AI Is Everywhere, But Where Is It Really Adding Value?

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AI took over the conversation of ads and media industry. It is located in the room, the road map, the terrain, qbr. If your company has not mentioned AI in the last 48 hours, make sure there is a pulse.

But while the buzz is real, the application still feels blurred. We entered the time of “Ai everywhere” without ever agreeing on where it really belongs.

Through the ecosystem, companies explore AI with a mixture of optimism, pressure and growing feeling “Wait – what do we do again?” AI is built into tools, marked in decks and thrown into the internal sprinters. But for agencies, publishers, brands and platforms, there is still no common definition of success. Hype is tall. Frames? Not so much.

Opportunity opposite the impact

The brand squares are looking for their AI strategy before they realized that Ai even fits their work on their work. Agencies get RFPs asking what intensifies, but few are following them connecting with the outcomes. Publishers are expected to sculate the content, clean the data and simplify the ops with AI, often without the added budget or staff. Sellers are racing to hit “AI drive” on each feature, whether or not something has changed.

In the meantime, operating teams report for incremental efficiency – faster submitted, more proposed answers, faster applications. That’s something. But is that a strategy? Is that performance? Is it meaningful at all? That part is still unclear. Everyone agrees that AI helps in places. But many still try to realize if it helps u right Places.

Let’s be honest: it’s not because we lack imagination. Everyone has a “AI mental list could do it”: audience modeling, dynamic creative, insights into a campaign, cleansing taxonomy, summarizing a meeting notes. Even measurement gets AI, with tools that appear in trends, indicating anomalies or automatic-camomical campaign performance-and-like connections with real business influence still ongoing. But when it comes to choosing a case for use, team alignment, ejection and measuring impact? That’s where things are slowing down.

If this feels familiar, it should. The ad did so before. We were racing into programming, clean rooms, CDPs, we persecute innovation without always intentionally aligning ourselves. Ai is just the latest wave.

Difference? Not limited to the media or measurement. Ai moves to every corner of business: creative, ops, analytics, strategy, even legal. All are required to evaluate tools that are not always trained to assess – and make decisions on which they will be weighed later.

Add a mixture of stimulating mismatch, supplier pressure and internal urgency, and you have the perfect environment for AI Chaos Theater. Features are priority over functionality. Demors are a priority for durability. And the “future forward” often means “no one is sure who else owns it.”

IAB hears it from the whole ecosystem: brands waiting for real value, agencies that juggling with pilots with vague payments, publishers trying to figure out what the operating scale actually looks like.

That is why this moment requires discipline, not just curiosity.

We need to start asking better questions. Does this tool solve a particular problem? What is the business goal bound to – cost, quality, time, transparency, performance? Which manual labor is reduced – or just gets ready? What supervision is still needed? And maybe the most important thing: what can we do now because this job is automated?

Without clarity on such issues, and the initiatives become empty calories: impressive in theory, unfulfilled in practice.

But if we slow down and align, there is real upside down. Agencies could transfer more resources to insight and planning. The stamps could act faster on the campaign signals. Publishers could invest more time in the quality content and development of the audience. Ai can do so, but only if we stop forcing him in a role that is not designed to fulfill.

It’s time to switch from proving that we use AI to prove that it is useful.

We no longer need AI features, more AI filters or other “powered AI” tones. We need a common understanding of how Ai fits into the actual work – and a willingness to say “yet” when he doesn’t.

Well, everyone talks about Ai. It’s an easy part.

Now make that sense.

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