HOW BRANDS CAN RESTRUCTURE CONTENT TO INCREASE VISIBILITY ON LLMS
As AI increasingly shapes product discovery, brands need to rethink how content is structured for Large Language Models. Clear information architecture, explicit relationships and machine-readable formats can help brands become more visible and accurately represented in AI-generated responses.
Industry Insights | AI Search | Content Strategy
The Context
As AI increasingly becomes part of the product-discovery journey, brands face a new visibility challenge.
Marketing content has traditionally been structured primarily for human readers and conventional search engines. However, Large Language Models (LLMs) process information differently.
Rather than simply reading content from top to bottom, LLMs process tokens, assess relationships between information, and resolve ambiguity based on patterns learned from large datasets.
When content lacks clear structural signals, AI systems may have to fill information gaps using generic knowledge. As a result, specific brand information can be overlooked or excluded from AI-generated responses.
The shift is becoming increasingly important as more consumers turn to AI during product discovery.
Research cited in the article shows that 35% of U.S. consumers now use AI during the product-discovery phase, compared with 13.6% using traditional search engines.
Research from Princeton University also found that applying appropriate structural signals can improve content visibility by more than 100% in AI-generated responses.
Why Content Structure Matters for AI Visibility
Traditional SEO focuses heavily on helping search engines discover, index and rank content.
LLM visibility requires a slightly different approach.
The goal is not only to make content searchable, but also to make the relationships between information clear and interpretable for AI systems.
This means brands need to think beyond keywords and consider how information is organized, connected and signposted throughout a page.
Four Ways to Make Content More LLM-Friendly
1. Lead With the Claim, Not the Context
Traditional editorial writing often begins with background information before introducing the main argument.
For LLMs, this structure may be less effective.
Because information appearing earlier in a passage can receive greater weight, brands should consider placing the core claim or answer first, followed by supporting context and evidence.
This makes the primary message easier for AI systems to identify and retrieve.
2. Separate Different Layers of Meaning
Avoid placing multiple types of information inside a single, dense paragraph.
Instructions, customer proof points, comparisons and tone markers should be clearly separated so that the model does not need to infer what each part represents.
Explicit structural signals can also help clarify relationships between ideas.
For example, phrases such as "in contrast to" or "the evidence suggests" can signal that a section represents a comparison, rebuttal or qualification.
The clearer the relationship between ideas, the less interpretation the model needs to perform.
3. Make Linked Content Explicit
LLMs do not interact with links in the same way humans do.
As a result, generic calls to action such as "Read more" provide little contextual information.
More descriptive language can communicate what the linked content actually contains.
For example:
"Download the product specification sheet."
is more informative than:
"Read more."
An even more specific description can communicate both the subject and the value of the linked content:
"Read the case study on how we reduced onboarding time by 60%."
The principle is simple: make the destination and its relevance explicit.
4. Use HTML Tables for Complex Comparisons
When presenting information about product ranges, capabilities, pricing or other structured comparisons, brands should consider using HTML tables rather than relying solely on prose or div-based layouts.
Tables provide a clear and parseable structure that helps AI systems understand the relationships between different entities and attributes.
For complex product information, better structure can therefore improve both machine comprehension and information accuracy.
What This Means for Business
AI visibility is becoming a content-structure challenge as much as a traditional SEO challenge.
Organizations should consider whether their websites are designed only for people to read or also for AI systems to interpret, connect and retrieve information accurately.
This does not mean abandoning traditional SEO.
Instead, organizations need to recognize that search rankings and LLM visibility are related but different challenges.
A page can perform well in traditional search while still being poorly represented in AI-generated answers if its underlying information lacks clear structural signals.
As AI becomes an increasingly important discovery layer, content strategy will need to account for both human readability and machine interpretability.
Key Takeaways
✅ Lead with the main claim so AI systems can identify the most important information quickly.
✅ Separate different layers of meaning to make relationships between information explicit.
✅ Replace generic link descriptions with specific, descriptive language that communicates what the destination contains.
✅ Use HTML tables when presenting complex product, pricing or capability comparisons.
✅ Traditional SEO and LLM visibility are not the same problem and require different considerations.
✅ Clear structural logic can help brands become more visible and accurately represented in AI-generated responses.
Key Quote
“Position in traditional search and quality of structural signal for LLMs are related but not the same problem.”
— Brendan Turner, SVP/Digital Experience, The MX Group
Reference
This article is summarized and adapted from research and analysis by Brendan Turner of The MX Group, examining how content structure can influence brand visibility in Large Language Model-generated responses.
© This article is an editorial summary intended for educational and industry knowledge-sharing purposes. It is not a verbatim translation or reproduction of the original publication.
