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In October 2025, Google published a number that should have triggered a marketing crisis at every B2B company.
60% of B2B buyers now use ChatGPT, Claude, Perplexity, or Gemini to research vendors and shape their shortlist before they ever talk to anyone at the company. The same Google research found that 94% of B2B buyers used a large language model at some point during their purchase journey in 2025. AI Overviews, the answers Google itself now shows above the search results, are reducing organic traffic by 15 to 25% across most B2B verticals.
Translation. The buyer's first impression of your category is no longer formed on your website. It is formed inside an AI assistant, which is reading other sources, summarising them, and recommending vendors based on what it finds.
If your brand is not in those answers, you are not in the consideration set. And almost none of the marketing teams I talk to are tracking this.
This is what GEO, generative engine optimisation, actually is. It is the discipline of becoming a brand that AI tools cite when they answer questions in your category. It is the natural successor to SEO, but it works on completely different signals, and the brands winning at it are not the same brands that won at SEO.
Here is what AI tools actually look at when they decide which brands to mention.
The first signal is consistency of mention across high authority sources. AI models are trained on enormous text corpora, but at inference time, they often check current sources, including Reddit, Wikipedia, industry publications, and verified review sites. A brand that gets mentioned consistently across these sources, even if not always positively, is more likely to be retrieved when the model answers a query.
The second signal is the depth and specificity of the content the brand has put into the world. AI models do not summarise generic marketing pages well. They summarise specific, original, well-structured content. A blog post that says "we are the best CRM for small teams" gets passed over. A blog post that says "our smallest customers run on Hubspot Starter, see customer service throughput improve 47% within 90 days, and typically expand to Professional in month seven" gives the model something to cite. The first is marketing. The second is structured information that serves the AI's job of giving the user a useful answer.
The third signal is community presence. Reddit, in particular, has become disproportionately important. Recent analysis of AI Overviews and citations across ChatGPT and Perplexity finds Reddit is the single most cited domain when these tools answer product or vendor questions. The brands appearing in helpful Reddit threads with full context and disclosure are getting cited by AI tools as a side effect, often without realising it.
The fourth signal is review presence on platforms that the AI tools actually weigh, including G2, Capterra, TrustRadius for B2B SaaS, and category-specific sources for other verticals. Volume of reviews matters less than recency, depth, and specific use case detail.
The fifth signal is what the industry is now calling AXO score, the answer engine visibility score. The Pedowitz Group's April 2026 analysis suggests the industry average is 28 % visibility across major LLMs for category-relevant queries. Top performers are above 60 %. Most brands have never measured this, which means they have no idea where they sit on this distribution.
What does this mean for your acquisition strategy?
It means there is now a parallel channel to SEO that is becoming more important every quarter, and most growth teams do not have anyone owning it. The traffic from this channel does not always show up cleanly in your analytics, because users often get the answer inside the AI tool and never click through. But the consideration is happening. The shortlist is being formed. And by the time the buyer eventually does visit your website, the decision has already been substantially made by an AI tool that read about you somewhere else.
The shift in budget that follows from this is significant. Brands that historically spent on SEO content optimised for keywords are starting to redirect budget toward content optimised for AI retrieval, which means more original research, more specific data, more first-hand customer stories with measurable outcomes, and more presence on the platforms AI tools actually cite.
The discipline is also organisational. Most companies do not have a person whose job it is to monitor what AI tools are saying about them. The brands winning are appointing someone to run weekly checks. Asking ChatGPT, Claude, Perplexity, and Google AI variants of "what is the best X for Y," recording where the brand shows up, where it does not, and what the AI is saying about competitors. This data becomes the input for the next quarter's content strategy.
This is happening fast enough that the first movers are establishing positions that will be very hard to dislodge. AI models retrain, but their behaviour around well-established sources is sticky. The brand that becomes "the obvious answer" for a category in 2026 will likely still be the obvious answer in 2028.
The question is whether you have someone owning this in your team, or whether you will look up in 18 months and realise your category has been quietly redistributed by tools you were not paying attention to.
Pick someone. Make this their quarterly project. The cost of starting now is low. The cost of starting in 18 months will be much higher.
See you at the next edition, Arindam