AI SEO vs. Regular SEO: What Actually Changed (and What Didn’t)
Regular SEO ranks pages. AI SEO gets your words into the answer provided by ChatGPT, Gemini, Claude, etc. You need both, working as one system.
Neil Patel, of NP Digital, recently asked: Do we need a brand-new SEO strategy for AI? It’s the right question to be asking.
And the answer is no, but we do need a smarter mix.
Google still rewards people-first content; AI just changes where that content shows up and how it gets credited. Think of SEO as your ground game and AI SEO (GEO/LLMO) as your passing game. Same field. New plays. See Google’s guidance on helpful content and how AI Overviews link to sources.
TL;DR
Why this matters now: You don’t have to rip up your playbook. You just need to make your best answers easy for both search engines and answer engines to find, trust, and quote. The steps are small but they have a big impact.
Keep the fundamentals and add the new AI-era tactics.
Google’s ranking systems still prioritize helpful, reliable, people-first content.
AI SEO (with GEO and LLMO) helps your content get retrieved, trusted, and cited in AI Overviews and chatbots.
Use micro-answers, clean schema, clear authorship, and monitor AI citations to win both classic search and AI answers. See Article and FAQ structured data.
SEO and GEO Definitions You Need to Know
To put it plainly: SEO gets you discovered. GEO/LLMO gets you cited.
Before we compare, let’s normalize terms. “AI SEO” isn’t a replacement for SEO; it’s a layer on top. GEO and LLMO just give you language and tactics for the AI parts—how answers pick your words, attribute them, and send traffic your way.
Regular SEO: Make pages rank. Match intent, deliver value, fix tech basics, earn links. (See Google’s people-first guidance.)
AI SEO: Make answers choose you. Structure content so AI systems can find, trust, and quote your lines. Read how AI Overviews surface links.
GEO: Generative Engine Optimization: Strategy for being retrievable, quotable, trusted by AI engines.
LLMO: Large Language Model Optimization: Tactics that make your content easier for LLMs to extract and attribute.
What changed (and what didn’t)
Inclusion beats position inside AI. Be the source.
Here’s the honest shift: Classic SEO is about position on a results page. AI SEO is about inclusion inside an answer. The fundamentals still matter—intent, information gain, clean markup—but your content also needs to be packaged into answer-ready units that tools can lift cleanly.
Inclusion vs. position
In SERPs, you fight for a ranking. In AI answers, you fight for citation—one of the few links in an AI Overview or chatbot response. Google notes Overviews link out in multiple ways; your job is to be one of those links.
Answer units > walls of text
LLMs prefer short, self-contained chunks. Add 2–3 sentence micro-answers under each section. Clear nouns. Defined acronyms. Fewer pronouns.
Schema still matters
Yes, Google reduced FAQ/HowTo rich result visibility for most sites. But structured data still helps Google understand your page and can improve how articles are presented. Keep Article and FAQ as defaults; add HowTo/Product when relevant. Start with Article and FAQ. Read the visibility update here.
Provenance = trust
Show author, headshot, bio, publish/updated dates, and citations. These are visible signals of quality (see Google’s rater guidelines overview PDF) and help both humans and models evaluate credibility.
Measure AI inclusion
Treat AI Overviews and chatbots as a channel. Test target queries monthly and log when your brand is cited. (Google also documents how AI Overviews are tracked and counted in updates to Search docs.)
AI SEO vs. Regular SEO — side-by-side
Why the table? You’re balancing two surfaces that reward similar inputs but show results differently. Use this as your quick gut-check when planning a post or updating an existing one.
| Dimension | AI SEO (GEO/LLMO) | Regular SEO |
|---|---|---|
| Primary goal | Inclusion inside AI answers (citations and links back). | Rankings and visibility in traditional SERPs. |
| Surface | Google AI Overviews, ChatGPT, Perplexity, Gemini. | Organic listings and SERP features (Top Stories, People Also Ask, etc.). |
| Signals | Clarity, answer-ready chunks, provenance (author/date/citations), and structured data. | Relevance, depth, internal links, backlinks, and technical health. |
| Content shape | Short micro-answers, summary tables, step lists, original charts/images. | Comprehensive pages with strong on-page SEO (titles, headings, meta, media). |
| KPIs | AI citations, share of answers (how often you’re linked inside AI results), branded quotes. | Impressions, average position, CTR, sessions, conversions. |
| Architecture | Pillars + clusters designed for clean retrieval; see GEO guide and LLMO. | Pillars + clusters for topical authority; see HubSpot on pillar pages. |
| Tools | RAG/readability checks, schema validators, AI inclusion logs/tests. | Google Search Console, analytics, crawlers, link analysis. |
Tip: Link related posts for context & crawl depth — GEO vs. SEO, Why GEO is the Next SEO, Guides hub, Blog index.
For architecture, lean on topic clusters. A pillar page is a deep hub that links to supporting conten, great for rankings and for AI retrieval.
Where each wins
Discovery favors SEO. Explanation favors AI. Big journeys need both.
It’s important to use the right tool for the job. Some searches demand listings and maps. Some need a tight explanation. Many journeys need both: a high-level answer in an Overview, then a deeper dive on your site.
Regular SEO wins for product, local, and comparison intent (maps, listings, catalogs).
