Agentic AI shows promise for direct deals. But if Google has its way, Buyer Direct could put an end to all sorts of agentic direct sales opportunities while they’re still in the cradle.
Historically, non-human traffic was viewed on a spectrum from benign to intentional fraud. But AI agents represent a legitimate audience that brands will have to understand and engage.
When an advertiser trains a model on its full organic converter base, it can better identify patterns that distinguish a casual browser from someone close to buying.
Advertising in AI-native destinations and experiences is already starting to look less like a series of experiments and more like an emerging market.
Instead of relying on rigid audience segments, modern systems can interpret a real-world moment in real time, combining layers of information to better predict what someone might actually care about right now.
The industry spent years building data verification frameworks for publishers and sellers. The buy side has no equivalent discipline. And it needs one.
In a world being agentically orchestrated at scale, human judgment is the indispensable guardrail preventing us from optimizing right over the cliff.
We must reject the fantasy of infinite creative variations tailored to individuals – not just because it is technically impossible but because it fundamentally misunderstands how advertising works for the vast majority of brands.
With deterministic data like cookies becoming less reliable, advertising’s real value comes from being able to predict where audiences will be and how and when to reach them.
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 real AI risk in digital advertising isn’t automation, it’s autonomous decision-making without clear accountability and sufficient oversight.
The programmatic intelligence layer is owned by the same parties that are extracting margin from it. That structure is now being dismantled by an AI-underpinned protocol that moves the intelligence layer into the open.
Binary measurement preserves margin and lets late-arriving impressions claim credit they may not deserve. We need a new measurement model for the AI era.
There is a myth that end-to-end platforms and sheer quantity of data yield efficiency and competitive advantage. But most clients need flexible, modular solutions that address their needs and work with their existing tech stacks.
Between signal loss, missing audience information and aging data, most of the data that companies have is spotty. But despite what you may have heard, agentic AI can deal with spotty data.
LLM environments introduce new dimensions to brand safety and suitability. Understanding them is the starting point for testing ChatGPT ads.
The AI content licensing boom is real. But without neutral, scalable infrastructure, it’s on track to repeat programmatic’s trust and transparency failures.
WPP is shifting to a pay-for-performance revenue model. But there are many barriers to clear before PFP becomes the industry standard.
We can’t simply trust AI to make up for bland creative with sheer volume. The real performance shift happens when we use AI to push creativity further.
Data brokers will need to process consumers data-deletion requests every 45 days starting August 1, 2026, or face heavy fines.
A proposed standard under discussion at W3C aims to redefine how ad effectiveness is measured across the web. This proposal would centralize measurement of ad effectiveness under the control of Google, Apple and Meta.
We now have plenty of evidence that AI can be an incredibly useful tool. But proof is also piling up that it erodes our creativity.
Machines can now flag anomalies, surface trends and spin insights in seconds. Yet, even with its speed, AI still can’t decode the “why” behind the “what.”
If you want to protect your margins, your data and your long-term strategy, here are five questions worth asking before you sign anything.
The play “Data” sparks conversations about AI, surveillance, data tracking and predictive modeling. The ad tech world should be paying attention.
As we head further into 2026, the biggest risk for marketing orgs isn’t choosing the wrong AI – it’s letting “AI perfectionism” delay decisions so long that no decision gets made at all.
Should businesses start advertising on AI media platforms? Yes. But it depends on how consumers in your category are already using AI and how quickly your organization can respond.
Customers don’t need perfection. But they do need to know that a brand will make it right when something goes wrong.
If a buyer wants real control, “domain rationalization” alone will never be enough. Buyers need “bid rationalization,” shaping the supply at the bid-request level so they see fewer, better, more outcome-relevant opportunities.
Many organizations are encountering a paradox: Performance appears strong, while durable growth remains elusive. How should performance be evaluated when the strongest signals no longer align cleanly with growth?
