Generative Engine Optimization (GEO) Software Market (2026-2036)

The growth of this market is driven by the insertion of generated answers between a query and the websites that would previously have received the click, by the consequent inability of established search analytics to explain where brand visibility is being won or lost, and by the concern of brand and communications functions about how their organisation is characterised in answers they cannot see. Spending is early, growing from a small base, and concentrated among organisations for which organic discovery was already a material acquisition channel.

Three features of this category deserve to be stated at the outset because they bear directly on how the forecast should be read. The evidence that generated answers suppress clicks is contested by the largest platform rather than settled. The flagship technical artefact that this software helps implement has no confirmed consumer among the engines. And the measurement on which the entire category rests is sampling rather than measurement, because a generated answer has no rank to observe. None of this prevents the market growing quickly, and it is growing quickly, but it does mean that a buyer is purchasing an estimate of an effect whose magnitude is disputed, and a supplier is building on a data supply the platforms can withdraw.

Generative engine optimization software exists because a generated answer occupies the position a ranked list used to occupy. When a user asks a question and receives a synthesised response that names some sources and omits others, the commercial question for an organisation changes from where it ranks to whether it is mentioned, how it is characterised and whether it is cited. Established search analytics cannot answer any of those questions, because they were built to report positions, impressions and clicks against a list of results. This category is the attempt to build an instrument for the new surface, and it is being built while that surface is still moving.

The demand evidence most frequently cited is also the most contested, and both halves of the dispute are facts. A Pew Research Center study published on 22 July 2025 examined 68,879 Google searches made by 900 United States adults who agreed to share their browsing activity, covering search behaviour in March 2025, with the AI Overview content and search result pages collected between 7 and 17 April 2025. About 58 percent of respondents ran at least one search that produced an AI summary, and about 18 percent of all Google searches in March 2025 generated one. Users clicked a link 8 percent of the time when an AI summary was present, against 15 percent when it was not, and they ended their browsing session on 26 percent of pages carrying an AI summary against 16 percent of pages showing only traditional results. Google responded on the same day the study was released. A spokesperson stated that the study used a flawed methodology and a skewed queryset that did not represent Search traffic, and said that Google had not seen significant drops in aggregate web traffic, maintaining that AI features create new opportunities for people to connect with websites. Earlier, on 21 May 2025, Google Vice President Nick Fox had argued that some studies of this kind examined time frames preceding the launch of AI Overviews. A prospective buyer of this software is therefore acting on a finding that the platform whose behaviour is being measured rejects as unrepresentative, while the platform concedes nothing about aggregate traffic. The honest reading is that the direction of the effect is credible and well evidenced in this study, that its magnitude across the whole of search traffic remains genuinely open, and that a vendor presenting the click figures as settled fact is overstating the position.

A second figure in the same study deserves more attention than it receives, because it disciplines the sizing of this market. About 18 percent of Google searches in March 2025 produced an AI summary, which means that rather more than four in five did not. The surface this software addresses was therefore a minority of search events at that point, and classic search optimisation continued to govern the remainder. That supports treating generative engine optimization software as an incremental and adjacent budget line rather than as a replacement for existing search spending, and it explains why the category grows very fast in percentage terms from a base that remains small in absolute terms.

The third structural feature is the one least often acknowledged by the industry itself. A ranked list is deterministic, auditable and the same for any observer issuing the same query, which is what made search rank measurable and what made an entire analytics industry possible. A generated answer is none of those things. It varies with sampling, with session context, with personalisation and with retrieval, so there is no rank to observe and no single correct value for the visibility of a brand. What these tools actually do is issue prompts repeatedly, record whether and how a brand appears, and aggregate the results into a rate. That is a survey instrument rather than a measurement instrument, and it follows that two vendors sampling the same brand with different prompt sets and different frequencies will report different numbers, not because either is wrong but because the quantity is defined by the sampling design.

The implication for buyers is uncomfortable and worth stating plainly. A figure described as share of voice in AI answers is an estimate whose value depends on choices the vendor made about which prompts to issue, how often, from which locations and with which personalisation state, and those choices are rarely disclosed in enough detail to be reproduced. Comparisons between vendors are therefore not meaningful, trend data within one vendor is meaningful only while its prompt set is stable, and a change in the underlying model can shift the reported number without any change in the behaviour of the organisation being measured. Mature vendors are beginning to publish methodology, and that disclosure is becoming a differentiator.

The data supply chain carries a structural risk that compounds this. Sampling an answer engine at scale requires issuing large numbers of automated queries against consumer products that generally offer no visibility interface for the purpose and whose terms discourage automated access. There is no equivalent of a search console for generated answers, which means the input to this entire industry rests on access arrangements that the platforms have not granted and can constrain at any time. A platform that introduced an official visibility interface would reshape the competitive field immediately, and one that tightened enforcement against automated querying would impair every vendor simultaneously.

The technical optimisation side of the category faces a related credibility problem. On 17 June 2025, John Mueller of Google stated publicly that no AI system currently uses llms.txt, a file format promoted as a means of guiding large language models to the most important content on a site. He noted that WordPress core automatically creates sitemaps so that sites are likely already covered, that multiple sitemap files cause no problems particularly where a single content management system generates them, and that his server logs showed consumer chatbots fetching pages for training and grounding but never fetching llms.txt. The Yoast WordPress plugin had by then added a feature generating an llms.txt file, marketed as guiding language models to important content, although take-up remained limited. The position is therefore that the most promoted technical artefact in generative engine optimisation was publicly stated by the search advocate of the largest engine to have no consumer, with his own server logs offered as evidence, while a widely installed publishing plugin generates it. Suppliers that sell implementation of this artefact as a visibility measure are selling work whose effect is unestablished, and buyers should expect technical recommendations in this category to be separated between those grounded in observed crawler behaviour and those grounded in proposed standards.

What remains, once those cautions are applied, is a real and defensible product. Brands genuinely need to know whether answer engines describe them accurately, whether competitors are being recommended in their place, whether the sources those engines cite are ones they can influence, and whether their content is reachable by the crawlers that ground these systems. Those are legitimate questions, the answers have commercial consequence, and no existing tool answers them. The category is sound. It is the precision of its metrics, not the existence of its purpose, that is overstated.