What is generative engine optimisation? The honest definition.

Where the term came from

The acronym GEO was used in a November 2023 academic paper titled "GEO: Generative Engine Optimization" (Aggarwal et al., 2023) - read on arXiv, by researchers from Princeton and Georgia Tech. The paper proposed nine optimisation strategies and tested them against citation rates in generative search interfaces. That paper is the citable origin. The research code and benchmark dataset are on GitHub if you want to reproduce the lift findings. The industry adopted the term through 2024 alongside parallel acronyms (AEO, LLMO) that describe roughly the same discipline.

Why GEO matters in 2026

The answer surfaces are already in front of buyers. Google now shows an AI Overview on a large share of informational queries - including on the query "generative engine optimisation" itself. LLM referral traffic to websites grew 72.7% year on year (Duda / Search Engine Journal, April 2026). Traditional Google search still drives more decisions overall, but the consideration step increasingly happens inside a generated answer before anyone clicks a blue link.

The practical consequence: a brand can hold position 1 in classic Google and still be absent from the answer a buyer actually reads. That absence is invisible in normal analytics. Nothing dropped, no alert fired - the buyer simply never saw you. GEO is the discipline of closing that gap.

What GEO actually involves

• Schema and structured data. Article, FAQPage, HowTo, Product, Person, Organization. The structural layer AI engines extract from.
• Content depth. AI engines compose answers from substantive sources. Thin pages get skipped.
• Named author signals. Person schema, credentials, links to professional profiles. Engines like Perplexity weight named authorship heavily.
• Bot access management. GPTBot, ClaudeBot, PerplexityBot, Google-Extended - each has its own controls. Accidentally blocking one removes you from that engine's citation pool.
• Community presence. Reddit and topic-specific forums feed certain engines (especially ChatGPT) heavily for context, even if not always for direct citation.
• Citation tracking. Tools that sample AI engine answers on a schedule and report what shows up. Profound, AIClicks, Otterly, plus the free tools on this site.

What GEO is not

• Not a replacement for SEO. Traditional Google search still drives more decisions than AI search in 2026 for most UK B2B verticals.
• Not a guaranteed channel. No AI engine has published a confirmed ranking formula. The signals correlate; they are not deterministic.
• Not a separate department. Most teams should treat GEO as 20% extra work on top of an SEO practice that has matured.
• Not something you can fully outsource. The named author, the community presence, the genuine expertise - these need a real person at the brand, not just an agency.

One thing to settle before any of this. If the site already carries a lot of content published in bulk, start there instead. Google's scaled content abuse policy targets volume without value regardless of who or what wrote it, and no amount of GEO work outruns that. It is an AI content cleanup job first, and a GEO job second.

How GEO differs from traditional SEO

Same underlying signals, different scoring weights. SEO optimises for ranking position in a results list. GEO optimises for citation inside a generated answer that may or may not show a list at all. The differences worth knowing:

• Surface vs page. SEO targets the SERP. GEO targets the AI answer surface that often sits above or replaces the SERP entirely.
• Click vs mention. SEO success is a click. GEO success can be a brand mention in the AI answer without a click - influenced pipeline, not direct traffic.
• Rank vs share-of-voice. SEO measures rank position. GEO measures citation frequency, citation share-of-voice against named competitors, and query coverage.
• One engine vs many. SEO is mostly Google + Bing. GEO covers ChatGPT, Perplexity, Claude, Gemini, AI Overviews, AI Mode, Copilot at minimum.
• Crawl + rank vs retrieve + generate. SEO assumes the engine fetches, indexes, ranks and serves your URL. GEO works through retrieval-augmented generation - your content gets pulled into the model's context window and summarised, not served directly.

The work overlaps but the decisions diverge. A page that ranks position 1 in Google can still fail to be cited in AI Overviews if it lacks the extractability signals (answer-first structure, named author, citable statements).

What GEO looks like in the UK right now: my own data

In May 2026 I audited 231 UK professional-services websites across three sectors - 73 law firms, 56 accountancy firms and 102 estate agents - measuring structured data, AI crawler access and captured citations. The headline numbers:

• Roughly a third of firms are invisible to AI engines. 29% of law firms and 32% of accountancy firms carried no structured data that identified what kind of business they were.
• Silent crawler blocks are common and accidental. 27% of accountancy firms - including PwC and BDO - returned errors to GPTBot or ClaudeBot at the hosting layer while serving browsers normally. Nobody at those firms chose that.
• Zero of 83 readable estate agent homepages used FAQ schema. The single easiest extraction format for an AI engine, and an entire sector ignores it.
• Citation is rarer than being named. ChatGPT named around half the estate agents in the sample, but only 5 of 100 appeared as a cited source. The rest were map pins, not authorities.

One finding is worth singling out because it shows GEO working. The 5 estate agents ChatGPT actually cited as sources all had one thing in common: an explicit awards or authority page - a Best Estate Agency Guide win, a regional award announcement - that the engine could quote directly. Not better homepages. A quotable, third-party-verifiable claim. That is the mechanism in miniature.

How do you measure whether GEO is working?

Not with rank positions. The working metrics are citation frequency (how often engines cite you across a fixed query set), citation share-of-voice against named competitors, and query coverage (what proportion of your buyer questions mention you at all). Sample the same queries across engines on a schedule and track the trend. I keep a full measurement walkthrough and a list of tracking tools with UK pricing if you want the detail.

The five-principle framework for AI visibility

The discipline shakes down to five working principles. Apply them in this order:

1. Bot access - verify GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended can all crawl. Silent blocks here remove you from the citation pool entirely.
2. Extractable structure - 30-second answer blocks at the top of each section, short declarative sentences, clean H2/H3 hierarchy. AI engines lift quotable passages, not paragraphs of brand voice.
3. Named-author E-E-A-T - byline + Person schema + credentials. Anonymous brand content loses to named-expert content for the same query.
4. Topical depth - 1,500-2,500 words on priority queries, covering the head plus 3-5 sub-questions. Pages bundling sub-topics win multiple citation rounds in query fan-out.
5. Off-site authority - Wikipedia entity, Wikidata, Reddit presence, citations from credible publications. Slowest layer; compounds over quarters.

That order matters. Bot access first (one-day fix, biggest impact if blocked). Then structure, then named author, then depth, then authority. Spending six months on Wikipedia entity work while GPTBot is blocked is the textbook failure mode.

The honest read on the discipline

GEO became a marketing category faster than it became a discipline. By 2026 the genuine practitioners and the repackagers are starting to separate, but the noise is still loud. A simple test: if a "GEO service" does not include schema work, named author setup and bot access verification, it is not GEO. It is SEO with a price uplift. If you want the end-to-end walkthrough rather than the definition, I wrote a deeper GEO field guide covering the whole process step by step.