Has Your SEO Role Been Repurposed by GEO? One CMO’s Testimony on Changing Strategy

A CMO calls me on a Monday morning. His Google traffic has dropped 47% in 18 months.

On LinkedIn, a CMO’s post is making the rounds among search professionals: « GEO and AEO have changed our user acquisition strategy. » The debate heats up. Same question on r/TechSEO: « Has your job been repurposed? »

The following Monday, a CMO I worked with 3 years ago calls me. His B2B marketplace, €20 million in annual revenue, shares his numbers. 12,000 organic visits per month in January 2024. 6,400 in June 2025. −47%. Traffic no longer responds to classic SEO.

« Our writers produce 12 articles per month, but none are picked up by AI. We’re invisible where it matters. »

Yet Search Console tells a different story. Clicks from Google’s « AI Overviews »: 1,200 monthly visits. Unmanaged. The 4-person SEO team didn’t know how these results were being generated. Worse, they couldn’t reproduce them or monetize them.

The audit is damning. Zero semantic structure. Zero cocoons. Zero schema markup for business entities. The site had no entity identifiers for its products, no pages connecting concepts — suppliers, features, industries. Google didn’t understand the business logic. The strategy boiled down to page volume, listicles, no connections between concepts.

We stopped mass content production. We structured the architecture into 3 thematic cocoons, fed the Knowledge Graph via JSON‑LD, and trained the team to manage entities rather than keywords.

6 months later, AI Overview citations climb to 4,800 per month. Clicks from these citations reach 2,800 monthly visits (+133%). Classic organic traffic rebounds to 9,200 visits. And the conversion rate from AI traffic exceeds classic traffic by 22%.

I’m observing this shift across all my clients since early 2025. The classic SEO role no longer cuts it.

The SEO role we knew: certainties, metrics, routine

Before 2025, the SEO profession was well-defined. A r/TechSEO contributor sums it up almost naively: « Crawling, indexing, rankings, links. You knew what to focus on. » And it was true.

Expectations were crystal clear. You audit server logs. You optimize title and meta tags. You build a link strategy. You clean up duplicate content. You track SERP positions. All of this was managed with established tools and concrete metrics: average positions, organic traffic, indexed pages, number of referring domains.

Job postings reflected this clarity. Role descriptions listed specific skills: Search Console expertise, ability to conduct technical audits, link-building expérience, CMS knowledge. KPIs were shared with leadership: improvement in rankings for strategic queries, growth in unpaid traffic.

This stability had an advantage: performance was measurable, predictable. When you took action on internal linking, you could observe its effect. When you deployed content, you knew how long before it indexed and ranked. The lag between effort and result was known.

But that comfort shattered. In 2025, Google shifted an increasing portion of its SERPs to AI-generated responses, and traditional mechanics began losing their grip. Historical rankings no longer protected against decline. Competitors producing less content but better structured for LLMs were capturing traffic flows we didn’t measure in rankings.

What GEO and AEO broke in the job description

With Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO), the goal is no longer simply to rank first. It’s to become the source of truth that language models cite when answering an intent.

That changes everything. The question is no longer « which keyword to target? » but « what entity does my site embody in the AI’s eyes? ». Search professionals feel it. On r/TechSEO, a user asks: « Are you trying to win a keyword or an intent or a category? » The answer is no longer obvious.

The role mutates toward a form of semantic data engineering. You must understand how conversational agents interpret content, what authority signals they use, how they construct answers. You’re no longer managing web pages — you’re sculpting a knowledge graph.

Concretely, this involves new tasks. Identify relevant entities in Google’s Knowledge Graph. Create entity identifiers (via Wikidata, for example) for each business concept. Implement complete JSON‑LD schemas — not just for rich snippets, but to describe relationships between subjects. Test how AI generates a response when questioned on your domain, and verify whether your brand appears as a source.

For the CMO I worked with, this was the biggest shock. His team knew how to write 2,000-word articles optimized for a primary keyword. But no one knew how to mark up a product entity. No writer understood the difference between a hierarchical link and an associative link in a cocoon. The transformation wasn’t technological first — it was cultural and organizational.

How this CMO transformed his SEO team in 6 months

The CMO made a radical decision. He renamed the SEO Manager role to « Data SEO Architect » and restructured the team around three pillars: entities, architecture, structured content.

