Over the past several months, I've been helping businesses rethink how they appear in an entirely new type of search: AI.
Instead of asking how a company ranks in Google, I'm asking a different question:
When someone asks ChatGPT, Google AI Overviews, or Perplexity for a recommendation, what does AI actually say about that business?
One of those engagements has now become my first measurable AEO (Answer Engine Optimization) case study.
The engagement
The client is a respected family-owned home builder with nearly two decades of experience and a strong reputation in its market. After rebuilding its website from the ground up—with structured data, schema markup, AI-readable site architecture, and content designed specifically around the questions prospective buyers ask—we measured the results using an enterprise-grade AI search analytics platform.
The outcome exceeded my expectations.
The results
Following the launch, the company ranked #1 in citation share within its competitive set, meaning AI systems referenced its website more frequently than any direct competitor. It also ranked #4 overall in AI brand visibility across 100 tracked brands, and when the company was cited, it appeared in an average position of 1.5—typically first or second in AI-generated responses.
Perhaps most encouraging, two articles published only days before the measurement were already responsible for approximately 5% of every AI citation across the entire domain. Because those articles did not exist before the redesign, their contribution is directly attributable to the new strategy rather than legacy content.
What the data revealed
Just as valuable as the successes were the opportunities the data uncovered.
For the category's highest-intent, non-branded buyer query—the type of search someone makes before they know which company to hire—the business ranked #32 out of 100.
That finding doesn't diminish the results; it validates why measurement matters.
Strong brands often perform well when customers already know their name. The larger opportunity is becoming the company AI recommends before a customer has chosen a brand. That insight now informs the next phase of optimization, and we'll measure again after those improvements are implemented.
Why most local businesses are invisible to AI
While this is only my first published case study, it reinforces a broader pattern I've begun seeing as I work with additional organizations.
Most niche and local businesses have spent years building outstanding real-world reputations. Very few have invested in making that expertise understandable to AI systems.
That creates a significant strategic opportunity.
AI cannot recommend what it cannot confidently understand. It relies on information that is structured, trustworthy, technically accessible, and easy to cite. Businesses that intentionally build for those systems today are establishing digital authority while much of their competition remains focused exclusively on traditional SEO.
The early-mover window
In my view, this is the early stage of a shift that will reshape how customers discover businesses.
Twenty years ago, companies that invested early in search engine optimization built durable competitive advantages before the market caught up. AI search feels remarkably similar—but the adoption curve is moving much faster.
This first case study doesn't prove every answer, but it provides something far more valuable: measurable evidence that thoughtful technical architecture and AI-focused content can materially influence how modern AI systems understand and recommend a business.
As I continue working with more organizations, I'll continue publishing both the successes and the lessons learned. The goal isn't simply to improve rankings—it's to help businesses become the answer AI is confident recommending.