A buyer at a mid-size company needs a new tool. Ten years ago, they typed a category name into Google, skimmed ten blue links, and built a shortlist from whichever landing pages ranked best.
That habit is breaking. Increasingly, the first move is a prompt: "what's the best [category] tool for a fifty-person sales team," typed straight into ChatGPT, Perplexity, or Claude.
The answer that comes back already contains a shortlist. If your product isn't on it, you may never get the click that used to be step one of your funnel.
This matters more for B2B SaaS than for almost any other category, because software buyers are exactly the audience most comfortable asking a chatbot for a recommendation instead of typing a query into a search box.
This shift isn't anecdotal. G2's 2026 buyer behavior research, based on a survey of more than 1,000 B2B software decision-makers conducted in March 2026, found that eight in ten buyers had sourced software recommendations from tools like ChatGPT or Google AI Mode within the past two years, and roughly half said AI was most useful specifically for narrowing and comparing vendor options.
Semrush ran a parallel survey of more than 600 B2B professionals in early 2026 and landed on a similar picture from a different angle: 92 percent said AI had shaped their vendor shortlist, and 97 percent reported discovering a vendor through AI that they hadn't previously considered.
Neither study claims search engines are finished. Semrush found a large share of buyers still cross-check an AI answer against a traditional search before committing. But the sequence has changed for a growing share of the market: the prompt comes first, and search has become the verification step rather than the entry point.
For a SaaS marketing team, that reordering is the whole story. Classic demand-gen SEO optimizes for the moment someone types a query into a search box and clicks a result. Generative engine optimization optimizes for the moment before that, when a model decides which three or four vendors are even worth naming out loud.
Software buyers are unusually well suited to asking a chatbot for advice, compared with buyers in categories like industrial equipment or professional services. Many of them already use ChatGPT or Copilot for their own job before they ever open a chatbot to research a purchase, so reaching for the same tool during a vendor search is a small step, not a new habit.
B2B SaaS purchases also tend to route through a buying committee rather than a single decision-maker. That means the same category question gets asked several times, by different people, in different words: an engineer wants integration details, a finance lead wants pricing structure, a marketing lead wants proof of results. Each of those angles is a separate prompt a model might field, and a separate chance to be named or left out of the answer.
The category is also crowded. Most SaaS verticals now carry a dozen or more credible vendors competing on overlapping feature sets, which is exactly the condition that pushes a buyer toward a shortcut. Asking a model to synthesize the landscape and name a few strong options is faster than reading fifteen homepages that all claim to be the leader.
None of this is unique to software forever. Other categories will feel the same pull as buyers get more comfortable with AI-assisted research. B2B SaaS is simply the leading edge, which means the cost of waiting to adapt is higher here than in almost any other market.
Ask an LLM to recommend software in a given category and watch what it leans on. Review platforms show up constantly: G2, Capterra, TrustRadius, and category-specific directories are exactly the kind of structured, frequently updated, third-party content that retrieval systems favor over a vendor's own marketing copy.
That isn't an accident. Review sites are built around the same signal a model is trying to approximate: what real users think of a product, compared with its nearest alternatives. In fact, G2's own research found that review sites have started to edge out AI chatbots as the single most cited source shaping shortlist decisions, at 38 percent versus 37 percent, the first time review platforms have taken that top spot in G2's tracking.
Practically, this means your G2 and Capterra profiles aren't just a trust badge for your website anymore. They function as retrieval material for the exact models your buyers are questioning. A thin profile, a stale category placement, or a pile of unanswered negative reviews can quietly keep you out of an AI answer before anyone on your team even sees the conversation happen.
The same logic extends to comparison content: "X versus Y" pages, "alternatives to X" roundups, and category buying guides, whether published by you, a review site, or an independent blog. These pages hand a model concrete, structured language to draw on when a buyer asks it to compare options. If your competitors have thorough comparison pages and you don't, the model simply has more material to describe them with, and less to describe you with.
There's a structural reason review platforms perform so well here. Their pages are consistently formatted: a product name, a category, a list of named alternatives, aggregate ratings, and dated user quotes. That consistency makes them easy for a retrieval system to parse and easy for a model to treat as reliable, compared with a marketing page that mixes claims, screenshots, and calls to action in whatever order a designer chose. If you want your own site to compete for the same kind of citation, borrowing that structure, a clear product name, an explicit category, named comparisons, dated proof points, is a reasonable place to start.
Not throw out the demand-gen playbook. Paid search, SEO, and content marketing still drive pipeline, and Semrush's data shows a large majority of buyers still touch a search engine somewhere in the process. The shift is additive to existing motions, not a replacement for them.
A few priorities do move up the list, though:
None of this is exotic. It's the same fundamentals of positioning and third-party proof that good demand-gen has always relied on. What's new is the channel: the results page your buyer sees is now a paragraph written by a model, assembled from the same signals above, just weighted differently than a search algorithm would weight them.
Traditional demand-gen wisdom treats comparison pages as bottom-of-funnel content: something a prospect reads in week three of evaluation, after they already know your name. That model assumed the buyer found you first, then went looking for comparisons to validate the choice.
GEO reshuffles that assumption. If a buyer's first move is asking an AI tool "what's the best [category] tool," the model's answer often already looks like a comparison: two or three names, a line on what each is good at, maybe a caveat. Your comparison content isn't validating a decision anymore. It's shaping the initial shortlist.
That means the old sequence, discover the brand, then read a comparison to confirm it, no longer holds for a meaningful share of buyers. A well-built comparison page can be the discovery mechanism itself, if a model retrieves it, or content that echoes its structure, when answering a category query.
It also raises the bar on honesty and specificity. Vague comparison pages that dodge real tradeoffs give a model little to work with. Specific, defensible claims about where you fit and where you don't are more likely to get pulled into an answer intact, because they read as substantive rather than promotional.
In practice, that means naming the alternatives your buyers actually consider rather than a flattering shortlist of weaker options, describing pricing structure and packaging in plain language instead of "contact sales," and being explicit about the use cases where a competitor might genuinely be a better fit. A model synthesizing an answer from thin, evasive copy has nothing solid to repeat. A model working from a page with real specifics has language it can quote almost directly.
We looked at what this competitive landscape looks like in practice in our piece on why some competitors are winning in AI search while others with stronger traditional SEO barely show up at all.
You don't need to rebuild your content strategy from scratch to make progress. A useful place to begin:
That last step is where most teams get stuck, because checking five models by hand for a growing list of prompts doesn't scale past a handful of queries. It's part of why we built Bold GEO: a daily refresh of how your brand is cited across ChatGPT, Perplexity, Gemini, Claude, and Copilot, so you're working from a number instead of a guess. You can see how the tracking works at boldgeo.co.
Your buyer's first search might already be a prompt. The question worth answering this quarter is whether your name shows up in the answer.
Bold GEO monitors how your brand is cited across ChatGPT, Perplexity, Gemini, Claude, and Copilot on a daily refresh. 7-day free trial, no credit card.