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    Home»Spotlight»Using Agentic AI to Connect Across the Funnel
    Spotlight

    Using Agentic AI to Connect Across the Funnel

    adminBy adminMay 5, 2026No Comments5 Mins Read
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    Using Agentic AI to Connect Across the Funnel
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    This post was created in partnership with Mutinex

    Key takeaways

    • Marketers are still testing how and where AI is most useful.
    • The right data can help marketers get the most out of AI tools.
    • AI tools can change the role of marketers, but strategy and creative work will always need a human touch.

    AI has moved from abstract promise to daily marketing work, but brands are still sorting out where it creates business value and where it adds another layer of noise.

    During an ADWEEK House Possible Group Chat co-hosted with Mutinex, leaders from top adtech and consumer brands explored how teams are using AI for measurement, workflow speed, creative production, and brand building—without handing over the parts of marketing that require taste, emotion, and judgment.

    Using Agentic AI to Connect Across the Funnel插图
    PubMatic’s Kyle Dozeman

    Making AI useful starts with the foundation

    The conversation opened with a discussion on how teams are trying to make AI useful across the business. Vinny Rinaldi, VP, consumer connections at The Hershey Company, shared that Hershey is applying AI across areas from supply chain to marketing—but only after putting the right data foundation in place.

    “Your data infrastructure is the most important part of implementing AI,” Rinaldi said.

    Mike Finnerty, U.S. president at Mutinex, added that brands often run into trouble when they try to layer AI on top of weak systems. “It always starts with data, right? And the adage of ‘crap in—crap out’ is true no matter what model you’re putting on top of it,” he said.

    Angela Vargo, VP, marketing, communications, and brand experience at Breeze Airways, said airlines have no shortage of data, but volume alone does not solve the problem. Breeze has customer accounts, operational information, customer service inputs, and NPS data, which makes the work complex before AI enters the picture. “It really is overwhelming when you’re looking at the sheer volume of it,” Vargo said.

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