AI Search Has Shifted: Super Intent Is Here
Search behavior changed in 2025 and it's reshaping 2026. Similarweb's analysis showed AI query length averaging 31.2 words versus 3.4 in traditional search - roughly a 900% jump in context and constraints users share.
Hawaii-based Plate Lunch Collective calls this "super intent" - users now ask detailed, multi-constraint questions and expect precise answers. "Visibility now depends on answering the detailed, multi-constraint questions people actually ask," said Hayden Bond, Founder of Plate Lunch Collective. "Users provide 900% more context to AI-assisted search than they ever gave traditional search."
What "Super Intent" Means for Marketers
- Queries are scenario-driven: people add budget, timing, location, audience, and must-have features.
- AI systems retrieve and synthesize content that best fits the full context - not single keywords.
- Generic pages lose ground when the user expects answers to specific combinations of needs.
How AI Retrieves Answers Now
When someone asks a 30-word question in ChatGPT, Gemini, AI Overviews, Perplexity, or a voice assistant, the system scans multiple sources for content that matches the combined constraints. It then composes a response and often cites sources that clearly cover those constraints together.
Keyword-matching alone doesn't cut it. Your content has to mirror the user's scenario, carry the facts they need, and structure those facts so AI can extract them cleanly.
A Simple Example
Old approach: "Costa Rica luxury resort."
Super intent query: "What Costa Rican resorts have ocean-view suites under $400, offer both zip-lining and surfing, accommodate families with teenagers, and include transportation from Liberia airport?"
- Pages that win: ones that explicitly confirm ocean-view suites, price ranges, family fit (teens), activity availability, and airport transfer options - all on one page or clearly linked blocks.
- Pages that lose: generic "luxury resort" pages with vague amenities and no clear pricing or policies.
Playbook: Structure for Retrieval, Not Just Rank
- Build scenario pages: Create pages for the highest-frequency combinations you see in sales calls and inboxes (budget + audience + use case + location).
- State constraints explicitly: Price ranges, dates, capacity, policies, inclusions/exclusions, accessibility, booking windows.
- Use modular content blocks: Amenities, pricing, FAQs, "good fit for," "not ideal for," and comparison sections that AI can excerpt.
- Publish decision FAQs: Answer the exact questions buyers ask late in the funnel, with specifics and numbers.
- Add structured data: Use schema to label products, services, FAQs, reviews, and locations so AI can parse facts fast. Google's structured data docs are a solid starting point.
- Show sources and proofs: Cite policies, link to activity partners, include real itineraries, and keep details current.
- Localize and segment: Separate pages for families vs. corporate groups; Liberia vs. San José arrivals; weekend vs. weekday pricing.
- Write for extraction: Short sentences, clear headings, lists with full nouns (not pronouns), and consistent terminology.
- Cover trade-offs: Be explicit about what you do and don't offer; clarity outperforms fluff in AI answers.
- Keep your knowledge fresh: Update availability, prices, and policies on-page - don't bury them in PDFs.
What Plate Lunch Collective Is Seeing
Across clients - from airline staffing in the Pacific Rim to luxury resorts in the Caribbean, transportation in Costa Rica, and hospitality in Canada - the same pattern holds. AI-referred traffic is smaller but it converts better, and visitors spend 68% more time on-site than those from traditional organic search.
The takeaway for 2026: structure content to answer multi-constraint questions directly. If your page can't satisfy the full scenario, someone else's will.
Measurement That Matters
- Segment AI-assisted traffic: Use UTMs for shared links from AI platforms and track branded queries that echo your scenarios.
- Watch depth and dwell: Pages built for scenarios should see higher time on page and more internal clicks.
- Monitor AI citations: Check which pages get referenced by AI assistants and double down on their structure.
- Score coverage: List your top 20 buyer scenarios and grade each page on how fully it answers them.
Quick Implementation Checklist
- Identify top 10 buyer scenarios from sales and support logs.
- Create or refactor one page per scenario with clear constraints and answers.
- Add FAQ and comparison blocks for each scenario.
- Mark up pages with appropriate schema (FAQ, Product/Service, LocalBusiness, Organization).
- Include concrete numbers: prices, capacities, timelines, SLAs.
- Link related scenarios to form a clean cluster.
- Add "Who this is for / not for" sections to filter intent.
- Refresh quarterly; timestamp updates.
- Track conversions by scenario page in your analytics.
- Test prompts: Ask AI the exact scenario queries and see if your pages appear or are cited.
Key Questions About the Shift to AI-Assisted Search
What is "super intent"?
It's the detailed, multi-faceted query format people use with AI assistants, combining multiple constraints like location, budget, audience, features, and timing.
How does optimizing for AI search differ from keyword-focused approaches?
AI matches content to the full context of the query. Pages built around generic terms won't surface when the user specifies constraints. You need clear, structured answers that address the combined needs.
How does this impact 2026 planning?
Restructure content around scenarios, add explicit constraints, and make information extractable. Visibility follows clarity.
About Plate Lunch Collective
Plate Lunch Collective is a Hawaii-based AI-native digital marketing consultancy specializing in the retrieval layer - the technical and semantic structure that determines how AI systems discover and cite businesses.
Sources and Further Learning
- Similarweb's analysis on AI search behavior: AI News and Insights
- Structured data fundamentals: Google Search Central
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