Rethinking the SEO Function for the GEO Era
Executive Summary
How a larger mid-market company converts SEO into an AI search strategy team
Search is moving from “find and click” to “get an answer and decide.” Google’s AI Overviews and AI Mode aim to give people a synthesized response with links for deeper reading. Independent research also reports CTR drops on queries that trigger AI Overviews. And search behavior is splitting. Classic search still matters, while genAI tools increasingly shape how people research, compare, find new brands and ultimately purchase products.
This raises one leadership question:
So what does our SEO team own now?
In the GEO era, SEO becomes an AI search strategy team. It keeps your public answers consistent, increases how often your best pages are cited, and makes the traffic you do win convert.
The mandate has changed
Classic SEO was straightforward: rankings → sessions.
Now you’re managing three outcomes:
Representation
How AI answers describe your category, what you do, and what makes you different.Citations and source use
Whether your pages, definitions, and proof show up as source material.Conversion per click
When clicks shrink, each visit has to do more work.
Google’s own guidance for AI search leans toward longer questions, follow-ups, and content that is “helpful and satisfying.”
Current reality: buyers increasingly form opinions before they land on your website. CTR drops on AI Overview queries make that more obvious.
What changes inside the SEO function
Own answer readiness with clarity, extractable structure, consistent entities and definitions.
Own brand accuracy in AI-mediated discovery. This is what AI systems say about you, and what you do when they get it wrong.
Run a steady test loop across surfaces (Google, Bing, assistants), not just “publish and hope.”
What a GEO-ready SEO team looks like on larger marketing teams
Less “keyword factory,” more strategy + ops with measurement, information architecture, writing clarity, and testing.
1) Measurement
Segment queries by intent (brand vs non-brand; problem vs solution vs evaluation)
Connect search activity to pipeline with proxy models and assisted conversion work
Track presence across AI features and assistants
Search Console now has a branded queries filter to separate branded and non-branded performance without regex lists.
2) Testing
Publish against hypotheses (structure, proof placement, schema)
Run “answer QA” checks (accuracy, citation presence, positioning)
Iterate on a small set of priority pages, fast
GEO research frames generative engines as multi-source synthesis and proposes new visibility metrics.
3) Information design
Build entity-first information architecture
Write answer-first pages that still include depth
Package proof (benchmarks, case studies, standards, FAQs) so it’s easy to cite and hard to misread
Five roles that fit into a mid-market team
You don’t need a 20-person SEO department. You need clear charters and a few updated roles.
1) AI Search Strategy Lead (your evolved Head of SEO)
Own search strategy across classic SEO, AEO, and GEO. Keep content, PMM, PR, and analytics moving in the same direction.
Set “reference page” standards for priority topics
Run a quarterly search narrative review (category framing, comparisons, accuracy risks)
Own the roadmap and test backlog
KPIs:
AI feature presence on priority query sets (weekly)
Branded vs non-branded trends (Search Console branded filter)
Pipeline per organic visit
2) Search Measurement & Testing Analyst
Build the measurement system and run tests that connect search visibility to business outcomes.
Maintain query sets by intent and funnel stage
Detect assistant referrals and clean up attribution
Design and read tests (structure, schema, internal links, proof placement)
Assistants often provide inline citations and links, which turns referral tracking into basic hygiene. GA4 supports custom channel groups, so you can define “AI assistants” as its own channel.
3) Content / Entity Architect
Design your site’s information around entities, questions, and proof so pages are easy to cite and hard to misinterpret.
Build hubs (product, persona, use case, proof)
Standardize definitions and answer-first patterns
Enforce internal linking rules and content governance
4) Technical SEO & Structured Data Lead
Keep the foundation clean so AI features can access and parse your content.
Crawlability, canonicals, performance
Structured data implementation and validation
Template-level changes that improve extractability
Reference: Google’s AI features docs for site owners.
5) Search Reputation & Earned Visibility (often PR + SEO together) as Comms Lead
Build third-party corroboration and point earned links at canonical reference pages.
