Most of what people call “AI optimization” is just good publishing with a new name on it. Here’s what actually moves the needle, and what to skip.
There’s a lot of noise right now about “AI-friendly content” as if it’s a separate discipline from SEO, with its own rules, its own file formats, and its own hacks. I don’t think that’s true, and I don’t think you need to buy into it.
Google’s own guidance on AI features in Search says the same thing plainly: the best practices for SEO remain relevant for AI features, and there are no extra requirements to show up in AI Overviews or AI Mode. OpenAI’s publisher documentation draws a similarly practical line: any public site can appear in ChatGPT search if it’s crawlable, and search discovery is handled separately from AI training access. Neither company is asking publishers to learn a new game. They’re asking for the same fundamentals, done well.
So instead of chasing a new checklist, I think it’s worth asking a more useful question: what does “better content” actually mean right now, and what’s just noise?
What “better content” actually means
Google’s helpful-content guidance asks a short list of honest questions: does this page offer original information, real analysis, a clear point of view, and evidence that someone with actual expertise wrote it? That’s not a new bar. It’s the same bar good editors have always held writers to. The only thing that’s changed is who’s grading the paper, because now it’s not just a person deciding whether your page is worth their time. It’s also a retrieval system deciding whether your page is worth citing.
That distinction matters because it kills the idea that AI-friendly content is somehow generic or templated. It’s the opposite. Google’s AI optimization guide explicitly separates commodity content, the kind that could have come from any of a dozen other sites, from non-commodity content that reflects a real point of view or first-hand experience. If your page reads like it could have been written by anyone about anything, that’s the problem to fix. Not your schema markup.
Why formatting isn’t cosmetic
Here’s where I think a lot of teams get the emphasis backwards. They treat structure and formatting as a technical afterthought, something to hand off after the writing is “done.” But formatting is part of the argument, not decoration on top of it.
Google recommends content organized into clear paragraphs and sections with headings that describe what’s actually in them. The W3C’s accessibility guidance on headings says the same thing from a different angle: headings communicate the organization of a page and let readers, and assistive technology, navigate it. And there’s research behind why this matters more than ever. A widely cited study on how language models handle long contexts found that models can lose track of information buried in the middle of a document, even when that information is technically present. Separate research on retrieval systems has found that preserving structure and hierarchy in HTML improves how well content is retrieved, compared with flattening everything into plain text.
Structure isn’t just for people scanning on their phones. It’s how machines find the point. Practically, that means leading with a clear answer instead of a slow windup, using headings that could stand alone out of context, and resisting the urge to bury your best insight three paragraphs into a section called “Overview.”
The infrastructure layer still matters
None of this replaces the basics. Descriptive titles, readable URLs, crawlable links using standard anchor markup, and structured data that actually matches what’s on the page, these are still the plumbing that makes everything else discoverable. Google is explicit that structured data helps it understand a page and can enable rich results, but it’s just as explicit that there’s no special schema required for generative AI. Schema is worth doing well. It’s not a shortcut around doing the content well.
The same goes for crawl access. If a page is worth AI visibility, it needs to be reachable in the first place, which means checking your robots.txt and noindex directives are doing what you actually intend, not blocking the pages you want found.
The measurement gap nobody’s fully closed
This is the part I think gets skipped over the most, and it’s the part that actually determines whether any of this work is paying off.
Google has started reporting on AI Overviews and AI Mode visibility directly inside Search Console. Google Analytics now has a dedicated AI Assistant channel. OpenAI tags ChatGPT referral traffic with a UTM parameter automatically. All of that is real progress. A year ago, this traffic was mostly invisible. It isn’t anymore.
But it’s still not a complete picture, and Google says so itself. Search Console and GA4 won’t always agree, because of implementation gaps, consent settings, time zone differences, canonicalization, bot filtering, and non-HTML pages. That’s not a bug to fix. It’s the nature of measuring something that happens partly outside your own site, in someone else’s interface, before a click ever occurs.
Which is why I’d tell any team, mine included, that the dashboards are necessary but not sufficient. You still need a qualitative layer alongside them: testing the questions your customers actually ask, checking whether your brand or your page shows up in the answer, and comparing that against what your competitors are getting cited for. Treat it like a monthly habit, not a one-time audit.
What to fix first
If you’re looking for where to actually spend the next few hours, here’s the order I’d work in:
- Lead with the answer, not the runway. Rewrite intros so the main point lands in the first few sentences. Save the supporting detail for after.
- Replace commodity framing with something only you could have written. A field observation, a specific failure case, a number from your own work. Anything a template couldn’t produce.
- Fix your heading architecture. One clear H1, and H2s that each summarize the section under them in plain language.
- Make every important internal link crawlable and descriptive. Standard links, real anchor text, nothing that only works after JavaScript runs.
- Treat schema as hygiene, not magic. Implement it cleanly, validate it, and make sure it reflects what a reader actually sees on the page.
- Audit what you’re accidentally blocking. Confirm the pages you want found aren’t sitting behind a stray robots.txt rule or a noindex tag from an old migration.
- Watch more than one report. Pair Search Console’s generative AI data with GA4’s AI Assistant channel, and expect them to disagree at the edges.
The practical reframe
I’ll say this plainly, because I think it’s the most useful thing in this whole piece: “AI-friendly content” isn’t a new specialty. It’s mostly the modern name for content that’s useful, clearly structured, honestly sourced, and measured instead of assumed. If you’re doing the fundamentals well, you’re already most of the way there. The work left is editorial discipline, not a new set of hacks, and that’s a much better problem to have.