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Your Website Isn’t Ready for AI—Yet

10 min read

After years of obsessing over rankings, metadata, and Core Web Vitals, most marketers are missing what’s right in front of them. AI doesn’t scan your site like a crawler. It processes your content like a reader.

And if it can’t find value fast, you’re out of the conversation. If you want your content to show up in Large Language Model (LLM)-powered answers like Claude, ChatGPT, Perplexity, or Google’s Search Generative Experience, you’ve got to rethink how you structure and present information.


 

Here’s how to get your site in shape.

1. Stop Hiding the Good Stuff in PDFs and Videos

LLMs right now aren’t parsing your slick marketing video or that 25-page whitepaper download behind a form or download link. They want digestible, on-page content: plaintext, clean HTML, no friction.

Do this:

  • Summarize whitepapers on the page itself. Use headers like “Key Takeaways” or “What You’ll Learn.”
  • Transcribe your videos. Better yet, pull out the insight and write it up like a blog.
  • Replace fluffy hero copy with clear, direct summaries of what you do.

Think like a machine trying to answer a question. If your site doesn’t give the answer in plain English, it won’t make the cut.


 

2. Write for Questions, Not Keywords

LLMs don’t match exact keywords. They connect intent with concepts. That means pages optimized around keyword variants like “Best commercial HVAC company in CT” are going to lose to pages that actually explain what makes a company the best HVAC provider.

Do this:

  • Build Q&A sections on core service pages.
  • Use natural, conversational phrasing in your subheads: “How does this work?” or “What’s included?”
  • Say what you do, who it’s for, and why it’s better. Fast.

 

3. Use Structured Content. Not Just Structured Data.

Schema markup is still very useful, but LLMs don’t care as much about JSON-LD as they do about how the content is written and laid out. When an LLM encounters poorly structured content, even with perfect schema markup, it struggles to extract coherent information to quote or reference.

Do this:

  • Use bullet points, tables, side-by-side comparisons.
  • Add “TL;DR” sections to long pages. Literally label them “TL;DR.”
  • Separate insight from filler. Clear headers. Short paragraphs. Make scanning stupid-simple.

The model isn’t ranking your page. It’s reading it. And if it can’t follow the structure, it won’t quote it.


 

4. Authority Signals Still Matter (But Not the Way You Think)

A single great page won’t cut it anymore. If your content doesn’t link out, reference credible sources, or show any sign of a broader presence, LLMs treat it like a dead end. Authority today means you’re part of a bigger conversation. Link up. Cite others. Be connected.

Do this:

  • Mention your company name, clearly and often.
  • Reference other known entities or stats (ideally with links, but they don’t have to be clickable).
  • Link between your own pages like you’re teaching someone how everything connects.

Think less like a marketer, more like an editor. If your site can’t support its own claims or connect the dots, LLMs won’t take it seriously.


 

5. Pages That Actually Answer Something Get Quoted

LLMs favor websites that answer questions directly, not sites that waffle or gate content. It’s not about ranking number one. It’s about being the best source in a sea of mediocre content.

Do this:

  • Turn “What We Offer” into “What’s Included in Our [Service].”
  • Include pricing if you can. If not, explain how pricing works.
  • Add use cases, step-by-step explanations, “who this is for” sections.

If you’re not the clearest voice on a topic, you won’t be the voice AI chooses.


 

Final Thought:

This isn’t a future problem. It’s happening now. AI is already shaping how people find answers. If your site doesn’t play nice with LLMs, you’re going to be invisible in a new kind of search landscape.

Clean up your content. Strip the fluff. Make your expertise undeniable.

LLM Definition: A Large Language Model (LLM) is a type of artificial intelligence system designed to understand and generate human language. It is built using deep learning techniques and trained on massive amounts of text data from books, websites, and other written sources.