Our recent work with clients across a range of industries has demonstrated how AI-driven search visibility is fast becoming a strategic priority for businesses. Tools including the likes of ChatGPT, Google’s AI Overviews, and Perplexity, are now widely used to research products and inform purchasing decisions. As a result, the question of whether and how your content appears in those responses has shifted from a point of curiosity to a matter of real commercial significance.
We previously analysed the relationship between traditional organic rankings and AI search visibility, finding these are far more connected than certain industry hype suggests. 76% of AI Overview citations come from pages already ranking in Google’s organic top 10, and when organic visibility drops, AI citations tend to follow.
Having said that, there is one area where AI search mechanics diverge from traditional SEO, and that’s how content gets extracted. AI systems read your page differently to the way a human would. They pull specific passages and data points from it, working within fixed token budgets that determine how much of any single source gets used. How your content is structured directly impacts what’s selected and what’s skipped.
Over the past year, several independent research teams have published data pinpointing how this extraction works. This article brings that research together and translates it into practical structural decisions for content strategy.
The grounding budget constraint
AI search extracts content from web pages, rather than reading the page in full, so the structure of your content is critical to determining which content is recognised by LLMs.
Google’s Gemini-powered AI systems
Recent research has shown Google’s Gemini-powered AI systems allocate a fixed budget of approximately 2,000 words per query, distributed across sources by organic ranking position:
- Rank #1 gets 531 words (28% of the total budget)
- Rank #2 gets 433 words (23%)
- Rank #3 gets 378 words (20%)
- Rank #4 gets 330 words (17%)
- Rank #5 gets 266 words (13%)
A 4,000-word guide doesn’t earn a bigger crawl budget than an 800-word article; it just gets a lower percentage of itself selected.
Pages under 1,000 words see 61% of their content extracted.
Pages with over 4,000 words see 13% of their content extracted.
For those who have built their content strategy around comprehensive guides and resources, this is a structural change that is crucial for the future of search.
Where on your page AI looks first
There’s a strong positional bias in what content gets selected by AI Overviews.
Opening paragraphs are extracted “almost wholesale, regardless of content”, meaning your first few sentences carry disproportionate weight in determining how you appear.
A study of approximately 3 million ChatGPT responses showed the same pattern.
44.2% of all ChatGPT citations come from the first third of a page’s content, with citation odds peaking within the first 20% before declining sharply, described as a “ski ramp” distribution pattern.
The data now quantifies why it matters specifically for AI. If your opening paragraphs don’t contain your most extractable content, the grounding budget gets spent on weaker material further down the page instead of your strongest points.
Structural patterns that correlate with citations
Clean heading hierarchy, lists, and data tables all correlate with higher AI citation rates across multiple studies.
AIROPS’ 2026 State of AI Search report found that 87% of pages cited by ChatGPT use a single H1, and 68.7% follow a logical heading hierarchy without skipping levels. Pages with sequential heading structures and schema markup showed 2.8x higher citation rates. Nearly 80% of cited pages include lists.
Separately, Wellows found comparison tables correlate with 2.8x higher citation rates than text-only content, while Presence AI found pages with original data tables earn 4.1x more AI citations.
Opening paragraphs that answer the query directly get cited 67% more often than those that build up to an answer.
These are familiar recommendations for anyone who’s worked in SEO since featured snippets launched in 2014, from clean heading hierarchy to answer-first structure.
What the AI citation data adds is specific multipliers showing how much these practices matter when AI systems evaluate content at the chunk level. Each section of your page is being assessed independently for relevance, which means a single well-structured section can get cited even if the rest of the page is average.
What this means for content length
The grounding budget data challenges the “longer is better” assumption that’s persisted in content strategy for years, but it doesn’t mean short content wins automatically.
76.1% of AI Overview citations still come from pages ranking in the organic top 10, and that overlap has been growing as BrightEdge found it had been rising from 32.3% to 54.5% over 16 months. Google traffic is still the single strongest predictor of AI citation, with high-traffic sites three times more likely to be cited.
Dense pages get better extraction coverage than long ones. A 1,200-word article that covers a topic with original data and specific examples will rank organically and get strong extraction coverage (roughly 45% of its content selected).
A 4,000-word article covering the same ground with additional padding might also rank, but only 13% gets extracted, and the budget may end up selecting filler paragraphs over your strongest points.
As we covered when analysing the difference when optimising for LLMs vs traditional SEO, this is one of the few areas where understanding AI search mechanics genuinely changes practical decision-making. The advice to write concisely and front-load value has always been good practice and the grounding budget data gives it a measurable mechanical basis.
Schema markup: useful, not transformative
Correlational data suggests schema markup improves AI citation rates, but Google has never confirmed schema as a ranking signal for AI Overviews. The correlation likely reflects that sites implementing schema also tend to have cleaner HTML and better-structured content overall.
Google’s own guidance from May 2025 was practical: ensure your structured data matches the visible content on the page. Schema helps machines parse content more accurately, which is worth doing. Article schema with author attribution supports E-E-A-T signals, and FAQ schema makes sense where content genuinely answers common questions. The benefit is in machine readability, and that’s a reasonable investment even without a confirmed ranking impact.
Where to start
The structural practices that improve AI extraction are the same ones that improve organic rankings and user experience.
Start with your highest-traffic pages and review the first 200 words of each: do they directly answer the query someone would type to find that page? If not, restructure so they do. This single change addresses both the grounding budget (front-loading value within your ~540 word allocation) and the citation position bias (the first third of content gets cited most heavily).
Check heading hierarchies are clean and sequential. Each H2 section should address one focused topic in roughly 200-400 words, which aligns with how retrieval systems chunk content for processing. Where you have data worth sharing, use tables, the 4.1x citation multiplier for original data tables is one of the strongest structural signals in the research.
Keep content fresh
Ahrefs found that 79% of pages cited by ChatGPT were updated in 2025, and AIROPS reported that pages not refreshed quarterly are three times more likely to lose citations.
Regular updates to ensure content is updated with recent developments or new context will help maintain both organic and AI visibility for your content with relatively low effort.
Getting your content structure right
The research covered in this article points to a consistent theme, AI systems reward clarity, density, and structure. These aren’t new principles, but the grounding budget data gives them a mechanical basis that makes them harder to ignore.
For organisations already investing in content, applying these structural changes to existing high-traffic pages is the highest-leverage starting point.
At SoBold, we build these structural principles into our WordPress development solutions from the platform level:
Clean heading hierarchies enforced through page templates, schema markup integrated into content types; and publishing workflows that make it straightforward for internal teams to create well-structured content without needing to think about the technical details.
