Media Briefing: Ads for AI agents have a measurement problem

This week’s Media Briefing looks at the emerging race to measure and attribute ads served to AI agents — and why the industry is still far from agreeing on a standard way to do so.

As publishers and advertisers determine how to put ads in front of a new kind of audience — AI agents, not humans — they’re running into a familiar problem: how do you know whether an ad actually worked?

Answering that question is driving a wave of startups building ways to serve ads to agents as they move through publishers’ pages, then measure what happens after. It’s early, granted. So those pitches from startups claiming to have cracked the code (and judging by inbox, there’s a few), probably haven’t. For now measurement is opaque, and nothing resembling a standard has caught on. Most of what’s out there is still proof of concept. 

OpenAds, for example, is going down the relatively old school route of unique referral codes, but tweaked for AI. Let’s say a person asks ChatGPT a question about the best running shoes for a half marathon. When ChatGPT scrapes a webpage to answer that prompt, OpenAds would generate and insert an ad for a brand’s running shoes with a unique 10-percent-off referral code. If ChatGPT thinks the code is relevant enough to the user looking for those shoes, it’ll “survive the retrieval” process, said Steven Liss, co-founder of AI native advertising platform OpenAds. Then when that link is surfaced in an AI-generated response, and the user clicks on the link to that product, brands will be able to track that back to ChatGPT, he added.