How to Appear in Google AI Mode: A First-Mover’s Playbook

By Anirudh Singla

Google AI Mode is the most important change in search since PageRank. Launched at Google I/O in May 2025 and powered by Gemini 2.5 – with the Gemini 3 Flash global rollout now underway – AI Mode is not another SGE-style overlay. It is a distinct, opt-in, conversational search surface where users ask a question, receive a response four times longer than an AI Overview, and then keep talking to Google.

The behavioural data is unprecedented. Session duration in AI Mode is roughly three times longer than in traditional Google Search. The zero-click rate is 93%. A single query now triggers what Google calls a query fan-out – up to sixteen parallel sub-searches, with 59% of prompts triggering between five and eleven at once. Your content is no longer being retrieved for “the query.” It is being retrieved for a dialogue chain that Google has constructed on your user’s behalf.

For marketers, this is both a threat and an opening. Studies of 730,000 responses found that AI Mode and AI Overviews reach 86% of the same conclusions – but their citations overlap only 13.7% of the time. Appearing in one does not guarantee appearing in the other. AI Mode is a new visibility surface with its own rules. And because it is early, the marketers who learn it first will compound the advantage for years.

At Index’25, the world’s first AI-Search conference we hosted at Pepper, I told the audience what I believe now more than ever:

“Search is undergoing the most profound transformation of our time. Generative AI is redefining how people discover, trust, and engage with information – moving us from keywords and rankings to intelligence and context at scale.” – Anirudh Singla, Co-founder & CEO, Pepper Content (Index’25 keynote)

AI Mode is the sharpest edge of that transformation. What follows is a first-mover’s playbook: how the conversational format changes content requirements, how to write for multi-turn context, how to anticipate follow-up queries, and what content structures perform across a dialogue chain rather than a single search.

What Makes AI Mode Different from AI Overviews

AI Mode is not AI Overviews. Confusing the two is the most common strategic mistake marketers are making right now. Three structural differences matter.

Responses are longer and deeper. AI Mode answers average four times the length of an AI Overview. Each answer can cite ten or more sources inside a single response – then cite a different set again on the follow-up.

Queries are conversational by default. Follow-up questions are not a user option; they are the expected flow. Users spend three times longer per session, ask three to five questions before leaving, and move from exploration to evaluation to decision inside a single conversation.

The retrieval model is fan-out, not match. When a user types “how to rank in AI Mode,” Google does not run that query. It runs “what is AI Mode,” “how is AI Mode different from AI Overviews,” “what is query fan-out,” “how does Gemini retrieve sources,” “E-E-A-T signals in AI Mode,” and a dozen more – in parallel – and fuses the results into one conversational answer.

This last point rewrites the content playbook. You are not optimizing for a single query anymore. You are optimizing to appear inside the fan-out map.

Query Fan-Out: The Core Mechanic

Query fan-out is AI Mode’s central innovation. Here is what happens under the hood.

A user sends a query. Gemini deconstructs it into intents, sub-intents, and related questions – anywhere from five to sixteen – and runs them in parallel. The model then synthesizes, ranks, and cites the sources that best answer the fan-out as a whole, not any single sub-query.

Research on AI Mode shows 59% of prompts trigger between five and eleven simultaneous sub-queries. Complex B2B and consideration queries average nine to eleven. ChatGPT, by comparison, runs 2.3 to 2.8 sub-queries per prompt. AI Mode is an order of magnitude more aggressive in how it expands the search before answering.

Operationally, this means the single most powerful SEO asset in 2026 is an interlinked cluster of pages that collectively cover every sub-query the model is likely to generate. If your topic has a fan-out map of eleven sub-queries, and you have strong, schema-marked answers on all eleven, you are not competing for a single citation – you are dominating the answer.

Content optimized for conversational fan-out achieves 40% higher coverage in simulations than content built for single queries. Content with strong ontological structure responds to 3x more contextual variations.

Mandy Dhaliwal, CMO of Nutanix, put the operational shift at Index’25 in one line: enterprise marketing is being re-architected around retrievability. For AI Mode, “retrievability” means one specific thing – being present across the fan-out.

Writing for Multi-Turn Context

A user rarely stops at one question in AI Mode. The session is a conversation, and Gemini carries context from turn to turn. Your content has to survive and re-appear across that chain.

Three principles for multi-turn writing

One sub-intent per H2. Each H2 should answer a single, cleanly-bounded sub-question. An article on AI Mode with an H2 called “Everything about AI Mode” will be cited less than an article with H2s like “What is query fan-out,” “How is AI Mode different from AI Overviews,” and “What content structures work best in AI Mode.” Gemini retrieves at the chunk level, and chunks mapped to a distinct sub-intent win.

Self-contained sections. Every section should be readable without the surrounding article. AI Mode extracts and quotes chunks; it does not display your full page. Write each section to stand alone – with its own definition, its own evidence, and its own concluding sentence.

Explicit next-question hooks. Inside each section, anticipate what the user will ask next and set up the link. “Once you have allowed the crawler, the next step is structuring content for citation triggers…” is not just good writing; it is a signal to Gemini that your page covers the adjacent sub-intent. AI Mode rewards that pattern.

