AdExchanger Guest Columnist, Author at Chief Marketer

AI Agents Are Making Marketing Decisions On Data No One Has Checked In Years

Ask anyone in ad tech about data governance, and they’ll talk about the supply side. The industry spent years building verification frameworks for publishers and sellers, proving that the data powering supply-side decisions is what it claims to be. Standards exist, enforcement is maturing and the consensus is clear: If automated systems act on your […]

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Data-Driven Thinking

Cannes 2026: The Humans Strike Back

I’ve been making the annual June pilgrimage to the Côte d’Azur long enough to recognize the difference between conversations that will still matter in September and those that evaporate faster than rosé in 95-degree heat. Threaded through every serious discussion at the Cannes Lions this year, I detected a growing consensus that humans in the […]

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Data-Driven Thinking

The Paradox Of Personalization: Billions Of AI-Tailored Ads Creates A Measurement Mess

The dawn of digital advertising came with a seductive promise: the right ad served at the right time to the right person. We were told this paradigm would finally solve John Wanamaker’s century-old conundrum, eliminating the legendary 50% of wasted ad spend. It was a beautiful fantasy. Setting aside the dystopian, “Minority Report”-style surveillance required […]

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Data-Driven Thinking

Why Prediction Is Replacing Precision For Outcome-Driven Advertising

For years, the advertising model was contextual. If an insurance provider wanted to sell its product, it would prioritize advertising on insurance-related sites. The thinking was that you had to go where the signals were strongest. Only those actively visiting a site that offers information about what insurance packages to buy could be guaranteed to […]

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Data-Driven Thinking

The W3C Is Making A Critical Mistake About Measuring Advertising Effectiveness

The W3C’s proposed “Attribution Level 1” browser standard deserves far more scrutiny from the advertising and measurement community than it has so far received. The public comment period remains open through June 10. At a moment when advertisers face enormous pressure to justify spending and many publishers outside the largest platforms are struggling economically, industry […]

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Data-Driven Thinking

Digital Advertising Needs Guardrails For AI

The biggest AI risk in digital advertising is probably not the one most people are talking about. It’s not creative generation. It’s not job replacement. It’s not whether AI can optimize campaigns faster than humans. The real risk is what happens when autonomous systems begin making commercial decisions without clear ownership, governance or accountability. That […]

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Data-Driven Thinking

From Open Internet To Open Agent: The Architecture Buyers Were Promised Is Finally Here

You know the call. It happens every month, sometimes every week: The DSP number doesn’t match the SSP number. Nobody is sure whose pixel fired twice. The deal you activated three weeks ago is still not delivering. The brand safety report came back clean, but the placement was anything but. A meaningful percentage of your […]

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Data-Driven Thinking

Why Agentic Measurement Will Reprice The Ad Market

Every era of advertising is defined by what its reporting layer cannot see. In the 1960s, the industry was defined by the Nielsen diary. Households recorded their viewing on paper and mailed it back. Advertisers and broadcasters waited weeks for the data. Nielsen’s diary wasn’t replaced because it was wrong, but because it was slow. […]

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Data-Driven Thinking

Making Marketing More Modular: What Agentic AI Can Learn From The Shipping Container Revolution

Today’s explosion of AI service providers has everyone looking back at the dot-com bubble, revisiting the technological boom and bust at the turn of the millennia for historical precedent to help us separate real innovation from BS. History is a valuable teacher, but I look beyond the history of the ad tech industry for inspiration. […]

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Data-Driven Thinking

Brands Don’t Need Perfect Data To Use AI

There is a common refrain that AI requires high-quality data to deliver high-quality results. “Garbage in/garbage out” refers to the idea that any AI trained on less than perfect data will not be able to produce valuable outputs. Before I get accused of dismissing the importance of quality data, high-quality data does produce the best […]

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