Jennie Roper, VP OOH Intelligence at WPP Media, asks what the rise of AI means for creativity and genuinely original thinking?

One of the things I have always loved about planning in OOH is that the most useful questions can come from the smallest details. The same street can feel completely different at 8am and 6pm. A familiar journey can mean something new when the weather turns, a train is delayed or a big event is taking place nearby.

AI is also  giving planners new ways to follow those clues. It can bring together information that may once have sat separately, helping us spot patterns and possibilities that are easy to miss when we look at each source in isolation.

That is exciting, but as AI becomes a more familiar part of the planning process, what turns all of that information into an idea that feels genuinely original?

The value lies in what we bring to AI

AI is at its most powerful when it is not working from the same starting point as everyone else. It can make sense of vast amounts of information, but the quality of what it uncovers depends on the data, knowledge, and original questions we bring to it.

This is where agencies have a real opportunity to create something different. A brand’s own data, alongside a planner’s knowledge of its category, audience, and media landscape, can give AI a far more useful starting point than the same broad inputs available to everyone else.

It also changes the questions we can ask. Instead of beginning with, "who are the biggest group of people we can reach?" planners can ask, "what is happening in someone’s day that makes this brand genuinely useful or relevant to them?"

That may sound like a small shift, but it can lead to very different thinking and, ultimately, different results. It takes us beyond audiences defined only by age, income or an online behaviour, and towards the real moments people are living through.

The growing use of agentic marketing platforms such as WPP Open can make it easier to bring different data sources into that process,  but the real value still lies in the questions planners choose to ask of them.

Why context changes the answer

This is where original thinking comes in. 

Trainline is a great example of this. It would have been easy to begin with a broad goal of reaching rail passengers around stations, but a station is not one single moment. For one person, it could be the start of a holiday. For another, it could be a familiar commute to work. For someone else, it is where a delayed journey has suddenly become stressful.

Trainline’s long-term presence around stations and within rail environments was built around this understanding. The question planners asked was not just, "how do we reach people travelling by train?" but more, "where can Trainline be most relevant / helpful to their journey?"

By showing up consistently in those moments, Trainline built familiarity and trust. Brand awareness reached 88% and 74% of audiences showed intent to act.

The audience did not change but the questions did. That is where original thinking comes in. AI can help planners build a richer picture of the people moving through a station, but it is the ability to look at that information differently that changes the strategy.

Don’t let the first answer be the final answer

Think about the moment a piece of data appears to give a very clear answer. It points to an audience, a location or a behaviour, and suddenly the route forward seems obvious. AI can now get us to that moment much quicker, by bringing together signals, spotting the pattern, and giving planners something to work with. But the work should not stop there.

If we take a step back, does the answer make sense in the real world? What is actually happening around that behaviour? And is there another way of looking at it that could lead somewhere more interesting?

This is not about treating every answer with suspicion but also giving ourselves permission to stay curious. The first answer may be right. Or it may be the clue that leads us to a better question.

So if five agencies begin with the same tools, the difference will not come from who gets to an answer first but from who notices the human story behind the data, asks the question nobody else has asked, and finds an opportunity the others have missed.