Okay, I need to talk about something that broke my brain a little bit this week. And not in the fun "oh wow look at this cool AI demo" way. More in the "I've been optimising content wrong for longer than I'd like to admit" way.

You know that feeling when you finally understand how a magic trick works and you're simultaneously impressed and annoyed? That's what happened when I properly understood query fan-out. And if you're doing anything related to search visibility in 2026 — SEO, content marketing, product listings, B2B lead gen, literally anything where being found online matters — this is the thing you need to wrap your head around next.

Here's what's actually happening behind the scenes when someone types a prompt into ChatGPT, Perplexity, Google AI Mode, or Claude.

Let's say someone asks: "What's the best accounting software for a small consultancy?"

You might assume the AI does what Google used to do — searches that phrase, pulls up some results, summarises. Simple. Logical. Completely wrong.

What actually happens is the AI decomposes that single question into somewhere between nine and twenty separate searches. It's looking up pricing tiers. It's searching for integrations with invoicing tools. It's checking mobile app quality. It's pulling tax compliance features for the user's jurisdiction. It's scanning third-party review sites. It's looking for comparison articles. It's checking whether free trials are available.

All of this happens simultaneously. In seconds. While the user sits there thinking the AI is just... thinking.

(I spent an embarrassingly long time watching the "thinking" indicators on various AI platforms this week, like a dog watching a washing machine. My partner walked past at one point and asked if I was okay. I was not okay. I was having a professional crisis.)

This process — one prompt becoming many searches, all running in parallel, results synthesised into a single answer — is called query fan-out. And it's not experimental. It's not coming soon. It's the retrieval architecture running behind every major AI search platform right now.

Here's where this gets uncomfortable for anyone who's spent the last decade doing keyword-focused SEO (raises hand slowly, avoids eye contact).

In traditional search, you optimised a page for a keyword. You ranked for that keyword. Traffic arrived. Commission or conversion happened. Everyone was happy. The system was comprehensible.

Query fan-out breaks this model in a way that's almost elegant in its cruelty. Your content isn't competing for one search term anymore. It's competing across an entire constellation of sub-queries that the AI generates without telling anyone.

Research from Seer Interactive and Nectiv found that a typical prompt generates nine to eleven sub-queries, with roughly a quarter of prompts triggering twelve or more. Deep research modes can run into the hundreds. The more complex or ambiguous the original question, the wider the fan-out.

And here's the part that really stings: the AI uses something called reciprocal rank fusion to decide what to cite. Each sub-query produces its own ranked results. Sources that appear across multiple lists — and rank well in each — accumulate higher composite scores. A page that shows up moderately well across six different fan-out queries will often outperform a page that ranks first for just one.

Read that again. Your beautifully optimised, first-position, trophy-on-the-shelf page can get outranked by something mediocre that just happens to be relevant in more directions.

I need a moment. And possibly a biscuit.

Not all fan-out looks the same. The sub-queries AI systems generate tend to follow six recurring patterns, and recognising which ones apply to your content category is the difference between strategic optimisation and expensive guesswork.

Disambiguation happens when a query is vague. Someone searches "best protein powder" and the AI doesn't know if they mean plant-based, whey, casein, or mass gainer — so it searches for all of them simultaneously. It's hedging, basically. The AI equivalent of ordering one of everything because you can't decide.

Entity attributes kick in when the AI needs to understand what something actually is across all its dimensions. For a product: material, compatibility, price range, warranty. For a service: credentials, service areas, response times, cancellation policies. It's building a complete mental model of the thing before recommending it.

Journey stages activate when a query spans an entire decision arc. Someone researching home renovation doesn't just need contractor recommendations — the AI also searches for budgeting guidance, permit information, timelines, and before-and-after examples. All at once. Because apparently AI has more patience for research than any human I've ever met.

Trust signals fire when stakes are high. Financial products, health queries, legal services, expensive purchases — these all trigger the AI to specifically search for credibility markers. Certifications, reviews, case studies, editorial coverage, regulatory standing. The AI is essentially doing due diligence on your behalf, which is either reassuring or terrifying depending on how confident you are in your own credibility infrastructure. (I just made myself nervous.)

