Cookies are disappearing, but the bigger change in marketing is happening underneath the targeting debate. Brands are being pushed to replace familiar audience shortcuts with systems that can interpret behavior, connect fragmented data and make faster decisions about media and creative.
That was the focus of Adweek’s Adspeak discussion with Cognitiv president Eric Danetz and Mars marketing executive Nicole Vinson. Their argument is practical: AI only becomes valuable when it improves the decisions around a campaign, not when it is added as a layer of technological theater.
The useful shift is from audience labels to behavior
The discussion puts behavioral data ahead of traditional panel research because it can offer a closer view of what people actually do. Clean room technology can then bring different data sources together, helping marketers build a more complete picture without relying on the old cookie-based model.
That does not mean data automatically becomes insight. It means the work of targeting is moving toward interpretation. A demographic label may describe who someone is supposed to be. Behavioral signals can show what they are currently interested in, how they respond to content and which message is more likely to matter.
The distinction is important because AI is also changing the speed of creative evaluation. Adweek reports that one example reduced creative analytics from 100 hours to under 10. That is not simply a productivity win. It gives teams more room to test, learn and adjust before a campaign has spent its budget.
Pilot first, promise later
The most credible idea in the discussion is a pilot-first approach. Instead of attempting a sweeping AI transformation, organizations can test a specific use case, measure the result and decide whether it deserves to scale. That makes adoption less about buying a large technology promise and more about proving a useful behavior change.
The reported example of Special K achieving a 36% lift through more precise audience targeting shows why the approach is attractive. It also needs to be treated as a reported case study, not a universal benchmark. Results depend on the data, the measurement model, the campaign and the decisions made around the tool.
That is where governance and partnerships become part of performance. Better targeting is not enough if teams cannot explain where the signals came from, how they were used or what guardrails apply. The next phase of AI marketing will therefore belong less to the brands with the loudest automation claims and more to the ones that can connect data quality, creative judgment and accountable testing.
The strategic consequence is clear: moving beyond cookies is not mainly a compliance exercise. It is a change in how marketing teams decide what to notice, what to make and what to scale.