Does Generative Engine Optimization Work? The Evidence So Far

For generative engine optimization to work, a change made to a page or a brand's presence would have to raise one of four measures that the business did not already get from SEO: citations in AI answers, brand mentions in AI answers, visits from AI engines, or revenue from those visits. Generative engine optimization is the practice of making a brand's pages, data and mentions the sources that large language model answers cite and recommend. "Works" means a measurable lift against a control, not a before-and-after on one page. Before anyone sells you the practice, it's worth knowing that Google itself says there are "no additional requirements to appear in AI Overviews or AI Mode" and "no special schema.org structured data" (Google Search Central, AI features and your website). And it's worth knowing why the claim level matters: Ahrefs quoted Wil Reynolds on a page whose ChatGPT citations rose 1,900% month on month "with little business impact" (July 2026). A number went up. Nothing else did.

The citation level is the one most studies measure, so the first honest question is whether a citation is worth anything on its own.

No, being cited by an AI engine is not the same as getting traffic from it, because most AI answers are read without a click and the citation is the only trace a brand gets. Ahrefs estimates AI Overviews cut clicks by about 58%, with the click-through rate for position one falling from 0.037 to 0.016 (Ahrefs, AI search trends, July 2026). Google's Search Console AI performance report shows impressions only, and AI Overview clicks arrive as google / organic in GA4, so they can't be separated. ChatGPT and Perplexity referrals do appear, as chatgpt.com and perplexity.ai. So a citation is a leading indicator and nothing more. It says the engine trusted the page enough to name it; it does not say anyone came. With the claim defined, the paper that started the term is the first piece of evidence.

The original GEO paper, "GEO: Generative Engine Optimization" by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande (Princeton, Georgia Tech, the Allen Institute for AI and IIT Delhi, first posted November 2023, published at KDD 2024), tested nine ways of rewriting a page and found that adding quotations, statistics and source citations raised the page's share of a generated answer by roughly 30 to 40% on its own metric. The benchmark was GEO-bench, 10,000 queries. The engine was a two-step setup that fetched the top five Google results and generated the answer with gpt-3.5-turbo. The two metrics were "Position-Adjusted Word Count" and "Subjective Impression".

The reported bests were 41% on word count and 28% on impression. The top three methods ran 30 to 40% on word count and 15 to 30% on impression. Keyword Stuffing showed "little to no improvement" and scored below the untouched baseline on word count. A 200-query check on Perplexity found Quotation Addition about 22% better on word count and Keyword Stuffing about 10% worse than baseline. The result is real inside the paper's setup. The next section explains why that setup is not a live engine.

The paper's gains do not transfer to a live engine because every tested page was already among the five sources handed to the model, so the experiment measured how much of an answer a retrieved page could claim, not whether a page gets retrieved at all. That distinction is the whole argument of this page, and two later papers make it precisely. Olivier Martinez's "Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023-2026)" (arXiv, July 2026) concludes that "no reviewed technique shows a stable, longitudinal, cross-platform causal effect on organic discoverability", with the narrower positive that "already-retrieved content can causally alter its citation or use". And a controlled two-document test of 252,000 trials across six models found that topical relevance and list position had the largest effect on which source was cited first, that price information and a recent timestamp helped, and that "formatting-only edits" had minimal impact (Rahul Vishwakarma, Shushant Kumar and Ratnesh Jamidar, "What Gets Cited: Competitive GEO in AI Answer Engines", arXiv, May 2026). One more thing: the paper's engine ran on gpt-3.5-turbo, a model no public engine uses in 2026. The tests that came after it are the ones a business should weigh.

Later tests of GEO tactics have found small, mixed or negative effects: a full GEO treatment lost to an untouched page in a 2026 test reported by Ahrefs, adding schema to 1,885 cited pages produced no uplift, llms.txt showed no link to citations across about 300,000 domains, and the factors that did hold up were relevance, freshness, mentions on other sites and original data. Every test is below with its sample, because a result without a sample is a slogan.

Two things to take from the table. The only controlled results are the schema test and the paired-document test, and both say content and relevance beat markup. And everything positive that survives is also what SEO has always rewarded: a relevant page, kept current, that other sites mention. The table sorts into tactics with evidence and tactics without.

The GEO tactics with evidence behind them are the ones that also work in search: answering the question first, covering the sub-questions an engine fans out to, earning mentions on sites the engines already cite, updating pages rather than only adding them, and publishing original data. If that list looks familiar, that's the point.

The list of tactics without evidence is shorter and more expensive.

The GEO tactics with no evidence behind them are llms.txt, keyword stuffing aimed at AI engines, mass-produced FAQ pages, and schema markup used as a citation lever rather than as a description of what is on the page. None of these is harmful in itself. Each one is a place money goes that could have gone to the first list.

Schema still helps engines read entities and dates, which is a different job from earning a citation and a good reason to keep it accurate. A tactic can also fail in one engine and hold in another, and the next section says why.

GEO results differ by engine because each engine retrieves from a different index, weights rankings differently, and re-runs its retrieval on different schedules, so the same page can be cited by Perplexity, ignored by ChatGPT and dropped by AI Overviews in the same month. Google's "query fan-out" wording, several related searches behind one query, is the reason its own overlap with the top 10 fell.

The full mechanism for Google's two features is on its own page: how Google AI Overviews and AI Mode choose their sources. The overlap figures also explain the case that worries most buyers: a page that ranks and still goes uncited.