Anticipating Follow-Up Queries

AI Mode users ask follow-ups. The exact follow-up varies, but it follows predictable patterns: definition → comparison → how-to → caveats → examples → alternatives. If your content covers that arc, you stay cited across the conversation.

A concrete exercise every content team should run:

1. Pick a primary query your brand cares about.
2. Run it through AI Mode and capture the first answer.
3. Ask the three follow-ups a real user would ask.
4. Log every sub-query implied in each response.
5. Map your existing content against those sub-queries.

The gaps are your content calendar. Every uncovered sub-query is a reason Gemini cited someone else in turn two, turn three, or turn four. Close the gaps, interlink the cluster, and your citation share across the conversation compounds.

Sydney Sloan, former CMO of G2, made a related observation at Index’25: AI search has collapsed the distance between brand and demand. On AI Mode specifically, that collapse happens across turns. 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. Your job is to be present in every one of those turns.

Content Structures That Perform Across a Dialogue Chain

Certain structural patterns disproportionately earn citations in multi-turn AI Mode sessions. Five matter most.

The 50–70 word answer block. Open every article with a concise, quote-ready answer to the primary query. This is what Gemini extracts first – factual, self-contained, no surrounding context required.

Named entity comparison tables. For any “X vs Y” sub-intent in your fan-out map, a side-by-side table with clear rows and columns outperforms prose. Gemini lifts entire table rows into conversational answers.

Ordered 3–7 step procedures. AI Mode heavily favours procedural content with a short, numbered step count. Convert anything that can be ordered into an explicit sequence.

Proprietary statistics. The fastest way to earn citation dominance in a fan-out is to publish a number that no other URL has. Original research, first-party benchmarks, and survey results are cited at multiples of the rate of generic content.

Schema density. Article + FAQPage + HowTo + Person + Organization schema, in JSON-LD, remains the highest-leverage technical investment. Pages with the full stack are cited at rates three to five times higher than unmarked pages across AI Mode fan-outs.

Angelique Bellmer Krembs, former CMO of PepsiCo, framed the underlying point at Index’25: in a world where AI summarizes everything, the brands that get summarized favourably are the ones with the clearest positioning and the most distinctive voice. Generic content cannot survive a fan-out. Distinct, structured, evidence-backed content thrives inside it.

Insights: What Marketing Leaders Are Saying About AI Mode

The CMOs and marketing leaders at Index’25 converged on a handful of insights that apply directly to AI Mode.

Linda Caplinger, Head of SEO and AI Search at NVIDIA, emphasized that AI discovery rewards content that proves it has been lived – first-hand experience, original photography, real deployment data. AI Mode’s fan-out amplifies this: the model looks for verifiable signal across every sub-query, and content that cannot prove its origin loses across all of them simultaneously.

Neil Patel, who keynoted Index’25, reduced the strategy to a single instruction:

“Be the source worth citing. Publish facts, stats, and expert insights that tools like ChatGPT and Perplexity can’t ignore.” – Neil Patel (Index’25)

AI Mode extends that instruction – be the source worth citing at every turn of a conversation.

From my own conversations with CMOs across Pepper’s enterprise customer base, the operational insight is this: AI Mode is a test of cluster completeness, not individual page quality. A single high-ranking page no longer wins. A complete, interlinked, schema-marked cluster wins.

Kishan Panpalia, part of Pepper’s founding team, captured the cultural moment at Index’25:

“Once in a generation, technology doesn’t just improve – it changes the way we see the world. GEO is not just a buzzword, but a new rule book for brand discovery, trust, and selection in an AI-first marketplace.” – Kishan Panpalia, Pepper Content

AI Mode is the rule book’s most aggressive chapter.

The AI Mode Readiness Checklist

Run any priority topic through these seven checks. Topics that score 7/7 are the ones that consistently appear in AI Mode fan-outs.

1. Fan-out map built. You have listed the five to fifteen sub-queries a real user will trigger by asking the primary question.
2. Cluster coverage. You have a dedicated, ranked, indexed page for each sub-query on the map.
3. Interlinking. Every page in the cluster links to every other page, with descriptive anchor text that matches the sub-intent.
4. Self-contained sections. Every H2 is a single, cleanly-bounded answer that can be extracted as a stand-alone chunk.
5. Schema stack. Article, FAQPage, HowTo, Person, and Organization schema in JSON-LD, validated, matching the on-page content.
6. Original data present. At least one proprietary statistic, benchmark, or piece of first-party data in the cluster.
7. Freshness. Every page updated in the last 90 days with current data and sources.

A cluster scoring 7/7 is the single most durable AI Mode asset a brand can build in 2026. It does not just earn one citation – it earns citations across an entire conversation, which compounds for every user who enters the same topic.

The Bigger Shift

Google AI Mode is early. Conventional wisdom has not caught up, which is exactly why the window is open. The marketers who treat AI Mode as “AI Overviews with extra steps” will be left behind. The marketers who treat it as a fan-out distribution surface – and build complete, interlinked, schema-marked clusters for the sub-queries their customers actually ask – will own the conversation before the rest of the market knows it has started.

AI Mode rewards operators, not publishers. Rewards clusters, not pages. Rewards anticipated questions, not only answered ones. Build for that, and you will be present at every turn of every conversation that matters to your brand.