How Technical SEO Adapts to the New AI Search Era

Hi everyone, this is Neo.

Recently, a deck from Nik Ranger, Senior Technical SEO Consultant at Dejan Marketing, caused quite a stir in the industry. It systematically explores how technical SEO will evolve in the AI era.

Before we dive in, it’s worth getting to know the speaker.

Nik Ranger Senior Technical SEO Consultant at Dejan Marketing

Nik Ranger is a Senior Technical SEO Consultant at Dejan Marketing, specializing in technical audits, content strategy, developer collaboration, and helping mid-to-large enterprises tackle complex SEO challenges. As director of SEO Collective Australia — a non-profit community with thousands of members — she’s dedicated to advancing SEO education and sharing cutting-edge insights.

Nik is also a Google Women Techmakers Ambassador and has been named by Search Engine Journal as one of the “top SEO experts to follow.” Her work has been published in multiple books by Majestic and Search Engine Journal, covering topics from machine learning and AI in SEO to semantic search and attribution.

Based in Victoria, Australia, her passion lies in combining technical precision with innovation to help brands build visibility in the ever-evolving AI-driven search landscape.

The deck’s core message hits hard: the future of SEO lies in understanding and influencing AI models — using technical means to build brand authority inside AI’s “mind.”

Today, combining the best of this deck with my latest takeaways, I’m bringing you a fully upgraded deep-dive.

AI’s Iceberg: The Interpretative Layer and the Agentic Layer

Modern search engines have fundamentally changed. They’ve added two distinct AI-enhanced layers:

  1. Interpretative Layer: this is the final output we see every day — like AI Overviews or chat replies.
  2. Agentic Layer: this is AI’s “back-end brain” for decision-making. It decides whether to expand a query, whether to search for new information, and which information sources to trust.

In the past, we did SEO to compete for rankings in the “interpretative layer”; now, our new battleground is influencing the decisions of the “agentic layer.”

Our new mission is no longer simply chasing rankings — it’s two core tasks: Diagnosis and Influence.

Part 1: Diagnosis — What Does Your Brand Look Like in AI’s Eyes Right Now?

Before forming a strategy, we have to accurately diagnose your brand’s current standing in the AI environment.

#1 Isolate Algorithm Impacts from Technical Issues

When site traffic drops, don’t rush to blame Google. We need to determine whether it stems from a Google algorithm update or from our own technical/content issues.

By establishing a historical traffic baseline and forecasting, we can measure the gap between expected traffic and actual traffic (called pDelta in the deck). This delta helps us precisely diagnose the root cause of traffic changes — whether it’s shifting keyword intent, AI result localization, or low content quality.

#2 Understand AI’s “Grounded” Queries

Next, we need to use GSC data to analyze how AI handles our relevant queries. The key is distinguishing between two query types:

  • Ungrounded Queries: AI generates results purely from its core training data — like a “closed-book exam.”
  • Grounded Queries: AI uses RAG (Retrieval-Augmented Generation) to combine real-time search results with its core memory when generating answers — like an “open-book exam.”

The SEO opportunity is hiding in “grounded queries.” Our goal is to become the most authoritative, most citable “standard answer” AI reaches for during its “open-book exam.”

#3 Audit SEO Fundamentals

Technical SEO, content quality, backlink health, even PPC ad data — these fundamentals remain critical in the AI era. A comprehensive audit helps you spot the telltale signs of problems and lays a solid foundation for the “influence” strategy that follows.

Part 2: Influence — How to Get AI Models to Recommend Your Brand

Diagnosis done. Now it’s time to go on the offensive and shape AI’s perception of our brand.

#4 Measure a New KPI: “AI Rank”

We need a new KPI to measure our work — AI Rank. It measures how frequently and authoritatively your brand appears in AI models (like Gemini, ChatGPT).

You can measure it using a “two-way probing” method:

  • Entity to brand: ask AI, “What are the top ten brands related to [your core service]?”
  • Brand to entity: ask AI, “What are the ten things related to [your brand]?”

This way, you can quantify your brand’s visibility in AI’s eyes and track it as a core KPI.

#5 Build Topical Authority Through “Query Fan-Out” Research

AI Overviews prefer citing pages that provide the most comprehensive, authoritative answers for an entire topic. So our strategy must shift from keyword research to topic modeling.

The core methodology is Query Fan-Out Research.

That means taking one core topic and fanning it out like an open fan to predict and cover a whole “constellation of related questions.”

A B2B example: your core product is “industrial robots.” You’d create a content cluster that comprehensively answers these questions:

  • What is it? (definition)
  • How does it differ from cobots? (comparison)
  • How does it improve production line efficiency? (solution)
  • How do I choose a supplier? (purchasing decision)
  • What programming languages does it use? (technical details)

Your goal is to become the ultimate source of information on your core topic, so AI treats your content as the most efficient, most logical source for any related query.

#6 Deconstruct AI’s Decision Process with “Tree Walker Analysis”

This is the highest-level play: deeply understanding AI’s “thought path” when generating answers — especially the answers it “almost” gave but ultimately abandoned.

By analyzing the confidence AI has in generating each word, we can uncover its semantic uncertainty.

For example, if AI has very low confidence in the middle word “and” when connecting your brand with “tours” and “activities,” that exposes a key insight: AI sees your brand as an authority in “tours,” but it’s not sure whether you also offer “activities.”

This finding can directly guide your content and PR strategy — precisely reinforce the connection between your brand and “activities,” and fill that “knowledge blind spot” in AI.

Summary: Building Measurable Authority in the Machine’s Mind

The future of SEO isn’t a choice between traditional methods and AI — it’s a deep integration of both.

Our ultimate goal is to build real, measurable authority for our brand inside the machine’s (AI’s) mind.

It’s time to re-examine your SEO strategy. Are you ready?

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