One question has dominated almost every conversation around Generative Engine Optimization over the past year: how to measure GEO. While it’s a reasonable question, much of the debate assumes AI visibility can be measured in the same way as traditional search performance.
The search industry has spent more than two decades relying on relatively stable metrics: Rankings, Clicks, Impressions, Traffic, Conversions; however, artificial intelligence doesn’t work that way.
Every answer is influenced by the model being used, the user’s prompt, their previous interactions, the sources available, retrieval methods and continual model updates. Two people asking the same question may receive different answers. That makes GEO fundamentally different from traditional search optimization.
Yet much of the market has rushed to offer certainty. AI Visibility Scores. GEO Scores. Citation Scores. Authority Scores.
These platforms undoubtedly provide useful insight, and many are becoming an important part of the measurement toolkit, but they should never be mistaken for a single source of truth.
David Taylor, SVP, DigitalThe recent publication of the AMEC GEO Principles is an important moment for the communications industry because it acknowledges something many practitioners have recognized for some time – there is no universally accepted methodology for measuring AI visibility and, more importantly, there probably never will be.
Instead, we should begin thinking about GEO measurement as a framework rather than a metric.
At Dig & Dig, we’ve found it helpful to separate measurement into three distinct layers.
The first is technical readiness. Can AI systems efficiently access, understand and interpret your digital assets? This includes structured data, crawlability, content clarity, entity consistency and machine-readable architecture.
The second is authority. Is your organization consistently referenced by credible third parties? Are subject matter experts visible? Are your claims independently validated? Does earned media reinforce the expertise your brand is trying to communicate?
The third is market visibility. How frequently does your organization appear in AI-generated responses across commercially important prompts? Which competitors are referenced instead? Which publishers appear most often? Where are the gaps?
No single metric answers all three. Equally, none should be viewed in isolation. This is where communications measurement becomes both more difficult and more interesting.
For years, earned media has represented one of the industry’s greatest attribution challenges. Coverage influences awareness, trust, search demand and commercial performance, but rarely in a directly attributable way. AI makes that relationship even more complex.
An article published today may influence an AI model weeks or months later. A journalist’s quote may be cited repeatedly without the original publication receiving another click. An executive interview may strengthen entity understanding without generating any immediate referral traffic.
The impact exists, but the attribution becomes increasingly diffuse. That doesn’t mean measurement becomes impossible; it means measurement becomes multidimensional.
Rather than asking, “Did this article generate traffic?”, organizations should increasingly ask:
Those are fundamentally communications questions.
The significance of the AMEC framework is not that it gives the industry all the answers. It doesn’t. If anything, it highlights just how complex this challenge really is. Its importance lies in recognizing that transparency, methodological rigor and consistency matter more than headline scores. For an industry that’s become accustomed to dashboards, rankings and performance scores, that can feel uncomfortable. But it’s also a reflection of reality.
Reputation, authority and trust have never been particularly easy to measure, yet they’ve always influenced how organizations are discovered and perceived. AI hasn’t changed that. It’s simply made those signals more visible. Perhaps that’s the real evolution. Not a new way of measuring communications, but a renewed appreciation for the influence that earned authority has always had.
As AI changes the way people discover information, brands need a strategy that goes beyond traditional search. At Dig & Dig, we’re built for a world of AI-first discovery, helping organizations improve visibility across both search engines and AI platforms by combining SEO and content, GEO, digital PR and data-led insight.
Rather than treating AI visibility as a standalone discipline, we focus on the signals that influence how brands are understood, referenced and recommended online. From technical readiness and content strategy to authority building and measurement frameworks, we help clients adapt to the changing search landscape with confidence.
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Whether you’re exploring how to measure GEO, improve AI visibility or prepare your brand for the future of search, get in touch and speak to our team of experts at hello@diganddig.com to learn how Dig & Dig can help.
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SVP, Digital
David is a commercially experienced Digital Marketing Leader with over 20 years of success across the UK, US, and EMEA. He specializes in driving business growth through full-funnel, digital-first strategies in B2C, B2B, and B2D markets. Fostering a data-driven, test-and-learn culture, he consistently translates complex challenges into simple, elegant digital solutions.
