AI Is Finally Doing Real Work In Ad Ops (But Only When It Works With Your Existing Tech) | AdExchanger

Publisher ad ops teams still spend significant time manually pulling GAM reports and reverse engineering revenue drops. Large language models provide real value when connected directly to systems publishers already use, such as Google Ad Manager, GitHub, and revenue reconciliation feeds. Practical use cases include diagnosing sudden revenue dips, assessing the impact of Prebid software updates, and reconciling SSP discrepancies without losing days in GAM. Reported outcomes include reducing investigations from two weeks to three hours, including identifying how a Prebid update later cannibalized outstream video revenue. Benefits require wiring models into bespoke GAM instances and teaching them business logic, since hallucinations remain and many agent tools are unreliable without proper integration.

"For all the AI-in-ad-ops talk, plenty of publisher teams are still trapped in the grind of pulling GAM reports by hand and trying to reverse engineer why revenue dropped. At Programmatic AI in Las Vegas, Jordan Cauly, who launched a publisher monetization tech consultancy after an eight-plus year stint as a product lead at Mediavine, made a simple argument that large language models only help with the ad tech grind when they plug directly into systems publishers already use. For instance, Google Ad Manager (GAM), GitHub and revenue reconciliation feeds."

"Cauly aimed his session squarely at publisher ad ops and product teams who want AI to take real work off their plates. His examples were all grounded in day-to-day tasks like diagnosing revenue dips, unpacking the impact of Prebid software updates and reconciling SSP discrepancies without losing days in GAM. For Cauly, the benefit of adding LLMs is saving time. His clients are cutting revenue mysteries that used to take two weeks to solve down to three hours."

"He described using AI to solve how a recent Prebid update quietly cannibalized a publisher's outstream video revenue months after launch. And he said his morning routine has gone from logging into three different platforms before coffee to looking at one synthesized view of GAM, GitHub and SSP revenue gaps. The catch is that none of this comes out of the box. Every GAM instance is bespoke. LLMs still hallucinate."

"Most "agents" on the market are, in Cauly's words, more Pinto than Ferrari. For publishers that want AI to do real work in ad ops, the challenge is wiring models into the right systems and teaching them how the business actually runs."

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