How to Leverage AI in Streaming Ad Tech in 2026
Whether it’s traditional machine learning (ML) or its trendier generative cousin, artificial intelligence (AI) is appearing in a range of streaming ad tech use cases for increasing under-the-hood efficiency in streaming monetization applications. Sargeway’s Sarge Sargent, Fox’s Amit Shetty, Google’s Inderpreet Sandhu, IAB Tech Lab’s Shailley Singh, and Reality Software’s Nadine Krefetz discuss AI’s fast-changing role in this clip from Streaming Media 2025.
A Combination of AIs
Krefetz, head of Reality Software and a contributing editor at Streaming Media, opens the discussion by asking Sargent, co-owner of Sargeway, to talk about what AI in advertising means to him.
Sargent calls AI a loaded term. Most people think of GPTs and generative AI when they think of it, but AI began with ML. “So for me, coming from the publishing workflow and specifically from the ad platforms at Disney, it meant using ... a combination of that,” he notes. “Every ad server is going to have some machine learning in it to build its algorithms and keep feeding and training its algorithms. You can use some AI workflows or some scene detection, object detection, for some ad assets, and that’s where we kind of talked about doing some QA for ad assets. And then something that I don’t think anyone’s talked about is using machine learning to look at data.”
Krefetz encourages him to hold that thought and invites other panelists to speak about AI in advertising.
A Tool That’s ‘A Fixture, Not a Feature’
Shetty, senior director, video ad technology product, at Fox, says he thinks about it as tools for better measurement, delivery, ad fraud detection, and more. “AI is able to help you to do those tasks much better, hopefully cheaper, though that part of it is still being sorted out. So that’s the way I look at AI. Basically a tool that helps various use cases, not a single thing,” he asserts.
Sandhu, global head of CTV partner development and growth at Google, calls AI “more of a fixture, not a feature, as in, it’s not what someone is actually buying or choosing to use. And Amit [Shetty], like you said, it’s about what are the outcomes that it’s going to help you drive.” He says the question should be, “How does it make you get to an answer faster or find problems before they become problems?” From an ad tech perspective, it’s been in use longer than a decade for improved forecasting, reporting, and more. A use case would be “someone who’s trying to just reduce the time between getting an RFP from an agency to actually executing and delivering on it and then having the core teams really have tools that help them find new opportunities or new experiences,” he explains. His publisher partners want help solving business problems: “I don’t care if [AI is] a hamster wheel in the background, powering your stuff, it’s, ‘How’s it going to help me do it quicker, faster, and with a lot more signal in the actual data than noise?’”
Disruption to Come
Krefetz invites Singh, EVP product and COO at IAB Tech Lab, to give his perspective.
“Like Sarge said, machine learning is not new to ad tech. I think probably a decade that it’s been used,” Singh replies. “And before generative AI came on the scene, in ’23, there was wide adoption of deep machine learning algorithms to understand patterns, to make decisions on bidding, and in data and all the other areas in adtech.” He believes generative AI will be disruptive to typical human tasks because of its ability to recognize patterns. “I think that’s the new thing that we are seeing, and how that supports the ad tech supply chain, that’s yet to be seen,” he notes.
Join us February 24–26, 2026 for more thought leadership, actionable insights, and lively debate at Streaming Media Connect 2026! Registration is open!
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