Every few months someone asks me whether AI has killed ASO. My honest answer: no, but it has made some parts of the job a lot faster and made some other parts more dangerous if you're not careful. After two-plus years of watching this play out — and trying most of these tools ourselves — here's what I've actually learned.
The hype is real. So is the mess.
When ChatGPT became mainstream in late 2022, the immediate reaction in app developer communities was predictable: "I can just generate my app store descriptions now." And you can, kind of. The first draft it produces is usually coherent and doesn't embarrass you. For developers who previously submitted things like "This app does what it says. Please download it." — genuine improvement.
But the bar for ASO was never just "grammatically correct." The bar is: does this description convert browsers into downloads, does it surface your app for the right search queries, and does it not get your update rejected. AI is inconsistently good at the first, genuinely useful for brainstorming the second, and surprisingly bad at the third.
Where AI actually earns its keep
I'll be upfront — I use AI tools regularly for ASO work. Here's where they genuinely save time:
Drafting and rewriting
If you give a model like Claude or ChatGPT your app's core features plus a few competitor descriptions you like, the first draft it produces is usually 70–80% of the way there. That's genuinely useful. Staring at a blank text box trying to write "Powerful. Simple. The task manager you've always wanted." is a miserable experience. Getting something to react to is much faster.
What I do: generate three or four variations, pull the best sentences from each, then rewrite the opening hook myself. The hook is the part that actually converts — it's the 2–3 lines visible before "more" — and AI tends to write safe, generic openers. A human who knows the product writes better hooks.
Keyword brainstorming
Ask an AI to generate 40 keyword variations for a fitness tracking app and it'll give you a solid list in about ten seconds. Most of them you've already thought of, but there are usually three or four phrasings you hadn't considered. It's a useful starting point before you go validate actual search volumes in a real ASO tool.
The keyword brainstorming is genuinely good. The keyword research is where things go wrong.
Reviewing translated descriptions
This is underrated. If you've localized your app into 15 languages, having a model do a quick sanity check on a translation — "does this sound natural to a native speaker, are there any obvious errors" — catches things before they go live. Not a replacement for a professional translator, but useful as a secondary pass on machine translations.
Where it falls apart
Keyword volume data
This is the big one. AI models do not have access to real-time App Store or Google Play search data. When you ask "what's the search volume for 'habit tracker' on the App Store?" you will get a confident, specific-sounding answer that is completely fabricated. I've seen developers build their entire metadata strategy around AI-generated keyword estimates, then wonder why their visibility didn't improve.
Actual keyword volume and difficulty data comes from tools that query real store data — not from a language model that learned from text on the internet. Treat any specific numbers an AI gives you about search volume as made up until proven otherwise.
Apple and Google guidelines compliance
This one catches people out. AI has a tendency to write app descriptions that include phrasing Apple specifically flags during review — things like superlative claims ("the best", "the #1"), references to price or temporary promotions in the description, or comparisons to competitors. Apple's review guidelines are specific and occasionally counterintuitive, and AI models don't reliably know them.
We had a case where a generated description included the phrase "loved by thousands of users" — which sounds harmless but triggered a metadata rejection because it was unsubstantiated. Small thing, but it delayed a release. Always read what the AI wrote against the actual guidelines before submitting.
Operating at scale
If you manage one app, copy-pasting AI output into App Store Connect is annoying but workable. If you manage ten apps across two stores, each with 20 locales, the manual overhead adds up fast. AI doesn't help with the mechanics of pushing metadata — it just gives you words. Someone still has to get those words into the right fields, in the right languages, for the right devices.
This is the part of ASO that hasn't changed at all with the AI wave: the actual publishing workflow is still a slog unless you have a tool that handles it. That's what we built App Store Manager to do — take the metadata you've got, in whatever shape, and handle the distribution side so you're not doing it by hand.
A workflow that actually makes sense
Here's roughly what we use internally, and what I'd suggest:
- Use a real ASO tool for keyword research. Get actual search volume and difficulty data. AI can help you brainstorm terms to look up, but the validation has to happen against real data.
- Use AI to generate description drafts. Give it your target keywords, the app's core value proposition, and a tone reference. Generate a few versions. Don't publish the first thing it produces.
- Rewrite the opening 2–3 lines yourself. This is your conversion copy. It matters more than the rest of the description combined. Don't outsource it to a model.
- Check against guidelines before submitting. Especially if you're updating a category that's had recent policy changes — in-app purchases, health apps, apps with user content.
- Use a dedicated tool for publishing. The "copy, paste, translate, re-paste" loop across multiple apps and locales is where time disappears. That's what tooling is for.
The honest take
I'm actually pretty optimistic about AI for the creative parts of ASO — the writing, the ideation, the translation review. It genuinely saves time. But there's a version of AI-assisted ASO that skips the parts that matter: the actual keyword research, the compliance check, the thoughtful conversion copy. That version looks efficient and produces mediocre results.
The developers I've seen get consistent ASO gains are the ones who use AI to go faster on the parts where speed is fine, and slow down on the parts where quality matters. That balance hasn't changed just because the tools have gotten smarter.
The stores are still competitive. The metadata still matters. Getting it right still takes some effort — just a bit less of it than it used to.