Search traffic has been sliding for two years as AI platforms answer questions directly instead of sending users to click through. New measurement framework from the IAB gives radio groups running digital news and content a vocabulary for tracking that exposure.
Measuring Visibility in the AI Era, part of the IAB’s broader Project Eidos initiative, was built to solve a specific market problem: more than 20 companies now sell AI visibility measurement tools, each using a different methodology, and two providers measuring the same brand or publisher can produce contradictory results.
Only 16% of brands currently track their AI visibility at all, and buyers with budgets to spend have had no basis for evaluating what they are buying.
The stakes for any broadcaster running a website are real. ChatGPT now logs more than 900 million weekly active users, and Google AI Overviews reach over 2.5 billion users a month, appearing on close to half of all searches and 14% of shopping queries.
McKinsey projects unprepared brands could see traditional search traffic decline 20% to 50%. Publishers are already feeling it: Chartbeat data reported by Axios in March found search referral traffic down 60% for small publishers, 47% for medium publishers, and 22% for large publishers over the past two years, while AI chatbot referrals, despite growing more than 200% since late 2024, still account for less than 1% of publisher page views.
For publishers specifically, the framework defines four measurable layers – presence, prominence, portrayal, and persuasion – that determine whether content is cited by an AI platform, how much of it is actually used, how accurately it is attributed, and whether that citation drives a click back to the source. It also sets disclosure requirements providers must meet before their data can be trusted, covering which AI platforms they cover, how their query sets are built, and how they detect hallucinated or factually inaccurate citations.
Futuri reported last year that large language models were excluding radio from AI-generated media mix models almost entirely, citing a lack of structured performance data.
Kathleen Fink argued in March that radio’s influence on the customer journey gets lost in attribution dashboards built for the last click. This framework is a different front in the same fight: it is about whether a station group’s own web content gets cited, summarized, or ignored by the AI platforms increasingly standing between searchers and a click.
The framework does not rate individual vendors, but IAB VP of AI Caroline Giegerich, who led the working group, frames it as the foundation for a future certification program.
The practical use of the framework is as a checklist rather than a mandate. IAB’s disclosure categories – platform coverage, query construction, and hallucination detection – are a reasonable list of questions to bring to any AI visibility vendor a group is evaluating for its news site or content hub. The framework also flags multimodal visibility, including how AI platforms handle voice responses, as a priority for future standardization, worth watching given how much of radio’s own content is voice-first.
