Classic SEO vs. SEO for AI (comparison table)
The differences aren't cosmetic. The unit you optimize, the metric you measure and the cadence you iterate all change. But — and this matters — 60-70% of the technical work is shared. Knowing what stays and what changes is what stops you reinventing the wheel and, equally, stops you applying obsolete techniques thinking they're still enough.
| Dimension | Classic SEO | SEO for AI (GEO) |
|---|---|---|
| Unit | Rankable page | Citable entity |
| Outcome | Top 10 with click | Quoted inside the answer |
| Metric | Position + CTR + clicks | Mention rate + relative position + sentiment |
| Cadence | Monthly | Weekly |
| Algorithm | Announced changes ~quarterly | Models change every 4-8 weeks |
| Tools | Search Console, Ahrefs | GEO-specific monitoring + residual Search Console |
What stays the same
Good news for any team with SEO already running: most of what you already do well still pays off.
- Domain authority (quality backlinks, age, trust) is still a strong signal.
- Technical quality (Core Web Vitals, indexability, semantic HTML) — models also drop slow or broken pages.
- Content depth and usefulness — shallow content was never good; now it's worse.
- Internal architecture — a clean sitemap and coherent linking helps crawling, whether it's Google's bot, OpenAI's or Anthropic's.
What changes and why
What changes concentrates in three areas: content format, advanced schema and monitoring. These aren't refinements — they're new pieces with their own rigor.
- Self-contained question-answer format. Every block must be citable without extra context. It's a change in how the copy is structured, not in what is said.
- FAQPage, HowTo and Article schema with extended properties.
Organizationalone doesn't cut it; LLMs especially leverage FAQ and HowTo for literal quotes. - Monitoring by prompts, not by keywords. The unit of measurement changes: you no longer measure "position for [keyword]"; you measure "presence for [natural prompt]".
The new technical stack (what tools, what not)
| Layer | Classic SEO tools | GEO tools |
|---|---|---|
| Technical audit | Screaming Frog, Sitebulb | Same + advanced schema review |
| Prompt research | Ahrefs, Semrush (keywords) | Otterly, Profound, Athena (prompts) + your own battery |
| Ranking monitoring | Search Console, Sistrix | Dedicated GEO tools + your own monitoring |
| Competitive analysis | Ahrefs Content Gap | Share-of-voice comparison by prompt |
The four generative engines that matter
ChatGPT (browsing and memory)
The most used, the one that combines the most signals (training + real-time browsing + user memory). Optimizing for ChatGPT forces you to work all three levers (entity, content, authority). If you have to pick one, pick this one.
Perplexity (explicit citation)
The most measurable because it cites explicitly with links. The key metric is how often you show up as a cited source. External authority and content freshness weigh more here than in ChatGPT.
Google AI Overviews
The one that benefits most from classic SEO done well (a lot of Google Search work carries over). But heads up: appearing in AI Overviews can cut classic SEO clicks — you have to measure the net.
Claude (when connected to the web)
Anthropic's model — more conservative in its citations, but with growing traction in technical B2B. Same levers, no structural quirks.
How SEO for AI is done in practice
- Month 1: baseline with monitoring + technical audit that adds advanced schema and cleans entity inconsistencies.
- Month 2: reformat the top-30 most relevant pages into citable format.
- Month 3: ship 6-10 new pieces covering prompts where you don't appear.
- Month 4: first round of external authority (PR, citations, partners).
- Month 5+: weekly iteration on the battery and adjustments based on results.