Most think GEO is an SEO problem. It's actually an organizational problem.
To do AI search well, you need two teams working in lockstep that have spent decades operating in silos: performance marketing and brand marketing.
That's not easy. At many large companies, these teams have different metrics, cultures, and expectations from higher-ups. There's very little built-in incentive for them to work together.
AI search can span that gap.
This talk introduces "The GEO Bridge" - a practical framework for understanding AI search as the connective layer between brand and performance marketing. Attendees will learn why traditional marketing structures break down in the AI era, how to create shared ownership and metrics across teams, and what leaders can do to turn GEO from a siloed SEO initiative into a cross-functional marketing capability.
The goal is to leave with a blueprint for aligning brand, SEO, content, PR, and performance teams around a common objective: increasing visibility in AI-generated experiences. I'll draw on my experiences at Paylocity founding and leading a cross-functional "AI search initiative," a multi-disciplinary group under our Go-to-Market department, whose work has tripled our AI visibility this past year.
The GEO Bridge: How AI Search Connects Performance & Brand
Predictive SEO: Forecasting rankings, demand & organic visibility using AI
In this talk, Purvi Sharma shows how SEO teams can move from reactive reporting to predictive, AI-assisted planning.
She explains how early search signals, such as trend momentum, SERP volatility, and competitive intensity, can be used to forecast organic growth and content demand before rankings visibly change.
The session focuses on practical application, demonstrating how SEOs can use accessible data and lightweight models to make earlier, more confident decisions and gain a timing advantage in search.
Local Search in an AI-First Era: What Enterprise Brands Must Rethink in 2026
I will be sharing how local has transformed and why Content, Entities, Experience, Engagement and TCO are critical pillars to scale local presence for enterprise in 2026.
Local 4.0: Winning When AI Chooses Before Customers Click
1️⃣ The shift from rankings to recommendations — how AI now selects local brands before a click happens.
2️⃣ The evolution from Local 1.0 → 4.0 and why traditional SEO is no longer enough.
3️⃣ Building AI trust: structured data, entity consistency, and verifiable brand signals at scale.
4️⃣ Unlocking trapped local data to become understandable, reusable, and safe for AI systems.
5️⃣ An enterprise blueprint to engineer AI-driven local visibility — not just traffic, but influence.
I ran 30+ GEO experiments so you don't have to: What 200M AI citations say actually works
Your boss wants an AI search strategy. LinkedIn gurus want to sell you one. Nobody has data - so I spent a year running 30+ controlled GEO experiments across 200M AI citations to test every tactic the industry swears by.
Some results confirmed what we suspected. Most didn't. Why do some Reddit communities earn 9x more AI citations than others with identical content?
Which LinkedIn format gets cited - and which one is a waste of time?
Where does Claude actually pull its citations from? (Hint: not where you think.)
Can AI even see your images and design? And what happened when I built a 1,000-blog AI slop site is stranger than you'd guess - Google noticed fast, but the AI engines are still citing it today.
You'll leave knowing which GEO tactics measurably move visibility on ChatGPT, Claude, Gemini, and AI Overviews, which are folklore, and how to run these experiments on your own site.