A page can rank first in Google and still be absent from AI answers because the engine answers the sub-questions behind the prompt rather than the prompt itself, prefers the passage that states the answer, and favors sources it has cited before, so a ranking page with a slow opening, no direct passage and no third-party mentions loses to a lower-ranked page that has them. The 38% figure says it in one number: by March 2026, most AI Overview citations came from outside the top 10. ChatGPT's most-cited domain is Wikipedia and Perplexity's is Reddit (Profound, 680M citations, 2025), neither of which ranks first for most commercial queries. And only about 12% of links cited by ChatGPT, Gemini and Copilot appear in Google's top 10 for the same prompt (Ahrefs, 2025).

The Bing, crawler and directory detail for ChatGPT lives in how ChatGPT chooses sources. With the evidence laid out, the business decision follows.

Yes, GEO is worth it for a business in 2026 when its buyers already ask assistants for recommendations and its pages already rank, and no, it is not worth paying for as a separate program when the site has no rankings, no mentions and no way to measure a citation. The honest call depends on where you're starting from, and the table below says it without a sales pitch attached.

Two practitioner views are worth holding side by side, and both are honest. Peter Rota, Senior Technical SEO Manager at HUB International, put it this way on the Noorscale podcast: "If you have a basic foundation of SEO, you're going to get 80% of the way there." His own caveat from the same conversation is that the remaining 20% is work SEO rarely covers, such as how assistants describe the brand and pages like support docs; Peter Rota on the Noorscale podcast has the full exchange. The other view is the title of an r/SEO thread from April 2026: "Most 'GEO experts' are just SEO consultants who changed their LinkedIn bio". The search demand tells the same story from a different angle. Searches for "generative engine optimization" fell by roughly 45% in the year to September 2026 while "AI SEO" grew, by third-party estimates. The term is losing ground. The work is not. A business that is unsure should test rather than buy, and the test takes 60 days.

You run your own 60-day GEO test by fixing a panel of prompts, recording which pages each engine cites on two dates 60 days apart, changing a set of test pages in between, and leaving a matched set of control pages untouched. The model to copy, at small scale, is the Ahrefs schema test: matched controls, a window before and after, and a comparison of the difference rather than the raw change. Bobbink's test is the reminder of why: a full checklist can lose to nothing.

A word on honesty, because it's the part that gets skipped. Otterly's untouched pages moved 60% or more in two weeks. A lift smaller than the control's own movement is noise, however good it looks in a slide. Free or cheap tooling is fine for all of this; a spreadsheet does the job. The panel is the part most people get wrong.

A prompt panel should contain the questions a buyer asks an assistant at each stage of choosing, written the way a person types them, fixed for the length of the test, and large enough that one prompt cannot move the result. Comparison, "how" and "vs" questions are what fan-out pulls (Semrush, AI Mode study), and "best X" lists made up a large share of the page types ChatGPT cited in an Ahrefs analysis, so a panel without them misses where the citations are.

Keep the same wording on both dates, and store everything in one sheet: date, engine, run number, cited URLs and mentioned brands. The control pages are what make the panel readable.

Control pages in a GEO test show how much citation rates move on their own, so that any lift on the test pages can be read against the engine's own drift rather than against zero. Ahrefs matched its 1,885 treated pages to 4,000 controls on prior citation level and on different domains, and the difference it made is the whole lesson: the raw before-and-after on AI Mode showed plus 43%, while the control-adjusted figure was plus 2.4%. Otterly's reference page rose 63 to 64% with no edit at all. So here is the sentence to keep: a before-and-after without a control is a story, not a result. That is true of agency case studies as well, including Noorscale's. With a method in place, the question is what to expect and when.

Working looks like crawled and re-indexed test pages at 30 days, a citation rate on the prompt panel that has moved more than the control pages' by 60 days, and the first referral sessions and AI-report impressions that can be tied to those pages by 90 days; none of it is guaranteed, and a flat result is a result.

Structure changes have been reported to show in AI citations in 4 to 8 weeks (several 2026 sources); rankings take 3 to 6 months. The Search Console AI performance report shows impressions only, and AI Overview clicks don't separate from google / organic, so the 90-day checkpoint is partial by nature. Anyone promising real results in 30 days is guessing. How to measure AI search traffic carries the GA4 and Search Console detail. What the test costs decides whether to run it in-house or buy it.

GEO costs about the same as the SEO work it rests on, because most of the work is shared, and it is not worth paying for as a separate line when a site has no rankings to build from, no buyers asking assistants, or no way to measure a citation. Agencies price it in one of three shapes, and a 60-day in-house test costs staff time and no tooling spend at all.

No agency can promise a citation, a ranking or a date, and any quote that does is telling you something. How Noorscale runs the test for clients is next.

Noorscale tests whether GEO is working with the same method this page describes: a fixed prompt panel recorded monthly across the engines a client's buyers use, matched control pages, one change set at a time, and a report that shows the control's movement next to the test pages' so a flat result is reported as flat. The method comes from the founder's training in semantic SEO (a Semantic SEO Expert Certificate recognized by Koray Tuğberk GÜBÜR, March 2024). AI search optimization is the service that runs the panel and the test. Noorscale does not quote citation lifts from client work without a control, and the only published client results on this site are search results, not AI citation results. When a controlled result exists, it will be published with its method.

This week, write the prompt panel. Twenty questions your buyers ask, in their words, in one sheet. Run them once in ChatGPT with search on and once in Perplexity, and write down who is cited. You don't have to change a page yet. You'll have your day 0, and the next time someone pitches you GEO you'll be able to ask them for a control.