AI search is introducing a new layer of app discovery that happens before users ever reach the App Store or Google Play, shifting the opportunity for app marketers from simply optimizing listings to understanding how LLMs evaluate apps, which signals they trust, and how discoverability is redefined; insights from a recent webinar on getting apps discovered in ChatGPT and LLM search—featuring experts from Reddit and Yodel Mobile—highlight that while interest in AI-driven discovery is rapidly growing, most app teams are still in early stages, with AppTweak data showing 34% have not started, 31% are researching, 29% are building strategies, and only 6% have a defined approach, creating a clear early-mover advantage for teams that begin aligning their strategy now.
Key Takeaways
- App discovery is no longer limited to app stores—AI search is now a critical entry point.
- Large Language Models (LLMs) prioritize intent, context, and credibility, not just rankings.
- Community discussions and real user insights strongly influence recommendations.
- AI visibility is probabilistic, not position-based.
- Consistent positioning across the web is essential for being recommended.
The Shift: App Discovery Is Expanding Beyond the Store
For years, the app discovery journey was simple:
Users opened the App Store or Google Play, searched, and installed.
That’s changing fast.
Today, users increasingly start their journey by asking AI tools like ChatGPT, Gemini, or Perplexity:
“What’s the best budgeting app for students?”
“Which fitness app is best for beginners?”
Only after receiving recommendations do they visit the app store.
What this means:
App stores are still where installs happen—but AI is where decisions begin.
How LLMs Actually Recommend Apps
AI models don’t “rank” apps the way app stores do. Instead, they:
- Understand the user’s intent
- Gather information from multiple sources
- Recommend apps that best match the need
This makes AI discovery more like recommendation logic than traditional search.
Intent > Keywords
LLMs don’t just match keywords—they interpret problems.
Example:
A query like “best budgeting app for students” may imply:
- Free or low-cost
- Easy to use
- Designed for beginners
- Student-friendly features
Apps that clearly match these signals across the web are more likely to be recommended.
You Must Be “Considered” Before You Can Be Recommended
Before an app appears in AI results, it must first be seen as a valid option.
That depends on how consistently your app is associated with a use case across:
- Your website
- App store listings
- Reviews and comparisons
- Community discussions
- Media and editorial coverage
The rule:
If your app isn’t clearly tied to a problem across multiple sources, it won’t show up.
Do App Store Listings Still Matter?
Yes—but differently.
What helps:
- Clear positioning (what the app does)
- Detailed use-case descriptions
- Real user feedback
What doesn’t matter (for AI):
- Hidden keywords
- Download counts
- Conversion rates
App store listings act as supporting signals, not the primary driver.
Why Community Discussions Are So Powerful
AI systems heavily rely on discussion-based content, especially for recommendation queries.
Why?
Because communities provide:
- Real-world use cases
- Comparisons
- Pros and cons
- Context and nuance
Example Insight:
- Broad discussions → Popular apps dominate
- Niche discussions → Specialized apps surface
Takeaway:
The more clearly your app is linked to a specific use case in discussions, the better your chances.
Early Trends in AI App Discovery
1. Rankings ≠ Visibility
Top-ranked apps in app stores don’t always appear in AI recommendations.
- Weak positioning → Low AI visibility
- Strong niche clarity → High AI visibility
2. Trust Beats Volume
More mentions help—but credible, in-depth discussions matter more.
AI prefers:
- Detailed conversations
- Real user experiences
- Balanced opinions
Over:
- Shallow promotional content
3. Structured Content Wins
LLMs favor content that’s easy to interpret, such as:
- Q&A pages
- Comparison articles
- “Best app” lists
- Problem-solution guides
- Pros & cons breakdowns
Why?
Because these formats map directly to user intent.
4. Sources Vary by Category
There is no universal strategy.
Different categories rely on different ecosystems:
- Communities (e.g., forums)
- Editorial content
- Review platforms
- Social platforms
Action: Identify where your category gets its signals.
How to Improve Your App’s AI Visibility
1. Audit Your Current Presence
Search for your app in AI tools:
- Which prompts show your app?
- Where are you missing?
- Which competitors appear more often?
- What sources are influencing results?
You can’t optimize what you don’t measure.
2. Align Around a Clear Problem
Your messaging should answer one core question:
“What problem does this app solve?”
Ensure consistency across:
- Website
- App store listing
- Content
- Community presence
If your positioning is unclear, AI will struggle to recommend you.
3. Participate in Communities (Strategically)
Communities are now part of the discovery layer.
Best practices:
- Be helpful, not promotional
- Contribute to real discussions
- Address misconceptions
- Add value to existing threads
This builds trust signals AI can reuse.
4. Create Intent-Focused Content
Instead of generic content, focus on:
- Specific use cases
- Clear user problems
- Structured answers
AI Visibility = Association, Not Ranking
Stop asking:
“How do I rank #1 in AI?”
Start asking:
- For which user intents is my app relevant?
- Where is my app clearly understood?
- Where is it missing or unclear?
AI visibility is about being associated with the right problems.
Final Thoughts: ASO Isn’t Dead—It’s Expanding
App Store Optimization still matters—but it’s no longer enough on its own.
The discovery journey now looks like this:
AI Search → Consideration → App Store → Install
To win in this new landscape, your app must be:
- Clearly positioned
- Consistently represented
- Widely discussed
- Contextually relevant
In short:
It’s no longer about being searchable.
It’s about being recommendable.