First pillar: one person dedicated to feeding the knowledge graph, managing schemas, tracking AI citations. She was trained in advanced JSON‑LD and the Knowledge Graph API. Second pillar: a cocoon architecture specialist who redesigned all internal linking to connect each page to its parent entity. Third pillar: writers, repositioned as « information node designers, » trained to write for both humans and agents, using the « entity → intent → content » method.

Content production was cut from 12 to 5 articles per month. Each new article is now designed as a node in a cocoon, linked to a pillar page, with explicit outbound links to neighboring entities. The team shifted from spending 80% of its time on writing to 30%, with the rest split between entity management, AI response monitoring, and cocoon optimization.

« The hardest part was getting people to accept that writing was no longer the core of the job, » the CMO acknowledged.

Hiring followed suit. The new « Data SEO Architect » didn’t come from SEO — he came from data. He knew how to query knowledge bases, not analyze link anchors. A skill completely outside traditional job descriptions. Six months later, monthly AI citations grew from 3,100 to 4,800, and clicks from AI Overviews reached 2,800 monthly visits — a lever now under control.

Novelty vs Familiarity: the real dilemma for teams in 2026

At the heart of this transformation lies a tension that the DOSE framework places at the center of decisions: Novelty versus Familiarity. The model, taught by Guillaume Attias at BMO Academy, distinguishes actions that bring novelty (exploration) from those that consolidate what exists (exploitation).

When the CMO approached me, his team was 90% on familiarity: link building, content spinning, optimizing existing pages. It was comfortable. But the environment had changed. Growth wouldn’t come from what already existed. It would come from the ability to structure the site for language models — new territory.

So we applied DOSE. For the first 6 months, 70% of effort went into novelty: feeding the Knowledge Graph, deploying entity schemas, building cocoons, testing prompts to evaluate brand presence in AI responses. The remaining 30% ensured familiarity maintenance: tracking traditional rankings, technical optimization, classic competitive monitoring.

This dial wasn’t easy to accept. The team saw content production shrink, position reporting simplify. But results spoke: +133% AI clicks in 6 months, with no additional ad spend. Proof that novelty, when managed well, isn’t a risk — it’s the only growth lever in an ecosystem where AI is rewriting the rules.

Today, I systematically ask my clients: how much energy are you investing in novelty? The answer tells a lot about their ability to survive the next 12 months.

Which metrics to track beyond citations?

One of the anxieties teams feel when shifting to GEO is measurement. Since we’re no longer just talking about rankings, what do we track daily? Here are the metrics I helped this CMO put in place — and the results observed over 6 months.

These numbers don’t come from a magic platform. Search Console already provides « AI Overviews » clicks when you filter by result type. Citations can be tracked via tools like Semrush or Sistrix, but I especially recommend daily manual tests: query Google or Bard on your key topics, and verify whether your brand appears, in what context, for which intent.

Another key metric: entity recall rate. For a corpus of 50 business queries, how many AI responses mention your company’s entity? This ratio gradually replaces tracking traditional rankings. For this client, it grew from 14% to 38% in six months. The CEO even integrated it into his monthly dashboard.

Finally, brand sentiment in AI responses. Does the AI speak positively about your services, or cite you only as one player among many? It’s not easy to automate, but it’s the new reputation criterion. And it’s what will make the difference between a cited site and an ignored one.

And you—has your SEO role already mutated?

The mutation is underway. The question on r/TechSEO isn’t theoretical: « Has your job been repurposed? » It’s being lived by thousands of professionals.

The SEO role doesn’t disappear. It’s moving up a level. From ranking technician, it becomes architect of machine understanding. That demands new skills, a new reading frame, and above all a new time allocation.

When I audited this marketplace, the diagnosis fit one sentence: the problem wasn’t content, it was data architecture. And that’s probably the same thing in your organization.

Look at your past few weeks. What percentage of your time did you spend producing content versus structuring your entities? How many of your pages are connected to a cocoon? When did you last open Search Console to specifically analyze your clicks from AI Overviews?

The answers to these questions define who will lead search tomorrow. And who will remain a spectator.

Stéphane Jambu

SEO & AI Engineer

I build growth systems / AI / Neuroscience | 650+ clients · 80 LinkedIn testimonials · 30 years of expertise · 15 years of systems running without me.

Follow on LinkedIn