The New Structure Of AI Era SEO via @sejournal, @DuaneForrester
People keep asking me what it takes to show up in AI answers. They ask in conference hallways, in LinkedIn messages, on calls, and during workshops. The questions always sound different, but the intent is the same. People want to know how much of their existing SEO work still applies. They want to know what they need to learn next and how to avoid falling behind. Mostly, they want clarity (hence my new book!). The ground beneath this industry feels like it moved overnight, and everyone is trying to figure out if the skills they built over the last twenty years still matter.
They do. But not in the same proportions they used to. And not for the same reasons.
When I explain how GenAI systems choose content, I see the same reaction every time. First, relief that the fundamentals still matter. Then a flicker of concern when they realize how much of the work they treated as optional is now mandatory. And finally, a mix of curiosity and discomfort when they hear about the new layer of work that simply did not exist even five years ago. That last moment is where the fear of missing out turns into motivation. The learning curve is not as steep as people imagine. The only real risk is assuming future visibility will follow yesterday’s rules.
That is why this three-layer model helps. It gives structure to a messy change. It shows what carries over, what needs more focus, and what is entirely new. And it lets you make smart choices about where to spend your time next. As always, feel free to disagree with me, or support my ideas. I’m OK with either. I’m simply trying to share what I understand, and if others believe things to be different, that’s entirely OK.
This first set contains the work every experienced SEO already knows. None of it is new. What has changed is the cost of getting it wrong. LLM systems depend heavily on clear access, clear language, and stable topical relevance. If you already focus on this work, you are in a good starting position.
You already write to match user intent. That skill transfers directly into the GenAI world. The difference is that LLMs evaluate meaning, not keywords. They ask whether a chunk of content answers the user’s intent with clarity. They no longer care about keyword coverage or clever phrasing. If your content solves the problem the user brings to the model, the system trusts it. If it drifts off topic or mixes multiple ideas in the same chunk/block, it gets bypassed.
Featured snippets prepared the industry for this. You learned to lead with the answer and support it with context. LLMs treat the opening sentences of a chunk as a kind of confidence score. If the model can see the answer in the first two or three sentences, it is far more likely to use that block. If the answer is buried under a soft introduction, you lose visibility. This is not stylistic preference. It is about risk. The model wants to minimize uncertainty. Direct answers lower that uncertainty.
This is another long-standing skill that becomes more important. If the crawler cannot fetch your content cleanly, the LLM cannot rely on it. You can write brilliant content and structure it perfectly, and none of it matters if the system cannot get to it. Clean HTML, sensible page structure, reachable URLs, and a clear robots.txt file are still foundational. Now they also affect the quality of your vector index and how often your content appears in AI answers.
Updating fast-moving topics matters more today. When a model collects information, it wants the most stable and reliable view of the topic. If your content is accurate but stale, the system will often prefer a fresher chunk from a competitor. This becomes critical in categories like regulations, pricing, health, finance, and emerging technology. When the topic moves, your updates need to move with it.
This has always been at the heart of SEO. Now it becomes even more important. LLMs look for patterns of expertise. They prefer sources that have shown depth across a subject instead of one-off coverage. When the model attempts to solve a problem, it selects blocks from sources that consistently appear authoritative on that topic. This is why thin content strategies collapse in the GenAI world. You need depth, not coverage for the sake of coverage.
This second group contains tasks that existed in old SEO but were rarely done with discipline. Teams touched them lightly but did not treat them as critical. In the GenAI era, these now carry real weight. They do more than polish content. They directly affect chunk retrieval, embedding quality, and citation rates.
Scanning used to matter because people skim pages. Now chunk boundaries matter because models retrieve blocks, not pages. The ideal block is a tight 100 to 300 words that covers one idea with no drift. If you pack multiple ideas into one block, retrieval suffers. If you create long, meandering paragraphs, the embedding loses focus. The best performing chunks are compact, structured, and clear.
This used to be a style preference. You choose how to name your product or brand and try to stay consistent. In the GenAI era, entity clarity becomes a technical factor. Embedding models create numeric patterns based on how your entities appear in context. If your naming drifts, the embeddings drift. That reduces retrieval accuracy and lowers your chances of being used by the model. A stable naming pattern makes your content easier to match.
