What Search Advertisers Need to Know About Google AI Mode

Google’s AI Mode is now available to all users in the U.S., outside of Labs. AI Mode provides an advanced search experience designed for complex, multi-intent, or conversational queries. It is powered by Gemini 2.5 and includes multimodal integrations. AI Mode differs from AI Overviews (AIOs); the latter provide summarized answers and relevant links embedded in search results. In contrast, AI Mode, located within a separate mode or tab, combines conversational AI and chat functions, along with follow-up questions, in Google Search. Users can search with text, voice, and images, and although still in testing, they can interact using the new “Search Live” feature for real-time, back-and-forth voice-powered conversations.

AI mode utilizes a “query fan-out” method, breaking down questions into subtopics and synthesizing answers from multiple sources, thereby providing more contextual results than traditional Google Search. For example, in AI Mode, a query like “How to plan a trip to Japan” might be silently split by Google into sub-questions such as “Best time to visit Japan,” “Top tourist destinations,” and “Visa requirements.” AI Mode then constructs an overview that integrates answers from trusted sources, delivering a multi-paragraph summary with citations, images, and even shopping suggestions for the trip. This change represents a significant shift in how users interact with search, emphasizing conversation over clicks and highlighting content that aligns with broader user intent.

Businesses in the U.S., particularly publishers, retailers, and service providers, may already be noticing the impact on traditional organic performance as AI Mode alters how information is surfaced and how users interact with results. AI Mode has not yet been released in EMEA, likely due to compliance and regulation concerns, but it is now live in India. Google began showing AIOs in the UK a few months after they became available in the U.S. last year (May 2024), leading to speculation that the rollout of AI Mode to EMEA might follow a similar timeline in Q3/Q4 this year. However, it’s important to note that AIOs were only recently launched in some other European countries (March 2025), so we will continue to closely monitor AI Mode rollouts.

Brands should prepare for AI Mode by optimizing for long-tail conversational queries, providing structured data, and ensuring content is high-quality and well-sourced, making it easy for AI systems to cite. E-E-A-T content remains as important as ever. Additionally, AI Mode’s multimodal features mean brands should explore innovative content formats. This is relevant for both U.S. and EMEA audiences because, although AI Mode isn’t yet available in the latter region, users are increasingly turning to AI for search, and the principles still apply. For example, 22% of people in the UK now use ChatGPT, a similar conversational search tool. 

That said, personalization adds new complexity: when answers in AI Mode depend on individual history, tracking, and attribution become more difficult. Traffic from AI Mode isn’t currently separated in tools like Google Search Console (GSC), which makes performance attribution harder. Additionally, we anticipate an increase in zero-click searches, which will result in even less traffic to the site. According to Ahrefs, AI summaries can decrease clicks by 35%. Therefore, there must be a shift in how we measure, set KPIs, and optimize strategies.

With search marketers no longer solely focused on traditional keywords, clicks, and rankings, we recommend considering: 

Assess brand visibility beyond rankings: Advertisers should expand how they evaluate visibility, moving beyond traditional keyword rankings to include brand mentions within AI-generated responses. This includes how frequently a brand appears and its position relative to competitors across LLM environments, including Google’s AI Mode. 

Track indirect performance signals: As AI-generated results alter user behavior, conventional metrics like click-through rate are becoming less reliable on their own. Advertisers should monitor changes in assisted conversions, branded search volume, and overall impression stability to better understand AI’s influence on performance.

Analyze AI referral traffic and source-level patterns: Referral traffic from AI systems is emerging as a distinct signal. While tracking capabilities are still evolving, early analysis can help advertisers identify patterns in user acquisition and inform future attribution models.

Adapt content for AI understanding and retrieval: Effective content strategies now require structured, semantically rich assets designed for how AI systems interpret and respond to queries. This includes addressing related sub-questions, using internal linking to establish content hierarchy, and ensuring clarity at the page level.

Support query fan-out with broader coverage: AI-generated search results often expand a single question into related topics or follow-ups. Advertisers should structure their content ecosystems to account for this behavior, offering comprehensive coverage across topic clusters and ensuring relevance across a range of related intents.

Test multimodal formats where applicable: As search experiences become more visual and multimodal, advertisers should experiment with formats such as imagery, video, and structured data to determine where and how they enhance visibility within AI-driven results.

Consistent qualitative analysis, paired with a proactive investment in high-quality, structured content, will be essential as AI continues to reshape how users search and discover information. While measurement frameworks and AI integrations are still evolving, advertisers that prioritize adaptability—grounded in a clear understanding of how content is interpreted, surfaced, and engaged with—will be best positioned to maintain relevance. This shift represents both a technical challenge and a strategic opportunity to redefine how brand value is conveyed in an increasingly AI-mediated environment.