Thought-leadership distribution (owned + earned)
Analyst relations support (definitions, data packs)
Earned coverage that links back to the right pages
This matters because generative answers often cite multiple sources; source diversity affects trust.
Org models that work
Pick a structure based on product complexity and compliance risk, not your old org chart.
Model A: Central Search Team
Best for one core product, lean marketing team, and low compliance risk
One team owns standards, measurement, and execution
Content executes inside that system
Tradeoff is while fast and consistent, it can bottleneck when product lines grow.
Model B: Hub-and-spoke (best for multi-product)
Central search team owns standards, tooling, governance
“Spokes” sit in PMM, Content, PR
Central team:
Entity map and IA rules
Measurement and query sets
Test backlog and QA
Spokes:
Product-line hubs, proof assets, industry pages
Earned distribution tied to canonical pages
This fits how buyers research now across products, proof, trust, and comparisons, across multiple surfaces.
Model C: Product-led search (SEO inside PMM)
Best for: product-led growth, strong PMM, heavy docs footprint.
PMM owns category narrative and reference assets
SEO owns measurement and technical standards
Docs/help center become major conversion surfaces
Tradeoff is strong evaluation coverage that needs tight governance to prevent drift.
How to measure SEO/GEO now
If you measure the old way, you’ll manage the old way.
CTR volatility on AI Overview queries pushes teams to care more about conversion per visit and source use, not just traffic.
A practical scorecard
1) Representation
Brand accuracy rate in sampled AI answers (weekly)
Category framing presence
Misrepresentation backlog (found → fixed)
2) Citations and source use
AI feature presence on priority queries
Citation share (how often your canonical pages are cited in sampled answers)
Proof page discovery (benchmarks, case studies)
3) Demand capture
Branded vs non-branded trends (Search Console branded filter)
Organic conversion rate and pipeline per organic visit
Conversion on evaluation pages (pricing, security, implementation, comparisons)
4) Test speed
Controlled tests published per month
Time-to-publish improvements on priority templates
What you learned, written down and reused
How to reward the team
Don’t score SEO only for engaged sessions. Reward the following:
fewer, stronger reference pages
higher conversion per visit
fewer brand-accuracy issues
more citation presence on priority topics
What SEO careers turn into
In many orgs, SEO was treated as a specialist lane. In GEO, it’s a strategy lane.
SEO Lead → AI Search Strategy Lead
Technical SEO → structured data + information systems
Content SEO → entity architect + narrative work
SEO analyst → measurement and testing lead
What this changes for CMOs
1) Positioning gets enforced
Clear entity maps and reference pages enable consistent product definitions and claims across the site, PR, and sales materials. This reduces your narrative drift in AI answers.
2) Demand gen gets cleaner
If AI answers compress early discovery, paid and lifecycle programs pick up more evaluation-stage intent. A GEO-ready SEO team improves those visits by strengthening proof pages (case studies, security, implementation, comparisons).
3) PR becomes part of discovery
If answers cite multiple sources, earned coverage becomes a visibility input, not just a reputation tool.
4) Legal/compliance becomes a standard partner
AI answers compress nuance. That increases risk around regulated claims, pricing, performance, and security posture. SEO needs a fast path to correct errors and a clear rulebook for what gets published.
5) Measurement becomes shared language
Use Search Console’s branded filter to separate demand from discovery.
Use GA4 custom channel groups to isolate assistant referrals where possible.
Then tie those signals to pipeline.
What you can do now
Run a 30-day SEO charter reset:
Rewrite the SEO team charter to include GEO and AEO responsibilities
Update 3–5 role descriptions (AI Search Strategy Lead, Measurement/Testing, Entity Architect, Tech SEO)
Pick one product line and pilot a hub-and-spoke model
Launch a GEO scorecard: representation, citations, demand capture, test speed
Present results to the exec team with one clear recommendation to scale
This is a clean path to turn SEO into an AI search strategy team throughout 2026.
Last updated 01-04-2026
