit statements usually say. The actual benefit, “you no longer have to export to a spreadsheet to run this calculation,” is too specific for the model to invent. It either comes from your brief or it doesn’t appear.
The origin of the feature. “We heard from customers that…” is the common pattern. AI will use it. If there’s a real customer behind the request, a real conversation that shaped how the feature works, that’s gone unless you put it in.
What this replaces. Features replace workflows. They make something easier that was previously annoying or impossible. AI writes about what the feature does, not what it replaces. The “before” is often more compelling than the “after,” and it’s almost always missing.
The scale of the limitation. “Supports up to 100 users” versus “now supports teams of any size” is a meaningful difference. AI will write something that sounds reasonable. Whether it’s accurate, or whether the limitation it describes was actually the thing customers complained about, requires someone who knows.
Try it on an AI-written feature announcement
An announcement for a new export feature. Correct structure. Missing the detail that makes it matter.
The edit that matters most
Reading an announcement draft, the question to hold is: would someone who doesn’t already know about this feature understand why they should care?
This is harder to answer than it sounds. If you built the feature, you know why it matters. The announcement will feel complete because your brain is supplying the context that’s actually missing from the text.
One way to test it: read the announcement out loud to someone who hasn’t been in the product conversations. If they ask “wait, but why would someone need that?” after hearing it, the reason is missing. If they nod along and then can’t explain it back, the reason is present but buried.
The fix is almost always addition, not rewriting. The AI draft usually has the right structure. What it needs is the real detail: the customer who asked for this, the workflow it replaces, the specific number that changed. Those come from the people who built the feature, and they’re rarely in the first draft.
“Add a sentence after the second paragraph: before this, anyone with more than 500 records had to run three separate exports and combine them manually.”
That sentence changes the announcement. It gives the reader a before. It makes “single export” feel like relief instead of a feature checkbox.
Before it goes to customers
Announcements are low-friction to write and high-consequence if they’re wrong. They reach everyone at once. A claim that’s slightly off, a benefit that’s described too broadly, a limitation that got misrepresented, those are harder to walk back than a support email.
The review that matters is the one where someone who built the feature reads the draft specifically looking for claims that don’t match what the feature actually does. Not for tone. Not for grammar. For accuracy.
That review takes ten minutes. The AI draft is a starting point, not an endpoint. The shape is usually right. The substance has to come from somewhere else.