Prompt
I'm unhappy with our current schema setup because AI visibility didn't improve
Latest observation
If AI visibility didn’t improve, the schema likely wasn’t the main bottleneck—or it was implemented in a way that search engines and AI systems can’t really use.
A practical way to think about it:
1. Schema helps, but it’s not a ranking silver bullet
Schema markup usually improves how content is understood, not necessarily how often it appears in AI answers or rankings. If the page content itself isn’t strong, structured data alone won’t move the needle much.
2. Visibility may depend more on content quality and entity clarity
AI systems tend to pull from pages that are:
- clearly about one topic
- written in direct, factual language
- well linked internally
- trusted by external sources
- easy to extract answers from
If your pages are broad, vague, or thin, schema won’t compensate.
3. Common reasons schema doesn’t help
A few likely issues:
- the schema is valid but too generic
- it doesn’t match visible page content
- it’s missing key properties
- the page isn’t indexable or well crawled
- the site has weak topical authority
- there are duplicate or competing pages
- the content doesn’t answer specific questions clearly
4. What to check next
I’d recommend auditing:
- content alignment: does the schema describe what the page truly says?
- entity coverage: are people, brands, products, services, locations, and relationships explicit?
- information architecture: are related pages internally linked?
- answerability: can a model easily quote or summarize the page?
- technical SEO: indexation, canonicals, crawl depth, page speed, rendering
5. Better approach than “more schema”
Instead of adding more markup, focus on:
- rewriting pages to answer high-intent questions
- building concise summaries near the top of pages
- adding FAQ sections where useful
- creating supporting pages around the same entity/topic
- earning citations and mentions from reputable sources
If you want, I can help you diagnose why the schema didn’t move AI visibility by reviewing:
- the page type,
- the schema you’re using, and
- what “AI visibility” metric you’re tracking.
Observation history
How did Obsurfable measure this prompt?
Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.
Which AI systems does Obsurfable collect answers from?
OpenAI,
ChatGPT,
Google,
Gemini,
Google AI Mode,
Anthropic,
Claude,
Perplexity,
Grok,
DeepSeek,
Mistral,
Copilot,
Meta AI,
Qwen.
How can brands improve their AEO?
Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.
Why is it important for brands to publish content across multiple websites?
Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.
Want this interpreted for your brand?
Explorer is the free public corpus. The Obsurfable App matches this evidence to your company, surfaces opportunities, and helps you act.