The Foundry

PR got deprioritized. Direct mail got cut. Events got shelved. Every channel decision your team has ever made had a reason behind it. Most of those reasons have never been revisited. Some of them have quietly expired — and a few of the tactics that were abandoned are now more valuable than the channels that replaced them.

One in three B2B buyers recently purchased from a vendor they’d never heard of before…introduced by AI during the research phase. That vendor didn’t get there by accident. There’s a specific strategic decision behind it, and most growth teams are skipping it entirely.

Most B2B companies that discover weak AI visibility immediately label it a “marketing problem” and throw it at the CMO to fix. That’s the wrong move. What shows up in AI-generated responses is a direct reflection of your go-to-market foundation. And until that foundation is solid, no amount of optimization will change the picture.

When growth stalls in B2B companies, it’s almost never an activity problem. Teams are busy. Dashboards are full. The pipeline is moving. And yet the revenue line stays unpredictable and nobody can quite agree on what’s actually wrong. There’s a structural reason for that… and it’s invisible from inside the silos.

Most marketing operating models were built for channels we can track. But buyers now outsource research to AI, and your existing footprint is shaping the answers. Someone needs to own this new perception layer. The job is straightforward: new objectives, expanded scope across functions, and governance that keeps AI’s representation accurate and credible.

Most companies don’t have an AI problem and a GTM problem — they have one structural problem showing up on two surfaces simultaneously. When AI can’t credibly recommend you, optimization won’t change that. But fixing the foundation will. Here’s what the right sequence actually looks like.

AI is shaping buyer shortlists long before your pipeline records an opportunity. An AI visibility assessment doesn’t just measure whether you show up, it tests whether AI tells the right story and treats you as a credible choice. Here’s the diagnostic framework we’ve been testing to score visibility, accuracy, and credibility across platforms.

After our first post in this series went live, people did what you’d expect… They asked AI about their own companies and braced for the results.

What was interesting wasn’t what AI got wrong. It was that every inaccuracy was traceable to signals the company had stopped managing.

The distortion follows a pattern. A four-layer pattern most teams have never examined as a connected system.