AI engines now source facts through grounding queries. Here's how to position your brand where machines look first — and measure the visibility that matters.
The search engine as we knew it is now split in two: the interface humans see, and the machine layer running beneath it. Google Gemini, ChatGPT, and their peers don’t just retrieve your page — they interrogate it with what researchers now call grounding queries, real-time sub-searches that verify facts before generating a response. If your content isn’t built to answer those machine questions, your brand simply doesn’t exist in the reply.
What Grounding Queries Actually Are (and Why Zero-Volume Keywords Miss the Point)
Dr. Peter J. Meyers at Moz has dug into real grounding query data, and the finding should reframe how Southeast Asian marketing teams brief their content teams. When Gemini composes an AI Overview or a cited answer, it runs background queries — short, factual, often question-shaped — to source specific claims. These queries frequently have near-zero search volume in traditional tools because humans rarely type them verbatim. Machines do.
The implication: a keyword strategy built solely on Search Console impressions is mapping a territory that AI engines don’t fully travel. Your content needs to be structured around answerable factual claims — precise figures, named entities, clear categorical statements — not just around search volume signals. For brands in markets like Indonesia or Thailand, where AI assistants are increasingly mediating product discovery on mobile, this is especially acute. A Tokopedia product category that answers “what is the average delivery time for [category] in Jakarta” in structured prose is far more grounding-query-ready than one optimised purely for head terms.
AI Share of Voice Is Now a Board-Level Metric
Semrush has released a framework for building an AI Brand Visibility Report that pairs AI share of voice, citation frequency, and sentiment with GA4 conversion data — all in a single report leadership can interrogate. This matters because CMOs across Southeast Asia are being asked, with increasing urgency, whether their brand appears when a customer asks an AI assistant for a recommendation.
The architecture of such a report is instructive. Track which AI platforms cite your domain, how often, in what sentiment context, and whether those citations correlate with measurable downstream intent signals. Brands like Grab and AirAsia have enough structured content and press coverage to generate natural citation surfaces — but mid-market brands in B2B SaaS or financial services often have citation deserts. Filling those deserts means publishing precise, attributable, frequently-updated content: pricing pages with real figures, comparison guides with named competitors, founder or analyst quotes that AI engines can lift as authoritative sources. The free Semrush template is a reasonable starting scaffold, but the strategic work is deciding which AI surfaces matter most for your specific buyer journey.
The YouTube Thumbnail Signal You’re Probably Misreading
YouTube’s Todd Beaupré confirmed that the platform’s desktop homepage redesign — featuring significantly larger thumbnails that display fewer videos per screen — measurably increased long-form video engagement. Search Engine Journal’s Matt G. Southern covered the announcement, and the finding carries a counterintuitive lesson for content strategists: showing users less but making each piece more visually prominent drove more consumption of deeper content.
For GEO and AEO strategy, the parallel is pointed. AI engines are essentially curating a smaller, higher-confidence set of sources to surface in responses. The brands that get cited aren’t necessarily publishing more — they’re publishing content with higher signal density per piece. A single, well-structured pillar page with embedded statistics, clear authorship credentials, and a named expert perspective will outperform ten thin articles in grounding query retrieval. Southeast Asian marketing teams under pressure to hit volume targets should take note: the machine search layer is already penalising content sprawl in favour of depth. The YouTube thumbnail redesign didn’t reduce quality — it concentrated attention. Your content architecture should do the same.
Building a Content Stack That Machines Trust
Putting this together into a practical framework: the brands that will dominate machine search over the next 18 months are those that treat AI citation as a distribution channel with its own editorial logic. That means three things running in parallel.
First, audit your existing content for grounding-query readiness: does each major page answer a specific, verifiable factual question with precision? Second, instrument your analytics to track AI-referred traffic and citation mentions across Gemini, Perplexity, and ChatGPT — not just organic SERP clicks. Third, compress your content investment into fewer, denser assets rather than spreading budget across high-volume but shallow clusters. For multilingual markets like Malaysia or the Philippines, this also means ensuring your structured data and hreflang signals are clean enough that grounding queries in Bahasa or Filipino resolve to the correct, locally-authoritative version of your content. A machine that can’t confidently attribute a claim to the right regional entity will simply skip the citation.
The deeper question worth sitting with: as AI engines increasingly decide which brands get named in the moments that matter most — a purchase consideration query, a competitive comparison, a regulatory question — is your organisation measuring the right kind of search visibility? Or are you still optimising for a SERP that fewer users are actually reading?
At grzzly, we work with marketing teams across Southeast Asia who are navigating exactly this shift — from traditional SEO metrics to AI citation strategy, grounding-query content architecture, and visibility reporting that leadership can actually act on. If you’re trying to understand where your brand stands in the machine-search layer, or how to close the gap, we’d like to think through it with you. Let’s talk
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Cosmic GrizzlyMapping the evolving cosmos of search — from traditional SERP dominance to answer engine optimisation and AI-cited authority. Obsessed with how machines decide what the world deserves to read.