Google’s Search Live will need high-quality data to succeed
Jul 2, 2025
Google wants to make AI assistants even more conversational by improving how we talk with AI search. The recent launch of Search Live within its AI Mode introduces a voice-first interface that lets users engage in real-time, back-and-forth conversations with Google’s AI.
The key to making Search Live thrive: high-quality data capable of supporting both natural voice interaction and accurate, grounded responses.
Voice-first interaction raises the bar for AI-powered user experiences
Search Live enables users to initiate and maintain voice conversations with Google’s AI directly through the Google app on Android and iOS. By tapping the new “Live” icon, users can ask questions aloud and receive spoken responses, with the ability to ask follow-up questions naturally.
This hands-free, conversational approach caters to users looking for more intuitive and accessible ways to interact with search engines, especially while multitasking or on the move.
Google’s goal with voice-based AI Mode is to reduce the friction between user intent and AI understanding. Speaking to an AI system is fundamentally different from typing a search query. People often use more casual language, incomplete sentences, and layered questions when speaking.
They also expect the AI to keep track of context across multiple turns in the conversation. The integration of voice input reflects a broader user demand for AI that feels more like interacting with a human assistant: understanding nuance, intent, and real-world context without the need for carefully worded prompts.
Voice-based AI demands stronger, more diverse data quality
Voice-first search introduces unique data quality challenges that go beyond traditional text-based AI models:
Speech recognition requires extensive, high-quality training data drawn from diverse voices, accents, and speaking styles to ensure the AI can accurately transcribe spoken input. Misunderstood words or phrases at this stage can derail the entire interaction.
Spoken language is less structured than typed queries. People are more likely to include ambiguity, idiomatic phrases, or incomplete references when speaking. Training the AI to handle this means exposing models to large, diverse datasets that capture real-world speech patterns and conversational context.
Delivering accurate, spoken answers requires precise grounding in reliable knowledge sources. AI must be trained to map freeform voice input to trustworthy web content or structured knowledge graphs; then summarize and deliver answers clearly and conversationally. Google’s AI Mode relies on its Knowledge Graph and up-to-date web content for this reason. When the AI’s confidence is low, it defaults to providing direct web links so users can fact-check or explore further.
The result is a heightened dependency on both breadth and quality of training and inference data. For AI builders, this highlights the growing importance of multimodal data pipelines that can support speech recognition, intent detection, conversational context tracking, and response accuracy, all in real time.
What’s next for AI search experiences
The launch of Search Live reflects two converging trends: growing user demand for natural, voice-led search experiences and an escalating need for robust, high-quality data to support AI understanding at every layer, from speech input to answer generation.
As Google continues to expand AI Mode with upcoming visual input features, the pressure on data quality will only increase. Centific is well suited to respond. We combine our AI data foundry platform with a global network of 1.8 million subject matter experts across 230+ markets to train multi-modal AI models, and we embed innovative uses of AI in our processes.
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