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AI has taken over the ad and media industry conversation. It’s in the room, the road map, the pitch, the QBR. If your company hasn’t mentioned AI in the last 48 hours, check for a pulse.
But while the buzz is real, the application still feels murky. We’ve entered the “AI is everywhere” era without ever agreeing on where it actually belongs.
Across the ecosystem, companies are exploring AI with a mix of optimism, pressure and a growing sense of “Wait – what are we doing again?” AI is being embedded in tools, tagged in decks and tossed into internal sprints. But for agencies, publishers, brands and platforms, there’s still no shared definition of success. The hype is high. The frameworks? Not so much.
Opportunity versus impact
Brand marketers are being asked for their AI strategy before they’ve figured out how AI even fits into their workflow. Agencies are getting RFPs that ask what’s AI-enhanced, but few follow up with how it ties to outcomes. Publishers are expected to scale content, clean data and streamline ops with AI, often without added budget or staff. Vendors are racing to slap “AI-powered” onto every feature, whether or not it changes anything.
Meanwhile, operations teams are reporting incremental efficiencies – faster briefs, more templated responses, quicker recaps. That’s something. But is it strategy? Is it performance? Is it even meaningful? That part’s still fuzzy. Everyone agrees AI is helping in places. But many are still trying to figure out if it’s helping in the right places.
Let’s be honest: It’s not because we lack imagination. Everyone has a mental list of “AI could do this”: audience modeling, dynamic creative, campaign insights, taxonomy cleanup, summarizing meeting notes. Even measurement is getting AI’d, with tools surfacing trends, flagging anomalies or auto-summarizing campaign performance – though tying that back to actual business impact is still a work in progress. But when it comes to picking a use case, aligning teams, rolling it out and measuring impact? That’s where things slow down.
If this feels familiar, it should. The ad industry has done this before. We’ve raced into programmatic, data clean rooms, CDPs, chasing innovation without always aligning on purpose. AI is just the newest wave.
The difference? It’s not limited to media or measurement. AI is creeping into every corner of the business: creative, ops, analytics, strategy, even legal. Everyone is being asked to evaluate tools they’re not always trained to assess – and make decisions they’ll be measured on later.
Add in a mix of incentive misalignment, vendor pressure and internal urgency, and you’ve got the perfect environment for AI chaos theater. Features are prioritized over functionality. Demos are prioritized over durability. And “future-forward” often means “no one’s sure who owns this yet.”
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The IAB hears this from across the ecosystem: brands waiting on real value, agencies juggling pilots with unclear payoffs, publishers trying to figure out what operational scale actually looks like.
That’s why this moment calls for discipline, not just curiosity.
We need to start asking better questions. Does this tool solve a specific problem? What business objective is it tied to – cost, quality, time, transparency, performance? What manual work is being reduced – or just repackaged? What oversight is still required? And perhaps most importantly: What are we now free to do because this work is automated?
Without clarity on questions like these, AI initiatives become empty calories: impressive in theory, unfulfilling in practice.
But if we slow down and align, there’s real upside. Agencies could shift more resources toward insight and planning. Brands could act faster on campaign signals. Publishers could invest more time into quality content and audience development. AI can make that happen, but only if we stop forcing it into roles it wasn’t designed to fill.
It’s time to shift from proving we’re using AI to proving it’s useful.
We don’t need more AI features, more AI filters or another “powered by AI” pitch slide. We need a shared understanding of how AI fits into the real work – and a willingness to say “not yet” when it doesn’t.
So yes, everyone’s talking about AI. That’s the easy part.
Now make it make sense.
“Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.
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