Opinion

For years, independent agencies won business by being different from holding companies: agile, responsive and flexible, with closer client relationships.

Connected TV has entered a new phase.

For years, the channel was primarily a reach vehicle, a way to follow audiences as viewing shifted from linear TV to streaming. That migration has reached critical mass. Audiences are engaged across CTV, and advertiser dollars are following. According to eMarketer, US CTV ad spending is projected to reach $53.42 billion, reflecting continued double-digit growth as streaming outpaces linear TV.

The pitch for dynamic take rates is reasonable. Total volume goes up, and the publisher sees more impressions clearing. But the problem is where the extra volume comes from. 

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.

Advertising has never been richer in data. With the right tools, marketers can now track competitor spend, campaigns and performance across media and markets, often in near-real time.

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.

Coming back from POSSIBLE last month, one thing stood out to me: Streaming TV has entered a much more operational phase. The conversation has evolved from where streaming is headed to how marketers drive better performance today. The content and conversations focused on how AI can improve campaign execution, how data can sharpen targeting and measurement and how channels work together within broader omnichannel strategies.

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 mass adoption of LLMs is shifting internet search for consumers from following blue links to synthesized answers from LLMs. Zero click is here. As generative engines and personal AI agents begin searching, comparing and acting on our behalf, discovery will no longer happen on a search page. It will happen inside the answers with personalized information and preferences.

The AI content licensing boom is real. But without neutral, scalable infrastructure, it’s on track to repeat programmatic’s trust and transparency failures.

The last decade of app marketing drove massive success in downloads thanks in large part to preloads, install campaigns and carrier partnerships. But the app download is just the beginning. EMarketer warns that 90% of new users stop using an app after seven days.

There is a clear set of capabilities that CTV OEMs and streamers can offer advertisers that the original content owner cannot. Here are the most material gaps that the buy side experiences between supply sent from the publisher and supply sent from distributors.

WPP is shifting to a pay-for-performance revenue model. But there are many barriers to clear before PFP becomes the industry standard.

As connected TV (CTV) matures, advertisers aren’t just raising expectations; they’re resetting them. And increasingly, transparency isn’t a value-add, it’s the cost of entry.

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.

Brands have gotten adept at finding and following their audiences across the media landscape. Today, that means everywhere: linear TV, CTV, YouTube and the open web, often in the same evening.

When it comes to identity, most marketers moved past third-party cookie concerns a long time ago. Identity today is not about a single technology or solution; it is about learning how to combine different signals in ways that allow campaigns to reach real people across channels while respecting privacy and maintaining performance.

Data brokers will need to process consumers data-deletion requests every 45 days starting August 1, 2026, or face heavy fines. 

In the rush to scale globally, US brands are often content with “accidental attention.” But attention isn’t a lottery; it’s a strategy that requires a deep familiarity with context in real time.

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.

For years, programmatic advertising has operated on the assumption that most buyers didn’t really want to know how the system works. That’s changing. More advertisers are asking harder questions about where their media dollars actually go, and those questions are increasingly come from mid-market agencies that would historically rely on DSPs to manage the complexity of supply for them.

AI is only as good as the data that fuels it. In advertising, however, that foundation is often flawed.

CTV never fit cleanly into campaign execution models built for RTB-based trading. Whether agentic AI solutions can simplify the complicated CTV landscape for publishers will be their first real test case.

When Apple launched AppTrackingTransparency (ATT) in 2021, access to deterministic identifiers fell sharply as roughly 80% of users opted out of tracking. User-level feedback loops became sparse and biased, and iOS performance marketing shifted into a different measurement environment under SKAdNetwork (SKAN). Apple’s AdAttributionKit (AAK) later delayed postbacks, compressed conversion values and set privacy thresholds that made signal availability dependent on campaign structure and scale.

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.

The industry has spent years debating third-party cookies, but AI has settled the debate. First-party data isn’t just preferred; it’s structurally necessary. And the capital is already moving.