Google links apps directly to Search's AI Mode

Google announced the rollout of app integrations within Search's AI Mode, beginning this week in the United States. The feature allows users to securely connect third-party services and interact with them without leaving Search. Three integration examples are highlighted: Instacart for shopping, Canva for design, and YouTube Music for playlist curation. In the Instacart example, a user working with AI Mode to generate a grocery list for a barbecue can add ingredients directly to an Instacart shopping cart and check out from the app or website with a few taps. For design work, users can ask Canva to display template options relevant to a project like creating a flyer. For music, AI Mode can help curate a playlist that users can instantly save to YouTube Music and play. Google notes that these connected apps enable "Personal Intelligence"—the ability for Search to deliver more tailored responses when it has access to user data from integrated services. The company states it is working with a range of partners and will launch with more apps in the future, though no specific timeline or additional app names are provided. Users can access the feature by heading to AI Mode in Search.

Google's move to embed app integrations directly in Search's AI Mode reflects a broader strategy to position Search as a central hub for user workflows, not just a query tool. By allowing seamless handoff to services like Instacart, Canva, and YouTube Music, Google reduces friction and increases the likelihood users stay within its ecosystem. The feature builds on existing integrations available in the Gemini app, extending that pattern into Search—where Google's traffic volume is far larger. The article emphasizes "securely link," suggesting privacy and data-handling are design priorities, though the body does not detail the security model. The mention of "Personal Intelligence" suggests that connected app data feeds into more tailored Search responses, creating a feedback loop: more integrations yield more granular user context, which in turn generates more relevant results and more reasons to use the feature.