AI search builds answers from patterns across owned content, earned media, and community conversations.

That shift is forcing SEO to evolve from a standalone channel into the operating system that aligns teams, data, and narratives to influence how brands are understood and recommended.

In this talk, Jen Cornwell will break down why the SEO silo is collapsing and how search leaders can lead cross-channel execution across PR, content, paid media, and social.

She will introduce a phased operating model that connects owned assets, earned coverage, and community signals to establish a clear source of truth and shape the consensus AI systems rely on to generate answers.

After this session, attendees will be able to:

- Design a cross-functional SEO operating model aligned to shared visibility and revenue goals

- Coordinate PR, content, paid media, and social efforts to strengthen AI-driven brand authority

- Turn search insights into organization-wide intelligence that improves speed, testing, and decision-making.

It’s 2026: plenty of teams have an AI citations dashboard, a list of schema updates, and a recurring slide about prompt visibility. And they're stuck. They can tell you where they show up in AI answers, but very little about how to change it.

The top performers have realized that monitoring isn't the work; it's the prerequisite to the work. The teams seeing genuine compounding gains in AI search are spending their time on what AI actually sees—how their agent traffic gets routed, how their pages render for AI bots, and where there are gaps that need filling. They've stopped asking "are we visible?" and started asking "is our site built for an AI audience?"

Matt Thompson, VP of Partnerships, sees this divide play out in real time, across the hundreds of brands and agencies Scrunch works with—and across Scrunch's data on agent traffic patterns and page-level AI search performance collected every day. In this 20-minute session, he walks through the specific work the top performers are doing beyond monitoring, the patterns hiding in the data, and a practical checklist anyone in the room can use to figure out where their own AI search performance is stuck and what to do about it.