GEO for B2B tech 

Generative Engine Optimisation (GEO) is the practice of improving how visible, and how accurately represented your content (and by extension, your brand) is inside answers produced by generative large language model (LLM) systems like ChatGPT, Gemini, CoPilot, Claude or Perplexity. 

There has been a bit of a sprint to label it, so you might hear AEO, LLMO (my favourite), or even just AI Search which are all the same thing, but GEO seems to have won the race for the name after being formalised in academic research authored by contributors from Princeton University, Georgia Tech, and the Allen Institute for AI (among others).

No, but you’re not the first to ask. This has been a common question in the formative days of GEO, not least because some unsophisticated SEO agencies are desperate to hitch themselves to the shiny new wagon. The truth is they might be siblings, but they are not identical twins.

GEO and SEO solve adjacent problems, but they do it in different ways. SEO optimises for retrieval and is about being returned as a result when someone searches. GEO optimises for synthesis, which means being included, described, and sometimes cited when an engine generates a single answer by combining information from multiple sources.  

As a useful shorthand: SEO is often about winning the click. GEO is about winning a place in the answer.  The playbooks overlap, but they are not interchangeable. 

Good SEO hygiene still forms the foundation of good GEO performance. Generative systems tend to reward content that is easy to extract, easy to interpret, and directly responsive to a question. That can be supported by SEO-style best practices like clear headings, direct language and descriptive subheads. The same Princeton et al report that named GEO as a ‘thing’ also outlined how straightforward textual enhancements on a website can increase measured visibility by up to 40% in generative engine responses, with effectiveness varying by domain.  

However, the easiest evidence that they are not the same thing comes from a report by Ahrefs, which analysed 15,000 prompts and compared the URLs cited by AI assistants against Google’s top results for the same prompts. On average, only 12% of the links cited by ChatGPT, Gemini, and Copilot appeared in Google’s top 10 results for that prompt. In other words, ranking well in Google is not a reliable proxy for being cited or referenced in AI answers.  

Authority remains a real concept across both disciplines, but it presents differently. SEO historically treated authority as a link-weighted signal on domains and pages, which is why in my early PR days clients were desperate to get backlinks into articles. In GEO, authority behaves more like a trust-weighted signal across an ecosystem. This means repeated, consistent, independent confirmation across sources the model retrieves and feels safe repeating. This is one reason communications and PR strategy suddenly matters in what used to feel like “search territory.”  

That last point is especially important for B2B tech. Optimisation is not universal, because the sources retrieved and weighted change with the type of question and the type of buyer, as well as which LLM the searcher is using (more on that later). 

A second practical difference is measurement. SEO gives clean outputs; rank, impressions, clicks. GEO gives messy outputs; mentions, citations, narrative accuracy, and inconsistency across engines. 

In short, SEO and GEO are related, but they reward different behaviours and they produce different outcomes. SEO remains a foundation. GEO is the next layer, where being understood, validated and repeated across the right sources increasingly determines whether a brand appears in the answer at the moment a buyer is trying to make a decision.

Okay, it’s not the same as SEO, how do these large language models (LLMs) produce answers?

Well, they are called “generative engines” because they don’t just retrieve an answer for you like a search engine, they generate one using a process called Retrieval Augmented Generation (RAG).

RAG is a process where the model retrieves external evidence from the live internet, like traditional SEO and augments it against its training data to generate an answer that combines all that context. When authoritative information is accessible, well-structured and consistently stated across the sources the system retrieves, it becomes easier for the model to include it accurately in the final answer. When high-quality information is inaccessible or absent from the retrievable ecosystem, the LLM may retrieve weaker substitutes and still generate an answer, which increases the risk of omission, ambiguity, or misinformation.

RAG is widely used because it allows outputs to incorporate newer, domain-specific, or otherwise external information without retraining the underlying model each time the world changes, and is also why recency bias can play a big role in rankings, particularly for more niche technical topics.

I like to think of it like this. Traditional search behaves like a library. You search something and the engine’s job was to retrieve something useful off the shelf, largely based on keywords. Generative systems are more like a brain, you ask it a question and it thinks about what it already knows (training data) and what it needs to read on the internet to then generate a concise answer.

In B2B, buyers are using LLMs to compress research and validate their decisions. A stat that is widely cited from Forrester claims 89% of B2B buyers reported using generative AI in at least one of their purchasing processes, describing it as impactful across phases of the buying journey. That stat is from 2024, but won’t have dropped since.

In practice, that means if you aren’t being surfaced by an LLM when it answers peoples’ questions, you are invisible to nine out of ten buyers. Bit of an uphill battle for your sales team!

Forrester has outlined a “zero-click” drift in B2B, where buyers increasingly get what they need from AI-generated summaries without visiting vendor sites.

A nuance worth flagging is that B2B buyers often use AI less for pure discovery than consumers do. They often have a list in mind already, and use LLMs to compress complexity and to validate a decision they are already moving toward. Forrester’s own description of the 2024 survey result makes this explicit, arguing buyers were not only using GenAI to discover vendors, but also to evaluate differences and justify the purchase commitment. 

