Key takeaways
- AI Mode is a conversation, not a query. Users ask three to five follow-up questions per session, and Gemini carries context from turn to turn, so your content has to stay relevant across an entire chain of sub-intents, not just the first question.
- A single AI Mode prompt can fan out into up to 16 parallel sub-searches, and 59% of prompts trigger between five and eleven sub-queries. You are optimizing for a map of questions, not one keyword.
- AI Mode and AI Overviews overlap on citations only 13.7% of the time, so ranking in one surface says almost nothing about the other.
- The practical tactics that matter most: one sub-intent per H2, self-contained sections, explicit next-question hooks, consistent entity signals, and a serious investment in Google-owned surfaces (YouTube, Business Profile, Maps).
- Citation share on AI Mode is concentrated at the top and wide open in the long tail. Below google.com, YouTube, and Reddit, every other domain sits under 1% share, which is where most brands can actually win.
What AI Mode actually is (and what it isn't)
The most common mistake I see teams make is treating AI Mode as a bigger version of AI Overviews. It isn't. The two surfaces share infrastructure, but they produce completely different user behavior, citation patterns, and content requirements.
AI Overviews are a snapshot: a synthesized answer with a handful of links, sitting above the blue links, that most users read and scroll past. AI Mode is an opt-in conversational surface where people ask questions in natural language, get multi-paragraph answers, and then keep going. Google's own positioning makes this explicit: you can ask a follow-up question right from an AI Overview and flow into a back-and-forth where "your context stays with you, and as you explore more deeply, the links and supporting articles get even more relevant."
| Dimension | AI Overviews | AI Mode |
|---|---|---|
| Entry point | Default surface, above blue links | Opt-in conversational interface |
| Format | Single synthesized answer, 3-7 cited sources | Multi-paragraph answer, 10+ cited sources per turn |
| Answer length | Short, paragraph or list | Roughly 4x the length of an Overview |
| User behavior | Read, scroll, sometimes click | 3-5 follow-up turns per session |
| Query style | Traditional search queries | Natural language, ~3x longer than classic queries |
The numbers behind this are worth sitting with. AI Mode passed 1 billion monthly active users roughly a year after launch, with queries more than doubling every quarter. More than 1 in 6 AI Mode searches in the US are multimodal, meaning voice or image. And the average AI Mode query is triple the length of a traditional Google search query. People are talking to this thing, not typing keywords into it.
Google's own developer documentation is the right starting point for the technical fundamentals, and it confirms something important: both AI Overviews and AI Mode use query fan-out to surface a wider, more diverse set of links. Which brings us to the mechanics.
How multi-turn actually works under the hood
Query fan-out: one question becomes many
When a user asks something in AI Mode, Gemini doesn't run one search. It runs a custom query fan-out process that decomposes the prompt into parallel sub-searches, each targeting a different sub-intent. A single query can trigger up to 16 parallel sub-searches, and 59% of prompts trigger between five and eleven sub-queries simultaneously. Complex B2B and consideration queries average nine to eleven.
For comparison, ChatGPT runs around two to three sub-queries per prompt on average. AI Mode's fan-out is an order of magnitude more aggressive. That difference is the whole ballgame for GEO: your content isn't competing for one retrieval slot, it's competing for slots across an entire map of decomposed questions.
And here's the part that makes multi-turn genuinely different from just "more queries": conversational follow-ups re-run query fan-out with the previous turns as context. Each turn builds on accumulated intent rather than starting fresh. If turn one was "best CRM for a 20-person agency," turn three might implicitly mean "best CRM for a 20-person agency, considering the API options we just discussed." Your content has to be retrievable for that accumulated intent, not just the literal words on the screen.
The turn-by-turn buyer journey
Sydney Sloan put this well at Index'25: a user "might discover you in turn one, evaluate you in turn three, and arrive at a decision by turn five, all without ever leaving the conversation." That's a compressed funnel happening inside a single session, with no clicks back to your site in between.
This reframes what GEO actually optimizes for. You're not trying to win a query. You're trying to survive a conversation.
What the citation data says
Two findings from the data should change how you prioritize.
First, AI Mode and AI Overviews barely share sources. Ahrefs analyzed 730,000 responses and found citation overlap of just 13.7% between the two surfaces for the same query. The two agree on what to say (86% semantic similarity in conclusions) but disagree on where they found it. Appearing in one gives you no guarantee of appearing in the other. If a brand is mentioned in AI Overviews, there's a 61% chance it also shows up in AI Mode's longer response, which is a decent tailwind but nowhere near a guarantee.
Second, AI Mode's citation mix is heavily concentrated in Google's own properties. In June 2026, google.com itself captured 7.31% of all AI Mode citations, more than YouTube (2.88%) and Reddit (2.52%) combined, according to Promptwatch's Google AI Mode citation share data. That google.com figure jumped 72% month over month on top of a sixfold jump the prior month. Below the top three, every other domain sits under 1% share.
Read that last sentence again. The open-web opportunity on AI Mode lives almost entirely in the long tail. That's either discouraging or an enormous opening depending on your perspective. I lean toward opening: the head terms are locked up by Google properties and giant platforms, but the sub-intent fan-out creates thousands of narrow retrieval slots that no single competitor can dominate.
One more data point worth knowing: AI Mode answers are long and citation-dense. Promptwatch's research on average sources per response shows Google's AI surfaces cite around 10 sources per answer, roughly double ChatGPT's ~5, and AI Mode responses run about 4x the length of an AI Overview with 2.5x more brand entities mentioned per response. Long answers with many citation slots are good news for mid-authority domains that would never crack a classic top-10 ranking.
