What Is AI Search Optimisation?

Definition (working, documented)
AI Search Optimisation is the practice of improving the likelihood that AI-powered search systems (e.g., Google AI Overviews/AI Mode, ChatGPT Atlas, Perplexity) select, cite, and recommend your content when synthesizing answers. It sits at the intersection of traditional SEO and emerging practices variously called Answer Engine Optimisation (AEO) and Generative Engine Optimisation (GEO). Unlike the classic goal of winning a ranked position on a ten-blue-links page, AI Search Optimisation focuses on being a credible, citable source inside generative summaries—and ensuring those citations translate into measurable visits and outcomes.

1) Why the term exists (and what it includes)

AI changes the surface. Google’s AI Overviews present AI-generated snapshots with links to learn more on some queries (especially complex ones). There is no AIO markup; Google’s public stance is to continue building helpful, reliable, people-first content and maintain technical eligibility, with AIO/AI Mode traffic counted under Search Console → Performance → Web.

Answer engines and task browsers.

Perplexity cites sources by default and its Deep Research runs dozens of searches and reads hundreds of pages to assemble a report.
ChatGPT Atlas is a browser with ChatGPT at its core, performing research and enabling agentic tasks while linking out to the open web.

Because these systems synthesize rather than merely rank, optimisation targets citation likelihood, justifiability (clear claims that can be quoted), and discoverability across related sub-queries.

2) AI Search Optimisation vs. SEO, AEO, and GEO

SEO (classic). Ensure crawlability, indexation, relevance, authority, and usability to rank a page in SERPs.
AEO (Answer Engine Optimisation). Emphasizes answer readiness: clearly structured, question-shaped content for systems that return direct answers.
GEO (Generative Engine Optimisation). An emerging term in research and industry describing the optimisation of content visibility inside generative engines, with early academic work proposing frameworks to measure and improve that visibility.

Practically, AI Search Optimisation draws from all three: SEO’s technical hygiene, AEO’s answer-first structure, and GEO’s focus on generative citations.

3) What the platforms say (and why it matters)

Google

What AIO is: Snapshot with links; shows when it can add benefit.
How to participate: There is no special markup; keep creating helpful, reliable, people-first content and use supported structured data where appropriate.
How it’s measured: AIO/AI Mode clicks/impressions are included in Search Console “Web” totals.

Perplexity

Deep Research conducts dozens of searches and reads hundreds of sources, then cites them. Your pages must be quotable and evidence-rich.

OpenAI (ChatGPT Atlas)

Atlas integrates ChatGPT into the browser to summarize and compare content and to perform tasks with agent mode—surfacing and following links to the open web. Structure and clarity improve the odds Atlas will pull and attribute your material.

4) The AI Search Optimisation methodology (8 components)

1) Intent mapping for complex questions

Identify decision-stage, multi-constraint problems where AIO tends to appear (comparisons, planning, troubleshooting). Build one page per distinct question with an answer-first structure.

2) Answer-first pages with evidence

Lead with a short verdict and its scope. Then provide if/then thresholds, steps, trade-offs, and link primary sources (standards, vendor manuals, laws). This makes your page safe to cite.

3) Machine-legible structure (no gimmicks)

Use JSON-LD Organization and page-relevant schema that matches visible text (e.g., FAQ when real Q&As exist; Product on PDPs). Keep sitemaps, canonicals, and internal links clean.

4) Page experience (Core Web Vitals)

Google recommends achieving good CWV and notes INP replaced FID as the responsiveness metric. Improve LCP/INP/CLS on the pages you want cited.

5) Authorship & maintenance (E-E-A-T spirit)

Show author identity, relevant experience, an updated-on note, and a change log. These quality cues support “helpful and reliable” content.

6) Conversation mapping & internal linking

Branch queries by constraints (budget, compliance, team size) and interlink hub ↔ spokes so engines exploring related sub-topics discover your full corpus—useful when Perplexity or AIO fans-out across variants.

7) Measurement loop

Presence & citations: Log whether AIO appears and whether your domain/URLs are linked, by market/device.
Impact: Join to Search Console (Web) clicks/CTR for cited pages; annotate content releases.
Perplexity checks: Run Deep Research on your topics to see if you’re cited and which statements are quoted.

8) Governance for generative content

If you use AI to draft, follow Google’s guidance on using gen-AI content responsibly (avoid scaled content abuse and always add human value).

5) Sample page template (reuse for AI Search Optimisation)

H1: “How to choose ___ for a ___ team when ___ applies”
Executive answer: 3–5 sentences with the recommendation and assumptions.
Decision thresholds: “If >X, do …; If ≤X, prefer …”
Comparison table: Criteria columns that reflect buyer trade-offs.
Limits & exceptions: Who shouldn’t use this approach.
References: Primary docs only.
Author & update stamp.

This template mirrors how AIO presents a snapshot + links, and gives Perplexity/Atlas quotable, checkable claims to cite.

6) What AI Search Optimisation is not

Not a set of secret tags to “turn on” AIO. Google explicitly says no special markup exists.
Not a replacement for SEO. Crawlability, indexation, and page experience are still prerequisites.
Not pure content volume. Google warns against scaled content abuse without user value.

7) A 30-day starter plan

Week 1 — Discovery

Export GSC Web queries; identify complex questions with impressions but low CTR. Annotate baseline metrics.

Week 2 — Production

Publish 5 answer-first pages with thresholds, tables, and primary citations; add Organization and any valid page-type schema.
Improve INP on these pages; aim ≤200ms at p75 where feasible.

Week 3 — Measurement

Log AIO presence and linked sources across markets/devices.
Run Perplexity Deep Research to check citation coverage.

Week 4 — Iteration

Close factual gaps and add missing thresholds; expand FAQs where genuinely helpful (avoid duplication).

Bottom line

AI Search Optimisation means publishing answer-first, evidence-rich, machine-legible content and maintaining it so that AIO, Atlas, and Perplexity can quote and link you confidently. There’s no hack—only disciplined execution and honest measurement mapped to what the platforms themselves document.

References

Google. AI features and your website.
Google. Find information in faster & easier ways with AI Overviews.
Google. Search Console: What are impressions, position, and clicks?
Google. Is AI Mode included in Search Console totals? (industry coverage & help updates).
Perplexity. Introducing Deep Research.
OpenAI. Introducing ChatGPT Atlas.
Google. Using generative AI content on your website (policy).
web.dev. INP is officially a Core Web Vital.