In this article, Laure Malergue of Displayce, explains how agentic AI can bring DOOH into planning earlier, make recommendations easier to defend and connect data, activation and learnings. She explores what makes a strong DOOH agent, why specialist data and workflows matter and how this approach could help the media secure a more influential role in media strategies from the very first planning conversation.
Walk along the Croisette during Cannes Lions, and advertising is everywhere in its most physical form: posters, digital screens, branded spaces and large outdoor moments. This year, another presence was just as visible: AI agents.
For the past two years, most of the AI conversation in advertising has focused on production. This year’s adtech announcements mark a shift from AI-assisted tools towards agentic systems able to make, coordinate and execute advertising decisions.
That shift raises a more important question for the industry: Will AI agents make media decisions better?
Media planning has always been a discipline of choices: Which audience matters most? Which context will make the message stronger? Which channel should enter the plan earlier? Which budget split is easiest to defend? These choices shape the campaign long before it goes live.
For digital out-of-home (DOOH), this could be a turning point.
DOOH is invited too late to the party
DOOH has many of the qualities brands are looking for today: real-world and brand safe visibility, public attention, contextual relevance with the ability to adapt to location, weather, time of day, audience flows and live events. Yet, it still lacks discoverability within the media mix.
Too often, it enters the media plan late, once the brief, strategy and main budget choices have already been shaped. DOOH is then considered at the execution stage, although its strongest value could have influenced the plan earlier.
The challenge is practical. DOOH requires strong knowledge of audience data, mobility patterns, local specificities, screen environments, formats, availability, pricing, context, timing and measurement. The value is clear, but turning these parameters into a confident recommendation takes time.
Programmatic DOOH has solved a large part of the activation challenge by automating the buying process. The next challenge sits earlier, when a media planner decides whether DOOH deserves a place at the table, how much budget it should receive and why one scenario is stronger than another.
Making DOOH easier to recommend
This is where agentic AI can change the role of DOOH in the media mix. The strongest agents will be judged by the quality of the decisions they support: can they explain why one scenario is stronger than another, help a planner defend DOOH in a wider media strategy and turn campaign results into learnings for the next brief?
This is the concept Displayce is bringing to the market with Agentic DOOH: a way to connect the brief, recommendation, activation and learning loop, so DOOH can influence media thinking before the plan is locked.
This conviction comes from more than a decade building programmatic DOOH technology. Our goal is now to make DOOH easier to recommend, with specialist intelligence available inside the environments where agencies and brands are already building media plans.
This is why Displayce’s agents are available via Model Context Protocol (MCP). The MCP allows AI applications such as ChatGPT or Claude to connect with external data sources and tools. For agencies building their own agentic workspaces, this means DOOH intelligence can be accessed where planners already work. For Displayce, MCP is a way to bring specialist DOOH expertise into the existing media ecosystem, with more openness and interoperability.
What makes a good agent for DOOH?
A generic AI model can understand language, but it does not understand media by itself. For DOOH, a good agent needs to be built on a strong data ecosystem, expert workflows and simulation algorithms. This is the foundation behind Displayce’s agents.
The data ecosystem gives the agent access to the signals that matter: audience data, mobility patterns, inventory context, screen environments, local context and historical campaign performance.
The expert workflow helps the agent understand how DOOH planning actually works: the objective, the constraints, context, timing and budget, and the level of explanation a planner needs to defend a recommendation.
The simulation algorithm allows the agent to compare scenarios, test assumptions and understand why a city, venue type, screen, audience group, daypart or context is relevant.
Without these foundations, an agent can create a recommendation that looks convincing but has limited media value. With them, it can support stronger, clearer and more transparent DOOH decisions.
Make DOOH impossible to ignore from the very first media conversation
By bringing DOOH expertise into planning workflows earlier, Agentic DOOH can change how the medium is considered by agencies, brands and media owners.
For agencies, this means less time assembling data and more time shaping the argument. For brands, it means clearer recommendations and greater confidence in DOOH investment. For media owners, it means making local knowledge and premium inventory visible while the plan is still being shaped.
Human judgment remains central. Planners still decide what is right for the brand, the role of agents is to give teams better scenarios, stronger explanations and more time to think.
Agentic DOOH should be measured by one question: Does it help the medium earn a stronger place in the media plan? If the answer is yes, AI agents will help bring DOOH further upstream in the media decision-making process, at the moment when budgets are shaped, strategies are built, and channel choices are made.