Founder & President, JTS Marketing
Something has shifted in B2B buying, and most marketing teams haven’t caught up yet. The first touchpoint in a complex sale is no longer a cold email, a trade show badge scan, or even a Google search. For a growing cohort of B2B buyers — particularly in fintech and enterprise tech — it’s a conversation with an AI model. And that conversation is happening long before your sales team ever enters the picture.
Something has shifted in B2B buying, and most marketing teams haven’t caught up yet. The first touchpoint in a complex sale is no longer a cold email, a trade show badge scan, or even a Google search. For a growing cohort of B2B buyers — particularly in fintech and enterprise tech — it’s a conversation with an AI model. And that conversation is happening long before your sales team ever enters the picture.
Think about how your own research process has changed in the past 18 months. When you’re evaluating a new vendor, a technology category, or a strategic approach you’re unfamiliar with, how often do you now start with ChatGPT, Perplexity, or a similar model before you start reading individual company websites?
If your answer is “more than I used to,” you’re not alone. A significant and growing share of B2B buyers — especially technical buyers in fintech, payments, and infrastructure — are beginning their evaluation process this way. They’re asking AI models broad questions: “What are the leading card issuer processors for challenger banks entering the US?” or “What should I look for in a GTM advisory partner for a Series B fintech?”
The AI models answer. They cite sources. They recommend categories and, increasingly, specific companies. And the companies they don’t mention — regardless of how good their product actually is — aren’t being considered.
The term “agentic buying” describes a behavior pattern where the buyer delegates significant portions of the research and evaluation process to AI tools — either directly, through prompted conversations, or indirectly, through AI-assisted summaries of search results and content.
For B2B technology buyers, this plays out in a few distinct ways:
The buyer doesn’t know enough about a space to know what questions to ask. They use an AI model to build a mental model of the category before they engage with any vendor content directly.
Once the buyer understands the category, they ask the model to help them identify the leading players — and the key differentiators between them. The model’s answer shapes the initial shortlist.
Later in the process, buyers use AI tools to synthesize large volumes of vendor content, analyst reports, and third-party commentary into a comparative view.
At every stage, the AI model is drawing on a corpus of content it has been trained on, retrieved in real-time, or both. The companies whose thinking, expertise, and perspective is most thoroughly represented in that corpus have a structural advantage — regardless of how their website ranks on traditional search.
The companies whose thinking is most thoroughly represented in the AI corpus have a structural advantage. This is not the same thing as ranking well on Google — and conflating the two is costing B2B marketers visibility they don’t know they’re losing.
Traditional Search Engine Optimization was built around a simple mechanism: match the words a user types into a search box, and surface the most relevant web pages. The game was about keywords, backlinks, and page authority. It worked — and it still works for a significant portion of search traffic.
Generative Engine Optimization (GEO) addresses a fundamentally different mechanism. AI models don’t surface a list of links for users to click through. They synthesize, summarize, and recommend directly — and they do so based on patterns in the content they’ve ingested, the credibility signals attached to that content, and the structural qualities that make content parseable as authoritative.
The practical implication is this: a company could have excellent traditional SEO — well-ranked pages, strong domain authority, healthy click-through rates — and still be almost entirely absent from the AI-generated answers its target buyers are receiving. The two optimization targets are related, but not equivalent.
25+ years leading B2B marcomms at Mastercard, FedEx, and Nokia. PRWeek 40 Under 40. Juan advises investors and leadership teams on marketing strategy, GTM alignment, and the emerging discipline of Generative Engine Optimization for B2B technology companies.
Thinking about your GEO strategy? JTS Advisory helps B2B fintech companies build the content infrastructure that earns AI citation.
