The Widening Gap: Why Most Websites Aren’t AI-Ready

I get this question a lot — and it’s a good one.

Traditional SEO focused on ranking. But with generative AI, visibility means showing up in the answers — not just the search results.

SEO focused on optimizing for search engines: keywords, metadata, backlinks, page structure, and page speed.

But here’s the thing: most company websites aren’t great at answering questions.

And that gap is widening – here’s why…

LLMs like ChatGPT are rapidly increasing their ability to deliver helpful answers — while most brand websites remain static and unprepared.

While LLMs continue to improve their “answer readiness”, the average brand site has barely moved. That’s a missed opportunity — and it’s exactly what content teams need to fix. Get AI-Ready

But now we’re dealing with a new layer: AI tools like ChatGPT, Gemini, Claude and Perplexity aren’t just pointing people to links — they’re answering questions directly.

So it’s no longer just about ranking in Google.

It’s about being *included* in the answer.

A link? Great if you get one. Whether a link to your content shows up depends on the query, the platform, and how the AI decides to generate the answer.

What you can control is how easy it is for your content to be found, quoted, and summarized by AI.

LLMs Don’t Crawl Like Search Engines

They don’t crawl or index live web pages in real time like search engines do.

Instead, they’re trained on massive datasets built from publicly available or licensed content — including Wikipedia, product listings, documentation, and structured data feeds.

Why? Because LLMs learn from chunks of content (called tokens), not full web pages.

They’re trained on snippets like headlines, summaries, FAQs, and structured content—not long, unstructured pages.

Some AI Tools Retrieve, Not Just Train

Not all AI tools rely only on training data. Some — like Google’s Search Generative Experience (SGE), Microsoft Copilot, and certain versions of ChatGPT and Claude with browsing — can retrieve fresh information from the web at runtime.

That means they aren’t just responding based on what they were trained on months ago — they’re also pulling from live sources like public websites, news articles, help docs, and product pages.

But even then, these tools don’t crawl the internet like a search engine. They rely on pre-indexed or structured content — and they prefer sources that are clear, scannable, and trustworthy.

In other words, if your content is hard to parse, buried in fluff, or scattered across PDFs and long pages, it’s less likely to be surfaced — even in real-time tools.

At ToTheWeb, we use Revere to monitor how brands show up in LLMs — from citations in chatbot responses to rankings in AI-generated answer summaries. It’s been an incredibly useful way to track visibility across platforms like ChatGPT, Gemini, and Claude.

AI Brand Management Score

This snapshot from Revere represents a small but valuable example of the types of brand insights that AI-powered brand management platforms can generate — including sentiment scores, brand perception summaries, and competitive positioning data.

What’s the best way to show up? Make your content clean, structured, and easy for machines to understand.

That’s why well-organized content often gets surfaced, while long, unstructured pages get overlooked.

Here’s the upside: many traditional SEO best practices still apply.
Use clear headers.
Write in short paragraphs.
Keep a logical structure.

All of that still improves visibility — just for a different kind of system. In practice, LLMs generally absorb content found in:

• Wikipedia entries
• Blog post intros
• News story summaries
• Product feeds
• FAQs and comparison tables
• Webinar transcripts and recap posts

Large Language Models favor content that’s easy to scan — not buried in dense walls of copy.

Why might that be??  Simple – there’s no structure to a block of text.

When I first started optimizing for LLMs, this insight changed everything about how we formatted client content.

“Across the board, we’re seeing more web traffic from LLMs like ChatGPT, and early data shows this traffic converts well, so clients are eager to get more of it… To help clients appear in LLM results more often, we’re guiding them toward this conversational, question-and-answer-focused writing style. For brands in complex industries, this is a big shift, so we work together to create content that maintains their brand voice and communicates technical ideas in ways that resonate with both buyers and LLMs.”

Carey Madsen, Vice President, The Fletcher Group
– PR and content marketing firm focused on fintech and payments

Authority Looks Different Now

In traditional SEO, authority came from backlinks, domain age, and ranking signals.

You built it over time — mostly through your website.

But with generative AI, the game has shifted.  I’ve watched this transformation happen over the past year, and it’s fascinating how quickly the metrics of influence have changed

What matters now is “entity authority” — whether your brand shows up consistently across trusted sources online.

I’ve seen companies pour resources into their websites while ignoring everything else.

But AI learns from all of it: product listings, news mentions, schema markup, customer reviews.

They published a series of thought leadership pieces, partner announcements, and product launches — all picked up by outlets like Business Wire, TechCrunch, and niche industry sites.

Because that content was well-structured, widely syndicated, and published on authoritative domains, it gave the AI multiple touchpoints to associate the brand with key topics.

A few weeks later, their name started showing up in chatbot summaries and SGE results — not just from their own site, but from quotes pulled directly from third-party coverage.

It’s no longer just about your website.

It’s about your entire digital footprint — everywhere your brand can be seen, named, structured, and understood.

Think Like a User, Not an Algorithm

LLMs don’t care about keyword stuffing.

They’re trained to understand what people are actually asking.

Content that answers real questions — in natural language — performs better.

Instead of writing “POS software,” ask:
“What’s the best POS system for a small retail store?”

That’s how users think.

And that’s how AI models evaluate relevance.
I’ve done the over-optimization thing in the early SEO days but this is better — and it feels better to write.

LLMs interpret intent – not just keywords

So optimize for clarity, usefulness, and the way people actually phrase their questions.

In an AI-first world, traditional keyword research isn’t the starting point — understanding your audience is.

Instead of chasing individual keywords, we:

1. Start with audience questions or pain points
2. Use ChatGPT to map topic clusters and buyer journey
3. Build a content model that addresses the full set of buyer needs
4. Track visibility in AI results, traffic quality, and content engagement

It’s not about who wrote the content or keywords — it’s about whether it’s useful.

You’re Now Optimizing for More Than Just Search

It’s not just about Google anymore.

Your content is now being used in AI-powered tools that serve answers directly to users, including:

• Public-facing chatbots
• Voice assistants
• Generative search experiences like Google’s SGE
• In-product assistants like Microsoft Copilot and Google Gemini

Each of these tools surfaces content differently.

Chatbots want clear product specs.

Voice tools need short, conversational answers.

AI search favors comparison tables and structured FAQs.

Copilot and Gemini work best with well-structured, scannable guides — especially when they pull from public knowledge sources.

Note: These tools don’t access private content. They surface what’s public — blog posts, FAQs, reviews, and structured data.

To show up, your content needs to be built for more than search engines. It needs to be AI-ready — wherever users ask, not just where they search.

Google prioritizes the quality and usefulness of content, regardless of whether it’s written by a human or AI.

Therefore, AI-generated content isn’t penalized per se; rather, low-quality content—regardless of whether it’s produced by AI or humans—is unlikely to perform well in search.

. The goal remains to deliver helpful, original, and relevant content.

Also worth noting: Google recently updated its guidance for human content raters. While raters have been asked to evaluate AI-written content, they weren’t given tools to detect it.

Frequently Asked Questions