How to adapt your GEO strategy for multi-turn
One sub-intent per H2, phrased like a user prompt
AI Mode extracts and cites at the H2 level. A broad section heading like "Everything about AI Mode" forfeits citation eligibility on specific sub-queries because the model can't map it cleanly to any single fan-out sub-search. Narrow H2s phrased as literal user prompts, "How much does AI Mode cost to use" or "Does AI Mode work in Europe," perform measurably better.
This is the single highest-leverage content change most teams can make, and it costs nothing but discipline.
Make every section self-contained
Because AI Mode quotes chunks rather than whole pages, every section needs its own definition, its own evidence, and its own concluding sentence to stand alone. If your pricing section only makes sense after reading the features section above it, the extracted chunk is useless to the model and it'll pull from a competitor instead.
Write explicit next-question hooks
Here's the tactic most teams miss. When you finish a section, anticipate the likely follow-up and either answer it on the same page or link directly to the page that does. This does two things: it signals to Gemini that your page covers the adjacent sub-intent, and it keeps the user (and the model) inside your content ecosystem across turns.
The classic failure mode is what practitioners call the one-shot optimization fallacy. You win turn one with a "Best CRM" listicle, then the user asks "which of these has the best API?" and that data isn't structured on your page or clearly interlinked. The AI pulls the follow-up answer from a competitor. You got discovered in turn one and eliminated by turn three.
Fix your entity signals
Inconsistent naming is quiet poison for AI attribution. If your brand is described slightly differently across your site, your schema markup, and third-party listings, the model's confidence in attributing mentions to you drops. Use keyword research to surface the long-tail "vs," "pricing," and "integration" follow-up questions buyers actually ask, and make sure the answers live on-page or one clean internal link away.
Treat Google-owned surfaces as GEO channels, not side projects
Given that google.com, YouTube, and Reddit together dominate AI Mode's citation mix, the recommendation from Promptwatch's June 2026 data is blunt: treat Google Business Profile, Maps, Merchant Center, and your YouTube channel as direct GEO channels. Video citations in Google's AI surfaces have been climbing steadily, reaching around 6.3% of AI Overviews citations by late July 2026, up from about 2.7% in January, per Promptwatch's AI Overviews citation type data.
The same report shows another shift worth acting on: product pages overtook listicles as the most cited format in AI Overviews in late July 2026, after listicles had led all year. If your GEO strategy is still "publish more listicles," the data has moved on. Structured, fact-dense product and comparison pages are where citations are going.
Measuring multi-turn visibility
You can't manage what you don't measure, and multi-turn visibility is genuinely hard to measure manually. Platform behavior also changes without warning: when ChatGPT Search started using the site: operator at scale in August 2026, site-scoped fan-out queries jumped from 0.37% to nearly 17% of all fan-out queries overnight. Continuous monitoring beats one-off audits, every time.
A few tools worth knowing depending on your setup:
| Tool | What it does | Best for |
|---|---|---|
| Promptwatch | Full AI visibility stack: prompt tracking with volumes and difficulty, citation trends, AI crawler logs, query fan-out data, content gap analysis, automated GEO content | Teams that want to measure and fix, not just monitor |
| Ahrefs Brand Radar | Brand tracking across AI search engines, backed by Ahrefs' citation research | Teams already in the Ahrefs ecosystem |
| Profound | Enterprise AI search visibility and analytics | Large orgs with analytics-heavy workflows |
| Peec AI | AI visibility tracking with optimization suggestions | Smaller teams on a budget |
Promptwatch is the platform I'd point to for this specific problem, because multi-turn visibility is exactly where tracker-only tools fall short. It tracks query fan-outs showing how AI expands a prompt into sub-queries, monitors which of your pages get cited across turns, logs AI crawler visits so you can see whether Gemini's crawlers can even read your pages, and generates content gap analysis against the actual AI responses in your space. It's used by 1,840+ brands and agencies and rated 4.7/5 on G2.
If you want to survey the broader market first, the GEO software directory at bestgeosoftware.com has a regularly updated catalog of platforms worth comparing.
Common mistakes to avoid
A few failure patterns come up again and again in practitioner reports:
- Measuring clicks instead of citations. In a surface where users stay in the conversation, citations and AI share of voice are the metric that predicts downstream outcomes. Clicks will lag badly.
- Marketing fluff with low information density. The model looks for verifiable signal across every sub-query in the fan-out, as Linda Caplinger of NVIDIA's SEO team puts it. Claims without data don't survive extraction.
- Treating GEO as a one-time sprint. Fan-out patterns and citation behavior shift month to month. The listicle-to-product-page swing in July 2026 is a good example of how fast the ground moves.
- Ignoring offsite entity consistency. Your Wikipedia description, your Reddit mentions, and your schema all feed the model's picture of who you are.
Where this is heading
Google's own usage data points in one direction: AI Mode is becoming a default exploration layer, not a separate destination. Planning-related queries are growing 80% faster than overall AI Mode query growth, and brainstorming queries 30% faster. People are using conversations to think through decisions, which means the multi-turn journey isn't an edge case, it's the core behavior.
The brands publishing structured, conversation-ready content now are the ones building the citation base the model draws from as the surface scales. If you'd rather have a senior team handle this end to end, 1001 SEO Media runs GEO and AI search programs as part of its SEO services, including AI visibility work built on Promptwatch. But whether you do it in-house or with an agency, the strategic shift is the same: stop optimizing for the query, and start optimizing for the conversation.