AI is rewriting the AdTech stack and what companies need from their teams

Something is shifting in AdTech, and it’s happening faster than most teams are prepared for. In the last few weeks alone, we’ve seen Sephora start using ChatGPT as a discovery layer for shoppers, Meta move toward AI-powered placements inside retail media environments, and players like Yahoo and Scout experiment with chatbot-first ad experiences. Even DOOH, which historically felt static, is starting to move toward formats that respond dynamically to who’s in the room.

What all of this points to is a deeper shift that goes beyond formats or channels. AI is no longer just an optimization layer sitting in the background of campaigns. It’s becoming the interface where ads are discovered, delivered, and interpreted. And when the interface changes, the way teams operate has to change with it.

What we’re seeing in the market is that many companies are still trying to operate in this new environment with teams that were designed for a previous one. Performance marketers whose workflows are heavily built around manual optimization, media planners who never had to consider how an LLM prioritizes or surfaces content, and account managers still anchored in last-click logic are all starting to feel that tension. These roles are not disappearing, but they are being compressed and redefined in ways that are not always obvious from the outside.

At the same time, demand is shifting toward a different kind of profile. There is a growing need for people who understand how AI layers on top of first-party data, who can connect CTV, programmatic, and retail media into a unified strategy, and who can operate comfortably across both technical and commercial conversations. One of the most consistent gaps we see is not purely technical capability, but the ability to translate what is happening under the hood into something a brand or stakeholder can actually understand and trust.

That translation layer is where many teams struggle. It’s also where the strongest candidates stand out. In multiple hiring processes, we’ve seen technically solid profiles stall because they couldn’t bridge that gap, while candidates who can move between systems, strategy, and client conversations tend to progress quickly and often receive competing offers.

The constraint right now is not demand, but profile availability. And this is where job descriptions tend to lag behind reality. Many are still written around tools, platforms, and execution-heavy expectations, rather than around interpretation, adaptability, and cross-functional thinking. The result is a mismatch between what companies ask for and what the market can realistically provide, which is one of the reasons searches feel longer and more frustrating than they should.

What’s changing is not just the format of ads, but the layer at which they operate. When that layer shifts, the required skill set doesn’t evolve gradually, it resets. Teams that recognize this early tend to adjust faster, both in how they hire and in how they define roles. The ones that don’t often find themselves consistently behind, trying to solve new problems with profiles that were never designed for them.