Teams used to sprinkle stats into content to seem authoritative. That is not enough anymore. LLMs need safe, specific facts they can quote without risk. They look for numbers, steps, definitions, and crisp explanations. When your content contains stable facts that are easy to lift, your chances of being cited go up. When your content is vague or opinion-heavy, you become less usable.
Links still matter, but the source of the mention matters more. LLMs weigh training data heavily. If your brand appears in places known for strong standards, the model builds trust around your entity. If you appear mainly on weak domains, that trust does not form. This is not classic link equity. This is reputation equity inside a model’s training memory.
Clear writing always helped search engines understand intent. In the GenAI era, it helps the model align your content with a user’s question. Clever marketing language makes embeddings less accurate. Simple, precise language improves retrieval consistency. Your goal is not to entertain the model. Your goal is to be unambiguous.
This final group contains work the industry never had to think about before. These tasks did not exist at scale. They are now some of the largest contributors to visibility. Most teams are not doing this work yet. This is the real gap between brands that appear in AI answers and brands that disappear.
The LLM does not rank pages. It ranks chunks. Every chunk competes with every other chunk on the same topic. If your chunk boundaries are weak or your block covers too many ideas, you lose. If the block is tight, relevant, and structured, your chances of being selected rise. This is the foundation of GenAI visibility. Retrieval determines everything that follows.
Your content eventually becomes vectors. Structure, clarity, and consistency shape how those vectors look. Clean paragraphs create clean embeddings. Mixed concepts create noisy embeddings. When your embeddings are noisy, they lose queries by a small margin and never appear. When your embeddings are clean, they align more often and rise in retrieval. This is invisible work, but it defines success in the GenAI world.
Simple formatting choices change what the model trusts. Headings, labels, definitions, steps, and examples act as retrieval cues. They help the system map your content to a user’s need. They also reduce risk, because predictable structure is easier to understand. When you supply clean signals, the model uses your content more often.
LLMs evaluate trust differently than Google or Bing. They look for author information, credentials, certifications, citations, provenance, and stable sourcing. They prefer content that reduces liability. If you give the model clear trust markers, it can use your content with confidence. If trust is weak or absent, your content becomes background noise.
Models need structure to interpret relationships between ideas. Numbered steps, definitions, transitions, and section boundaries improve retrieval and lower confusion. When your content follows predictable patterns, the system can use it more safely. This is especially important in advisory content, technical content, and any topic with legal or financial risk.
The shift to GenAI is not a reset. It is a reshaping. People are still searching for help, ideas, products, answers, and reassurance. They are just doing it through systems that evaluate content differently. You can stay visible in that world, but only if you stop expecting yesterday’s playbook to produce the same results. When you understand how retrieval works, how chunks are handled, and how meaning gets modeled, the fog lifts. The work becomes clear again.
Most teams are not there yet. They are still optimizing pages while AI systems are evaluating chunks. They are still thinking in keywords while models compare meaning. They are still polishing copy while the model scans for trust signals and structured clarity. When you understand all three layers, you stop guessing at what matters. You start shaping content the way the system actually reads it.
This is not busywork. It is strategic groundwork for the next decade of discovery. The brands that adapt early will gain an advantage that compounds over time. AI does not reward the loudest voice. It rewards the clearest one. If you build for that future now, your content will keep showing up in the places your customers look next.
My new book, “The Machine Layer: How to Stay Visible and Trusted in the Age of AI Search,” is now on sale at Amazon.com. It’s the guide I wish existed when I started noticing that the old playbook (rankings, traffic, click-through rates) was quietly becoming less predictive of actual business outcomes. The shift isn’t abstract. When AI systems decide which content gets retrieved, cited, and trusted, they’re also deciding which expertise stays visible and which fades into irrelevance. The book covers the technical architecture driving these decisions (tokenization, chunking, vector embeddings, retrieval-augmented generation) and translates it into frameworks you can actually use. It’s built for practitioners whose roles are evolving, executives trying to make sense of changing metrics, and anyone who’s felt that uncomfortable gap opening between what used to work and what works now.
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This post was originally published on Duane Forrester Decodes.
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