Validation questions have a different psychological texture than discovery questions. A discovery prompt is exploratory. A validation prompt is someone trying to defend their decision. It’s the classic, “nobody got fired for buying IBM” scenario. That makes the output feel authoritative by default, even when it shouldn’t. Research on automation bias consistently shows that humans can over-rely on automated recommendations, particularly when they appear competent or confident, and that users’ understanding of AI does not reliably protect them from miscalibrated trust, with more confident AI users actually trusting the outputs more/interrogating them less than a novice.

B2B decisions are heavily influenced by repeated claims across trusted environments (analysts, trade press, vendor explainers, peer commentary). Humans are wired to treat familiarity as a cue for truth, and generative systems do something similar at scale. They synthesise from patterns in what they retrieve, and repetition across credible sources becomes a confidence signal for the answer they produce.  

This means the earned media consensus to validate where a prospect is already leaning is crucial in B2B buying.

One of the clearest large-scale data points on citation behaviour comes from Muck Rack’s “What Is AI Reading?” research. In the May 2026 edition, analysing 25M+ links across ChatGPT, Claude and Gemini, earned media accounts for 84% of AI citations. Journalism alone accounts for 27%. 

I can hear you, surprise surprise the PR people are telling me I need media coverage. Well, we kind of are, but we’re trying to be fair about it, so let us explain more.

Firstly, those percentages are averages across many industries and prompt types. The same Muck Rack write-up is clear that query type materially changes what gets cited.  For example, “industry trend” questions drive journalism citations at more than double the rate of “how-to” questions. They also note that for recent queries, journalism can make up a much larger share of citations.  

A lot of GEO commentary defaults to consumer behaviour because consumer prompts are easy to imagine, easy to test, and easy to generalise. “Best headphones”, “best hairdryer”, “is this product worth it”, “give me a plan for this holiday”. 

That prompt shape tends to reward broad consensus sources (like those “ten best” articles, review aggregators) and brands with mass visibility. B2B tech behaves differently because the questions are different, and the pool of credible sources is narrower.  

Reddit is a good example of why B2B GEO needs its own playbook. A lot of generic GEO advice has treated Reddit as almost universally important because it has historically appeared frequently in consumer-oriented AI answers. That can make sense for prompts asking for first-hand experience like restaurants, holidays, recipes, or consumer products, where community discussion is useful.

But that does not mean Reddit is equally important for a buyer asking about a private network platform, data centre supplier or OSS/BSS vendor. In the B2B digital infrastructure testing we run at Temono, Reddit rarely features as heavily as generic GEO advice would suggest. Vendor websites, trade media, customer and partner announcements, analyst content and other specialist sources tend to matter far more.

It is “horses for courses”. Look at the sources that actually shape the questions your buyers ask, rather than assuming one platform matters because somebody else's GEO report says it does.

In B2B tech, the most influential sources are the specialist titles, analyst-adjacent commentary (when not gated), standards bodies, and technical sources that get retrieved for niche prompts. The “trust set” gets narrower as the prompt gets more specialist.  

That means authoritative vendor explainers can also carry disproportionate weight. Consumer GEO narratives often underplay the role of brand-owned content because self-published claims look biased and because many consumer prompts are better served by lived experience and comparative reviews. In B2B tech, the equation shifts because many categories are complex, technical, and jargon-heavy, and the web’s clearest explanations are frequently published by vendors, standards bodies, or specialist technical organisations. When a generative system is trying to answer an explanatory prompt (“what is X”, “how does Y work”, “what are the trade-offs”), authoritative technical explainers can become highly retrievable inputs in the synthesis stage. 

This does not mean “owned beats earned” in B2B. It means owned content can be a primary source of clarity and can act as a verification layer, particularly when third-party coverage isn’t present. Consistently publishing informative content as part of your content marketing strategy can massively increase your chances of being cited by LLMs.

Don’t ignore the role of your own blog and newsroom pages!

Sadly not, that would be too easy…

The broad principles we have discussed are the same across all and should form the basis of your strategy, but there are some differences worth talking about.

A Yext study from 2025 outlined how Gemini takes more than half its references from brand-owned websites, which makes sense considering it’s owned by Google so has that SEO foundation baked in. Own the owned like you would for Google and Gemini will still support you. It also favours content published on YouTube, which shock horror, is owned by Google. Publishing some educational videos – crucially with the transcript in the description – can really help on Gemini.

Perplexity is known for deeper research. Get in your trade press and analyst reports and it will reward you. It can also favour recent news. ChatGPT meanwhile looks for consensus, which in consumer searches often means ‘top ten’ listicle articles. 

In B2B it is less clear, it will depend on the search, but third-party validation still matters. CoPilot meanwhile uses Microsoft-owned Bing instead of Google as its retrieval foundation, and can pull consensus much more frequently from LinkedIn which is owned by, you guessed it, Microsoft!

Citation behaviour can also change very quickly though. Again, Reddit is a useful recent example. Promptwatch recorded Reddit’s share of ChatGPT Search citations falling sharply during August 2026 after an algorithm update. 

ChatGPT is still the market leader in general so the temptation would be to optimise for how it works, but enterprise use is steadily moving more towards Gemini, Claude and CoPilot depending on whether your company uses Google or Microsoft, as well as Perplexity. Therefore, you need to think about how they all work and success in GEO will be a result of a mix of your owned, earned, and social comms strategy.