For years, B2B marketers have focused on one core question: how do buyers find us?
That question is changing.
Increasingly, the first layer of vendor discovery isn’t happening through search engines or websites. It’s happening inside AI systems. Tools like ChatGPT and Perplexity are no longer just helping buyers research options. They’re shaping which options get considered in the first place.
According to Shane H. Tepper, co-founder of Resonate Labs, the shift is already well underway.
“There’s a clean line between using a model to research and letting it shape discovery,” Tepper explains. “Ask it to summarize a vendor page, and it’s assisting. Ask it who the best vendors are, and it decides who’s in the room.”
The Shortlist Is Forming Earlier And Without You
In B2B buying, shortlists have always formed early. Research from 6sense shows that buyers typically fill most of their vendor shortlist before engaging with sales teams.
What’s changed is who’s helping build that list.
“The model isn’t waiting at the end of the funnel,” Tepper says. “It’s at the front, deciding who gets considered.”
That means the moment of discovery, the point where a buyer first encounters potential vendors, is increasingly mediated by AI. And if a company isn’t included in that initial set, it may never enter the conversation at all.
Why Some Brands Are Invisible to AI
One of the most surprising aspects of this shift is that visibility in traditional search doesn’t guarantee visibility in AI systems.
A company can rank well on Google, have strong brand recognition, and still be effectively invisible to AI tools. The reason is often technical.
“A model can only recommend what it can retrieve and read,” Tepper says. “And a lot of sites fail that second part.”
In one case, a B2B software company had a well-performing website that appeared fully functional to users and search engines. But its content was rendered client-side using JavaScript. AI crawlers—unlike Google—don’t execute JavaScript, meaning they saw an empty page.
The result: near-zero visibility across AI-driven queries.
Once the company shifted to server-side rendering and restructured its content, visibility improved significantly within weeks.
“Legibility is mechanical before it’s editorial,” Tepper says. “You can be the best-known brand in your category and still be invisible to the systems your buyers ask first.”
A Fragmented Landscape With No Single Strategy
Even for companies that are visible, another challenge emerges: inconsistency.
Different AI platforms produce different answers, and often recommend different vendors for the same query.
Data from multiple studies shows that overlap between platforms like ChatGPT and Perplexity is surprisingly low, with only a small percentage of shared citations. That means a brand highly visible in one system may be absent in another.
Adding to the complexity, results are not always stable. The same query can produce different recommendations on different runs.
But there is a pattern beneath the variability.
“The noise is real,” Tepper says. “But durable authority cuts through it.”
Companies that consistently appear across platforms tend to share three characteristics: content that machines can easily process, depth on the questions buyers actually ask, and validation from third-party sources.
Competition Is Shrinking Before It Starts
As AI systems generate shortlists, they compress the number of options buyers consider.
Instead of evaluating dozens of vendors, buyers are often presented with just a handful.
That has a direct impact on competition.
“In a crowded category, you’re either in the answer or you’re not in the deal,” Tepper says.
Historically, companies could work their way into consideration through outbound efforts, events, or strong sales processes. But when AI defines the initial shortlist, those opportunities narrow.
The advantage shifts toward companies whose positioning is clear, structured, and widely corroborated across the web.
The Signals You Can’t See Yet
For many organizations, the hardest part of this transition is that its impact is difficult to measure.
AI-driven interactions often don’t show up clearly in analytics. Buyers may consult an AI tool, then visit a company’s website directly or search for it by name. The influence is real, but the attribution is hidden.
That creates what Tepper describes as a “dark traffic” effect.
Teams may notice indirect signals: rising branded search, more informed prospects, or sales conversations that begin with “I saw you mentioned in ChatGPT.”
But by the time those signals appear, the underlying shift has already taken place.
“The pipeline effect is real well before it’s measurable,” Tepper says. “And by the time it shows up in your dashboards, someone else may already have a head start you couldn’t see.”
Be First to Comment