AI SEO wins for explainers, definitions, “how-to” steps, and nuanced “why” questions.
Both matter for complex buying journeys where users skim an AI answer, then click your pillar. Link these posts to help them move: GEO vs. SEO and Why GEO Is the Next SEO.
Tactics to add now (stacked on top of SEO)
Let’s make this practical. Keep your SEO engine running. Layer these AI-era tactics on top. Each one is small. Together, they make you easy to quote and credit.
1. Micro-answers & snippet engineering
Write answers, not just articles. You want to give models a clean grab handle.
Under every H2, add a 2–3 sentence answer that stands alone. Use small tables and numbered steps. Make lines easy to quote verbatim. For more, see our post on Snippet Engineering, RAG Testing, and Provenance Tagging.
2. Schema as standard
Schema is how you speak machine (e.g., LLMs). Speak in structured data, not just prose.
Add Article across the site. Mark up Q&A with FAQ (visibility is limited, but clarity still helps). If you sell products, pair Product schema with Merchant Center feeds. Validate with Google’s Rich Results Test inside the structured data docs.
3. Provenance tagging (show your receipts)
Trust should be visible.
Add author name, headshot, bio, role; show published and last updated dates; cite sources and include original data/visuals. Consider a short “How we did this” note on data-heavy posts. This aligns with Google’s page experience + helpful content ecosystem.
4. Topic clusters (same play, new stakes)
Build rails for discovery and retrieval. Pillars and clusters are retrieval rails.
Create a pillar with 4–6 supporting posts; interlink both ways. Anchor the cluster from navigational hubs: Guides and Blog for crawl depth. Cross-link high-intent readers to:
5) Monitor AI inclusion (treat it like a channel)
If you’re not measuring citations, you’re guessing. And that’s a potentially major waste of your time and money.
Create a monthly query set (25 questions). Test in Google (AI Overviews), ChatGPT, Perplexity, Gemini. Log which lines get cited and which URLs are linked. Track “answer share” (how often your brand appears among linked sources). For process, see Monitoring Your Brand in LLM Results.
The AI + SEO metric stack
Measure both layers in one view. Keep your classic SEO dashboard. Add a small panel for AI outcomes. You’ll see patterns fast: the pages that rank often fuel the answers that cite you.
Classic SEO metrics
Rankings, impressions, CTR, sessions, conversions (GSC + analytics)
AI layer
AI citations/mentions across engines
Answer share for a query set
Assisted conversions from AI-sourced visits (pages often linked in AI answers)
Workflow
Pick 25 high-intent questions.
Run AI tests monthly; record citations and linked URLs.
Add/adjust micro-answers, schema, titles, visuals.
Re-test and track movement.
30-day action plan
Speed matters, but so does sequence. This plan ships weekly wins that stack. You’ll improve trust signals, answer quality, structure, and measurement in one month.
Week 1 — Provenance & FAQ pass
Add author bios, headshots, publish/updated dates to your top 25 posts.
Add a short FAQ to each relevant post and mark it up with FAQ schema.
Week 2 — Micro-answers + Article schema
Insert 2–3 sentence micro-answers under every H2.
Ensure Article schema on all posts; validate with Google’s Rich Results Test.
Week 3 — Build one new cluster
Choose a core topic; create a pillar + 4–6 support posts.
Week 4 — AI inclusion tests
Run your standardized query set in AI engines; log citations and answer share.
Ship edits to weak sections; add original charts or short videos where useful.
FAQs
Do I need a whole new strategy for AI?
No. Keep the SEO fundamentals. Layer in GEO/LLMO so your best lines get included in AI answers. Start here: GEO and LLMO.
Will SEO die because of AI Overviews?
No. It’s a channel mix shift. Overviews summarize and link out; your play is to be one of the cited links and hold strong rankings. (See Google’s AI features.)
Should I still use FAQ schema after Google limited its visibility?
Yes, for meaning and machine understanding. You may not always get a FAQ rich result, but the markup still clarifies Q&A content for Google and others. (Change note, FAQ docs)
How do I measure AI SEO?
Track AI citations and answer share alongside rankings, CTR, sessions, and conversions. See: Monitoring Your Brand in LLM Results.
Where do topic clusters fit in?
They power both ranking and retrieval. Build a pillar page that links to high-quality subtopics. (See HubSpot’s pillar page guidance.)
Wrap-up: Make SEO and AI Work Together
You don’t need a new playbook. You just need to add a new layer.
Regular SEO stays your ground game: intent, value, links, and clean tech. AI SEO, powered by GEO and LLMO, is the layer that gets your words quoted inside AI answers. Run them together.
Here’s the move you need to make:
Keep the fundamentals tight.
Add micro-answers and schema so machines can grab and credit you.
Show provenance (author, dates, sources).
Build topic clusters and interlink for depth.
Measure AI inclusion like a real channel.
If you want a simple starting point, follow the 30-day plan in this post. It stacks quick, low-risk wins into a compounding edge.
And if you want hands-on help, our team can blueprint and ship this with you—start with the GEO guide, the LLMO explainer, or schedule a time to talk with our team.
Author: Noah Swanson
Noah Swanson is the founder and Chief Content Officer of Type and Tale.