The IAB is developing a framework to tackle AI advertising measurement

The IAB is working to “create a shared framework for measuring and crediting AI’s role in conversions, especially when traditional signals are minimized,” according to Caroline Giegerich, vp of AI at the IAB. The IAB is exploring new measurement frameworks and signals to quantify AI’s influence on purchase decisions, she added.

The stakes are bigger than they sound. If an AI agent reads a product page, weighs it against competitors and buys on a user’s behalf, who gets paid for that exposure? The publisher whose content shaped the answer? The platform that ran the agent? Nobody, because the evidence trail doesn’t exist yet? Because if traditional attribution signals (like UTM tracking parameters and referrals data) don’t reliably survive the customer journey in AI platforms, what evidence should count as proof that an ad encountered by AI contributed to a conversion?

“What does it mean to advertise to an agent? One side might think that ‘this is an interesting test,’ and the other side is like, ‘that’s deception,’” Giegerich said. She is drafting the framework now, based on conversations with a working group made up of tech companies, publishers, agencies, measurement vendors and brands. Giegerich declined to name which companies were in the working group.

“I think underneath it all is a desire to want to figure out what comes next in a marketplace that’s literally being upended as we speak,” said Giegerich. “We, as the IAB, are trying to catch up as much as we can to the flurry of activity that is happening around that concept of agents bypassing human traffic.”

The biggest issue the framework will address is how to connect AI visibility with measuring the attribution of conversions influenced by AI, Giegerich said. It will likely separate AI impact into two categories: when AI serves something to a user (or an awareness or intent layer) and when AI helps to make a decision by that user, she added. (This month, the IAB released frameworks around measuring AI visibility and disclosing AI usage in content production.)

But the main challenge so far has been how to assign credit, and what evidence is needed to do so — especially when AI platforms and tech companies are not known for providing this kind of information.

“Some of that evidence doesn’t exist. As the IAB, we want to influence those conversations to happen. So, if the evidence doesn’t exist, if there’s a certain party who could potentially provide it, well, this is a good open discussion to have,” Giegerich said.

The IAB’s work could help create a better foundation for the dynamic between publishers and tech platforms, creating a model in which publishers see more tangible value from the companies that benefit from their content, said Jaime Schultheis, head of global data partnerships at Bombora, where she leads a network of B2B publishers, brand sites and data providers.

“Many big tech partners have grown big audiences off of publishers, and there has been very little reciprocation. This is such a tremendous opportunity for it to be truly a reciprocal relationship,” Schultheis said.

But it won’t be easy. When asked what was the hardest thing for all parties in the working group to agree on, Giegerich said “everything.” Publishers, in particular, are arguing that their content informed the response that the AI system is serving to the user — and don’t want to be left out of the attribution conversation, she said.

A framework for AI advertising measurement would have to answer some key (and frankly, difficult to answer) questions, including what is being measured, who is doing the measurement, how it’s being measured, and at what layer is that measurement happening, said Michael Bishop, co-founder of AI native advertising platform OpenAds.

Bishop noted that even if the IAB can put together an industry standard to answer some of those questions, AI platforms may remain black boxes. If tech companies like OpenAI control the signals showing how AI influenced marketing outcomes, outside measurement firms may have to rely on integrations or data supplied by those same platforms – not unlike how Facebook built and controlled integrations with vendors to provide measurement data, without giving away full visibility into Facebook’s systems, he said.

“If you look at how those measurement vendors worked in Facebook, for example, they were not running their own JavaScript tags. Facebook actually coded the integration to measure their own homework effectively, and had the measurement vendors basically manually testing to rubber stamp that integration. So the black box was basically maintained, and the measurement vendors were effectively operating as like a third-party auditor trust layer,” Bishop said. “And it does not look very good for anyone.”

This story has been updated to clarify that the IAB attribution framework focuses on classifying and crediting conversions influenced by AI, whether or not an ad was ever served.