I keep seeing three claims about "AI SEO" for websites:

"Just add llms.txt."

"Schema is enough."

"Google SEO and AI visibility are now separate games."

Two of those are wrong. One is still unproven.

I spent time looking at the boring structure issues that decide whether a page can be crawled, parsed, summarized, and cited. The punchline is not glamorous: AI website optimization still starts with plain SEO optimization.

TL;DR

• No, AI website optimization is not a prompt trick. It is mostly page structure: intent, title, H1-H2, schema, tables, FAQ, internal links, sitemap, and crawlable HTML.
• Google's own guidance says optimizing for generative AI search still starts with Search fundamentals, not a separate magic playbook.
• A page can look fine to a human and still be weak for AI retrieval if it hides facts in paragraphs, skips schema, or has no direct answers.
• The TokenMix SEO/GEO audit costs $0.035 for a standard report and $0.5 for an advanced report. That makes broad triage cheap.
• I'd audit every important URL with a cheap pass first, then use advanced review only for money pages.

What AI website optimization actually means

AI website optimization means making a page easy for both search engines and answer engines to understand.

That sounds abstract, so here is the practical version:

Google's AI optimization guide is blunt about this: if you want to appear in AI Overviews and AI Mode, you still need Search fundamentals.

That matters because a lot of "AI SEO" advice online skips the fundamentals and jumps straight to fashionable files, hacks, and prompts. I don't think that is where most sites are failing.

Most sites are failing much earlier.

They have vague titles.

They have no self-contained lead.

They bury numbers in prose.

They have no FAQ.

They have schema that does not match the visible content.

They have orphaned blog posts with no internal links.

That is not an AI problem. That is a structure problem.

The $0.035 check vs the $0.5 check

The reason I like cheap audits is simple: most websites do not need a 40-page consultant deck before fixing obvious structural misses.

TokenMix exposes two SEO/GEO audit modes:

The price gap is 14.29x.

That does not mean the advanced report is expensive. It means the jobs are different.

I would not run advanced analysis on 1,000 low-priority pages first. I would run a standard scan to find the obvious problems, sort the URLs, and only then spend deeper analysis on pages that can actually move revenue or traffic.

The math changes how you audit

Here is the part that changed my mind.

For a content-heavy site, that is a very different workflow.

If I had 200 blog posts, I would not start by rewriting all of them. I would spend $7 to find which pages have the structural problems:

• missing or weak H1
• bad title/meta
• no FAQ
• no tables
• no schema
• weak internal links
• no direct first answer
• canonical/sitemap issues

Then I would fix the top 20 pages.

If those pages already get impressions, backlinks, or conversions, the audit cost is basically noise.

The "AI SEO" decision tree I would actually use

I would not treat every site the same.

Here is the decision tree I would use:

def ai_website_optimization_plan(site):
    if site["pages"] <= 20 and site["revenue_pages"]:
        return "Run advanced audits on every revenue page, then fix H1, schema, FAQ, tables, and internal links."

    if site["pages"] > 100 and site["traffic_declining"]:
        return "Run standard audit across the full cluster. Sort by impressions, then repair the top 20 pages first."

    if site["new_blog_program"]:
        return "Add a standard SEO/GEO audit to every publish checklist before indexing."

    if site["ai_visibility_goal"] and not site["schema"]:
        return "Fix schema and visible page structure before thinking about llms.txt."

    if site["mostly_javascript_rendered"]:
        return "Verify rendered HTML first. AI visibility starts with crawlability."

    return "Start with 10 representative pages. Look for repeated template-level failures."

This is boring on purpose.

The highest-leverage SEO work is often boring. That is why teams skip it.

What I would fix first

If I were optimizing a website for AI search visibility this week, I would fix things in this order:

The llms.txt point is where I differ from a lot of current AI SEO posts.

I am not against it. I just would not start there.

If a page has a vague title, no FAQ, no tables, weak schema, and no internal links, adding llms.txt is like labeling a messy warehouse. Maybe it helps a robot find the door. It does not organize the shelves.

The bigger picture

AI search does not remove the need for SEO.

It punishes weak structure faster.

A human can skim a messy article and still understand it. A retrieval system is less forgiving. It wants:

• clear entities
• clear sections
• short answers
• stable facts
• source links
• related pages
• machine-readable schema

That is why I think "AI website optimization" will become less about secret prompts and more about disciplined publishing systems.

The sites that win will not be the ones that add the most AI buzzwords.

They will be the ones with pages that are easiest to parse, trust, and cite.

What I'd do today

If I ran a SaaS site:

• I would audit every pricing, product, comparison, and integration page.
• I would add FAQ sections to every page with commercial search intent.
• I would make every H2 start with the answer, not a warm-up sentence.
• I would add schema only where it matches visible content.
• I would link every blog post into a real cluster.

If I ran a content site:

• I would scan the top 100 pages by impressions.
• I would fix pages with weak titles first.
• I would rewrite intros so the answer appears immediately.
• I would turn comparison paragraphs into tables.
• I would prune or merge pages with no clicks and no unique intent.

If I ran an agency:

• I would use cheap standard audits for discovery.
• I would reserve advanced audits for the pages clients actually care about.
• I would turn audit output into a 7-day fix queue.
• I would stop selling AI SEO as magic and start selling structure.

Disclosure

If you want to audit URL structure for SEO and AI answer-engine visibility, that is what TokenMix SEO/GEO Structure Audit does. Disclosure: I work on the research side. Full data-cited breakdown is on the original article.

Bottom line

AI website optimization is not separate from SEO optimization. It is stricter SEO.

If your page is unclear to Google, weakly structured for humans, and hard for machines to summarize, it will not become AI-ready because you added one trendy file.

What is the most common structural SEO failure you see on websites: titles, schema, headings, internal links, or something else?