Inside a Recent GEO Win: What’s Working Right Now

by Reggie Sudduth II

We’ve been deep in the GEO trenches lately, and because discovery changes almost weekly, it’s worth sharing what’s actually working right now.

Here’s how we helped a client move from traditional SEO to scalable, generative discovery – what worked, what we learned, and what’s next.

1. Discovery Beyond Traditional Search

Conventional search (e.g., Google organic) remains important, but discovery now happens in AI-Overview panels, generative platforms (ChatGPT, Gemini, Perplexity, Copilot), and other machine-driven discovery surfaces.

Lesson: If you optimize only for keywords, you’ll disappear in the next wave of discovery.

Implication: Create for both humans and machines. Write clearly, structure content intelligently, and make it easy for AI to understand, cite, and trust your brand.

2. Building a GEO Framework = Structure + Scalability

Our biggest takeaway is that GEO isn’t about writing more content, it’s more about building smarter systems. Here’s what worked in this case: 

• Researching how LLMs parse and cite content.
• Mapping which pages appeared in AI Overviews and which didn’t.
• Adding semantic optimization: schema markup, aligned entities, and citation-ready data.
• Creating a sustainable, repeatable framework instead of one-off tactics.

Lesson: GEO is neither “SEO 2.0” nor “just content marketing.” Think about it as an ecosystem design for generative-engine discoverability: structure, semantics, entities, trust signals, and sustained output.

Implication: Bring content, tech, and analytics together early. GEO wins when everyone’s in the same room.

3. Measure What Matters

One year in, the results spoke for themselves: 10,000+ citations in Google AI Overviews (up nearly 10K year-over-year), plus new visibility across ChatGPT, Gemini, and Perplexity.

Lesson: Traditional SEO metrics (rankings, clicks) are necessary but not enough. GEO adds a new dimension: how often your content is cited by AI engines.

Implication: Track new metrics:

• Number of AI Overview appearances
• Citation counts in LLMs
• Entity alignment and authority share vs. competitors

It’s early days – build your instrumentation now.

4. Diversification of Discovery = Risk Mitigation

Before GEO, this brand’s discovery footprint lived almost entirely on Google. After GEO, they were showing up across multiple AI-driven platforms.

Lesson: Depending on one channel is risky when the ground keeps shifting and discovery mechanisms evolve. By expanding into GEO, you’re future-proofing discoverability.

Implication: Map current and emerging discovery channels for your organization. Consider how your content appears via voice assistants, widgets, AI chat responses, generative knowledge panels, etc. 

Invest in being “there” where users ask questions, not just in search results pages.

5. Authority & Trust Still Win

We spent just as much time on authority signals as we did on content itself — things like schema, expert bylines, and clear sourcing.

Lesson: While tactics change (from keywords → semantics, from backlinks → entity trust), the underlying principle remains: signals of authority, expertise, and trust matter more than ever. In generative discovery, those signals help the engine decide which content to cite. TL;DR: Machines reward the same things humans do: clarity, credibility, and expertise.

Implication: Ensure your content (and your brand) show up as the credible voice in your field. This includes structured data, clear expertise, citation-friendly content, and alignment with known entities. Show your authority in ways both people and AI can read.

6. Early Movers Have the Edge

This client went from zero visibility in AI ecosystems to becoming “a consistently cited authority” across major generative surfaces.

Lesson: Being early in GEO gives you a head start. But it requires deliberate investment and sustained effort. It’s not “set-and-forget.”

Implication: Treat GEO like a long game. The advantage compounds if you stay curious and keep testing.

7. Quick Start Checklist

Here are actionable steps you can take based on these lessons:

1. Audit your current discovery footprint. Which pages appear in AI-overview/LLM surfaces? Where are your visibility gaps?
2. Map your entities and content architecture. Do you have clearly defined topics, aligned schemas, and structured markup?
3. Create citation-ready content. Focus on content that machines can parse — e.g., clear definitions, entity relationships, FAQs, structured data.
4. Build sustainable processes. Set up workflows for content + technical optimization + analytics + feedback loops.
5. Measure new metrics. Beyond clicks and rankings: citation count in AI engines, number of pages surfaced in AI-overviews, entity-to-brand alignment, etc.
6. Diversify discovery. Think beyond search: chatbots, widgets, voice assistants, generative knowledge panels.
7. Communicate internally. GEO is cross-discipline: content, tech, brand, analytics. Secure buy-in and allocate resources accordingly.

These early GEO wins show that when you focus on structure, semantics, and authority, you can show up where it really counts. Start small, learn fast, and don’t wait for a perfect playbook…things are changing fast, and the early movers will reap disproportionate benefits.

Get a fast read on what’s blocking your brand across all three, and a clear roadmap to capture more clicks, citations, and conversions. Request an AI Visibility Snapshot of your website.

About the Author:

Reggie Sudduth II is the Director of SEO at BMG360, where he leads strategy, client services, and production for a portfolio of high-end national and international brands. A digital marketing expert with deep experience in SEO, social, email, and content marketing, Reggie builds scalable search strategies that align with each client’s broader business goals. Known for his hands-on approach and strategic clarity, he partners closely with clients to turn insights into measurable growth.