The question I get asked most in 2026 is: How do we measure this?
- How do we measure whether our brand is showing up in ChatGPT?
- How do we measure whether Perplexity is recommending us?
- How do we measure whether the work we did last quarter on grounding for AI Mode moved the needle?
Nobody has solved this.
Anyone selling you a clean dashboard for tracking presence in grounding, visibility in display, or action at won across search, assistive, and agent simultaneously is selling you a snapshot view that amounts to a bad best guess.
The standard advice is “track these queries that we think people might ask,” or “track these queries that are a best-guess adaptation of search keywords.”
That advice is unhelpful because prebuilt keyword lists pick queries that are easy to track, map to existing marketing efforts, or would be ideal if the audience were predictable.
The visibility question is right. The precise-number answer it expects is wrong.
The measurement question, as the industry currently frames it, uses the wrong reference discipline. Brands still hunting for the perfect AI-era visibility KPI are hunting for something that doesn’t exist and never will.
The right answer is a methodology that takes its discipline from how economists measure systems too complex and opaque to measure precisely. My methodology is the Funnel Query Pathway, and it does more than measurement. It’s one operational artifact that does three jobs simultaneously: strategy, measurement, and analysis.
Marketers want a number on a dashboard, tracking week over week, tied to a specific query on a specific engine for any user, the way search delivered for 20 years. Search could deliver that number because the surface was finite, the rankings were stable, the click was measurable, and the journey was observable. Assistive and agential surfaces deliver none of that.
We’re operating in a new environment now, and that environment forces us to ask different questions, measure different signals, and act on different proof.
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Why AI visibility is a macro measurement problem
I studied economics and statistical analysis at Liverpool John Moores University, which is why the shape of this measurement problem looks familiar. The same shape shows up whenever a discipline that worked at one scale tries to operate at a scale where its instruments stop applying.
Microeconomics versus macroeconomics is the canonical case. The corner shop measures inventory precisely, the central bank can’t measure inflation precisely, and both disciplines are correct at their scales. Neither discipline’s instruments work in the other’s environment. The discipline…
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