Apple has begun implementing AI-generated tags in the developer beta version of iOS 26 as part of its efforts to improve app discoverability on the App Store. However, these tags are not yet visible on the public App Store nor do they currently influence the public Search algorithm.
At Apple’s Worldwide Developers Conference (WWDC 25), the company revealed that it would use AI techniques to analyze app metadata—such as descriptions, categories, and screenshots—to extract relevant information. This data helps Apple assign tags that better categorize apps for discovery.
Unlike previous methods that relied on manual keywords in the app’s name, subtitle, or keyword list, this AI-driven approach can uncover details buried deep within an app’s assets.
What This Means for Developers
Developers are advised that they do not need to add keywords to screenshots or take additional steps to influence these AI tags. Apple plans to give developers control over which AI-generated tags associate with their apps.
Apple also assured that all tags would undergo human review before appearing publicly, ensuring accuracy and relevance.
App intelligence firm Appfigures recently analyzed the beta and suggested that text extracted from screenshot captions might influence app rankings. While the idea that screenshots affect discoverability is correct, Apple confirmed the process uses AI rather than optical character recognition (OCR) as Appfigures initially speculated.
Once these AI-generated tags roll out globally, understanding and optimizing around them will be crucial for developers aiming to enhance their app’s visibility.
What The Author Thinks
AI-generated tags represent a significant step forward in how app stores categorize and recommend apps, potentially making it easier for users to find relevant software without developers jumping through keyword hoops. However, the success of this system hinges on transparency and fairness in how tags are assigned and reviewed. Developers will need clear communication and tools to manage these tags effectively. Done right, AI tagging could level the playing field, but mishandled, it risks introducing new biases or inaccuracies that may frustrate both developers and users.
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