Comparison criteria appear when the AI detects a choice needs to be made. It doesn't just list features — it searches for evaluation frameworks, trade-off analyses, and side-by-side breakdowns. It wants to help the user decide, not just browse.

Recency signals fire when a topic moves fast. Pricing, regulation, software updates, market conditions — anything with a short shelf life triggers sub-queries with temporal modifiers. "2026," "latest," "current." If your content is from 2024 and you haven't updated it, the AI knows. And it will find someone who has.

Understanding the concept is nice. It makes for good conversation at the kind of parties I apparently attend. But knowing what to do about it is what actually matters. So here's a practical framework that works whether you're an ecommerce seller, a SaaS company, a local service provider, or a B2B content publisher.

First: Figure out which fan-out patterns apply to you. Ask ChatGPT, Claude, or Perplexity the same questions your customers ask. Most platforms now expose their "thinking" or search steps. Watch what sub-queries they generate. Are they attribute-heavy? Journey-heavy? Trust-heavy? This tells you where your gaps are before you spend a single penny on new content.

Second: Audit your existing content against those patterns. For each major topic or product category, map whether you have content — on-site or off-site — that addresses each dimension the AI is searching. If you sell premium kitchen knives and the AI fans out into blade steel types, handle ergonomics, maintenance, warranty, and chef endorsements, check which of those you actually cover. (Spoiler: it's probably fewer than you think. It was for me.)

Third: Close the gaps with the right content type for each pattern. This is where it diverges from traditional SEO. You're not just writing more blog posts and hoping for the best.

For entity-heavy patterns, prioritise structured data completeness. Schema markup, merchant feed specs, image alt text describing specific features, collection pages grouped by attributes.

For journey-heavy patterns, prioritise content breadth. Awareness explainers, comparison frameworks, implementation guides, post-purchase support. They don't all need to live on one page — but they need to exist and be internally linked.

For trust-heavy patterns, prioritise credibility infrastructure. Author bios, third-party citations, case studies, certifications, review presence across platforms. The AI explicitly searches for this stuff. If it's not findable, it's not helping you.

For comparison patterns, prioritise honest evaluation content. Side-by-side breakdowns, transparent pricing, genuine discussion of trade-offs. If you don't provide this, someone else will — and they'll get the citation.

Fourth: Build your off-site presence. Many fan-out sub-queries specifically look for third-party validation. Review sites, industry directories, editorial roundups, community forums — these all function as external evidence. Figure out which sources are being cited for your priority topics and make sure you're in them.

Fifth: Measure at the topic level, not the keyword level. This is the hardest mental shift. Tracking rank position for a single keyword is increasingly meaningless in AI search. What matters is whether your content is being cited across the full spread of sub-queries the AI generates. Track citation frequency across AI platforms. Monitor which pages appear in AI-generated answers. Look at topic clusters, not individual terms.

Query fan-out is not a future development to keep an eye on. It is the mechanism running behind every AI search platform that determines which brands, products, publishers, and service providers get cited — and which ones get politely ignored.

The shift it requires isn't primarily technical. It's a shift in orientation: from keywords to comprehensiveness. From single pages to topic coverage. From your own site as the only thing that matters to recognising that AI pulls evidence from everywhere — and you need to be everywhere that matters.

The operators who build their content and data infrastructure around this reality will have a genuine advantage as AI search keeps absorbing a larger share of how people discover, evaluate, and buy. The ones who keep optimising for one keyword at a time will keep wondering why their traffic is declining even as their rankings look stable.

That's the structural reality of search in 2026. Query fan-out is the engine. And it's already running.

(Now if you'll excuse me, I need to go audit my own content against approximately seventeen fan-out patterns I've been cheerfully ignoring. Wish me luck. Send coffee.)

Jo Lambadjieva is an entrepreneur and AI expert in the e-commerce industry. She is the founder and CEO of Amazing Wave, an agency specializing in AI-driven solutions for e-commerce businesses. With over 13 years of experience in digital marketing, agency work, and e-commerce, Joanna has established herself as a thought leader in integrating AI technologies for business growth.

😍It was great, all I needed

🙂Good, not great

🙄It sucked